From 601ee1775a3c06c9b4de1fa7d808af8625b2fcd5 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Mon, 27 Oct 2025 20:54:00 -0700
Subject: [PATCH 01/56] Add a bat to run comfyui portable without api nodes.
 (#10504)

---
 .../advanced/run_nvidia_gpu_disable_api_nodes.bat               | 2 ++
 1 file changed, 2 insertions(+)
 create mode 100644 .ci/windows_nvidia_base_files/advanced/run_nvidia_gpu_disable_api_nodes.bat

diff --git a/.ci/windows_nvidia_base_files/advanced/run_nvidia_gpu_disable_api_nodes.bat b/.ci/windows_nvidia_base_files/advanced/run_nvidia_gpu_disable_api_nodes.bat
new file mode 100644
index 000000000..cfe4b9f0e
--- /dev/null
+++ b/.ci/windows_nvidia_base_files/advanced/run_nvidia_gpu_disable_api_nodes.bat
@@ -0,0 +1,2 @@
+..\python_embeded\python.exe -s ..\ComfyUI\main.py --windows-standalone-build --disable-api-nodes
+pause

From c305deed56a6ed259563b2047d9fcd51471e6590 Mon Sep 17 00:00:00 2001
From: ComfyUI Wiki <contact@comfyui-wiki.com>
Date: Tue, 28 Oct 2025 13:24:16 +0800
Subject: [PATCH 02/56] Update template to 0.2.3 (#10503)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 8570c66b6..121301669 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.28.7
-comfyui-workflow-templates==0.2.2
+comfyui-workflow-templates==0.2.3
 comfyui-embedded-docs==0.3.0
 torch
 torchsde

From 55bad303754eb60fa98f3ccf598e95502b819149 Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Tue, 28 Oct 2025 07:25:29 +0200
Subject: [PATCH 03/56] feat(api-nodes): add LTXV API nodes (#10496)

---
 comfy_api_nodes/nodes_ltxv.py | 191 ++++++++++++++++++++++++++++++++++
 nodes.py                      |   1 +
 2 files changed, 192 insertions(+)
 create mode 100644 comfy_api_nodes/nodes_ltxv.py

diff --git a/comfy_api_nodes/nodes_ltxv.py b/comfy_api_nodes/nodes_ltxv.py
new file mode 100644
index 000000000..e6ad6e27a
--- /dev/null
+++ b/comfy_api_nodes/nodes_ltxv.py
@@ -0,0 +1,191 @@
+from io import BytesIO
+from typing import Optional
+
+import torch
+from pydantic import BaseModel, Field
+from typing_extensions import override
+
+from comfy_api.input_impl import VideoFromFile
+from comfy_api.latest import IO, ComfyExtension
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    get_number_of_images,
+    sync_op_raw,
+    upload_images_to_comfyapi,
+    validate_string,
+)
+
+MODELS_MAP = {
+    "LTX-2 (Pro)": "ltx-2-pro",
+    "LTX-2 (Fast)": "ltx-2-fast",
+}
+
+
+class ExecuteTaskRequest(BaseModel):
+    prompt: str = Field(...)
+    model: str = Field(...)
+    duration: int = Field(...)
+    resolution: str = Field(...)
+    fps: Optional[int] = Field(25)
+    generate_audio: Optional[bool] = Field(True)
+    image_uri: Optional[str] = Field(None)
+
+
+class TextToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="LtxvApiTextToVideo",
+            display_name="LTXV Text To Video",
+            category="api node/video/LTXV",
+            description="Professional-quality videos with customizable duration and resolution.",
+            inputs=[
+                IO.Combo.Input("model", options=list(MODELS_MAP.keys())),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                ),
+                IO.Combo.Input("duration", options=[6, 8, 10], default=8),
+                IO.Combo.Input(
+                    "resolution",
+                    options=[
+                        "1920x1080",
+                        "2560x1440",
+                        "3840x2160",
+                    ],
+                ),
+                IO.Combo.Input("fps", options=[25, 50], default=25),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=False,
+                    optional=True,
+                    tooltip="When true, the generated video will include AI-generated audio matching the scene.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        duration: int,
+        resolution: str,
+        fps: int = 25,
+        generate_audio: bool = False,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=10000)
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint("/proxy/ltx/v1/text-to-video", "POST"),
+            data=ExecuteTaskRequest(
+                prompt=prompt,
+                model=MODELS_MAP[model],
+                duration=duration,
+                resolution=resolution,
+                fps=fps,
+                generate_audio=generate_audio,
+            ),
+            as_binary=True,
+            max_retries=1,
+        )
+        return IO.NodeOutput(VideoFromFile(BytesIO(response)))
+
+
+class ImageToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="LtxvApiImageToVideo",
+            display_name="LTXV Image To Video",
+            category="api node/video/LTXV",
+            description="Professional-quality videos with customizable duration and resolution based on start image.",
+            inputs=[
+                IO.Image.Input("image", tooltip="First frame to be used for the video."),
+                IO.Combo.Input("model", options=list(MODELS_MAP.keys())),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                ),
+                IO.Combo.Input("duration", options=[6, 8, 10], default=8),
+                IO.Combo.Input(
+                    "resolution",
+                    options=[
+                        "1920x1080",
+                        "2560x1440",
+                        "3840x2160",
+                    ],
+                ),
+                IO.Combo.Input("fps", options=[25, 50], default=25),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=False,
+                    optional=True,
+                    tooltip="When true, the generated video will include AI-generated audio matching the scene.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: torch.Tensor,
+        model: str,
+        prompt: str,
+        duration: int,
+        resolution: str,
+        fps: int = 25,
+        generate_audio: bool = False,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=10000)
+        if get_number_of_images(image) != 1:
+            raise ValueError("Currently only one input image is supported.")
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint("/proxy/ltx/v1/image-to-video", "POST"),
+            data=ExecuteTaskRequest(
+                image_uri=(await upload_images_to_comfyapi(cls, image, max_images=1, mime_type="image/png"))[0],
+                prompt=prompt,
+                model=MODELS_MAP[model],
+                duration=duration,
+                resolution=resolution,
+                fps=fps,
+                generate_audio=generate_audio,
+            ),
+            as_binary=True,
+            max_retries=1,
+        )
+        return IO.NodeOutput(VideoFromFile(BytesIO(response)))
+
+
+class LtxvApiExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            TextToVideoNode,
+            ImageToVideoNode,
+        ]
+
+
+async def comfy_entrypoint() -> LtxvApiExtension:
+    return LtxvApiExtension()
diff --git a/nodes.py b/nodes.py
index 7cfa8ca14..12e365ca9 100644
--- a/nodes.py
+++ b/nodes.py
@@ -2349,6 +2349,7 @@ async def init_builtin_api_nodes():
         "nodes_kling.py",
         "nodes_bfl.py",
         "nodes_bytedance.py",
+        "nodes_ltxv.py",
         "nodes_luma.py",
         "nodes_recraft.py",
         "nodes_pixverse.py",

From 6abc30aae9bd13f31dafd32552a365f2df2cf715 Mon Sep 17 00:00:00 2001
From: ComfyUI Wiki <contact@comfyui-wiki.com>
Date: Tue, 28 Oct 2025 13:56:30 +0800
Subject: [PATCH 04/56] Update template to 0.2.4 (#10505)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 121301669..cc3d4ca94 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.28.7
-comfyui-workflow-templates==0.2.3
+comfyui-workflow-templates==0.2.4
 comfyui-embedded-docs==0.3.0
 torch
 torchsde

From 614b8d3345424481d94a22fe7496d908c1a5c526 Mon Sep 17 00:00:00 2001
From: Jedrzej Kosinski <kosinkadink1@gmail.com>
Date: Tue, 28 Oct 2025 00:01:13 -0700
Subject: [PATCH 05/56] frontend bump to 1.28.8 (#10506)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index cc3d4ca94..4d84b0d3e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.28.7
+comfyui-frontend-package==1.28.8
 comfyui-workflow-templates==0.2.4
 comfyui-embedded-docs==0.3.0
 torch

From f2bb3230b796f6a486894fc3b597db2c0b9538c9 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 28 Oct 2025 03:03:59 -0400
Subject: [PATCH 06/56] ComfyUI version v0.3.67

---
 comfyui_version.py | 2 +-
 pyproject.toml     | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfyui_version.py b/comfyui_version.py
index 33a06bbb0..db48b05c4 100644
--- a/comfyui_version.py
+++ b/comfyui_version.py
@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.3.66"
+__version__ = "0.3.67"
diff --git a/pyproject.toml b/pyproject.toml
index fcc4854a5..ab054355c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.3.66"
+version = "0.3.67"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"

From b61a40cbc9c2eb648b4d22bb513ed3ab2e2f0fd7 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Tue, 28 Oct 2025 00:21:45 -0700
Subject: [PATCH 07/56] Bump stable portable to cu130 python 3.13.9 (#10508)

---
 .github/workflows/release-stable-all.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/release-stable-all.yml b/.github/workflows/release-stable-all.yml
index 5c1024599..7dca7277b 100644
--- a/.github/workflows/release-stable-all.yml
+++ b/.github/workflows/release-stable-all.yml
@@ -18,9 +18,9 @@ jobs:
     uses: ./.github/workflows/stable-release.yml
     with:
       git_tag: ${{ inputs.git_tag }}
-      cache_tag: "cu129"
+      cache_tag: "cu130"
       python_minor: "13"
-      python_patch: "6"
+      python_patch: "9"
       rel_name: "nvidia"
       rel_extra_name: ""
       test_release: true

From 8cf2ba4ba64203551276513068ee81145e90f0bc Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Tue, 28 Oct 2025 00:23:52 -0700
Subject: [PATCH 08/56] Remove comfy api key from queue api. (#10502)

---
 execution.py |  8 +++-----
 main.py      | 11 +++++++++--
 server.py    | 11 ++++++++---
 3 files changed, 20 insertions(+), 10 deletions(-)

diff --git a/execution.py b/execution.py
index 78c36a4b0..b14bb14c7 100644
--- a/execution.py
+++ b/execution.py
@@ -1116,7 +1116,7 @@ class PromptQueue:
         messages: List[str]
 
     def task_done(self, item_id, history_result,
-                  status: Optional['PromptQueue.ExecutionStatus']):
+                  status: Optional['PromptQueue.ExecutionStatus'], process_item=None):
         with self.mutex:
             prompt = self.currently_running.pop(item_id)
             if len(self.history) > MAXIMUM_HISTORY_SIZE:
@@ -1126,10 +1126,8 @@ class PromptQueue:
             if status is not None:
                 status_dict = copy.deepcopy(status._asdict())
 
-            # Remove sensitive data from extra_data before storing in history
-            for sensitive_val in SENSITIVE_EXTRA_DATA_KEYS:
-                if sensitive_val in prompt[3]:
-                    prompt[3].pop(sensitive_val)
+            if process_item is not None:
+                prompt = process_item(prompt)
 
             self.history[prompt[1]] = {
                 "prompt": prompt,
diff --git a/main.py b/main.py
index 4b4c5dcc4..8d466d2eb 100644
--- a/main.py
+++ b/main.py
@@ -192,14 +192,21 @@ def prompt_worker(q, server_instance):
             prompt_id = item[1]
             server_instance.last_prompt_id = prompt_id
 
-            e.execute(item[2], prompt_id, item[3], item[4])
+            sensitive = item[5]
+            extra_data = item[3].copy()
+            for k in sensitive:
+                extra_data[k] = sensitive[k]
+
+            e.execute(item[2], prompt_id, extra_data, item[4])
             need_gc = True
+
+            remove_sensitive = lambda prompt: prompt[:5] + prompt[6:]
             q.task_done(item_id,
                         e.history_result,
                         status=execution.PromptQueue.ExecutionStatus(
                             status_str='success' if e.success else 'error',
                             completed=e.success,
-                            messages=e.status_messages))
+                            messages=e.status_messages), process_item=remove_sensitive)
             if server_instance.client_id is not None:
                 server_instance.send_sync("executing", {"node": None, "prompt_id": prompt_id}, server_instance.client_id)
 
diff --git a/server.py b/server.py
index fe58db286..5d773b10a 100644
--- a/server.py
+++ b/server.py
@@ -691,8 +691,9 @@ class PromptServer():
         async def get_queue(request):
             queue_info = {}
             current_queue = self.prompt_queue.get_current_queue_volatile()
-            queue_info['queue_running'] = current_queue[0]
-            queue_info['queue_pending'] = current_queue[1]
+            remove_sensitive = lambda queue: [x[:5] for x in queue]
+            queue_info['queue_running'] = remove_sensitive(current_queue[0])
+            queue_info['queue_pending'] = remove_sensitive(current_queue[1])
             return web.json_response(queue_info)
 
         @routes.post("/prompt")
@@ -728,7 +729,11 @@ class PromptServer():
                     extra_data["client_id"] = json_data["client_id"]
                 if valid[0]:
                     outputs_to_execute = valid[2]
-                    self.prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute))
+                    sensitive = {}
+                    for sensitive_val in execution.SENSITIVE_EXTRA_DATA_KEYS:
+                        if sensitive_val in extra_data:
+                            sensitive[sensitive_val] = extra_data.pop(sensitive_val)
+                    self.prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute, sensitive))
                     response = {"prompt_id": prompt_id, "number": number, "node_errors": valid[3]}
                     return web.json_response(response)
                 else:

From 3bea4efc6b23d76c6b0672cd90421a9024e13fdb Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Tue, 28 Oct 2025 01:45:45 -0700
Subject: [PATCH 09/56] Tell users to update nvidia drivers if problem with
 portable. (#10510)

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 434d4ff06..4204777e9 100644
--- a/README.md
+++ b/README.md
@@ -176,6 +176,8 @@ Simply download, extract with [7-Zip](https://7-zip.org) and run. Make sure you
 
 If you have trouble extracting it, right click the file -> properties -> unblock
 
+Update your Nvidia drivers if it doesn't start.
+
 #### Alternative Downloads:
 
 [Experimental portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z)

From 22e40d2ace0f53da025b3a41cbe4b664ef807097 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Tue, 28 Oct 2025 12:08:08 -0700
Subject: [PATCH 10/56] Tell users to update their nvidia drivers if portable
 doesn't start. (#10518)

---
 .../advanced/run_nvidia_gpu_disable_api_nodes.bat                | 1 +
 .ci/windows_nvidia_base_files/run_nvidia_gpu.bat                 | 1 +
 .../run_nvidia_gpu_fast_fp16_accumulation.bat                    | 1 +
 3 files changed, 3 insertions(+)

diff --git a/.ci/windows_nvidia_base_files/advanced/run_nvidia_gpu_disable_api_nodes.bat b/.ci/windows_nvidia_base_files/advanced/run_nvidia_gpu_disable_api_nodes.bat
index cfe4b9f0e..ed00583b6 100644
--- a/.ci/windows_nvidia_base_files/advanced/run_nvidia_gpu_disable_api_nodes.bat
+++ b/.ci/windows_nvidia_base_files/advanced/run_nvidia_gpu_disable_api_nodes.bat
@@ -1,2 +1,3 @@
 ..\python_embeded\python.exe -s ..\ComfyUI\main.py --windows-standalone-build --disable-api-nodes
+echo If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest.
 pause
diff --git a/.ci/windows_nvidia_base_files/run_nvidia_gpu.bat b/.ci/windows_nvidia_base_files/run_nvidia_gpu.bat
index 274d7c948..4898a424f 100755
--- a/.ci/windows_nvidia_base_files/run_nvidia_gpu.bat
+++ b/.ci/windows_nvidia_base_files/run_nvidia_gpu.bat
@@ -1,2 +1,3 @@
 .\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build
+echo If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest.
 pause
diff --git a/.ci/windows_nvidia_base_files/run_nvidia_gpu_fast_fp16_accumulation.bat b/.ci/windows_nvidia_base_files/run_nvidia_gpu_fast_fp16_accumulation.bat
index 38f06ecb2..32611e4af 100644
--- a/.ci/windows_nvidia_base_files/run_nvidia_gpu_fast_fp16_accumulation.bat
+++ b/.ci/windows_nvidia_base_files/run_nvidia_gpu_fast_fp16_accumulation.bat
@@ -1,2 +1,3 @@
 .\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --fast fp16_accumulation
+echo If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest.
 pause

From 8817f8fc148c5a63ffd3f854975df8e72c740540 Mon Sep 17 00:00:00 2001
From: contentis <lspindler@nvidia.com>
Date: Tue, 28 Oct 2025 21:20:53 +0100
Subject: [PATCH 11/56] Mixed Precision Quantization System (#10498)

* Implement mixed precision operations with a registry design and metadate for quant spec in checkpoint.

* Updated design using Tensor Subclasses

* Fix FP8 MM

* An actually functional POC

* Remove CK reference and ensure correct compute dtype

* Update unit tests

* ruff lint

* Implement mixed precision operations with a registry design and metadate for quant spec in checkpoint.

* Updated design using Tensor Subclasses

* Fix FP8 MM

* An actually functional POC

* Remove CK reference and ensure correct compute dtype

* Update unit tests

* ruff lint

* Fix missing keys

* Rename quant dtype parameter

* Rename quant dtype parameter

* Fix unittests for CPU build
---
 comfy/model_base.py                           |  10 +-
 comfy/model_detection.py                      |  20 +
 comfy/ops.py                                  | 146 +++++-
 comfy/quant_ops.py                            | 437 ++++++++++++++++++
 comfy/sd.py                                   |  13 +-
 comfy/supported_models_base.py                |   1 +
 .../comfy_quant/test_mixed_precision.py       | 232 ++++++++++
 tests-unit/comfy_quant/test_quant_registry.py | 190 ++++++++
 8 files changed, 1030 insertions(+), 19 deletions(-)
 create mode 100644 comfy/quant_ops.py
 create mode 100644 tests-unit/comfy_quant/test_mixed_precision.py
 create mode 100644 tests-unit/comfy_quant/test_quant_registry.py

diff --git a/comfy/model_base.py b/comfy/model_base.py
index e877f19ac..7c788d085 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -134,7 +134,7 @@ class BaseModel(torch.nn.Module):
         if not unet_config.get("disable_unet_model_creation", False):
             if model_config.custom_operations is None:
                 fp8 = model_config.optimizations.get("fp8", False)
-                operations = comfy.ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype, fp8_optimizations=fp8, scaled_fp8=model_config.scaled_fp8)
+                operations = comfy.ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype, fp8_optimizations=fp8, scaled_fp8=model_config.scaled_fp8, model_config=model_config)
             else:
                 operations = model_config.custom_operations
             self.diffusion_model = unet_model(**unet_config, device=device, operations=operations)
@@ -333,6 +333,14 @@ class BaseModel(torch.nn.Module):
         if self.model_config.scaled_fp8 is not None:
             unet_state_dict["scaled_fp8"] = torch.tensor([], dtype=self.model_config.scaled_fp8)
 
+        # Save mixed precision metadata
+        if hasattr(self.model_config, 'layer_quant_config') and self.model_config.layer_quant_config:
+            metadata = {
+                "format_version": "1.0",
+                "layers": self.model_config.layer_quant_config
+            }
+            unet_state_dict["_quantization_metadata"] = metadata
+
         unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict)
 
         if self.model_type == ModelType.V_PREDICTION:
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index 141f1e164..3142a7fc3 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -6,6 +6,20 @@ import math
 import logging
 import torch
 
+
+def detect_layer_quantization(metadata):
+    quant_key = "_quantization_metadata"
+    if metadata is not None and quant_key in metadata:
+        quant_metadata = metadata.pop(quant_key)
+        quant_metadata = json.loads(quant_metadata)
+        if isinstance(quant_metadata, dict) and "layers" in quant_metadata:
+            logging.info(f"Found quantization metadata (version {quant_metadata.get('format_version', 'unknown')})")
+            return quant_metadata["layers"]
+        else:
+            raise ValueError("Invalid quantization metadata format")
+    return None
+
+
 def count_blocks(state_dict_keys, prefix_string):
     count = 0
     while True:
@@ -701,6 +715,12 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
         else:
             model_config.optimizations["fp8"] = True
 
+    # Detect per-layer quantization (mixed precision)
+    layer_quant_config = detect_layer_quantization(metadata)
+    if layer_quant_config:
+        model_config.layer_quant_config = layer_quant_config
+        logging.info(f"Detected mixed precision quantization: {len(layer_quant_config)} layers quantized")
+
     return model_config
 
 def unet_prefix_from_state_dict(state_dict):
diff --git a/comfy/ops.py b/comfy/ops.py
index 934e21261..93731eedf 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -344,6 +344,10 @@ class manual_cast(disable_weight_init):
 
 
 def fp8_linear(self, input):
+    """
+    Legacy FP8 linear function for backward compatibility.
+    Uses QuantizedTensor subclass for dispatch.
+    """
     dtype = self.weight.dtype
     if dtype not in [torch.float8_e4m3fn]:
         return None
@@ -355,9 +359,9 @@ def fp8_linear(self, input):
 
     input_shape = input.shape
     input_dtype = input.dtype
+
     if len(input.shape) == 3:
         w, bias = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype)
-        w = w.t()
 
         scale_weight = self.scale_weight
         scale_input = self.scale_input
@@ -368,23 +372,18 @@ def fp8_linear(self, input):
 
         if scale_input is None:
             scale_input = torch.ones((), device=input.device, dtype=torch.float32)
-            input = torch.clamp(input, min=-448, max=448, out=input)
-            input = input.reshape(-1, input_shape[2]).to(dtype).contiguous()
         else:
             scale_input = scale_input.to(input.device)
-            input = (input * (1.0 / scale_input).to(input_dtype)).reshape(-1, input_shape[2]).to(dtype).contiguous()
 
-        if bias is not None:
-            o = torch._scaled_mm(input, w, out_dtype=input_dtype, bias=bias, scale_a=scale_input, scale_b=scale_weight)
-        else:
-            o = torch._scaled_mm(input, w, out_dtype=input_dtype, scale_a=scale_input, scale_b=scale_weight)
-
-        if isinstance(o, tuple):
-            o = o[0]
+        # Wrap weight in QuantizedTensor - this enables unified dispatch
+        # Call F.linear - __torch_dispatch__ routes to fp8_linear handler in quant_ops.py!
+        layout_params_weight = {'scale': scale_weight, 'orig_dtype': input_dtype}
+        quantized_weight = QuantizedTensor(w, TensorCoreFP8Layout, layout_params_weight)
+        quantized_input = QuantizedTensor.from_float(input.reshape(-1, input_shape[2]), TensorCoreFP8Layout, scale=scale_input, dtype=dtype)
+        o = torch.nn.functional.linear(quantized_input, quantized_weight, bias)
 
         if tensor_2d:
             return o.reshape(input_shape[0], -1)
-
         return o.reshape((-1, input_shape[1], self.weight.shape[0]))
 
     return None
@@ -478,7 +477,128 @@ if CUBLAS_IS_AVAILABLE:
             def forward(self, *args, **kwargs):
                 return super().forward(*args, **kwargs)
 
-def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, scaled_fp8=None):
+
+# ==============================================================================
+# Mixed Precision Operations
+# ==============================================================================
+from .quant_ops import QuantizedTensor, TensorCoreFP8Layout
+
+QUANT_FORMAT_MIXINS = {
+    "float8_e4m3fn": {
+        "dtype": torch.float8_e4m3fn,
+        "layout_type": TensorCoreFP8Layout,
+        "parameters": {
+            "weight_scale": torch.nn.Parameter(torch.zeros((), dtype=torch.float32), requires_grad=False),
+            "input_scale": torch.nn.Parameter(torch.zeros((), dtype=torch.float32), requires_grad=False),
+        }
+    }
+}
+
+class MixedPrecisionOps(disable_weight_init):
+    _layer_quant_config = {}
+    _compute_dtype = torch.bfloat16
+
+    class Linear(torch.nn.Module, CastWeightBiasOp):
+        def __init__(
+            self,
+            in_features: int,
+            out_features: int,
+            bias: bool = True,
+            device=None,
+            dtype=None,
+        ) -> None:
+            super().__init__()
+
+            self.factory_kwargs = {"device": device, "dtype": MixedPrecisionOps._compute_dtype}
+            # self.factory_kwargs = {"device": device, "dtype": dtype}
+
+            self.in_features = in_features
+            self.out_features = out_features
+            if bias:
+                self.bias = torch.nn.Parameter(torch.empty(out_features, **self.factory_kwargs))
+            else:
+                self.register_parameter("bias", None)
+
+            self.tensor_class = None
+
+        def reset_parameters(self):
+            return None
+
+        def _load_from_state_dict(self, state_dict, prefix, local_metadata,
+                                  strict, missing_keys, unexpected_keys, error_msgs):
+
+            device = self.factory_kwargs["device"]
+            layer_name = prefix.rstrip('.')
+            weight_key = f"{prefix}weight"
+            weight = state_dict.pop(weight_key, None)
+            if weight is None:
+                raise ValueError(f"Missing weight for layer {layer_name}")
+
+            manually_loaded_keys = [weight_key]
+
+            if layer_name not in MixedPrecisionOps._layer_quant_config:
+                self.weight = torch.nn.Parameter(weight.to(device=device, dtype=MixedPrecisionOps._compute_dtype), requires_grad=False)
+            else:
+                quant_format = MixedPrecisionOps._layer_quant_config[layer_name].get("format", None)
+                if quant_format is None:
+                    raise ValueError(f"Unknown quantization format for layer {layer_name}")
+
+                mixin = QUANT_FORMAT_MIXINS[quant_format]
+                self.layout_type = mixin["layout_type"]
+
+                scale_key = f"{prefix}weight_scale"
+                layout_params = {
+                    'scale': state_dict.pop(scale_key, None),
+                    'orig_dtype': MixedPrecisionOps._compute_dtype
+                }
+                if layout_params['scale'] is not None:
+                    manually_loaded_keys.append(scale_key)
+
+                self.weight = torch.nn.Parameter(
+                    QuantizedTensor(weight.to(device=device, dtype=mixin["dtype"]), self.layout_type, layout_params),
+                    requires_grad=False
+                )
+
+                for param_name, param_value in mixin["parameters"].items():
+                    param_key = f"{prefix}{param_name}"
+                    _v = state_dict.pop(param_key, None)
+                    if _v is None:
+                        continue
+                    setattr(self, param_name, torch.nn.Parameter(_v.to(device=device), requires_grad=False))
+                    manually_loaded_keys.append(param_key)
+
+            super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
+
+            for key in manually_loaded_keys:
+                if key in missing_keys:
+                    missing_keys.remove(key)
+
+        def _forward(self, input, weight, bias):
+            return torch.nn.functional.linear(input, weight, bias)
+
+        def forward_comfy_cast_weights(self, input):
+            weight, bias = cast_bias_weight(self, input)
+            return self._forward(input, weight, bias)
+
+        def forward(self, input, *args, **kwargs):
+            run_every_op()
+
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
+                return self.forward_comfy_cast_weights(input, *args, **kwargs)
+            if (getattr(self, 'layout_type', None) is not None and
+                getattr(self, 'input_scale', None) is not None and
+                not isinstance(input, QuantizedTensor)):
+                input = QuantizedTensor.from_float(input, self.layout_type, scale=self.input_scale, fp8_dtype=self.weight.dtype)
+            return self._forward(input, self.weight, self.bias)
+
+
+def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, scaled_fp8=None, model_config=None):
+    if model_config and hasattr(model_config, 'layer_quant_config') and model_config.layer_quant_config:
+        MixedPrecisionOps._layer_quant_config = model_config.layer_quant_config
+        MixedPrecisionOps._compute_dtype = compute_dtype
+        logging.info(f"Using mixed precision operations: {len(model_config.layer_quant_config)} quantized layers")
+        return MixedPrecisionOps
+
     fp8_compute = comfy.model_management.supports_fp8_compute(load_device)
     if scaled_fp8 is not None:
         return scaled_fp8_ops(fp8_matrix_mult=fp8_compute and fp8_optimizations, scale_input=fp8_optimizations, override_dtype=scaled_fp8)
diff --git a/comfy/quant_ops.py b/comfy/quant_ops.py
new file mode 100644
index 000000000..b14e03084
--- /dev/null
+++ b/comfy/quant_ops.py
@@ -0,0 +1,437 @@
+import torch
+import logging
+from typing import Tuple, Dict
+
+_LAYOUT_REGISTRY = {}
+_GENERIC_UTILS = {}
+
+
+def register_layout_op(torch_op, layout_type):
+    """
+    Decorator to register a layout-specific operation handler.
+    Args:
+        torch_op: PyTorch operation (e.g., torch.ops.aten.linear.default)
+        layout_type: Layout class (e.g., TensorCoreFP8Layout)
+    Example:
+        @register_layout_op(torch.ops.aten.linear.default, TensorCoreFP8Layout)
+        def fp8_linear(func, args, kwargs):
+            # FP8-specific linear implementation
+            ...
+    """
+    def decorator(handler_func):
+        if torch_op not in _LAYOUT_REGISTRY:
+            _LAYOUT_REGISTRY[torch_op] = {}
+        _LAYOUT_REGISTRY[torch_op][layout_type] = handler_func
+        return handler_func
+    return decorator
+
+
+def register_generic_util(torch_op):
+    """
+    Decorator to register a generic utility that works for all layouts.
+    Args:
+        torch_op: PyTorch operation (e.g., torch.ops.aten.detach.default)
+
+    Example:
+        @register_generic_util(torch.ops.aten.detach.default)
+        def generic_detach(func, args, kwargs):
+            # Works for any layout
+            ...
+    """
+    def decorator(handler_func):
+        _GENERIC_UTILS[torch_op] = handler_func
+        return handler_func
+    return decorator
+
+
+def _get_layout_from_args(args):
+    for arg in args:
+        if isinstance(arg, QuantizedTensor):
+            return arg._layout_type
+        elif isinstance(arg, (list, tuple)):
+            for item in arg:
+                if isinstance(item, QuantizedTensor):
+                    return item._layout_type
+    return None
+
+
+def _move_layout_params_to_device(params, device):
+    new_params = {}
+    for k, v in params.items():
+        if isinstance(v, torch.Tensor):
+            new_params[k] = v.to(device=device)
+        else:
+            new_params[k] = v
+    return new_params
+
+
+def _copy_layout_params(params):
+    new_params = {}
+    for k, v in params.items():
+        if isinstance(v, torch.Tensor):
+            new_params[k] = v.clone()
+        else:
+            new_params[k] = v
+    return new_params
+
+
+class QuantizedLayout:
+    """
+    Base class for quantization layouts.
+
+    A layout encapsulates the format-specific logic for quantization/dequantization
+    and provides a uniform interface for extracting raw tensors needed for computation.
+
+    New quantization formats should subclass this and implement the required methods.
+    """
+    @classmethod
+    def quantize(cls, tensor, **kwargs) -> Tuple[torch.Tensor, Dict]:
+        raise NotImplementedError(f"{cls.__name__} must implement quantize()")
+
+    @staticmethod
+    def dequantize(qdata, **layout_params) -> torch.Tensor:
+        raise NotImplementedError("TensorLayout must implement dequantize()")
+
+    @classmethod
+    def get_plain_tensors(cls, qtensor) -> torch.Tensor:
+        raise NotImplementedError(f"{cls.__name__} must implement get_plain_tensors()")
+
+
+class QuantizedTensor(torch.Tensor):
+    """
+    Universal quantized tensor that works with any layout.
+
+    This tensor subclass uses a pluggable layout system to support multiple
+    quantization formats (FP8, INT4, INT8, etc.) without code duplication.
+
+    The layout_type determines format-specific behavior, while common operations
+    (detach, clone, to) are handled generically.
+
+    Attributes:
+        _qdata: The quantized tensor data
+        _layout_type: Layout class (e.g., TensorCoreFP8Layout)
+        _layout_params: Dict with layout-specific params (scale, zero_point, etc.)
+    """
+
+    @staticmethod
+    def __new__(cls, qdata, layout_type, layout_params):
+        """
+        Create a quantized tensor.
+
+        Args:
+            qdata: The quantized data tensor
+            layout_type: Layout class (subclass of QuantizedLayout)
+            layout_params: Dict with layout-specific parameters
+        """
+        return torch.Tensor._make_subclass(cls, qdata, require_grad=False)
+
+    def __init__(self, qdata, layout_type, layout_params):
+        self._qdata = qdata.contiguous()
+        self._layout_type = layout_type
+        self._layout_params = layout_params
+
+    def __repr__(self):
+        layout_name = self._layout_type.__name__
+        param_str = ", ".join(f"{k}={v}" for k, v in list(self._layout_params.items())[:2])
+        return f"QuantizedTensor(shape={self.shape}, layout={layout_name}, {param_str})"
+
+    @property
+    def layout_type(self):
+        return self._layout_type
+
+    def __tensor_flatten__(self):
+        """
+        Tensor flattening protocol for proper device movement.
+        """
+        inner_tensors = ["_qdata"]
+        ctx = {
+            "layout_type": self._layout_type,
+        }
+
+        tensor_params = {}
+        non_tensor_params = {}
+        for k, v in self._layout_params.items():
+            if isinstance(v, torch.Tensor):
+                tensor_params[k] = v
+            else:
+                non_tensor_params[k] = v
+
+        ctx["tensor_param_keys"] = list(tensor_params.keys())
+        ctx["non_tensor_params"] = non_tensor_params
+
+        for k, v in tensor_params.items():
+            attr_name = f"_layout_param_{k}"
+            object.__setattr__(self, attr_name, v)
+            inner_tensors.append(attr_name)
+
+        return inner_tensors, ctx
+
+    @staticmethod
+    def __tensor_unflatten__(inner_tensors, ctx, outer_size, outer_stride):
+        """
+        Tensor unflattening protocol for proper device movement.
+        Reconstructs the QuantizedTensor after device movement.
+        """
+        layout_type = ctx["layout_type"]
+        layout_params = dict(ctx["non_tensor_params"])
+
+        for key in ctx["tensor_param_keys"]:
+            attr_name = f"_layout_param_{key}"
+            layout_params[key] = inner_tensors[attr_name]
+
+        return QuantizedTensor(inner_tensors["_q_data"], layout_type, layout_params)
+
+    @classmethod
+    def from_float(cls, tensor, layout_type, **quantize_kwargs) -> 'QuantizedTensor':
+        qdata, layout_params = layout_type.quantize(tensor, **quantize_kwargs)
+        return cls(qdata, layout_type, layout_params)
+
+    def dequantize(self) -> torch.Tensor:
+        return self._layout_type.dequantize(self._qdata, **self._layout_params)
+
+    @classmethod
+    def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
+        kwargs = kwargs or {}
+
+        # Step 1: Check generic utilities first (detach, clone, to, etc.)
+        if func in _GENERIC_UTILS:
+            return _GENERIC_UTILS[func](func, args, kwargs)
+
+        # Step 2: Check layout-specific handlers (linear, matmul, etc.)
+        layout_type = _get_layout_from_args(args)
+        if layout_type and func in _LAYOUT_REGISTRY:
+            handler = _LAYOUT_REGISTRY[func].get(layout_type)
+            if handler:
+                return handler(func, args, kwargs)
+
+        # Step 3: Fallback to dequantization
+        if isinstance(args[0] if args else None, QuantizedTensor):
+            logging.info(f"QuantizedTensor: Unhandled operation {func}, falling back to dequantization. kwargs={kwargs}")
+        return cls._dequant_and_fallback(func, args, kwargs)
+
+    @classmethod
+    def _dequant_and_fallback(cls, func, args, kwargs):
+        def dequant_arg(arg):
+            if isinstance(arg, QuantizedTensor):
+                return arg.dequantize()
+            elif isinstance(arg, (list, tuple)):
+                return type(arg)(dequant_arg(a) for a in arg)
+            return arg
+
+        new_args = dequant_arg(args)
+        new_kwargs = dequant_arg(kwargs)
+        return func(*new_args, **new_kwargs)
+
+
+# ==============================================================================
+# Generic Utilities (Layout-Agnostic Operations)
+# ==============================================================================
+
+def _create_transformed_qtensor(qt, transform_fn):
+    new_data = transform_fn(qt._qdata)
+    new_params = _copy_layout_params(qt._layout_params)
+    return QuantizedTensor(new_data, qt._layout_type, new_params)
+
+
+def _handle_device_transfer(qt, target_device, target_dtype=None, target_layout=None, op_name="to"):
+    if target_dtype is not None and target_dtype != qt.dtype:
+        logging.warning(
+            f"QuantizedTensor: dtype conversion requested to {target_dtype}, "
+            f"but not supported for quantized tensors. Ignoring dtype."
+        )
+
+    if target_layout is not None and target_layout != torch.strided:
+        logging.warning(
+            f"QuantizedTensor: layout change requested to {target_layout}, "
+            f"but not supported. Ignoring layout."
+        )
+
+    # Handle device transfer
+    current_device = qt._qdata.device
+    if target_device is not None:
+        # Normalize device for comparison
+        if isinstance(target_device, str):
+            target_device = torch.device(target_device)
+        if isinstance(current_device, str):
+            current_device = torch.device(current_device)
+
+        if target_device != current_device:
+            logging.debug(f"QuantizedTensor.{op_name}: Moving from {current_device} to {target_device}")
+            new_q_data = qt._qdata.to(device=target_device)
+            new_params = _move_layout_params_to_device(qt._layout_params, target_device)
+            new_qt = QuantizedTensor(new_q_data, qt._layout_type, new_params)
+            logging.debug(f"QuantizedTensor.{op_name}: Created new tensor on {target_device}")
+            return new_qt
+
+    logging.debug(f"QuantizedTensor.{op_name}: No device change needed, returning original")
+    return qt
+
+
+@register_generic_util(torch.ops.aten.detach.default)
+def generic_detach(func, args, kwargs):
+    """Detach operation - creates a detached copy of the quantized tensor."""
+    qt = args[0]
+    if isinstance(qt, QuantizedTensor):
+        return _create_transformed_qtensor(qt, lambda x: x.detach())
+    return func(*args, **kwargs)
+
+
+@register_generic_util(torch.ops.aten.clone.default)
+def generic_clone(func, args, kwargs):
+    """Clone operation - creates a deep copy of the quantized tensor."""
+    qt = args[0]
+    if isinstance(qt, QuantizedTensor):
+        return _create_transformed_qtensor(qt, lambda x: x.clone())
+    return func(*args, **kwargs)
+
+
+@register_generic_util(torch.ops.aten._to_copy.default)
+def generic_to_copy(func, args, kwargs):
+    """Device/dtype transfer operation - handles .to(device) calls."""
+    qt = args[0]
+    if isinstance(qt, QuantizedTensor):
+        return _handle_device_transfer(
+            qt,
+            target_device=kwargs.get('device', None),
+            target_dtype=kwargs.get('dtype', None),
+            op_name="_to_copy"
+        )
+    return func(*args, **kwargs)
+
+
+@register_generic_util(torch.ops.aten.to.dtype_layout)
+def generic_to_dtype_layout(func, args, kwargs):
+    """Handle .to(device) calls using the dtype_layout variant."""
+    qt = args[0]
+    if isinstance(qt, QuantizedTensor):
+        return _handle_device_transfer(
+            qt,
+            target_device=kwargs.get('device', None),
+            target_dtype=kwargs.get('dtype', None),
+            target_layout=kwargs.get('layout', None),
+            op_name="to"
+        )
+    return func(*args, **kwargs)
+
+
+@register_generic_util(torch.ops.aten.copy_.default)
+def generic_copy_(func, args, kwargs):
+    qt_dest = args[0]
+    src = args[1]
+
+    if isinstance(qt_dest, QuantizedTensor):
+        if isinstance(src, QuantizedTensor):
+            # Copy from another quantized tensor
+            qt_dest._qdata.copy_(src._qdata)
+            qt_dest._layout_type = src._layout_type
+            qt_dest._layout_params = _copy_layout_params(src._layout_params)
+        else:
+            # Copy from regular tensor - just copy raw data
+            qt_dest._qdata.copy_(src)
+        return qt_dest
+    return func(*args, **kwargs)
+
+
+@register_generic_util(torch.ops.aten._has_compatible_shallow_copy_type.default)
+def generic_has_compatible_shallow_copy_type(func, args, kwargs):
+    return True
+
+# ==============================================================================
+# FP8 Layout + Operation Handlers
+# ==============================================================================
+class TensorCoreFP8Layout(QuantizedLayout):
+    """
+    Storage format:
+    - qdata: FP8 tensor (torch.float8_e4m3fn or torch.float8_e5m2)
+    - scale: Scalar tensor (float32) for dequantization
+    - orig_dtype: Original dtype before quantization (for casting back)
+    """
+    @classmethod
+    def quantize(cls, tensor, scale=None, dtype=torch.float8_e4m3fn):
+        orig_dtype = tensor.dtype
+
+        if scale is None:
+            scale = torch.amax(tensor.abs()) / torch.finfo(dtype).max
+
+        if not isinstance(scale, torch.Tensor):
+            scale = torch.tensor(scale)
+        scale = scale.to(device=tensor.device, dtype=torch.float32)
+
+        lp_amax = torch.finfo(dtype).max
+        tensor_scaled = tensor.float() / scale
+        torch.clamp(tensor_scaled, min=-lp_amax, max=lp_amax, out=tensor_scaled)
+        qdata = tensor_scaled.to(dtype, memory_format=torch.contiguous_format)
+
+        layout_params = {
+            'scale': scale,
+            'orig_dtype': orig_dtype
+        }
+        return qdata, layout_params
+
+    @staticmethod
+    def dequantize(qdata, scale, orig_dtype, **kwargs):
+        plain_tensor = torch.ops.aten._to_copy.default(qdata, dtype=orig_dtype)
+        return plain_tensor * scale
+
+    @classmethod
+    def get_plain_tensors(cls, qtensor):
+        return qtensor._qdata, qtensor._layout_params['scale']
+
+
+@register_layout_op(torch.ops.aten.linear.default, TensorCoreFP8Layout)
+def fp8_linear(func, args, kwargs):
+    input_tensor = args[0]
+    weight = args[1]
+    bias = args[2] if len(args) > 2 else None
+
+    if isinstance(input_tensor, QuantizedTensor) and isinstance(weight, QuantizedTensor):
+        plain_input, scale_a = TensorCoreFP8Layout.get_plain_tensors(input_tensor)
+        plain_weight, scale_b = TensorCoreFP8Layout.get_plain_tensors(weight)
+
+        out_dtype = kwargs.get("out_dtype")
+        if out_dtype is None:
+            out_dtype = input_tensor._layout_params['orig_dtype']
+
+        weight_t = plain_weight.t()
+
+        tensor_2d = False
+        if len(plain_input.shape) == 2:
+            tensor_2d = True
+            plain_input = plain_input.unsqueeze(1)
+
+        input_shape = plain_input.shape
+        if len(input_shape) != 3:
+            return None
+
+        try:
+            output = torch._scaled_mm(
+                plain_input.reshape(-1, input_shape[2]),
+                weight_t,
+                bias=bias,
+                scale_a=scale_a,
+                scale_b=scale_b,
+                out_dtype=out_dtype,
+            )
+            if not tensor_2d:
+                output = output.reshape((-1, input_shape[1], weight.shape[0]))
+
+            if output.dtype in [torch.float8_e4m3fn, torch.float8_e5m2]:
+                output_scale = scale_a * scale_b
+                output_params = {
+                    'scale': output_scale,
+                    'orig_dtype': input_tensor._layout_params['orig_dtype']
+                }
+                return QuantizedTensor(output, TensorCoreFP8Layout, output_params)
+            else:
+                return output
+
+        except Exception as e:
+            raise RuntimeError(f"FP8 _scaled_mm failed, falling back to dequantization: {e}")
+
+    # Case 2: DQ Fallback
+    if isinstance(weight, QuantizedTensor):
+        weight = weight.dequantize()
+    if isinstance(input_tensor, QuantizedTensor):
+        input_tensor = input_tensor.dequantize()
+
+    return torch.nn.functional.linear(input_tensor, weight, bias)
diff --git a/comfy/sd.py b/comfy/sd.py
index 28bee248d..6411bb27d 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -1262,7 +1262,7 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
     return (model_patcher, clip, vae, clipvision)
 
 
-def load_diffusion_model_state_dict(sd, model_options={}):
+def load_diffusion_model_state_dict(sd, model_options={}, metadata=None):
     """
     Loads a UNet diffusion model from a state dictionary, supporting both diffusers and regular formats.
 
@@ -1296,7 +1296,7 @@ def load_diffusion_model_state_dict(sd, model_options={}):
     weight_dtype = comfy.utils.weight_dtype(sd)
 
     load_device = model_management.get_torch_device()
-    model_config = model_detection.model_config_from_unet(sd, "")
+    model_config = model_detection.model_config_from_unet(sd, "", metadata=metadata)
 
     if model_config is not None:
         new_sd = sd
@@ -1330,7 +1330,10 @@ def load_diffusion_model_state_dict(sd, model_options={}):
     else:
         unet_dtype = dtype
 
-    manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
+    if hasattr(model_config, "layer_quant_config"):
+        manual_cast_dtype = model_management.unet_manual_cast(None, load_device, model_config.supported_inference_dtypes)
+    else:
+        manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
     model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
     model_config.custom_operations = model_options.get("custom_operations", model_config.custom_operations)
     if model_options.get("fp8_optimizations", False):
@@ -1346,8 +1349,8 @@ def load_diffusion_model_state_dict(sd, model_options={}):
 
 
 def load_diffusion_model(unet_path, model_options={}):
-    sd = comfy.utils.load_torch_file(unet_path)
-    model = load_diffusion_model_state_dict(sd, model_options=model_options)
+    sd, metadata = comfy.utils.load_torch_file(unet_path, return_metadata=True)
+    model = load_diffusion_model_state_dict(sd, model_options=model_options, metadata=metadata)
     if model is None:
         logging.error("ERROR UNSUPPORTED DIFFUSION MODEL {}".format(unet_path))
         raise RuntimeError("ERROR: Could not detect model type of: {}\n{}".format(unet_path, model_detection_error_hint(unet_path, sd)))
diff --git a/comfy/supported_models_base.py b/comfy/supported_models_base.py
index 54573abb1..e4bd74514 100644
--- a/comfy/supported_models_base.py
+++ b/comfy/supported_models_base.py
@@ -50,6 +50,7 @@ class BASE:
     manual_cast_dtype = None
     custom_operations = None
     scaled_fp8 = None
+    layer_quant_config = None  # Per-layer quantization configuration for mixed precision
     optimizations = {"fp8": False}
 
     @classmethod
diff --git a/tests-unit/comfy_quant/test_mixed_precision.py b/tests-unit/comfy_quant/test_mixed_precision.py
new file mode 100644
index 000000000..267bc177b
--- /dev/null
+++ b/tests-unit/comfy_quant/test_mixed_precision.py
@@ -0,0 +1,232 @@
+import unittest
+import torch
+import sys
+import os
+
+# Add comfy to path
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
+
+def has_gpu():
+    return torch.cuda.is_available()
+
+from comfy.cli_args import args
+if not has_gpu():
+    args.cpu = True
+
+from comfy import ops
+from comfy.quant_ops import QuantizedTensor, TensorCoreFP8Layout
+
+
+class SimpleModel(torch.nn.Module):
+    def __init__(self, operations=ops.disable_weight_init):
+        super().__init__()
+        self.layer1 = operations.Linear(10, 20, device="cpu", dtype=torch.bfloat16)
+        self.layer2 = operations.Linear(20, 30, device="cpu", dtype=torch.bfloat16)
+        self.layer3 = operations.Linear(30, 40, device="cpu", dtype=torch.bfloat16)
+
+    def forward(self, x):
+        x = self.layer1(x)
+        x = torch.nn.functional.relu(x)
+        x = self.layer2(x)
+        x = torch.nn.functional.relu(x)
+        x = self.layer3(x)
+        return x
+
+
+class TestMixedPrecisionOps(unittest.TestCase):
+
+    def test_all_layers_standard(self):
+        """Test that model with no quantization works normally"""
+        # Configure no quantization
+        ops.MixedPrecisionOps._layer_quant_config = {}
+
+        # Create model
+        model = SimpleModel(operations=ops.MixedPrecisionOps)
+
+        # Initialize weights manually
+        model.layer1.weight = torch.nn.Parameter(torch.randn(20, 10, dtype=torch.bfloat16))
+        model.layer1.bias = torch.nn.Parameter(torch.randn(20, dtype=torch.bfloat16))
+        model.layer2.weight = torch.nn.Parameter(torch.randn(30, 20, dtype=torch.bfloat16))
+        model.layer2.bias = torch.nn.Parameter(torch.randn(30, dtype=torch.bfloat16))
+        model.layer3.weight = torch.nn.Parameter(torch.randn(40, 30, dtype=torch.bfloat16))
+        model.layer3.bias = torch.nn.Parameter(torch.randn(40, dtype=torch.bfloat16))
+
+        # Initialize weight_function and bias_function
+        for layer in [model.layer1, model.layer2, model.layer3]:
+            layer.weight_function = []
+            layer.bias_function = []
+
+        # Forward pass
+        input_tensor = torch.randn(5, 10, dtype=torch.bfloat16)
+        output = model(input_tensor)
+
+        self.assertEqual(output.shape, (5, 40))
+        self.assertEqual(output.dtype, torch.bfloat16)
+
+    def test_mixed_precision_load(self):
+        """Test loading a mixed precision model from state dict"""
+        # Configure mixed precision: layer1 is FP8, layer2 and layer3 are standard
+        layer_quant_config = {
+            "layer1": {
+                "format": "float8_e4m3fn",
+                "params": {}
+            },
+            "layer3": {
+                "format": "float8_e4m3fn",
+                "params": {}
+            }
+        }
+        ops.MixedPrecisionOps._layer_quant_config = layer_quant_config
+
+        # Create state dict with mixed precision
+        fp8_weight1 = torch.randn(20, 10, dtype=torch.float32).to(torch.float8_e4m3fn)
+        fp8_weight3 = torch.randn(40, 30, dtype=torch.float32).to(torch.float8_e4m3fn)
+
+        state_dict = {
+            # Layer 1: FP8 E4M3FN
+            "layer1.weight": fp8_weight1,
+            "layer1.bias": torch.randn(20, dtype=torch.bfloat16),
+            "layer1.weight_scale": torch.tensor(2.0, dtype=torch.float32),
+
+            # Layer 2: Standard BF16
+            "layer2.weight": torch.randn(30, 20, dtype=torch.bfloat16),
+            "layer2.bias": torch.randn(30, dtype=torch.bfloat16),
+
+            # Layer 3: FP8 E4M3FN
+            "layer3.weight": fp8_weight3,
+            "layer3.bias": torch.randn(40, dtype=torch.bfloat16),
+            "layer3.weight_scale": torch.tensor(1.5, dtype=torch.float32),
+        }
+
+        # Create model and load state dict (strict=False because custom loading pops keys)
+        model = SimpleModel(operations=ops.MixedPrecisionOps)
+        model.load_state_dict(state_dict, strict=False)
+
+        # Verify weights are wrapped in QuantizedTensor
+        self.assertIsInstance(model.layer1.weight, QuantizedTensor)
+        self.assertEqual(model.layer1.weight._layout_type, TensorCoreFP8Layout)
+
+        # Layer 2 should NOT be quantized
+        self.assertNotIsInstance(model.layer2.weight, QuantizedTensor)
+
+        # Layer 3 should be quantized
+        self.assertIsInstance(model.layer3.weight, QuantizedTensor)
+        self.assertEqual(model.layer3.weight._layout_type, TensorCoreFP8Layout)
+
+        # Verify scales were loaded
+        self.assertEqual(model.layer1.weight._layout_params['scale'].item(), 2.0)
+        self.assertEqual(model.layer3.weight._layout_params['scale'].item(), 1.5)
+
+        # Forward pass
+        input_tensor = torch.randn(5, 10, dtype=torch.bfloat16)
+        output = model(input_tensor)
+
+        self.assertEqual(output.shape, (5, 40))
+
+    def test_state_dict_quantized_preserved(self):
+        """Test that quantized weights are preserved in state_dict()"""
+        # Configure mixed precision
+        layer_quant_config = {
+            "layer1": {
+                "format": "float8_e4m3fn",
+                "params": {}
+            }
+        }
+        ops.MixedPrecisionOps._layer_quant_config = layer_quant_config
+
+        # Create and load model
+        fp8_weight = torch.randn(20, 10, dtype=torch.float32).to(torch.float8_e4m3fn)
+        state_dict1 = {
+            "layer1.weight": fp8_weight,
+            "layer1.bias": torch.randn(20, dtype=torch.bfloat16),
+            "layer1.weight_scale": torch.tensor(3.0, dtype=torch.float32),
+            "layer2.weight": torch.randn(30, 20, dtype=torch.bfloat16),
+            "layer2.bias": torch.randn(30, dtype=torch.bfloat16),
+            "layer3.weight": torch.randn(40, 30, dtype=torch.bfloat16),
+            "layer3.bias": torch.randn(40, dtype=torch.bfloat16),
+        }
+
+        model = SimpleModel(operations=ops.MixedPrecisionOps)
+        model.load_state_dict(state_dict1, strict=False)
+
+        # Save state dict
+        state_dict2 = model.state_dict()
+
+        # Verify layer1.weight is a QuantizedTensor with scale preserved
+        self.assertIsInstance(state_dict2["layer1.weight"], QuantizedTensor)
+        self.assertEqual(state_dict2["layer1.weight"]._layout_params['scale'].item(), 3.0)
+        self.assertEqual(state_dict2["layer1.weight"]._layout_type, TensorCoreFP8Layout)
+
+        # Verify non-quantized layers are standard tensors
+        self.assertNotIsInstance(state_dict2["layer2.weight"], QuantizedTensor)
+        self.assertNotIsInstance(state_dict2["layer3.weight"], QuantizedTensor)
+
+    def test_weight_function_compatibility(self):
+        """Test that weight_function (LoRA) works with quantized layers"""
+        # Configure FP8 quantization
+        layer_quant_config = {
+            "layer1": {
+                "format": "float8_e4m3fn",
+                "params": {}
+            }
+        }
+        ops.MixedPrecisionOps._layer_quant_config = layer_quant_config
+
+        # Create and load model
+        fp8_weight = torch.randn(20, 10, dtype=torch.float32).to(torch.float8_e4m3fn)
+        state_dict = {
+            "layer1.weight": fp8_weight,
+            "layer1.bias": torch.randn(20, dtype=torch.bfloat16),
+            "layer1.weight_scale": torch.tensor(2.0, dtype=torch.float32),
+            "layer2.weight": torch.randn(30, 20, dtype=torch.bfloat16),
+            "layer2.bias": torch.randn(30, dtype=torch.bfloat16),
+            "layer3.weight": torch.randn(40, 30, dtype=torch.bfloat16),
+            "layer3.bias": torch.randn(40, dtype=torch.bfloat16),
+        }
+
+        model = SimpleModel(operations=ops.MixedPrecisionOps)
+        model.load_state_dict(state_dict, strict=False)
+
+        # Add a weight function (simulating LoRA)
+        # This should trigger dequantization during forward pass
+        def apply_lora(weight):
+            lora_delta = torch.randn_like(weight) * 0.01
+            return weight + lora_delta
+
+        model.layer1.weight_function.append(apply_lora)
+
+        # Forward pass should work with LoRA (triggers weight_function path)
+        input_tensor = torch.randn(5, 10, dtype=torch.bfloat16)
+        output = model(input_tensor)
+
+        self.assertEqual(output.shape, (5, 40))
+
+    def test_error_handling_unknown_format(self):
+        """Test that unknown formats raise error"""
+        # Configure with unknown format
+        layer_quant_config = {
+            "layer1": {
+                "format": "unknown_format_xyz",
+                "params": {}
+            }
+        }
+        ops.MixedPrecisionOps._layer_quant_config = layer_quant_config
+
+        # Create state dict
+        state_dict = {
+            "layer1.weight": torch.randn(20, 10, dtype=torch.bfloat16),
+            "layer1.bias": torch.randn(20, dtype=torch.bfloat16),
+            "layer2.weight": torch.randn(30, 20, dtype=torch.bfloat16),
+            "layer2.bias": torch.randn(30, dtype=torch.bfloat16),
+            "layer3.weight": torch.randn(40, 30, dtype=torch.bfloat16),
+            "layer3.bias": torch.randn(40, dtype=torch.bfloat16),
+        }
+
+        # Load should raise KeyError for unknown format in QUANT_FORMAT_MIXINS
+        model = SimpleModel(operations=ops.MixedPrecisionOps)
+        with self.assertRaises(KeyError):
+            model.load_state_dict(state_dict, strict=False)
+
+if __name__ == "__main__":
+    unittest.main()
+
diff --git a/tests-unit/comfy_quant/test_quant_registry.py b/tests-unit/comfy_quant/test_quant_registry.py
new file mode 100644
index 000000000..477811029
--- /dev/null
+++ b/tests-unit/comfy_quant/test_quant_registry.py
@@ -0,0 +1,190 @@
+import unittest
+import torch
+import sys
+import os
+
+# Add comfy to path
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
+
+def has_gpu():
+    return torch.cuda.is_available()
+
+from comfy.cli_args import args
+if not has_gpu():
+    args.cpu = True
+
+from comfy.quant_ops import QuantizedTensor, TensorCoreFP8Layout
+
+
+class TestQuantizedTensor(unittest.TestCase):
+    """Test the QuantizedTensor subclass with FP8 layout"""
+
+    def test_creation(self):
+        """Test creating a QuantizedTensor with TensorCoreFP8Layout"""
+        fp8_data = torch.randn(256, 128, dtype=torch.float32).to(torch.float8_e4m3fn)
+        scale = torch.tensor(2.0)
+        layout_params = {'scale': scale, 'orig_dtype': torch.bfloat16}
+
+        qt = QuantizedTensor(fp8_data, TensorCoreFP8Layout, layout_params)
+
+        self.assertIsInstance(qt, QuantizedTensor)
+        self.assertEqual(qt.shape, (256, 128))
+        self.assertEqual(qt.dtype, torch.float8_e4m3fn)
+        self.assertEqual(qt._layout_params['scale'], scale)
+        self.assertEqual(qt._layout_params['orig_dtype'], torch.bfloat16)
+        self.assertEqual(qt._layout_type, TensorCoreFP8Layout)
+
+    def test_dequantize(self):
+        """Test explicit dequantization"""
+
+        fp8_data = torch.ones(10, 20, dtype=torch.float32).to(torch.float8_e4m3fn)
+        scale = torch.tensor(3.0)
+        layout_params = {'scale': scale, 'orig_dtype': torch.float32}
+
+        qt = QuantizedTensor(fp8_data, TensorCoreFP8Layout, layout_params)
+        dequantized = qt.dequantize()
+
+        self.assertEqual(dequantized.dtype, torch.float32)
+        self.assertTrue(torch.allclose(dequantized, torch.ones(10, 20) * 3.0, rtol=0.1))
+
+    def test_from_float(self):
+        """Test creating QuantizedTensor from float tensor"""
+        float_tensor = torch.randn(64, 32, dtype=torch.float32)
+        scale = torch.tensor(1.5)
+
+        qt = QuantizedTensor.from_float(
+            float_tensor,
+            TensorCoreFP8Layout,
+            scale=scale,
+            dtype=torch.float8_e4m3fn
+        )
+
+        self.assertIsInstance(qt, QuantizedTensor)
+        self.assertEqual(qt.dtype, torch.float8_e4m3fn)
+        self.assertEqual(qt.shape, (64, 32))
+
+        # Verify dequantization gives approximately original values
+        dequantized = qt.dequantize()
+        mean_rel_error = ((dequantized - float_tensor).abs() / (float_tensor.abs() + 1e-6)).mean()
+        self.assertLess(mean_rel_error, 0.1)
+
+
+class TestGenericUtilities(unittest.TestCase):
+    """Test generic utility operations"""
+
+    def test_detach(self):
+        """Test detach operation on quantized tensor"""
+        fp8_data = torch.randn(10, 20, dtype=torch.float32).to(torch.float8_e4m3fn)
+        scale = torch.tensor(1.5)
+        layout_params = {'scale': scale, 'orig_dtype': torch.float32}
+        qt = QuantizedTensor(fp8_data, TensorCoreFP8Layout, layout_params)
+
+        # Detach should return a new QuantizedTensor
+        qt_detached = qt.detach()
+
+        self.assertIsInstance(qt_detached, QuantizedTensor)
+        self.assertEqual(qt_detached.shape, qt.shape)
+        self.assertEqual(qt_detached._layout_type, TensorCoreFP8Layout)
+
+    def test_clone(self):
+        """Test clone operation on quantized tensor"""
+        fp8_data = torch.randn(10, 20, dtype=torch.float32).to(torch.float8_e4m3fn)
+        scale = torch.tensor(1.5)
+        layout_params = {'scale': scale, 'orig_dtype': torch.float32}
+        qt = QuantizedTensor(fp8_data, TensorCoreFP8Layout, layout_params)
+
+        # Clone should return a new QuantizedTensor
+        qt_cloned = qt.clone()
+
+        self.assertIsInstance(qt_cloned, QuantizedTensor)
+        self.assertEqual(qt_cloned.shape, qt.shape)
+        self.assertEqual(qt_cloned._layout_type, TensorCoreFP8Layout)
+
+        # Verify it's a deep copy
+        self.assertIsNot(qt_cloned._qdata, qt._qdata)
+
+    @unittest.skipUnless(has_gpu(), "GPU not available")
+    def test_to_device(self):
+        """Test device transfer"""
+        fp8_data = torch.randn(10, 20, dtype=torch.float32).to(torch.float8_e4m3fn)
+        scale = torch.tensor(1.5)
+        layout_params = {'scale': scale, 'orig_dtype': torch.float32}
+        qt = QuantizedTensor(fp8_data, TensorCoreFP8Layout, layout_params)
+
+        # Moving to same device should work (CPU to CPU)
+        qt_cpu = qt.to('cpu')
+
+        self.assertIsInstance(qt_cpu, QuantizedTensor)
+        self.assertEqual(qt_cpu.device.type, 'cpu')
+        self.assertEqual(qt_cpu._layout_params['scale'].device.type, 'cpu')
+
+
+class TestTensorCoreFP8Layout(unittest.TestCase):
+    """Test the TensorCoreFP8Layout implementation"""
+
+    def test_quantize(self):
+        """Test quantization method"""
+        float_tensor = torch.randn(32, 64, dtype=torch.float32)
+        scale = torch.tensor(1.5)
+
+        qdata, layout_params = TensorCoreFP8Layout.quantize(
+            float_tensor,
+            scale=scale,
+            dtype=torch.float8_e4m3fn
+        )
+
+        self.assertEqual(qdata.dtype, torch.float8_e4m3fn)
+        self.assertEqual(qdata.shape, float_tensor.shape)
+        self.assertIn('scale', layout_params)
+        self.assertIn('orig_dtype', layout_params)
+        self.assertEqual(layout_params['orig_dtype'], torch.float32)
+
+    def test_dequantize(self):
+        """Test dequantization method"""
+        float_tensor = torch.ones(10, 20, dtype=torch.float32) * 3.0
+        scale = torch.tensor(1.0)
+
+        qdata, layout_params = TensorCoreFP8Layout.quantize(
+            float_tensor,
+            scale=scale,
+            dtype=torch.float8_e4m3fn
+        )
+
+        dequantized = TensorCoreFP8Layout.dequantize(qdata, **layout_params)
+
+        # Should approximately match original
+        self.assertTrue(torch.allclose(dequantized, float_tensor, rtol=0.1, atol=0.1))
+
+
+class TestFallbackMechanism(unittest.TestCase):
+    """Test fallback for unsupported operations"""
+
+    def test_unsupported_op_dequantizes(self):
+        """Test that unsupported operations fall back to dequantization"""
+        # Set seed for reproducibility
+        torch.manual_seed(42)
+
+        # Create quantized tensor
+        a_fp32 = torch.randn(10, 20, dtype=torch.float32)
+        scale = torch.tensor(1.0)
+        a_q = QuantizedTensor.from_float(
+            a_fp32,
+            TensorCoreFP8Layout,
+            scale=scale,
+            dtype=torch.float8_e4m3fn
+        )
+
+        # Call an operation that doesn't have a registered handler
+        # For example, torch.abs
+        result = torch.abs(a_q)
+
+        # Should work via fallback (dequantize → abs → return)
+        self.assertNotIsInstance(result, QuantizedTensor)
+        expected = torch.abs(a_fp32)
+        # FP8 introduces quantization error, so use loose tolerance
+        mean_error = (result - expected).abs().mean()
+        self.assertLess(mean_error, 0.05, f"Mean error {mean_error:.4f} is too large")
+
+
+if __name__ == "__main__":
+    unittest.main()

From d202c2ba7404affd58a2199aeb514b3cc48e0ef3 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Wed, 29 Oct 2025 06:22:08 +1000
Subject: [PATCH 12/56] execution: Allow a subgraph nodes to execute multiple
 times (#10499)

In the case of --cache-none lazy and subgraph execution can cause
anything to be run multiple times per workflow. If that rerun nodes is
in itself a subgraph generator, this will crash for two reasons.

pending_subgraph_results[] does not cleanup entries after their use.
So when a pending_subgraph_result is consumed, remove it from the list
so that if the corresponding node is fully re-executed this misses
lookup and it fall through to execute the node as it should.

Secondly, theres is an explicit enforcement against dups in the
addition of subgraphs nodes as ephemerals to the dymprompt. Remove this
enforcement as the use case is now valid.
---
 execution.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/execution.py b/execution.py
index b14bb14c7..20e106213 100644
--- a/execution.py
+++ b/execution.py
@@ -445,6 +445,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
                     resolved_outputs.append(tuple(resolved_output))
             output_data = merge_result_data(resolved_outputs, class_def)
             output_ui = []
+            del pending_subgraph_results[unique_id]
             has_subgraph = False
         else:
             get_progress_state().start_progress(unique_id)
@@ -527,10 +528,6 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
                 if new_graph is None:
                     cached_outputs.append((False, node_outputs))
                 else:
-                    # Check for conflicts
-                    for node_id in new_graph.keys():
-                        if dynprompt.has_node(node_id):
-                            raise DuplicateNodeError(f"Attempt to add duplicate node {node_id}. Ensure node ids are unique and deterministic or use graph_utils.GraphBuilder.")
                     for node_id, node_info in new_graph.items():
                         new_node_ids.append(node_id)
                         display_id = node_info.get("override_display_id", unique_id)

From 210f7a1ba580d57d817ca68346cb72b8d0a26ad2 Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Tue, 28 Oct 2025 23:38:05 +0200
Subject: [PATCH 13/56] convert nodes_recraft.py to V3 schema (#10507)

---
 comfy_api_nodes/nodes_recraft.py | 1319 +++++++++++++-----------------
 1 file changed, 585 insertions(+), 734 deletions(-)

diff --git a/comfy_api_nodes/nodes_recraft.py b/comfy_api_nodes/nodes_recraft.py
index 8ee7e55c4..dee186cd6 100644
--- a/comfy_api_nodes/nodes_recraft.py
+++ b/comfy_api_nodes/nodes_recraft.py
@@ -1,82 +1,71 @@
-from __future__ import annotations
-from inspect import cleandoc
-from typing import Optional
+from io import BytesIO
+from typing import Optional, Union
+
+import aiohttp
+import torch
+from PIL import UnidentifiedImageError
+from typing_extensions import override
+
 from comfy.utils import ProgressBar
-from comfy_extras.nodes_images import SVG # Added
-from comfy.comfy_types.node_typing import IO
+from comfy_api.latest import IO, ComfyExtension
+from comfy_api_nodes.apinode_utils import (
+    resize_mask_to_image,
+)
 from comfy_api_nodes.apis.recraft_api import (
-    RecraftImageGenerationRequest,
-    RecraftImageGenerationResponse,
-    RecraftImageSize,
-    RecraftModel,
-    RecraftStyle,
-    RecraftStyleV3,
     RecraftColor,
     RecraftColorChain,
     RecraftControls,
+    RecraftImageGenerationRequest,
+    RecraftImageGenerationResponse,
+    RecraftImageSize,
     RecraftIO,
+    RecraftModel,
+    RecraftStyle,
+    RecraftStyleV3,
     get_v3_substyles,
 )
-from comfy_api_nodes.apis.client import (
+from comfy_api_nodes.util import (
     ApiEndpoint,
-    HttpMethod,
-    SynchronousOperation,
-    EmptyRequest,
+    bytesio_to_image_tensor,
+    download_url_as_bytesio,
+    sync_op,
+    tensor_to_bytesio,
+    validate_string,
 )
-from comfy_api_nodes.apinode_utils import (
-    download_url_to_bytesio,
-    resize_mask_to_image,
-)
-from comfy_api_nodes.util import validate_string, tensor_to_bytesio, bytesio_to_image_tensor
-from server import PromptServer
-
-import torch
-from io import BytesIO
-from PIL import UnidentifiedImageError
-import aiohttp
+from comfy_extras.nodes_images import SVG
 
 
 async def handle_recraft_file_request(
+    cls: type[IO.ComfyNode],
     image: torch.Tensor,
     path: str,
-    mask: torch.Tensor=None,
-    total_pixels=4096*4096,
-    timeout=1024,
+    mask: Optional[torch.Tensor] = None,
+    total_pixels: int = 4096 * 4096,
+    timeout: int = 1024,
     request=None,
-    auth_kwargs: dict[str,str] = None,
 ) -> list[BytesIO]:
-    """
-    Handle sending common Recraft file-only request to get back file bytes.
-    """
-    if request is None:
-        request = EmptyRequest()
+    """Handle sending common Recraft file-only request to get back file bytes."""
 
-    files = {
-        'image': tensor_to_bytesio(image, total_pixels=total_pixels).read()
-    }
+    files = {"image": tensor_to_bytesio(image, total_pixels=total_pixels).read()}
     if mask is not None:
-        files['mask'] = tensor_to_bytesio(mask, total_pixels=total_pixels).read()
+        files["mask"] = tensor_to_bytesio(mask, total_pixels=total_pixels).read()
 
-    operation = SynchronousOperation(
-        endpoint=ApiEndpoint(
-            path=path,
-            method=HttpMethod.POST,
-            request_model=type(request),
-            response_model=RecraftImageGenerationResponse,
-        ),
-        request=request,
+    response = await sync_op(
+        cls,
+        endpoint=ApiEndpoint(path=path, method="POST"),
+        response_model=RecraftImageGenerationResponse,
+        data=request if request else None,
         files=files,
         content_type="multipart/form-data",
-        auth_kwargs=auth_kwargs,
         multipart_parser=recraft_multipart_parser,
+        max_retries=1,
     )
-    response: RecraftImageGenerationResponse = await operation.execute()
     all_bytesio = []
     if response.image is not None:
-        all_bytesio.append(await download_url_to_bytesio(response.image.url, timeout=timeout))
+        all_bytesio.append(await download_url_as_bytesio(response.image.url, timeout=timeout))
     else:
         for data in response.data:
-            all_bytesio.append(await download_url_to_bytesio(data.url, timeout=timeout))
+            all_bytesio.append(await download_url_as_bytesio(data.url, timeout=timeout))
 
     return all_bytesio
 
@@ -84,11 +73,11 @@ async def handle_recraft_file_request(
 def recraft_multipart_parser(
     data,
     parent_key=None,
-    formatter: callable = None,
-    converted_to_check: list[list] = None,
+    formatter: Optional[type[callable]] = None,
+    converted_to_check: Optional[list[list]] = None,
     is_list: bool = False,
-    return_mode: str = "formdata"  # "dict" | "formdata"
-) -> dict | aiohttp.FormData:
+    return_mode: str = "formdata",  # "dict" | "formdata"
+) -> Union[dict, aiohttp.FormData]:
     """
     Formats data such that multipart/form-data will work with aiohttp library when both files and data are present.
 
@@ -108,8 +97,8 @@ def recraft_multipart_parser(
     # Modification of a function that handled a different type of multipart parsing, big ups:
     # https://gist.github.com/kazqvaizer/4cebebe5db654a414132809f9f88067b
 
-    def handle_converted_lists(item, parent_key, lists_to_check=tuple[list]):
-        # if list already exists exists, just extend list with data
+    def handle_converted_lists(item, parent_key, lists_to_check=list[list]):
+        # if list already exists, just extend list with data
         for check_list in lists_to_check:
             for conv_tuple in check_list:
                 if conv_tuple[0] == parent_key and isinstance(conv_tuple[1], list):
@@ -125,7 +114,7 @@ def recraft_multipart_parser(
         formatter = lambda v: v  # Multipart representation of value
 
     if not isinstance(data, dict):
-        # if list already exists exists, just extend list with data
+        # if list already exists, just extend list with data
         added = handle_converted_lists(data, parent_key, converted_to_check)
         if added:
             return {}
@@ -146,7 +135,9 @@ def recraft_multipart_parser(
         elif isinstance(value, list):
             for ind, list_value in enumerate(value):
                 iter_key = f"{current_key}[]"
-                converted.extend(recraft_multipart_parser(list_value, iter_key, formatter, next_check, is_list=True).items())
+                converted.extend(
+                    recraft_multipart_parser(list_value, iter_key, formatter, next_check, is_list=True).items()
+                )
         else:
             converted.append((current_key, formatter(value)))
 
@@ -166,6 +157,7 @@ class handle_recraft_image_output:
     """
     Catch an exception related to receiving SVG data instead of image, when Infinite Style Library style_id is in use.
     """
+
     def __init__(self):
         pass
 
@@ -174,243 +166,225 @@ class handle_recraft_image_output:
 
     def __exit__(self, exc_type, exc_val, exc_tb):
         if exc_type is not None and exc_type is UnidentifiedImageError:
-            raise Exception("Received output data was not an image; likely an SVG. If you used style_id, make sure it is not a Vector art style.")
+            raise Exception(
+                "Received output data was not an image; likely an SVG. "
+                "If you used style_id, make sure it is not a Vector art style."
+            )
 
 
-class RecraftColorRGBNode:
-    """
-    Create Recraft Color by choosing specific RGB values.
-    """
-
-    RETURN_TYPES = (RecraftIO.COLOR,)
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
-    RETURN_NAMES = ("recraft_color",)
-    FUNCTION = "create_color"
-    CATEGORY = "api node/image/Recraft"
+class RecraftColorRGBNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftColorRGB",
+            display_name="Recraft Color RGB",
+            category="api node/image/Recraft",
+            description="Create Recraft Color by choosing specific RGB values.",
+            inputs=[
+                IO.Int.Input("r", default=0, min=0, max=255, tooltip="Red value of color."),
+                IO.Int.Input("g", default=0, min=0, max=255, tooltip="Green value of color."),
+                IO.Int.Input("b", default=0, min=0, max=255, tooltip="Blue value of color."),
+                IO.Custom(RecraftIO.COLOR).Input("recraft_color", optional=True),
+            ],
+            outputs=[
+                IO.Custom(RecraftIO.COLOR).Output(display_name="recraft_color"),
+            ],
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "r": (IO.INT, {
-                    "default": 0,
-                    "min": 0,
-                    "max": 255,
-                    "tooltip": "Red value of color."
-                }),
-                "g": (IO.INT, {
-                    "default": 0,
-                    "min": 0,
-                    "max": 255,
-                    "tooltip": "Green value of color."
-                }),
-                "b": (IO.INT, {
-                    "default": 0,
-                    "min": 0,
-                    "max": 255,
-                    "tooltip": "Blue value of color."
-                }),
-            },
-            "optional": {
-                "recraft_color": (RecraftIO.COLOR,),
-            }
-        }
-
-    def create_color(self, r: int, g: int, b: int, recraft_color: RecraftColorChain=None):
+    def execute(cls, r: int, g: int, b: int, recraft_color: RecraftColorChain = None) -> IO.NodeOutput:
         recraft_color = recraft_color.clone() if recraft_color else RecraftColorChain()
         recraft_color.add(RecraftColor(r, g, b))
-        return (recraft_color, )
+        return IO.NodeOutput(recraft_color)
 
 
-class RecraftControlsNode:
-    """
-    Create Recraft Controls for customizing Recraft generation.
-    """
-
-    RETURN_TYPES = (RecraftIO.CONTROLS,)
-    RETURN_NAMES = ("recraft_controls",)
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
-    FUNCTION = "create_controls"
-    CATEGORY = "api node/image/Recraft"
+class RecraftControlsNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftControls",
+            display_name="Recraft Controls",
+            category="api node/image/Recraft",
+            description="Create Recraft Controls for customizing Recraft generation.",
+            inputs=[
+                IO.Custom(RecraftIO.COLOR).Input("colors", optional=True),
+                IO.Custom(RecraftIO.COLOR).Input("background_color", optional=True),
+            ],
+            outputs=[
+                IO.Custom(RecraftIO.CONTROLS).Output(display_name="recraft_controls"),
+            ],
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-            },
-            "optional": {
-                "colors": (RecraftIO.COLOR,),
-                "background_color": (RecraftIO.COLOR,),
-            }
-        }
-
-    def create_controls(self, colors: RecraftColorChain=None, background_color: RecraftColorChain=None):
-        return (RecraftControls(colors=colors, background_color=background_color), )
+    def execute(cls, colors: RecraftColorChain = None, background_color: RecraftColorChain = None) -> IO.NodeOutput:
+        return IO.NodeOutput(RecraftControls(colors=colors, background_color=background_color))
 
 
-class RecraftStyleV3RealisticImageNode:
-    """
-    Select realistic_image style and optional substyle.
-    """
-
-    RETURN_TYPES = (RecraftIO.STYLEV3,)
-    RETURN_NAMES = ("recraft_style",)
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
-    FUNCTION = "create_style"
-    CATEGORY = "api node/image/Recraft"
-
+class RecraftStyleV3RealisticImageNode(IO.ComfyNode):
     RECRAFT_STYLE = RecraftStyleV3.realistic_image
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "substyle": (get_v3_substyles(s.RECRAFT_STYLE),),
-            }
-        }
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftStyleV3RealisticImage",
+            display_name="Recraft Style - Realistic Image",
+            category="api node/image/Recraft",
+            description="Select realistic_image style and optional substyle.",
+            inputs=[
+                IO.Combo.Input("substyle", options=get_v3_substyles(cls.RECRAFT_STYLE)),
+            ],
+            outputs=[
+                IO.Custom(RecraftIO.STYLEV3).Output(display_name="recraft_style"),
+            ],
+        )
 
-    def create_style(self, substyle: str):
+    @classmethod
+    def execute(cls, substyle: str) -> IO.NodeOutput:
         if substyle == "None":
             substyle = None
-        return (RecraftStyle(self.RECRAFT_STYLE, substyle),)
+        return IO.NodeOutput(RecraftStyle(cls.RECRAFT_STYLE, substyle))
 
 
 class RecraftStyleV3DigitalIllustrationNode(RecraftStyleV3RealisticImageNode):
-    """
-    Select digital_illustration style and optional substyle.
-    """
-
     RECRAFT_STYLE = RecraftStyleV3.digital_illustration
 
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftStyleV3DigitalIllustration",
+            display_name="Recraft Style - Digital Illustration",
+            category="api node/image/Recraft",
+            description="Select realistic_image style and optional substyle.",
+            inputs=[
+                IO.Combo.Input("substyle", options=get_v3_substyles(cls.RECRAFT_STYLE)),
+            ],
+            outputs=[
+                IO.Custom(RecraftIO.STYLEV3).Output(display_name="recraft_style"),
+            ],
+        )
+
 
 class RecraftStyleV3VectorIllustrationNode(RecraftStyleV3RealisticImageNode):
-    """
-    Select vector_illustration style and optional substyle.
-    """
-
     RECRAFT_STYLE = RecraftStyleV3.vector_illustration
 
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftStyleV3VectorIllustrationNode",
+            display_name="Recraft Style - Realistic Image",
+            category="api node/image/Recraft",
+            description="Select realistic_image style and optional substyle.",
+            inputs=[
+                IO.Combo.Input("substyle", options=get_v3_substyles(cls.RECRAFT_STYLE)),
+            ],
+            outputs=[
+                IO.Custom(RecraftIO.STYLEV3).Output(display_name="recraft_style"),
+            ],
+        )
+
 
 class RecraftStyleV3LogoRasterNode(RecraftStyleV3RealisticImageNode):
-    """
-    Select vector_illustration style and optional substyle.
-    """
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "substyle": (get_v3_substyles(s.RECRAFT_STYLE, include_none=False),),
-            }
-        }
-
     RECRAFT_STYLE = RecraftStyleV3.logo_raster
 
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftStyleV3LogoRaster",
+            display_name="Recraft Style - Logo Raster",
+            category="api node/image/Recraft",
+            description="Select realistic_image style and optional substyle.",
+            inputs=[
+                IO.Combo.Input("substyle", options=get_v3_substyles(cls.RECRAFT_STYLE, include_none=False)),
+            ],
+            outputs=[
+                IO.Custom(RecraftIO.STYLEV3).Output(display_name="recraft_style"),
+            ],
+        )
 
-class RecraftStyleInfiniteStyleLibrary:
-    """
-    Select style based on preexisting UUID from Recraft's Infinite Style Library.
-    """
 
-    RETURN_TYPES = (RecraftIO.STYLEV3,)
-    RETURN_NAMES = ("recraft_style",)
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
-    FUNCTION = "create_style"
-    CATEGORY = "api node/image/Recraft"
+class RecraftStyleInfiniteStyleLibrary(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftStyleV3InfiniteStyleLibrary",
+            display_name="Recraft Style - Infinite Style Library",
+            category="api node/image/Recraft",
+            description="Select style based on preexisting UUID from Recraft's Infinite Style Library.",
+            inputs=[
+                IO.String.Input("style_id", default="", tooltip="UUID of style from Infinite Style Library."),
+            ],
+            outputs=[
+                IO.Custom(RecraftIO.STYLEV3).Output(display_name="recraft_style"),
+            ],
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "style_id": (IO.STRING, {
-                    "default": "",
-                    "tooltip": "UUID of style from Infinite Style Library.",
-                })
-            }
-        }
-
-    def create_style(self, style_id: str):
+    def execute(cls, style_id: str) -> IO.NodeOutput:
         if not style_id:
             raise Exception("The style_id input cannot be empty.")
-        return (RecraftStyle(style_id=style_id),)
+        return IO.NodeOutput(RecraftStyle(style_id=style_id))
 
 
-class RecraftTextToImageNode:
-    """
-    Generates images synchronously based on prompt and resolution.
-    """
-
-    RETURN_TYPES = (IO.IMAGE,)
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
-    FUNCTION = "api_call"
-    API_NODE = True
-    CATEGORY = "api node/image/Recraft"
+class RecraftTextToImageNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftTextToImageNode",
+            display_name="Recraft Text to Image",
+            category="api node/image/Recraft",
+            description="Generates images synchronously based on prompt and resolution.",
+            inputs=[
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Prompt for the image generation."),
+                IO.Combo.Input(
+                    "size",
+                    options=[res.value for res in RecraftImageSize],
+                    default=RecraftImageSize.res_1024x1024,
+                    tooltip="The size of the generated image.",
+                ),
+                IO.Int.Input(
+                    "n",
+                    default=1,
+                    min=1,
+                    max=6,
+                    tooltip="The number of images to generate.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+                IO.Custom(RecraftIO.STYLEV3).Input("recraft_style", optional=True),
+                IO.String.Input(
+                    "negative_prompt",
+                    default="",
+                    force_input=True,
+                    tooltip="An optional text description of undesired elements on an image.",
+                    optional=True,
+                ),
+                IO.Custom(RecraftIO.CONTROLS).Input(
+                    "recraft_controls",
+                    tooltip="Optional additional controls over the generation via the Recraft Controls node.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "prompt": (
-                    IO.STRING,
-                    {
-                        "multiline": True,
-                        "default": "",
-                        "tooltip": "Prompt for the image generation.",
-                    },
-                ),
-                "size": (
-                    [res.value for res in RecraftImageSize],
-                    {
-                        "default": RecraftImageSize.res_1024x1024,
-                        "tooltip": "The size of the generated image.",
-                    },
-                ),
-                "n": (
-                    IO.INT,
-                    {
-                        "default": 1,
-                        "min": 1,
-                        "max": 6,
-                        "tooltip": "The number of images to generate.",
-                    },
-                ),
-                "seed": (
-                    IO.INT,
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "max": 0xFFFFFFFFFFFFFFFF,
-                        "control_after_generate": True,
-                        "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
-                    },
-                ),
-            },
-            "optional": {
-                "recraft_style": (RecraftIO.STYLEV3,),
-                "negative_prompt": (
-                    IO.STRING,
-                    {
-                        "default": "",
-                        "forceInput": True,
-                        "tooltip": "An optional text description of undesired elements on an image.",
-                    },
-                ),
-                "recraft_controls": (
-                    RecraftIO.CONTROLS,
-                    {
-                        "tooltip": "Optional additional controls over the generation via the Recraft Controls node."
-                    },
-                ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-                "unique_id": "UNIQUE_ID",
-            },
-        }
-
-    async def api_call(
-        self,
+    async def execute(
+        cls,
         prompt: str,
         size: str,
         n: int,
@@ -418,9 +392,7 @@ class RecraftTextToImageNode:
         recraft_style: RecraftStyle = None,
         negative_prompt: str = None,
         recraft_controls: RecraftControls = None,
-        unique_id: Optional[str] = None,
-        **kwargs,
-    ):
+    ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=False, max_length=1000)
         default_style = RecraftStyle(RecraftStyleV3.realistic_image)
         if recraft_style is None:
@@ -433,14 +405,11 @@ class RecraftTextToImageNode:
         if not negative_prompt:
             negative_prompt = None
 
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/recraft/image_generation",
-                method=HttpMethod.POST,
-                request_model=RecraftImageGenerationRequest,
-                response_model=RecraftImageGenerationResponse,
-            ),
-            request=RecraftImageGenerationRequest(
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/recraft/image_generation", method="POST"),
+            response_model=RecraftImageGenerationResponse,
+            data=RecraftImageGenerationRequest(
                 prompt=prompt,
                 negative_prompt=negative_prompt,
                 model=RecraftModel.recraftv3,
@@ -451,109 +420,83 @@ class RecraftTextToImageNode:
                 style_id=recraft_style.style_id,
                 controls=controls_api,
             ),
-            auth_kwargs=kwargs,
+            max_retries=1,
         )
-        response: RecraftImageGenerationResponse = await operation.execute()
         images = []
-        urls = []
         for data in response.data:
             with handle_recraft_image_output():
-                if unique_id and data.url:
-                    urls.append(data.url)
-                    urls_string = '\n'.join(urls)
-                    PromptServer.instance.send_progress_text(
-                        f"Result URL: {urls_string}", unique_id
-                    )
-                image = bytesio_to_image_tensor(
-                    await download_url_to_bytesio(data.url, timeout=1024)
-                )
+                image = bytesio_to_image_tensor(await download_url_as_bytesio(data.url, timeout=1024))
             if len(image.shape) < 4:
                 image = image.unsqueeze(0)
             images.append(image)
-        output_image = torch.cat(images, dim=0)
 
-        return (output_image,)
+        return IO.NodeOutput(torch.cat(images, dim=0))
 
 
-class RecraftImageToImageNode:
-    """
-    Modify image based on prompt and strength.
-    """
-
-    RETURN_TYPES = (IO.IMAGE,)
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
-    FUNCTION = "api_call"
-    API_NODE = True
-    CATEGORY = "api node/image/Recraft"
+class RecraftImageToImageNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftImageToImageNode",
+            display_name="Recraft Image to Image",
+            category="api node/image/Recraft",
+            description="Modify image based on prompt and strength.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Prompt for the image generation."),
+                IO.Int.Input(
+                    "n",
+                    default=1,
+                    min=1,
+                    max=6,
+                    tooltip="The number of images to generate.",
+                ),
+                IO.Float.Input(
+                    "strength",
+                    default=0.5,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    tooltip="Defines the difference with the original image, should lie in [0, 1], "
+                    "where 0 means almost identical, and 1 means miserable similarity.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+                IO.Custom(RecraftIO.STYLEV3).Input("recraft_style", optional=True),
+                IO.String.Input(
+                    "negative_prompt",
+                    default="",
+                    force_input=True,
+                    tooltip="An optional text description of undesired elements on an image.",
+                    optional=True,
+                ),
+                IO.Custom(RecraftIO.CONTROLS).Input(
+                    "recraft_controls",
+                    tooltip="Optional additional controls over the generation via the Recraft Controls node.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": (IO.IMAGE, ),
-                "prompt": (
-                    IO.STRING,
-                    {
-                        "multiline": True,
-                        "default": "",
-                        "tooltip": "Prompt for the image generation.",
-                    },
-                ),
-                "n": (
-                    IO.INT,
-                    {
-                        "default": 1,
-                        "min": 1,
-                        "max": 6,
-                        "tooltip": "The number of images to generate.",
-                    },
-                ),
-                "strength": (
-                    IO.FLOAT,
-                    {
-                        "default": 0.5,
-                        "min": 0.0,
-                        "max": 1.0,
-                        "step": 0.01,
-                        "tooltip": "Defines the difference with the original image, should lie in [0, 1], where 0 means almost identical, and 1 means miserable similarity."
-                    }
-                ),
-                "seed": (
-                    IO.INT,
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "max": 0xFFFFFFFFFFFFFFFF,
-                        "control_after_generate": True,
-                        "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
-                    },
-                ),
-            },
-            "optional": {
-                "recraft_style": (RecraftIO.STYLEV3,),
-                "negative_prompt": (
-                    IO.STRING,
-                    {
-                        "default": "",
-                        "forceInput": True,
-                        "tooltip": "An optional text description of undesired elements on an image.",
-                    },
-                ),
-                "recraft_controls": (
-                    RecraftIO.CONTROLS,
-                    {
-                        "tooltip": "Optional additional controls over the generation via the Recraft Controls node."
-                    },
-                ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-            },
-        }
-
-    async def api_call(
-        self,
+    async def execute(
+        cls,
         image: torch.Tensor,
         prompt: str,
         n: int,
@@ -562,8 +505,7 @@ class RecraftImageToImageNode:
         recraft_style: RecraftStyle = None,
         negative_prompt: str = None,
         recraft_controls: RecraftControls = None,
-        **kwargs,
-    ):
+    ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=False, max_length=1000)
         default_style = RecraftStyle(RecraftStyleV3.realistic_image)
         if recraft_style is None:
@@ -593,83 +535,69 @@ class RecraftImageToImageNode:
         pbar = ProgressBar(total)
         for i in range(total):
             sub_bytes = await handle_recraft_file_request(
+                cls,
                 image=image[i],
                 path="/proxy/recraft/images/imageToImage",
                 request=request,
-                auth_kwargs=kwargs,
             )
             with handle_recraft_image_output():
                 images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0))
             pbar.update(1)
 
-        images_tensor = torch.cat(images, dim=0)
-        return (images_tensor, )
+        return IO.NodeOutput(torch.cat(images, dim=0))
 
 
-class RecraftImageInpaintingNode:
-    """
-    Modify image based on prompt and mask.
-    """
-
-    RETURN_TYPES = (IO.IMAGE,)
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
-    FUNCTION = "api_call"
-    API_NODE = True
-    CATEGORY = "api node/image/Recraft"
+class RecraftImageInpaintingNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftImageInpaintingNode",
+            display_name="Recraft Image Inpainting",
+            category="api node/image/Recraft",
+            description="Modify image based on prompt and mask.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Mask.Input("mask"),
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Prompt for the image generation."),
+                IO.Int.Input(
+                    "n",
+                    default=1,
+                    min=1,
+                    max=6,
+                    tooltip="The number of images to generate.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+                IO.Custom(RecraftIO.STYLEV3).Input("recraft_style", optional=True),
+                IO.String.Input(
+                    "negative_prompt",
+                    default="",
+                    force_input=True,
+                    tooltip="An optional text description of undesired elements on an image.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": (IO.IMAGE, ),
-                "mask": (IO.MASK, ),
-                "prompt": (
-                    IO.STRING,
-                    {
-                        "multiline": True,
-                        "default": "",
-                        "tooltip": "Prompt for the image generation.",
-                    },
-                ),
-                "n": (
-                    IO.INT,
-                    {
-                        "default": 1,
-                        "min": 1,
-                        "max": 6,
-                        "tooltip": "The number of images to generate.",
-                    },
-                ),
-                "seed": (
-                    IO.INT,
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "max": 0xFFFFFFFFFFFFFFFF,
-                        "control_after_generate": True,
-                        "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
-                    },
-                ),
-            },
-            "optional": {
-                "recraft_style": (RecraftIO.STYLEV3,),
-                "negative_prompt": (
-                    IO.STRING,
-                    {
-                        "default": "",
-                        "forceInput": True,
-                        "tooltip": "An optional text description of undesired elements on an image.",
-                    },
-                ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-            },
-        }
-
-    async def api_call(
-        self,
+    async def execute(
+        cls,
         image: torch.Tensor,
         mask: torch.Tensor,
         prompt: str,
@@ -677,8 +605,7 @@ class RecraftImageInpaintingNode:
         seed,
         recraft_style: RecraftStyle = None,
         negative_prompt: str = None,
-        **kwargs,
-    ):
+    ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=False, max_length=1000)
         default_style = RecraftStyle(RecraftStyleV3.realistic_image)
         if recraft_style is None:
@@ -705,96 +632,73 @@ class RecraftImageInpaintingNode:
         pbar = ProgressBar(total)
         for i in range(total):
             sub_bytes = await handle_recraft_file_request(
+                cls,
                 image=image[i],
-                mask=mask[i:i+1],
+                mask=mask[i : i + 1],
                 path="/proxy/recraft/images/inpaint",
                 request=request,
-                auth_kwargs=kwargs,
             )
             with handle_recraft_image_output():
                 images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0))
             pbar.update(1)
 
-        images_tensor = torch.cat(images, dim=0)
-        return (images_tensor, )
+        return IO.NodeOutput(torch.cat(images, dim=0))
 
 
-class RecraftTextToVectorNode:
-    """
-    Generates SVG synchronously based on prompt and resolution.
-    """
-
-    RETURN_TYPES = ("SVG",) # Changed
-    DESCRIPTION = cleandoc(__doc__ or "") if 'cleandoc' in globals() else __doc__ # Keep cleandoc if other nodes use it
-    FUNCTION = "api_call"
-    API_NODE = True
-    CATEGORY = "api node/image/Recraft"
+class RecraftTextToVectorNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftTextToVectorNode",
+            display_name="Recraft Text to Vector",
+            category="api node/image/Recraft",
+            description="Generates SVG synchronously based on prompt and resolution.",
+            inputs=[
+                IO.String.Input("prompt", default="", tooltip="Prompt for the image generation.", multiline=True),
+                IO.Combo.Input("substyle", options=get_v3_substyles(RecraftStyleV3.vector_illustration)),
+                IO.Combo.Input(
+                    "size",
+                    options=[res.value for res in RecraftImageSize],
+                    default=RecraftImageSize.res_1024x1024,
+                    tooltip="The size of the generated image.",
+                ),
+                IO.Int.Input("n", default=1, min=1, max=6, tooltip="The number of images to generate."),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    default="",
+                    force_input=True,
+                    tooltip="An optional text description of undesired elements on an image.",
+                    optional=True,
+                ),
+                IO.Custom(RecraftIO.CONTROLS).Input(
+                    "recraft_controls",
+                    tooltip="Optional additional controls over the generation via the Recraft Controls node.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.SVG.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "prompt": (
-                    IO.STRING,
-                    {
-                        "multiline": True,
-                        "default": "",
-                        "tooltip": "Prompt for the image generation.",
-                    },
-                ),
-                "substyle": (get_v3_substyles(RecraftStyleV3.vector_illustration),),
-                "size": (
-                    [res.value for res in RecraftImageSize],
-                    {
-                        "default": RecraftImageSize.res_1024x1024,
-                        "tooltip": "The size of the generated image.",
-                    },
-                ),
-                "n": (
-                    IO.INT,
-                    {
-                        "default": 1,
-                        "min": 1,
-                        "max": 6,
-                        "tooltip": "The number of images to generate.",
-                    },
-                ),
-                "seed": (
-                    IO.INT,
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "max": 0xFFFFFFFFFFFFFFFF,
-                        "control_after_generate": True,
-                        "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
-                    },
-                ),
-            },
-            "optional": {
-                "negative_prompt": (
-                    IO.STRING,
-                    {
-                        "default": "",
-                        "forceInput": True,
-                        "tooltip": "An optional text description of undesired elements on an image.",
-                    },
-                ),
-                "recraft_controls": (
-                    RecraftIO.CONTROLS,
-                    {
-                        "tooltip": "Optional additional controls over the generation via the Recraft Controls node."
-                    },
-                ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-                "unique_id": "UNIQUE_ID",
-            },
-        }
-
-    async def api_call(
-        self,
+    async def execute(
+        cls,
         prompt: str,
         substyle: str,
         size: str,
@@ -802,9 +706,7 @@ class RecraftTextToVectorNode:
         seed,
         negative_prompt: str = None,
         recraft_controls: RecraftControls = None,
-        unique_id: Optional[str] = None,
-        **kwargs,
-    ):
+    ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=False, max_length=1000)
         # create RecraftStyle so strings will be formatted properly (i.e. "None" will become None)
         recraft_style = RecraftStyle(RecraftStyleV3.vector_illustration, substyle=substyle)
@@ -816,14 +718,11 @@ class RecraftTextToVectorNode:
         if not negative_prompt:
             negative_prompt = None
 
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/recraft/image_generation",
-                method=HttpMethod.POST,
-                request_model=RecraftImageGenerationRequest,
-                response_model=RecraftImageGenerationResponse,
-            ),
-            request=RecraftImageGenerationRequest(
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/recraft/image_generation", method="POST"),
+            response_model=RecraftImageGenerationResponse,
+            data=RecraftImageGenerationRequest(
                 prompt=prompt,
                 negative_prompt=negative_prompt,
                 model=RecraftModel.recraftv3,
@@ -833,139 +732,105 @@ class RecraftTextToVectorNode:
                 substyle=recraft_style.substyle,
                 controls=controls_api,
             ),
-            auth_kwargs=kwargs,
+            max_retries=1,
         )
-        response: RecraftImageGenerationResponse = await operation.execute()
         svg_data = []
-        urls = []
         for data in response.data:
-            if unique_id and data.url:
-                urls.append(data.url)
-                # Print result on each iteration in case of error
-                PromptServer.instance.send_progress_text(
-                    f"Result URL: {' '.join(urls)}", unique_id
-                )
-            svg_data.append(await download_url_to_bytesio(data.url, timeout=1024))
+            svg_data.append(await download_url_as_bytesio(data.url, timeout=1024))
 
-        return (SVG(svg_data),)
+        return IO.NodeOutput(SVG(svg_data))
 
 
-class RecraftVectorizeImageNode:
-    """
-    Generates SVG synchronously from an input image.
-    """
-
-    RETURN_TYPES = ("SVG",) # Changed
-    DESCRIPTION = cleandoc(__doc__ or "") if 'cleandoc' in globals() else __doc__ # Keep cleandoc if other nodes use it
-    FUNCTION = "api_call"
-    API_NODE = True
-    CATEGORY = "api node/image/Recraft"
+class RecraftVectorizeImageNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftVectorizeImageNode",
+            display_name="Recraft Vectorize Image",
+            category="api node/image/Recraft",
+            description="Generates SVG synchronously from an input image.",
+            inputs=[
+                IO.Image.Input("image"),
+            ],
+            outputs=[
+                IO.SVG.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": (IO.IMAGE, ),
-            },
-            "optional": {
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-            },
-        }
-
-    async def api_call(
-        self,
-        image: torch.Tensor,
-        **kwargs,
-    ):
+    async def execute(cls, image: torch.Tensor) -> IO.NodeOutput:
         svgs = []
         total = image.shape[0]
         pbar = ProgressBar(total)
         for i in range(total):
             sub_bytes = await handle_recraft_file_request(
+                cls,
                 image=image[i],
                 path="/proxy/recraft/images/vectorize",
-                auth_kwargs=kwargs,
             )
             svgs.append(SVG(sub_bytes))
             pbar.update(1)
 
-        return (SVG.combine_all(svgs), )
+        return IO.NodeOutput(SVG.combine_all(svgs))
 
 
-class RecraftReplaceBackgroundNode:
-    """
-    Replace background on image, based on provided prompt.
-    """
-
-    RETURN_TYPES = (IO.IMAGE,)
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
-    FUNCTION = "api_call"
-    API_NODE = True
-    CATEGORY = "api node/image/Recraft"
+class RecraftReplaceBackgroundNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftReplaceBackgroundNode",
+            display_name="Recraft Replace Background",
+            category="api node/image/Recraft",
+            description="Replace background on image, based on provided prompt.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.String.Input("prompt", tooltip="Prompt for the image generation.", default="", multiline=True),
+                IO.Int.Input("n", default=1, min=1, max=6, tooltip="The number of images to generate."),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+                IO.Custom(RecraftIO.STYLEV3).Input("recraft_style", optional=True),
+                IO.String.Input(
+                    "negative_prompt",
+                    default="",
+                    force_input=True,
+                    tooltip="An optional text description of undesired elements on an image.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": (IO.IMAGE, ),
-                "prompt": (
-                    IO.STRING,
-                    {
-                        "multiline": True,
-                        "default": "",
-                        "tooltip": "Prompt for the image generation.",
-                    },
-                ),
-                "n": (
-                    IO.INT,
-                    {
-                        "default": 1,
-                        "min": 1,
-                        "max": 6,
-                        "tooltip": "The number of images to generate.",
-                    },
-                ),
-                "seed": (
-                    IO.INT,
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "max": 0xFFFFFFFFFFFFFFFF,
-                        "control_after_generate": True,
-                        "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
-                    },
-                ),
-            },
-            "optional": {
-                "recraft_style": (RecraftIO.STYLEV3,),
-                "negative_prompt": (
-                    IO.STRING,
-                    {
-                        "default": "",
-                        "forceInput": True,
-                        "tooltip": "An optional text description of undesired elements on an image.",
-                    },
-                ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-            },
-        }
-
-    async def api_call(
-        self,
+    async def execute(
+        cls,
         image: torch.Tensor,
         prompt: str,
         n: int,
         seed,
         recraft_style: RecraftStyle = None,
         negative_prompt: str = None,
-        **kwargs,
-    ):
+    ) -> IO.NodeOutput:
         default_style = RecraftStyle(RecraftStyleV3.realistic_image)
         if recraft_style is None:
             recraft_style = default_style
@@ -988,165 +853,151 @@ class RecraftReplaceBackgroundNode:
         pbar = ProgressBar(total)
         for i in range(total):
             sub_bytes = await handle_recraft_file_request(
+                cls,
                 image=image[i],
                 path="/proxy/recraft/images/replaceBackground",
                 request=request,
-                auth_kwargs=kwargs,
             )
             images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0))
             pbar.update(1)
 
-        images_tensor = torch.cat(images, dim=0)
-        return (images_tensor, )
+        return IO.NodeOutput(torch.cat(images, dim=0))
 
 
-class RecraftRemoveBackgroundNode:
-    """
-    Remove background from image, and return processed image and mask.
-    """
-
-    RETURN_TYPES = (IO.IMAGE, IO.MASK)
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
-    FUNCTION = "api_call"
-    API_NODE = True
-    CATEGORY = "api node/image/Recraft"
+class RecraftRemoveBackgroundNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftRemoveBackgroundNode",
+            display_name="Recraft Remove Background",
+            category="api node/image/Recraft",
+            description="Remove background from image, and return processed image and mask.",
+            inputs=[
+                IO.Image.Input("image"),
+            ],
+            outputs=[
+                IO.Image.Output(),
+                IO.Mask.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": (IO.IMAGE, ),
-            },
-            "optional": {
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-            },
-        }
-
-    async def api_call(
-        self,
-        image: torch.Tensor,
-        **kwargs,
-    ):
+    async def execute(cls, image: torch.Tensor) -> IO.NodeOutput:
         images = []
         total = image.shape[0]
         pbar = ProgressBar(total)
         for i in range(total):
             sub_bytes = await handle_recraft_file_request(
+                cls,
                 image=image[i],
                 path="/proxy/recraft/images/removeBackground",
-                auth_kwargs=kwargs,
             )
             images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0))
             pbar.update(1)
 
         images_tensor = torch.cat(images, dim=0)
         # use alpha channel as masks, in B,H,W format
-        masks_tensor = images_tensor[:,:,:,-1:].squeeze(-1)
-        return (images_tensor, masks_tensor)
+        masks_tensor = images_tensor[:, :, :, -1:].squeeze(-1)
+        return IO.NodeOutput(images_tensor, masks_tensor)
 
 
-class RecraftCrispUpscaleNode:
-    """
-    Upscale image synchronously.
-    Enhances a given raster image using ‘crisp upscale’ tool, increasing image resolution, making the image sharper and cleaner.
-    """
-
-    RETURN_TYPES = (IO.IMAGE,)
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
-    FUNCTION = "api_call"
-    API_NODE = True
-    CATEGORY = "api node/image/Recraft"
-
+class RecraftCrispUpscaleNode(IO.ComfyNode):
     RECRAFT_PATH = "/proxy/recraft/images/crispUpscale"
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": (IO.IMAGE, ),
-            },
-            "optional": {
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-            },
-        }
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftCrispUpscaleNode",
+            display_name="Recraft Crisp Upscale Image",
+            category="api node/image/Recraft",
+            description="Upscale image synchronously.\n"
+            "Enhances a given raster image using ‘crisp upscale’ tool, "
+            "increasing image resolution, making the image sharper and cleaner.",
+            inputs=[
+                IO.Image.Input("image"),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
-    async def api_call(
-        self,
-        image: torch.Tensor,
-        **kwargs,
-    ):
+    @classmethod
+    async def execute(cls, image: torch.Tensor) -> IO.NodeOutput:
         images = []
         total = image.shape[0]
         pbar = ProgressBar(total)
         for i in range(total):
             sub_bytes = await handle_recraft_file_request(
+                cls,
                 image=image[i],
-                path=self.RECRAFT_PATH,
-                auth_kwargs=kwargs,
+                path=cls.RECRAFT_PATH,
             )
             images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0))
             pbar.update(1)
 
-        images_tensor = torch.cat(images, dim=0)
-        return (images_tensor,)
+        return IO.NodeOutput(torch.cat(images, dim=0))
 
 
 class RecraftCreativeUpscaleNode(RecraftCrispUpscaleNode):
-    """
-    Upscale image synchronously.
-    Enhances a given raster image using ‘creative upscale’ tool, boosting resolution with a focus on refining small details and faces.
-    """
-
-    RETURN_TYPES = (IO.IMAGE,)
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
-    FUNCTION = "api_call"
-    API_NODE = True
-    CATEGORY = "api node/image/Recraft"
-
     RECRAFT_PATH = "/proxy/recraft/images/creativeUpscale"
 
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftCreativeUpscaleNode",
+            display_name="Recraft Creative Upscale Image",
+            category="api node/image/Recraft",
+            description="Upscale image synchronously.\n"
+            "Enhances a given raster image using ‘creative upscale’ tool, "
+            "boosting resolution with a focus on refining small details and faces.",
+            inputs=[
+                IO.Image.Input("image"),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
-# A dictionary that contains all nodes you want to export with their names
-# NOTE: names should be globally unique
-NODE_CLASS_MAPPINGS = {
-    "RecraftTextToImageNode": RecraftTextToImageNode,
-    "RecraftImageToImageNode": RecraftImageToImageNode,
-    "RecraftImageInpaintingNode": RecraftImageInpaintingNode,
-    "RecraftTextToVectorNode": RecraftTextToVectorNode,
-    "RecraftVectorizeImageNode": RecraftVectorizeImageNode,
-    "RecraftRemoveBackgroundNode": RecraftRemoveBackgroundNode,
-    "RecraftReplaceBackgroundNode": RecraftReplaceBackgroundNode,
-    "RecraftCrispUpscaleNode": RecraftCrispUpscaleNode,
-    "RecraftCreativeUpscaleNode": RecraftCreativeUpscaleNode,
-    "RecraftStyleV3RealisticImage": RecraftStyleV3RealisticImageNode,
-    "RecraftStyleV3DigitalIllustration": RecraftStyleV3DigitalIllustrationNode,
-    "RecraftStyleV3LogoRaster": RecraftStyleV3LogoRasterNode,
-    "RecraftStyleV3InfiniteStyleLibrary": RecraftStyleInfiniteStyleLibrary,
-    "RecraftColorRGB": RecraftColorRGBNode,
-    "RecraftControls": RecraftControlsNode,
-}
 
-# A dictionary that contains the friendly/humanly readable titles for the nodes
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "RecraftTextToImageNode": "Recraft Text to Image",
-    "RecraftImageToImageNode": "Recraft Image to Image",
-    "RecraftImageInpaintingNode": "Recraft Image Inpainting",
-    "RecraftTextToVectorNode": "Recraft Text to Vector",
-    "RecraftVectorizeImageNode": "Recraft Vectorize Image",
-    "RecraftRemoveBackgroundNode": "Recraft Remove Background",
-    "RecraftReplaceBackgroundNode": "Recraft Replace Background",
-    "RecraftCrispUpscaleNode": "Recraft Crisp Upscale Image",
-    "RecraftCreativeUpscaleNode": "Recraft Creative Upscale Image",
-    "RecraftStyleV3RealisticImage": "Recraft Style - Realistic Image",
-    "RecraftStyleV3DigitalIllustration": "Recraft Style - Digital Illustration",
-    "RecraftStyleV3LogoRaster": "Recraft Style - Logo Raster",
-    "RecraftStyleV3InfiniteStyleLibrary": "Recraft Style - Infinite Style Library",
-    "RecraftColorRGB": "Recraft Color RGB",
-    "RecraftControls": "Recraft Controls",
-}
+class RecraftExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            RecraftTextToImageNode,
+            RecraftImageToImageNode,
+            RecraftImageInpaintingNode,
+            RecraftTextToVectorNode,
+            RecraftVectorizeImageNode,
+            RecraftRemoveBackgroundNode,
+            RecraftReplaceBackgroundNode,
+            RecraftCrispUpscaleNode,
+            RecraftCreativeUpscaleNode,
+            RecraftStyleV3RealisticImageNode,
+            RecraftStyleV3DigitalIllustrationNode,
+            RecraftStyleV3LogoRasterNode,
+            RecraftStyleInfiniteStyleLibrary,
+            RecraftColorRGBNode,
+            RecraftControlsNode,
+        ]
+
+
+async def comfy_entrypoint() -> RecraftExtension:
+    return RecraftExtension()

From 3fa7a5c04ae69ad168a875e8d3d453783d60899d Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Tue, 28 Oct 2025 21:21:01 -0700
Subject: [PATCH 14/56] Speed up offloading using pinned memory. (#10526)

To enable this feature use: --fast pinned_memory
---
 comfy/cli_args.py         |  1 +
 comfy/model_management.py | 30 ++++++++++++++++++++++++++++++
 comfy/model_patcher.py    | 26 +++++++++++++++++++++++++-
 3 files changed, 56 insertions(+), 1 deletion(-)

diff --git a/comfy/cli_args.py b/comfy/cli_args.py
index cc1f12482..001abd843 100644
--- a/comfy/cli_args.py
+++ b/comfy/cli_args.py
@@ -144,6 +144,7 @@ class PerformanceFeature(enum.Enum):
     Fp8MatrixMultiplication = "fp8_matrix_mult"
     CublasOps = "cublas_ops"
     AutoTune = "autotune"
+    PinnedMem = "pinned_memory"
 
 parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: {}".format(" ".join(map(lambda c: c.value, PerformanceFeature))))
 
diff --git a/comfy/model_management.py b/comfy/model_management.py
index afe78f36e..3e5b977d4 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -1080,6 +1080,36 @@ def cast_to_device(tensor, device, dtype, copy=False):
     non_blocking = device_supports_non_blocking(device)
     return cast_to(tensor, dtype=dtype, device=device, non_blocking=non_blocking, copy=copy)
 
+def pin_memory(tensor):
+    if PerformanceFeature.PinnedMem not in args.fast:
+        return False
+
+    if not is_nvidia():
+        return False
+
+    if not is_device_cpu(tensor.device):
+        return False
+
+    if torch.cuda.cudart().cudaHostRegister(tensor.data_ptr(), tensor.numel() * tensor.element_size(), 1) == 0:
+        return True
+
+    return False
+
+def unpin_memory(tensor):
+    if PerformanceFeature.PinnedMem not in args.fast:
+        return False
+
+    if not is_nvidia():
+        return False
+
+    if not is_device_cpu(tensor.device):
+        return False
+
+    if torch.cuda.cudart().cudaHostUnregister(tensor.data_ptr()) == 0:
+        return True
+
+    return False
+
 def sage_attention_enabled():
     return args.use_sage_attention
 
diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index c0b68fb8c..aec73349c 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -238,6 +238,7 @@ class ModelPatcher:
         self.force_cast_weights = False
         self.patches_uuid = uuid.uuid4()
         self.parent = None
+        self.pinned = set()
 
         self.attachments: dict[str] = {}
         self.additional_models: dict[str, list[ModelPatcher]] = {}
@@ -618,6 +619,21 @@ class ModelPatcher:
         else:
             set_func(out_weight, inplace_update=inplace_update, seed=string_to_seed(key))
 
+    def pin_weight_to_device(self, key):
+        weight, set_func, convert_func = get_key_weight(self.model, key)
+        if comfy.model_management.pin_memory(weight):
+            self.pinned.add(key)
+
+    def unpin_weight(self, key):
+        if key in self.pinned:
+            weight, set_func, convert_func = get_key_weight(self.model, key)
+            comfy.model_management.unpin_memory(weight)
+            self.pinned.remove(key)
+
+    def unpin_all_weights(self):
+        for key in list(self.pinned):
+            self.unpin_weight(key)
+
     def _load_list(self):
         loading = []
         for n, m in self.model.named_modules():
@@ -683,6 +699,8 @@ class ModelPatcher:
                             patch_counter += 1
 
                     cast_weight = True
+                    for param in params:
+                        self.pin_weight_to_device("{}.{}".format(n, param))
                 else:
                     if hasattr(m, "comfy_cast_weights"):
                         wipe_lowvram_weight(m)
@@ -713,7 +731,9 @@ class ModelPatcher:
                         continue
 
                 for param in params:
-                    self.patch_weight_to_device("{}.{}".format(n, param), device_to=device_to)
+                    key = "{}.{}".format(n, param)
+                    self.unpin_weight(key)
+                    self.patch_weight_to_device(key, device_to=device_to)
 
                 logging.debug("lowvram: loaded module regularly {} {}".format(n, m))
                 m.comfy_patched_weights = True
@@ -762,6 +782,7 @@ class ModelPatcher:
         self.eject_model()
         if unpatch_weights:
             self.unpatch_hooks()
+            self.unpin_all_weights()
             if self.model.model_lowvram:
                 for m in self.model.modules():
                     move_weight_functions(m, device_to)
@@ -857,6 +878,9 @@ class ModelPatcher:
                         memory_freed += module_mem
                         logging.debug("freed {}".format(n))
 
+                        for param in params:
+                            self.pin_weight_to_device("{}.{}".format(n, param))
+
             self.model.model_lowvram = True
             self.model.lowvram_patch_counter += patch_counter
             self.model.model_loaded_weight_memory -= memory_freed

From e525673f7201b6c49af0fa0e6baf44e4e98bb10c Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Tue, 28 Oct 2025 21:37:00 -0700
Subject: [PATCH 15/56] Fix issue. (#10527)

---
 comfy/sd.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index 6411bb27d..de4eee96e 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -1330,7 +1330,7 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None):
     else:
         unet_dtype = dtype
 
-    if hasattr(model_config, "layer_quant_config"):
+    if model_config.layer_quant_config is not None:
         manual_cast_dtype = model_management.unet_manual_cast(None, load_device, model_config.supported_inference_dtypes)
     else:
         manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)

From 6c14f3afac0ea28dba24fe8783e7c1f09c03b31f Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Wed, 29 Oct 2025 20:14:56 +0200
Subject: [PATCH 16/56] use new API client in Luma and Minimax nodes (#10528)

---
 comfy_api_nodes/apinode_utils.py         |  81 ------
 comfy_api_nodes/apis/minimax_api.py      | 120 ++++++++
 comfy_api_nodes/nodes_ideogram.py        |   2 +-
 comfy_api_nodes/nodes_luma.py            | 354 ++++++-----------------
 comfy_api_nodes/nodes_minimax.py         | 237 +++++----------
 comfy_api_nodes/util/client.py           |   2 +-
 comfy_api_nodes/util/download_helpers.py |   3 +-
 7 files changed, 283 insertions(+), 516 deletions(-)
 create mode 100644 comfy_api_nodes/apis/minimax_api.py

diff --git a/comfy_api_nodes/apinode_utils.py b/comfy_api_nodes/apinode_utils.py
index 4182c8f80..6a72b9d1d 100644
--- a/comfy_api_nodes/apinode_utils.py
+++ b/comfy_api_nodes/apinode_utils.py
@@ -3,14 +3,6 @@ import aiohttp
 import mimetypes
 from typing import Optional, Union
 from comfy.utils import common_upscale
-from comfy_api_nodes.apis.client import (
-    ApiClient,
-    ApiEndpoint,
-    HttpMethod,
-    SynchronousOperation,
-    UploadRequest,
-    UploadResponse,
-)
 from server import PromptServer
 from comfy.cli_args import args
 
@@ -19,7 +11,6 @@ from PIL import Image
 import torch
 import math
 import base64
-from .util import tensor_to_bytesio, bytesio_to_image_tensor
 from io import BytesIO
 
 
@@ -148,11 +139,6 @@ async def download_url_to_bytesio(
             return BytesIO(await resp.read())
 
 
-def process_image_response(response_content: bytes | str) -> torch.Tensor:
-    """Uses content from a Response object and converts it to a torch.Tensor"""
-    return bytesio_to_image_tensor(BytesIO(response_content))
-
-
 def text_filepath_to_base64_string(filepath: str) -> str:
     """Converts a text file to a base64 string."""
     with open(filepath, "rb") as f:
@@ -169,73 +155,6 @@ def text_filepath_to_data_uri(filepath: str) -> str:
     return f"data:{mime_type};base64,{base64_string}"
 
 
-async def upload_file_to_comfyapi(
-    file_bytes_io: BytesIO,
-    filename: str,
-    upload_mime_type: Optional[str],
-    auth_kwargs: Optional[dict[str, str]] = None,
-) -> str:
-    """
-    Uploads a single file to ComfyUI API and returns its download URL.
-
-    Args:
-        file_bytes_io: BytesIO object containing the file data.
-        filename: The filename of the file.
-        upload_mime_type: MIME type of the file.
-        auth_kwargs: Optional authentication token(s).
-
-    Returns:
-        The download URL for the uploaded file.
-    """
-    if upload_mime_type is None:
-        request_object = UploadRequest(file_name=filename)
-    else:
-        request_object = UploadRequest(file_name=filename, content_type=upload_mime_type)
-    operation = SynchronousOperation(
-        endpoint=ApiEndpoint(
-            path="/customers/storage",
-            method=HttpMethod.POST,
-            request_model=UploadRequest,
-            response_model=UploadResponse,
-        ),
-        request=request_object,
-        auth_kwargs=auth_kwargs,
-    )
-
-    response: UploadResponse = await operation.execute()
-    await ApiClient.upload_file(response.upload_url, file_bytes_io, content_type=upload_mime_type)
-    return response.download_url
-
-
-async def upload_images_to_comfyapi(
-    image: torch.Tensor,
-    max_images=8,
-    auth_kwargs: Optional[dict[str, str]] = None,
-    mime_type: Optional[str] = None,
-) -> list[str]:
-    """
-    Uploads images to ComfyUI API and returns download URLs.
-    To upload multiple images, stack them in the batch dimension first.
-
-    Args:
-        image: Input torch.Tensor image.
-        max_images: Maximum number of images to upload.
-        auth_kwargs: Optional authentication token(s).
-        mime_type: Optional MIME type for the image.
-    """
-    # if batch, try to upload each file if max_images is greater than 0
-    download_urls: list[str] = []
-    is_batch = len(image.shape) > 3
-    batch_len = image.shape[0] if is_batch else 1
-
-    for idx in range(min(batch_len, max_images)):
-        tensor = image[idx] if is_batch else image
-        img_io = tensor_to_bytesio(tensor, mime_type=mime_type)
-        url = await upload_file_to_comfyapi(img_io, img_io.name, mime_type, auth_kwargs)
-        download_urls.append(url)
-    return download_urls
-
-
 def resize_mask_to_image(
     mask: torch.Tensor,
     image: torch.Tensor,
diff --git a/comfy_api_nodes/apis/minimax_api.py b/comfy_api_nodes/apis/minimax_api.py
new file mode 100644
index 000000000..d747e177a
--- /dev/null
+++ b/comfy_api_nodes/apis/minimax_api.py
@@ -0,0 +1,120 @@
+from enum import Enum
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+
+class MinimaxBaseResponse(BaseModel):
+    status_code: int = Field(
+        ...,
+        description='Status code. 0 indicates success, other values indicate errors.',
+    )
+    status_msg: str = Field(
+        ..., description='Specific error details or success message.'
+    )
+
+
+class File(BaseModel):
+    bytes: Optional[int] = Field(None, description='File size in bytes')
+    created_at: Optional[int] = Field(
+        None, description='Unix timestamp when the file was created, in seconds'
+    )
+    download_url: Optional[str] = Field(
+        None, description='The URL to download the video'
+    )
+    backup_download_url: Optional[str] = Field(
+        None, description='The backup URL to download the video'
+    )
+
+    file_id: Optional[int] = Field(None, description='Unique identifier for the file')
+    filename: Optional[str] = Field(None, description='The name of the file')
+    purpose: Optional[str] = Field(None, description='The purpose of using the file')
+
+
+class MinimaxFileRetrieveResponse(BaseModel):
+    base_resp: MinimaxBaseResponse
+    file: File
+
+
+class MiniMaxModel(str, Enum):
+    T2V_01_Director = 'T2V-01-Director'
+    I2V_01_Director = 'I2V-01-Director'
+    S2V_01 = 'S2V-01'
+    I2V_01 = 'I2V-01'
+    I2V_01_live = 'I2V-01-live'
+    T2V_01 = 'T2V-01'
+    Hailuo_02 = 'MiniMax-Hailuo-02'
+
+
+class Status6(str, Enum):
+    Queueing = 'Queueing'
+    Preparing = 'Preparing'
+    Processing = 'Processing'
+    Success = 'Success'
+    Fail = 'Fail'
+
+
+class MinimaxTaskResultResponse(BaseModel):
+    base_resp: MinimaxBaseResponse
+    file_id: Optional[str] = Field(
+        None,
+        description='After the task status changes to Success, this field returns the file ID corresponding to the generated video.',
+    )
+    status: Status6 = Field(
+        ...,
+        description="Task status: 'Queueing' (in queue), 'Preparing' (task is preparing), 'Processing' (generating), 'Success' (task completed successfully), or 'Fail' (task failed).",
+    )
+    task_id: str = Field(..., description='The task ID being queried.')
+
+
+class SubjectReferenceItem(BaseModel):
+    image: Optional[str] = Field(
+        None, description='URL or base64 encoding of the subject reference image.'
+    )
+    mask: Optional[str] = Field(
+        None,
+        description='URL or base64 encoding of the mask for the subject reference image.',
+    )
+
+
+class MinimaxVideoGenerationRequest(BaseModel):
+    callback_url: Optional[str] = Field(
+        None,
+        description='Optional. URL to receive real-time status updates about the video generation task.',
+    )
+    first_frame_image: Optional[str] = Field(
+        None,
+        description='URL or base64 encoding of the first frame image. Required when model is I2V-01, I2V-01-Director, or I2V-01-live.',
+    )
+    model: MiniMaxModel = Field(
+        ...,
+        description='Required. ID of model. Options: T2V-01-Director, I2V-01-Director, S2V-01, I2V-01, I2V-01-live, T2V-01',
+    )
+    prompt: Optional[str] = Field(
+        None,
+        description='Description of the video. Should be less than 2000 characters. Supports camera movement instructions in [brackets].',
+        max_length=2000,
+    )
+    prompt_optimizer: Optional[bool] = Field(
+        True,
+        description='If true (default), the model will automatically optimize the prompt. Set to false for more precise control.',
+    )
+    subject_reference: Optional[list[SubjectReferenceItem]] = Field(
+        None,
+        description='Only available when model is S2V-01. The model will generate a video based on the subject uploaded through this parameter.',
+    )
+    duration: Optional[int] = Field(
+        None,
+        description="The length of the output video in seconds."
+    )
+    resolution: Optional[str] = Field(
+        None,
+        description="The dimensions of the video display. 1080p corresponds to 1920 x 1080 pixels, 768p corresponds to 1366 x 768 pixels."
+    )
+
+
+class MinimaxVideoGenerationResponse(BaseModel):
+    base_resp: MinimaxBaseResponse
+    task_id: str = Field(
+        ..., description='The task ID for the asynchronous video generation task.'
+    )
diff --git a/comfy_api_nodes/nodes_ideogram.py b/comfy_api_nodes/nodes_ideogram.py
index 9eae5f11a..d8fd3378b 100644
--- a/comfy_api_nodes/nodes_ideogram.py
+++ b/comfy_api_nodes/nodes_ideogram.py
@@ -20,9 +20,9 @@ from comfy_api_nodes.apis.client import (
 
 from comfy_api_nodes.apinode_utils import (
     download_url_to_bytesio,
-    bytesio_to_image_tensor,
     resize_mask_to_image,
 )
+from comfy_api_nodes.util import bytesio_to_image_tensor
 from server import PromptServer
 
 V1_V1_RES_MAP = {
diff --git a/comfy_api_nodes/nodes_luma.py b/comfy_api_nodes/nodes_luma.py
index e74441e5e..894f2b08c 100644
--- a/comfy_api_nodes/nodes_luma.py
+++ b/comfy_api_nodes/nodes_luma.py
@@ -1,69 +1,51 @@
-from __future__ import annotations
-from inspect import cleandoc
 from typing import Optional
+
+import torch
 from typing_extensions import override
-from comfy_api.latest import ComfyExtension, IO
-from comfy_api.input_impl.video_types import VideoFromFile
+
+from comfy_api.latest import IO, ComfyExtension
 from comfy_api_nodes.apis.luma_api import (
-    LumaImageModel,
-    LumaVideoModel,
-    LumaVideoOutputResolution,
-    LumaVideoModelOutputDuration,
     LumaAspectRatio,
-    LumaState,
-    LumaImageGenerationRequest,
-    LumaGenerationRequest,
-    LumaGeneration,
     LumaCharacterRef,
-    LumaModifyImageRef,
+    LumaConceptChain,
+    LumaGeneration,
+    LumaGenerationRequest,
+    LumaImageGenerationRequest,
     LumaImageIdentity,
+    LumaImageModel,
+    LumaImageReference,
+    LumaIO,
+    LumaKeyframes,
+    LumaModifyImageRef,
     LumaReference,
     LumaReferenceChain,
-    LumaImageReference,
-    LumaKeyframes,
-    LumaConceptChain,
-    LumaIO,
+    LumaVideoModel,
+    LumaVideoModelOutputDuration,
+    LumaVideoOutputResolution,
     get_luma_concepts,
 )
-from comfy_api_nodes.apis.client import (
+from comfy_api_nodes.util import (
     ApiEndpoint,
-    HttpMethod,
-    SynchronousOperation,
-    PollingOperation,
-    EmptyRequest,
-)
-from comfy_api_nodes.apinode_utils import (
+    download_url_to_image_tensor,
+    download_url_to_video_output,
+    poll_op,
+    sync_op,
     upload_images_to_comfyapi,
-    process_image_response,
+    validate_string,
 )
-from server import PromptServer
-from comfy_api_nodes.util import validate_string
-
-import aiohttp
-import torch
-from io import BytesIO
 
 LUMA_T2V_AVERAGE_DURATION = 105
 LUMA_I2V_AVERAGE_DURATION = 100
 
-def image_result_url_extractor(response: LumaGeneration):
-    return response.assets.image if hasattr(response, "assets") and hasattr(response.assets, "image") else None
-
-def video_result_url_extractor(response: LumaGeneration):
-    return response.assets.video if hasattr(response, "assets") and hasattr(response.assets, "video") else None
 
 class LumaReferenceNode(IO.ComfyNode):
-    """
-    Holds an image and weight for use with Luma Generate Image node.
-    """
-
     @classmethod
     def define_schema(cls) -> IO.Schema:
         return IO.Schema(
             node_id="LumaReferenceNode",
             display_name="Luma Reference",
             category="api node/image/Luma",
-            description=cleandoc(cls.__doc__ or ""),
+            description="Holds an image and weight for use with Luma Generate Image node.",
             inputs=[
                 IO.Image.Input(
                     "image",
@@ -83,17 +65,10 @@ class LumaReferenceNode(IO.ComfyNode):
                 ),
             ],
             outputs=[IO.Custom(LumaIO.LUMA_REF).Output(display_name="luma_ref")],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
         )
 
     @classmethod
-    def execute(
-        cls, image: torch.Tensor, weight: float, luma_ref: LumaReferenceChain = None
-    ) -> IO.NodeOutput:
+    def execute(cls, image: torch.Tensor, weight: float, luma_ref: LumaReferenceChain = None) -> IO.NodeOutput:
         if luma_ref is not None:
             luma_ref = luma_ref.clone()
         else:
@@ -103,17 +78,13 @@ class LumaReferenceNode(IO.ComfyNode):
 
 
 class LumaConceptsNode(IO.ComfyNode):
-    """
-    Holds one or more Camera Concepts for use with Luma Text to Video and Luma Image to Video nodes.
-    """
-
     @classmethod
     def define_schema(cls) -> IO.Schema:
         return IO.Schema(
             node_id="LumaConceptsNode",
             display_name="Luma Concepts",
             category="api node/video/Luma",
-            description=cleandoc(cls.__doc__ or ""),
+            description="Camera Concepts for use with Luma Text to Video and Luma Image to Video nodes.",
             inputs=[
                 IO.Combo.Input(
                     "concept1",
@@ -138,11 +109,6 @@ class LumaConceptsNode(IO.ComfyNode):
                 ),
             ],
             outputs=[IO.Custom(LumaIO.LUMA_CONCEPTS).Output(display_name="luma_concepts")],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
         )
 
     @classmethod
@@ -161,17 +127,13 @@ class LumaConceptsNode(IO.ComfyNode):
 
 
 class LumaImageGenerationNode(IO.ComfyNode):
-    """
-    Generates images synchronously based on prompt and aspect ratio.
-    """
-
     @classmethod
     def define_schema(cls) -> IO.Schema:
         return IO.Schema(
             node_id="LumaImageNode",
             display_name="Luma Text to Image",
             category="api node/image/Luma",
-            description=cleandoc(cls.__doc__ or ""),
+            description="Generates images synchronously based on prompt and aspect ratio.",
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -237,45 +199,30 @@ class LumaImageGenerationNode(IO.ComfyNode):
         aspect_ratio: str,
         seed,
         style_image_weight: float,
-        image_luma_ref: LumaReferenceChain = None,
-        style_image: torch.Tensor = None,
-        character_image: torch.Tensor = None,
+        image_luma_ref: Optional[LumaReferenceChain] = None,
+        style_image: Optional[torch.Tensor] = None,
+        character_image: Optional[torch.Tensor] = None,
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=3)
-        auth_kwargs = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
         # handle image_luma_ref
         api_image_ref = None
         if image_luma_ref is not None:
-            api_image_ref = await cls._convert_luma_refs(
-                image_luma_ref, max_refs=4, auth_kwargs=auth_kwargs,
-            )
+            api_image_ref = await cls._convert_luma_refs(image_luma_ref, max_refs=4)
         # handle style_luma_ref
         api_style_ref = None
         if style_image is not None:
-            api_style_ref = await cls._convert_style_image(
-                style_image, weight=style_image_weight, auth_kwargs=auth_kwargs,
-            )
+            api_style_ref = await cls._convert_style_image(style_image, weight=style_image_weight)
         # handle character_ref images
         character_ref = None
         if character_image is not None:
-            download_urls = await upload_images_to_comfyapi(
-                character_image, max_images=4, auth_kwargs=auth_kwargs,
-            )
-            character_ref = LumaCharacterRef(
-                identity0=LumaImageIdentity(images=download_urls)
-            )
+            download_urls = await upload_images_to_comfyapi(cls, character_image, max_images=4)
+            character_ref = LumaCharacterRef(identity0=LumaImageIdentity(images=download_urls))
 
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/luma/generations/image",
-                method=HttpMethod.POST,
-                request_model=LumaImageGenerationRequest,
-                response_model=LumaGeneration,
-            ),
-            request=LumaImageGenerationRequest(
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/luma/generations/image", method="POST"),
+            response_model=LumaGeneration,
+            data=LumaImageGenerationRequest(
                 prompt=prompt,
                 model=model,
                 aspect_ratio=aspect_ratio,
@@ -283,41 +230,21 @@ class LumaImageGenerationNode(IO.ComfyNode):
                 style_ref=api_style_ref,
                 character_ref=character_ref,
             ),
-            auth_kwargs=auth_kwargs,
         )
-        response_api: LumaGeneration = await operation.execute()
-
-        operation = PollingOperation(
-            poll_endpoint=ApiEndpoint(
-                path=f"/proxy/luma/generations/{response_api.id}",
-                method=HttpMethod.GET,
-                request_model=EmptyRequest,
-                response_model=LumaGeneration,
-            ),
-            completed_statuses=[LumaState.completed],
-            failed_statuses=[LumaState.failed],
+        response_poll = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/luma/generations/{response_api.id}"),
+            response_model=LumaGeneration,
             status_extractor=lambda x: x.state,
-            result_url_extractor=image_result_url_extractor,
-            node_id=cls.hidden.unique_id,
-            auth_kwargs=auth_kwargs,
         )
-        response_poll = await operation.execute()
-
-        async with aiohttp.ClientSession() as session:
-            async with session.get(response_poll.assets.image) as img_response:
-                img = process_image_response(await img_response.content.read())
-        return IO.NodeOutput(img)
+        return IO.NodeOutput(await download_url_to_image_tensor(response_poll.assets.image))
 
     @classmethod
-    async def _convert_luma_refs(
-        cls, luma_ref: LumaReferenceChain, max_refs: int, auth_kwargs: Optional[dict[str,str]] = None
-    ):
+    async def _convert_luma_refs(cls, luma_ref: LumaReferenceChain, max_refs: int):
         luma_urls = []
         ref_count = 0
         for ref in luma_ref.refs:
-            download_urls = await upload_images_to_comfyapi(
-                ref.image, max_images=1, auth_kwargs=auth_kwargs
-            )
+            download_urls = await upload_images_to_comfyapi(cls, ref.image, max_images=1)
             luma_urls.append(download_urls[0])
             ref_count += 1
             if ref_count >= max_refs:
@@ -325,27 +252,19 @@ class LumaImageGenerationNode(IO.ComfyNode):
         return luma_ref.create_api_model(download_urls=luma_urls, max_refs=max_refs)
 
     @classmethod
-    async def _convert_style_image(
-        cls, style_image: torch.Tensor, weight: float, auth_kwargs: Optional[dict[str,str]] = None
-    ):
-        chain = LumaReferenceChain(
-            first_ref=LumaReference(image=style_image, weight=weight)
-        )
-        return await cls._convert_luma_refs(chain, max_refs=1, auth_kwargs=auth_kwargs)
+    async def _convert_style_image(cls, style_image: torch.Tensor, weight: float):
+        chain = LumaReferenceChain(first_ref=LumaReference(image=style_image, weight=weight))
+        return await cls._convert_luma_refs(chain, max_refs=1)
 
 
 class LumaImageModifyNode(IO.ComfyNode):
-    """
-    Modifies images synchronously based on prompt and aspect ratio.
-    """
-
     @classmethod
     def define_schema(cls) -> IO.Schema:
         return IO.Schema(
             node_id="LumaImageModifyNode",
             display_name="Luma Image to Image",
             category="api node/image/Luma",
-            description=cleandoc(cls.__doc__ or ""),
+            description="Modifies images synchronously based on prompt and aspect ratio.",
             inputs=[
                 IO.Image.Input(
                     "image",
@@ -395,68 +314,37 @@ class LumaImageModifyNode(IO.ComfyNode):
         image_weight: float,
         seed,
     ) -> IO.NodeOutput:
-        auth_kwargs = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
-        # first, upload image
-        download_urls = await upload_images_to_comfyapi(
-            image, max_images=1, auth_kwargs=auth_kwargs,
-        )
+        download_urls = await upload_images_to_comfyapi(cls, image, max_images=1)
         image_url = download_urls[0]
-        # next, make Luma call with download url provided
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/luma/generations/image",
-                method=HttpMethod.POST,
-                request_model=LumaImageGenerationRequest,
-                response_model=LumaGeneration,
-            ),
-            request=LumaImageGenerationRequest(
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/luma/generations/image", method="POST"),
+            response_model=LumaGeneration,
+            data=LumaImageGenerationRequest(
                 prompt=prompt,
                 model=model,
                 modify_image_ref=LumaModifyImageRef(
-                    url=image_url, weight=round(max(min(1.0-image_weight, 0.98), 0.0), 2)
+                    url=image_url, weight=round(max(min(1.0 - image_weight, 0.98), 0.0), 2)
                 ),
             ),
-            auth_kwargs=auth_kwargs,
         )
-        response_api: LumaGeneration = await operation.execute()
-
-        operation = PollingOperation(
-            poll_endpoint=ApiEndpoint(
-                path=f"/proxy/luma/generations/{response_api.id}",
-                method=HttpMethod.GET,
-                request_model=EmptyRequest,
-                response_model=LumaGeneration,
-            ),
-            completed_statuses=[LumaState.completed],
-            failed_statuses=[LumaState.failed],
+        response_poll = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/luma/generations/{response_api.id}"),
+            response_model=LumaGeneration,
             status_extractor=lambda x: x.state,
-            result_url_extractor=image_result_url_extractor,
-            node_id=cls.hidden.unique_id,
-            auth_kwargs=auth_kwargs,
         )
-        response_poll = await operation.execute()
-
-        async with aiohttp.ClientSession() as session:
-            async with session.get(response_poll.assets.image) as img_response:
-                img = process_image_response(await img_response.content.read())
-        return IO.NodeOutput(img)
+        return IO.NodeOutput(await download_url_to_image_tensor(response_poll.assets.image))
 
 
 class LumaTextToVideoGenerationNode(IO.ComfyNode):
-    """
-    Generates videos synchronously based on prompt and output_size.
-    """
-
     @classmethod
     def define_schema(cls) -> IO.Schema:
         return IO.Schema(
             node_id="LumaVideoNode",
             display_name="Luma Text to Video",
             category="api node/video/Luma",
-            description=cleandoc(cls.__doc__ or ""),
+            description="Generates videos synchronously based on prompt and output_size.",
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -498,7 +386,7 @@ class LumaTextToVideoGenerationNode(IO.ComfyNode):
                     "luma_concepts",
                     tooltip="Optional Camera Concepts to dictate camera motion via the Luma Concepts node.",
                     optional=True,
-                )
+                ),
             ],
             outputs=[IO.Video.Output()],
             hidden=[
@@ -519,24 +407,17 @@ class LumaTextToVideoGenerationNode(IO.ComfyNode):
         duration: str,
         loop: bool,
         seed,
-        luma_concepts: LumaConceptChain = None,
+        luma_concepts: Optional[LumaConceptChain] = None,
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=False, min_length=3)
         duration = duration if model != LumaVideoModel.ray_1_6 else None
         resolution = resolution if model != LumaVideoModel.ray_1_6 else None
 
-        auth_kwargs = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/luma/generations",
-                method=HttpMethod.POST,
-                request_model=LumaGenerationRequest,
-                response_model=LumaGeneration,
-            ),
-            request=LumaGenerationRequest(
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/luma/generations", method="POST"),
+            response_model=LumaGeneration,
+            data=LumaGenerationRequest(
                 prompt=prompt,
                 model=model,
                 resolution=resolution,
@@ -545,47 +426,25 @@ class LumaTextToVideoGenerationNode(IO.ComfyNode):
                 loop=loop,
                 concepts=luma_concepts.create_api_model() if luma_concepts else None,
             ),
-            auth_kwargs=auth_kwargs,
         )
-        response_api: LumaGeneration = await operation.execute()
-
-        if cls.hidden.unique_id:
-            PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", cls.hidden.unique_id)
-
-        operation = PollingOperation(
-            poll_endpoint=ApiEndpoint(
-                path=f"/proxy/luma/generations/{response_api.id}",
-                method=HttpMethod.GET,
-                request_model=EmptyRequest,
-                response_model=LumaGeneration,
-            ),
-            completed_statuses=[LumaState.completed],
-            failed_statuses=[LumaState.failed],
+        response_poll = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/luma/generations/{response_api.id}"),
+            response_model=LumaGeneration,
             status_extractor=lambda x: x.state,
-            result_url_extractor=video_result_url_extractor,
-            node_id=cls.hidden.unique_id,
             estimated_duration=LUMA_T2V_AVERAGE_DURATION,
-            auth_kwargs=auth_kwargs,
         )
-        response_poll = await operation.execute()
-
-        async with aiohttp.ClientSession() as session:
-            async with session.get(response_poll.assets.video) as vid_response:
-                return IO.NodeOutput(VideoFromFile(BytesIO(await vid_response.content.read())))
+        return IO.NodeOutput(await download_url_to_video_output(response_poll.assets.video))
 
 
 class LumaImageToVideoGenerationNode(IO.ComfyNode):
-    """
-    Generates videos synchronously based on prompt, input images, and output_size.
-    """
-
     @classmethod
     def define_schema(cls) -> IO.Schema:
         return IO.Schema(
             node_id="LumaImageToVideoNode",
             display_name="Luma Image to Video",
             category="api node/video/Luma",
-            description=cleandoc(cls.__doc__ or ""),
+            description="Generates videos synchronously based on prompt, input images, and output_size.",
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -637,7 +496,7 @@ class LumaImageToVideoGenerationNode(IO.ComfyNode):
                     "luma_concepts",
                     tooltip="Optional Camera Concepts to dictate camera motion via the Luma Concepts node.",
                     optional=True,
-                )
+                ),
             ],
             outputs=[IO.Video.Output()],
             hidden=[
@@ -662,25 +521,15 @@ class LumaImageToVideoGenerationNode(IO.ComfyNode):
         luma_concepts: LumaConceptChain = None,
     ) -> IO.NodeOutput:
         if first_image is None and last_image is None:
-            raise Exception(
-                "At least one of first_image and last_image requires an input."
-            )
-        auth_kwargs = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
-        keyframes = await cls._convert_to_keyframes(first_image, last_image, auth_kwargs=auth_kwargs)
+            raise Exception("At least one of first_image and last_image requires an input.")
+        keyframes = await cls._convert_to_keyframes(first_image, last_image)
         duration = duration if model != LumaVideoModel.ray_1_6 else None
         resolution = resolution if model != LumaVideoModel.ray_1_6 else None
-
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/luma/generations",
-                method=HttpMethod.POST,
-                request_model=LumaGenerationRequest,
-                response_model=LumaGeneration,
-            ),
-            request=LumaGenerationRequest(
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/luma/generations", method="POST"),
+            response_model=LumaGeneration,
+            data=LumaGenerationRequest(
                 prompt=prompt,
                 model=model,
                 aspect_ratio=LumaAspectRatio.ratio_16_9,  # ignored, but still needed by the API for some reason
@@ -690,54 +539,31 @@ class LumaImageToVideoGenerationNode(IO.ComfyNode):
                 keyframes=keyframes,
                 concepts=luma_concepts.create_api_model() if luma_concepts else None,
             ),
-            auth_kwargs=auth_kwargs,
         )
-        response_api: LumaGeneration = await operation.execute()
-
-        if cls.hidden.unique_id:
-            PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", cls.hidden.unique_id)
-
-        operation = PollingOperation(
-            poll_endpoint=ApiEndpoint(
-                path=f"/proxy/luma/generations/{response_api.id}",
-                method=HttpMethod.GET,
-                request_model=EmptyRequest,
-                response_model=LumaGeneration,
-            ),
-            completed_statuses=[LumaState.completed],
-            failed_statuses=[LumaState.failed],
+        response_poll = await poll_op(
+            cls,
+            poll_endpoint=ApiEndpoint(path=f"/proxy/luma/generations/{response_api.id}"),
+            response_model=LumaGeneration,
             status_extractor=lambda x: x.state,
-            result_url_extractor=video_result_url_extractor,
-            node_id=cls.hidden.unique_id,
             estimated_duration=LUMA_I2V_AVERAGE_DURATION,
-            auth_kwargs=auth_kwargs,
         )
-        response_poll = await operation.execute()
-
-        async with aiohttp.ClientSession() as session:
-            async with session.get(response_poll.assets.video) as vid_response:
-                return IO.NodeOutput(VideoFromFile(BytesIO(await vid_response.content.read())))
+        return IO.NodeOutput(await download_url_to_video_output(response_poll.assets.video))
 
     @classmethod
     async def _convert_to_keyframes(
         cls,
         first_image: torch.Tensor = None,
         last_image: torch.Tensor = None,
-        auth_kwargs: Optional[dict[str,str]] = None,
     ):
         if first_image is None and last_image is None:
             return None
         frame0 = None
         frame1 = None
         if first_image is not None:
-            download_urls = await upload_images_to_comfyapi(
-                first_image, max_images=1, auth_kwargs=auth_kwargs,
-            )
+            download_urls = await upload_images_to_comfyapi(cls, first_image, max_images=1)
             frame0 = LumaImageReference(type="image", url=download_urls[0])
         if last_image is not None:
-            download_urls = await upload_images_to_comfyapi(
-                last_image, max_images=1, auth_kwargs=auth_kwargs,
-            )
+            download_urls = await upload_images_to_comfyapi(cls, last_image, max_images=1)
             frame1 = LumaImageReference(type="image", url=download_urls[0])
         return LumaKeyframes(frame0=frame0, frame1=frame1)
 
diff --git a/comfy_api_nodes/nodes_minimax.py b/comfy_api_nodes/nodes_minimax.py
index e3722e79b..05cbb700f 100644
--- a/comfy_api_nodes/nodes_minimax.py
+++ b/comfy_api_nodes/nodes_minimax.py
@@ -1,71 +1,57 @@
-from inspect import cleandoc
 from typing import Optional
-import logging
-import torch
 
+import torch
 from typing_extensions import override
-from comfy_api.latest import ComfyExtension, IO
-from comfy_api.input_impl.video_types import VideoFromFile
-from comfy_api_nodes.apis import (
+
+from comfy_api.latest import IO, ComfyExtension
+from comfy_api_nodes.apis.minimax_api import (
+    MinimaxFileRetrieveResponse,
+    MiniMaxModel,
+    MinimaxTaskResultResponse,
     MinimaxVideoGenerationRequest,
     MinimaxVideoGenerationResponse,
-    MinimaxFileRetrieveResponse,
-    MinimaxTaskResultResponse,
     SubjectReferenceItem,
-    MiniMaxModel,
 )
-from comfy_api_nodes.apis.client import (
+from comfy_api_nodes.util import (
     ApiEndpoint,
-    HttpMethod,
-    SynchronousOperation,
-    PollingOperation,
-    EmptyRequest,
-)
-from comfy_api_nodes.apinode_utils import (
-    download_url_to_bytesio,
+    download_url_to_video_output,
+    poll_op,
+    sync_op,
     upload_images_to_comfyapi,
+    validate_string,
 )
-from comfy_api_nodes.util import validate_string
-from server import PromptServer
-
 
 I2V_AVERAGE_DURATION = 114
 T2V_AVERAGE_DURATION = 234
 
 
 async def _generate_mm_video(
+    cls: type[IO.ComfyNode],
     *,
-    auth: dict[str, str],
-    node_id: str,
     prompt_text: str,
     seed: int,
     model: str,
-    image: Optional[torch.Tensor] = None,   # used for ImageToVideo
-    subject: Optional[torch.Tensor] = None, # used for SubjectToVideo
+    image: Optional[torch.Tensor] = None,  # used for ImageToVideo
+    subject: Optional[torch.Tensor] = None,  # used for SubjectToVideo
     average_duration: Optional[int] = None,
 ) -> IO.NodeOutput:
     if image is None:
         validate_string(prompt_text, field_name="prompt_text")
-    # upload image, if passed in
     image_url = None
     if image is not None:
-        image_url = (await upload_images_to_comfyapi(image, max_images=1, auth_kwargs=auth))[0]
+        image_url = (await upload_images_to_comfyapi(cls, image, max_images=1))[0]
 
     # TODO: figure out how to deal with subject properly, API returns invalid params when using S2V-01 model
     subject_reference = None
     if subject is not None:
-        subject_url = (await upload_images_to_comfyapi(subject, max_images=1, auth_kwargs=auth))[0]
+        subject_url = (await upload_images_to_comfyapi(cls, subject, max_images=1))[0]
         subject_reference = [SubjectReferenceItem(image=subject_url)]
 
-
-    video_generate_operation = SynchronousOperation(
-        endpoint=ApiEndpoint(
-            path="/proxy/minimax/video_generation",
-            method=HttpMethod.POST,
-            request_model=MinimaxVideoGenerationRequest,
-            response_model=MinimaxVideoGenerationResponse,
-        ),
-        request=MinimaxVideoGenerationRequest(
+    response = await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/minimax/video_generation", method="POST"),
+        response_model=MinimaxVideoGenerationResponse,
+        data=MinimaxVideoGenerationRequest(
             model=MiniMaxModel(model),
             prompt=prompt_text,
             callback_url=None,
@@ -73,81 +59,50 @@ async def _generate_mm_video(
             subject_reference=subject_reference,
             prompt_optimizer=None,
         ),
-        auth_kwargs=auth,
     )
-    response = await video_generate_operation.execute()
 
     task_id = response.task_id
     if not task_id:
         raise Exception(f"MiniMax generation failed: {response.base_resp}")
 
-    video_generate_operation = PollingOperation(
-        poll_endpoint=ApiEndpoint(
-            path="/proxy/minimax/query/video_generation",
-            method=HttpMethod.GET,
-            request_model=EmptyRequest,
-            response_model=MinimaxTaskResultResponse,
-            query_params={"task_id": task_id},
-        ),
-        completed_statuses=["Success"],
-        failed_statuses=["Fail"],
+    task_result = await poll_op(
+        cls,
+        ApiEndpoint(path="/proxy/minimax/query/video_generation", query_params={"task_id": task_id}),
+        response_model=MinimaxTaskResultResponse,
         status_extractor=lambda x: x.status.value,
         estimated_duration=average_duration,
-        node_id=node_id,
-        auth_kwargs=auth,
     )
-    task_result = await video_generate_operation.execute()
 
     file_id = task_result.file_id
     if file_id is None:
         raise Exception("Request was not successful. Missing file ID.")
-    file_retrieve_operation = SynchronousOperation(
-        endpoint=ApiEndpoint(
-            path="/proxy/minimax/files/retrieve",
-            method=HttpMethod.GET,
-            request_model=EmptyRequest,
-            response_model=MinimaxFileRetrieveResponse,
-            query_params={"file_id": int(file_id)},
-        ),
-        request=EmptyRequest(),
-        auth_kwargs=auth,
+    file_result = await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/minimax/files/retrieve", query_params={"file_id": int(file_id)}),
+        response_model=MinimaxFileRetrieveResponse,
     )
-    file_result = await file_retrieve_operation.execute()
 
     file_url = file_result.file.download_url
     if file_url is None:
-        raise Exception(
-            f"No video was found in the response. Full response: {file_result.model_dump()}"
-        )
-    logging.info("Generated video URL: %s", file_url)
-    if node_id:
-        if hasattr(file_result.file, "backup_download_url"):
-            message = f"Result URL: {file_url}\nBackup URL: {file_result.file.backup_download_url}"
-        else:
-            message = f"Result URL: {file_url}"
-        PromptServer.instance.send_progress_text(message, node_id)
-
-    # Download and return as VideoFromFile
-    video_io = await download_url_to_bytesio(file_url)
-    if video_io is None:
-        error_msg = f"Failed to download video from {file_url}"
-        logging.error(error_msg)
-        raise Exception(error_msg)
-    return IO.NodeOutput(VideoFromFile(video_io))
+        raise Exception(f"No video was found in the response. Full response: {file_result.model_dump()}")
+    if file_result.file.backup_download_url:
+        try:
+            return IO.NodeOutput(await download_url_to_video_output(file_url, timeout=10, max_retries=2))
+        except Exception:  # if we have a second URL to retrieve the result, try again using that one
+            return IO.NodeOutput(
+                await download_url_to_video_output(file_result.file.backup_download_url, max_retries=3)
+            )
+    return IO.NodeOutput(await download_url_to_video_output(file_url))
 
 
 class MinimaxTextToVideoNode(IO.ComfyNode):
-    """
-    Generates videos synchronously based on a prompt, and optional parameters using MiniMax's API.
-    """
-
     @classmethod
     def define_schema(cls) -> IO.Schema:
         return IO.Schema(
             node_id="MinimaxTextToVideoNode",
             display_name="MiniMax Text to Video",
             category="api node/video/MiniMax",
-            description=cleandoc(cls.__doc__ or ""),
+            description="Generates videos synchronously based on a prompt, and optional parameters.",
             inputs=[
                 IO.String.Input(
                     "prompt_text",
@@ -189,11 +144,7 @@ class MinimaxTextToVideoNode(IO.ComfyNode):
         seed: int = 0,
     ) -> IO.NodeOutput:
         return await _generate_mm_video(
-            auth={
-                "auth_token": cls.hidden.auth_token_comfy_org,
-                "comfy_api_key": cls.hidden.api_key_comfy_org,
-            },
-            node_id=cls.hidden.unique_id,
+            cls,
             prompt_text=prompt_text,
             seed=seed,
             model=model,
@@ -204,17 +155,13 @@ class MinimaxTextToVideoNode(IO.ComfyNode):
 
 
 class MinimaxImageToVideoNode(IO.ComfyNode):
-    """
-    Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API.
-    """
-
     @classmethod
     def define_schema(cls) -> IO.Schema:
         return IO.Schema(
             node_id="MinimaxImageToVideoNode",
             display_name="MiniMax Image to Video",
             category="api node/video/MiniMax",
-            description=cleandoc(cls.__doc__ or ""),
+            description="Generates videos synchronously based on an image and prompt, and optional parameters.",
             inputs=[
                 IO.Image.Input(
                     "image",
@@ -261,11 +208,7 @@ class MinimaxImageToVideoNode(IO.ComfyNode):
         seed: int = 0,
     ) -> IO.NodeOutput:
         return await _generate_mm_video(
-            auth={
-                "auth_token": cls.hidden.auth_token_comfy_org,
-                "comfy_api_key": cls.hidden.api_key_comfy_org,
-            },
-            node_id=cls.hidden.unique_id,
+            cls,
             prompt_text=prompt_text,
             seed=seed,
             model=model,
@@ -276,17 +219,13 @@ class MinimaxImageToVideoNode(IO.ComfyNode):
 
 
 class MinimaxSubjectToVideoNode(IO.ComfyNode):
-    """
-    Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API.
-    """
-
     @classmethod
     def define_schema(cls) -> IO.Schema:
         return IO.Schema(
             node_id="MinimaxSubjectToVideoNode",
             display_name="MiniMax Subject to Video",
             category="api node/video/MiniMax",
-            description=cleandoc(cls.__doc__ or ""),
+            description="Generates videos synchronously based on an image and prompt, and optional parameters.",
             inputs=[
                 IO.Image.Input(
                     "subject",
@@ -333,11 +272,7 @@ class MinimaxSubjectToVideoNode(IO.ComfyNode):
         seed: int = 0,
     ) -> IO.NodeOutput:
         return await _generate_mm_video(
-            auth={
-                "auth_token": cls.hidden.auth_token_comfy_org,
-                "comfy_api_key": cls.hidden.api_key_comfy_org,
-            },
-            node_id=cls.hidden.unique_id,
+            cls,
             prompt_text=prompt_text,
             seed=seed,
             model=model,
@@ -348,15 +283,13 @@ class MinimaxSubjectToVideoNode(IO.ComfyNode):
 
 
 class MinimaxHailuoVideoNode(IO.ComfyNode):
-    """Generates videos from prompt, with optional start frame using the new MiniMax Hailuo-02 model."""
-
     @classmethod
     def define_schema(cls) -> IO.Schema:
         return IO.Schema(
             node_id="MinimaxHailuoVideoNode",
             display_name="MiniMax Hailuo Video",
             category="api node/video/MiniMax",
-            description=cleandoc(cls.__doc__ or ""),
+            description="Generates videos from prompt, with optional start frame using the new MiniMax Hailuo-02 model.",
             inputs=[
                 IO.String.Input(
                     "prompt_text",
@@ -420,10 +353,6 @@ class MinimaxHailuoVideoNode(IO.ComfyNode):
         resolution: str = "768P",
         model: str = "MiniMax-Hailuo-02",
     ) -> IO.NodeOutput:
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
         if first_frame_image is None:
             validate_string(prompt_text, field_name="prompt_text")
 
@@ -435,16 +364,13 @@ class MinimaxHailuoVideoNode(IO.ComfyNode):
         # upload image, if passed in
         image_url = None
         if first_frame_image is not None:
-            image_url = (await upload_images_to_comfyapi(first_frame_image, max_images=1, auth_kwargs=auth))[0]
+            image_url = (await upload_images_to_comfyapi(cls, first_frame_image, max_images=1))[0]
 
-        video_generate_operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/minimax/video_generation",
-                method=HttpMethod.POST,
-                request_model=MinimaxVideoGenerationRequest,
-                response_model=MinimaxVideoGenerationResponse,
-            ),
-            request=MinimaxVideoGenerationRequest(
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/minimax/video_generation", method="POST"),
+            response_model=MinimaxVideoGenerationResponse,
+            data=MinimaxVideoGenerationRequest(
                 model=MiniMaxModel(model),
                 prompt=prompt_text,
                 callback_url=None,
@@ -453,67 +379,42 @@ class MinimaxHailuoVideoNode(IO.ComfyNode):
                 duration=duration,
                 resolution=resolution,
             ),
-            auth_kwargs=auth,
         )
-        response = await video_generate_operation.execute()
 
         task_id = response.task_id
         if not task_id:
             raise Exception(f"MiniMax generation failed: {response.base_resp}")
 
         average_duration = 120 if resolution == "768P" else 240
-        video_generate_operation = PollingOperation(
-            poll_endpoint=ApiEndpoint(
-                path="/proxy/minimax/query/video_generation",
-                method=HttpMethod.GET,
-                request_model=EmptyRequest,
-                response_model=MinimaxTaskResultResponse,
-                query_params={"task_id": task_id},
-            ),
-            completed_statuses=["Success"],
-            failed_statuses=["Fail"],
+        task_result = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/minimax/query/video_generation", query_params={"task_id": task_id}),
+            response_model=MinimaxTaskResultResponse,
             status_extractor=lambda x: x.status.value,
             estimated_duration=average_duration,
-            node_id=cls.hidden.unique_id,
-            auth_kwargs=auth,
         )
-        task_result = await video_generate_operation.execute()
 
         file_id = task_result.file_id
         if file_id is None:
             raise Exception("Request was not successful. Missing file ID.")
-        file_retrieve_operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/minimax/files/retrieve",
-                method=HttpMethod.GET,
-                request_model=EmptyRequest,
-                response_model=MinimaxFileRetrieveResponse,
-                query_params={"file_id": int(file_id)},
-            ),
-            request=EmptyRequest(),
-            auth_kwargs=auth,
+        file_result = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/minimax/files/retrieve", query_params={"file_id": int(file_id)}),
+            response_model=MinimaxFileRetrieveResponse,
         )
-        file_result = await file_retrieve_operation.execute()
 
         file_url = file_result.file.download_url
         if file_url is None:
-            raise Exception(
-                f"No video was found in the response. Full response: {file_result.model_dump()}"
-            )
-        logging.info("Generated video URL: %s", file_url)
-        if cls.hidden.unique_id:
-            if hasattr(file_result.file, "backup_download_url"):
-                message = f"Result URL: {file_url}\nBackup URL: {file_result.file.backup_download_url}"
-            else:
-                message = f"Result URL: {file_url}"
-            PromptServer.instance.send_progress_text(message, cls.hidden.unique_id)
+            raise Exception(f"No video was found in the response. Full response: {file_result.model_dump()}")
 
-        video_io = await download_url_to_bytesio(file_url)
-        if video_io is None:
-            error_msg = f"Failed to download video from {file_url}"
-            logging.error(error_msg)
-            raise Exception(error_msg)
-        return IO.NodeOutput(VideoFromFile(video_io))
+        if file_result.file.backup_download_url:
+            try:
+                return IO.NodeOutput(await download_url_to_video_output(file_url, timeout=10, max_retries=2))
+            except Exception:  # if we have a second URL to retrieve the result, try again using that one
+                return IO.NodeOutput(
+                    await download_url_to_video_output(file_result.file.backup_download_url, max_retries=3)
+                )
+        return IO.NodeOutput(await download_url_to_video_output(file_url))
 
 
 class MinimaxExtension(ComfyExtension):
diff --git a/comfy_api_nodes/util/client.py b/comfy_api_nodes/util/client.py
index 9c036d64b..9ae512fe5 100644
--- a/comfy_api_nodes/util/client.py
+++ b/comfy_api_nodes/util/client.py
@@ -78,7 +78,7 @@ class _PollUIState:
 
 _RETRY_STATUS = {408, 429, 500, 502, 503, 504}
 COMPLETED_STATUSES = ["succeeded", "succeed", "success", "completed"]
-FAILED_STATUSES = ["cancelled", "canceled", "failed", "error"]
+FAILED_STATUSES = ["cancelled", "canceled", "fail", "failed", "error"]
 QUEUED_STATUSES = ["created", "queued", "queueing", "submitted"]
 
 
diff --git a/comfy_api_nodes/util/download_helpers.py b/comfy_api_nodes/util/download_helpers.py
index f89045e12..364874bed 100644
--- a/comfy_api_nodes/util/download_helpers.py
+++ b/comfy_api_nodes/util/download_helpers.py
@@ -232,11 +232,12 @@ async def download_url_to_video_output(
     video_url: str,
     *,
     timeout: float = None,
+    max_retries: int = 5,
     cls: type[COMFY_IO.ComfyNode] = None,
 ) -> VideoFromFile:
     """Downloads a video from a URL and returns a `VIDEO` output."""
     result = BytesIO()
-    await download_url_to_bytesio(video_url, result, timeout=timeout, cls=cls)
+    await download_url_to_bytesio(video_url, result, timeout=timeout, max_retries=max_retries, cls=cls)
     return VideoFromFile(result)
 
 

From 1a58087ac2eb64be3645934d0025aafaa5bdce38 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Wed, 29 Oct 2025 12:43:51 -0700
Subject: [PATCH 17/56] Reduce memory usage for fp8 scaled op. (#10531)

---
 comfy/quant_ops.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/quant_ops.py b/comfy/quant_ops.py
index b14e03084..fb35a0d40 100644
--- a/comfy/quant_ops.py
+++ b/comfy/quant_ops.py
@@ -358,7 +358,7 @@ class TensorCoreFP8Layout(QuantizedLayout):
         scale = scale.to(device=tensor.device, dtype=torch.float32)
 
         lp_amax = torch.finfo(dtype).max
-        tensor_scaled = tensor.float() / scale
+        tensor_scaled = tensor * (1.0 / scale).to(tensor.dtype)
         torch.clamp(tensor_scaled, min=-lp_amax, max=lp_amax, out=tensor_scaled)
         qdata = tensor_scaled.to(dtype, memory_format=torch.contiguous_format)
 

From ec4fc2a09a390d0d81500c51fb9e4d8a7a5ce1fc Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Wed, 29 Oct 2025 12:48:06 -0700
Subject: [PATCH 18/56] Fix case of weights not being unpinned. (#10533)

---
 comfy/model_patcher.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index aec73349c..119119e96 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -1283,5 +1283,6 @@ class ModelPatcher:
         self.clear_cached_hook_weights()
 
     def __del__(self):
+        self.unpin_all_weights()
         self.detach(unpatch_all=False)
 

From ab7ab5be23fb9b71d1790f424e7dcf91dc1fe0cc Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Thu, 30 Oct 2025 07:17:46 +1000
Subject: [PATCH 19/56] Fix Race condition in --async-offload that can cause
 corruption (#10501)

* mm: factor out the current stream getter

Make this a reusable function.

* ops: sync the offload stream with the consumption of w&b

This sync is nessacary as pytorch will queue cuda async frees on the
same stream as created to tensor. In the case of async offload, this
will be on the offload stream.

Weights and biases can go out of scope in python which then
triggers the pytorch garbage collector to queue the free operation on
the offload stream possible before the compute stream has used the
weight. This causes a use after free on weight data leading to total
corruption of some workflows.

So sync the offload stream with the compute stream after the weight
has been used so the free has to wait for the weight to be used.

The cast_bias_weight is extended in a backwards compatible way with
the new behaviour opt-in on a defaulted parameter. This handles
custom node packs calling cast_bias_weight and defeatures
async-offload for them (as they do not handle the race).

The pattern is now:

cast_bias_weight(... , offloadable=True) #This might be offloaded
thing(weight, bias, ...)
uncast_bias_weight(...)

* controlnet: adopt new cast_bias_weight synchronization scheme

This is nessacary for safe async weight offloading.

* mm: sync the last stream in the queue, not the next

Currently this peeks ahead to sync the next stream in the queue of
streams with the compute stream. This doesnt allow a lot of
parallelization, as then end result is you can only get one weight load
ahead regardless of how many streams you have.

Rotate the loop logic here to synchronize the end of the queue before
returning the next stream. This allows weights to be loaded ahead of the
compute streams position.
---
 comfy/controlnet.py       |  17 +++---
 comfy/model_management.py |  28 +++++----
 comfy/ops.py              | 121 ++++++++++++++++++++++++++++----------
 3 files changed, 114 insertions(+), 52 deletions(-)

diff --git a/comfy/controlnet.py b/comfy/controlnet.py
index f08ff4b36..0b5e30f52 100644
--- a/comfy/controlnet.py
+++ b/comfy/controlnet.py
@@ -310,11 +310,13 @@ class ControlLoraOps:
             self.bias = None
 
         def forward(self, input):
-            weight, bias = comfy.ops.cast_bias_weight(self, input)
+            weight, bias, offload_stream = comfy.ops.cast_bias_weight(self, input, offloadable=True)
             if self.up is not None:
-                return torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias)
+                x = torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias)
             else:
-                return torch.nn.functional.linear(input, weight, bias)
+                x = torch.nn.functional.linear(input, weight, bias)
+            comfy.ops.uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
     class Conv2d(torch.nn.Module, comfy.ops.CastWeightBiasOp):
         def __init__(
@@ -350,12 +352,13 @@ class ControlLoraOps:
 
 
         def forward(self, input):
-            weight, bias = comfy.ops.cast_bias_weight(self, input)
+            weight, bias, offload_stream = comfy.ops.cast_bias_weight(self, input, offloadable=True)
             if self.up is not None:
-                return torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups)
+                x = torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups)
             else:
-                return torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups)
-
+                x = torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups)
+            comfy.ops.uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
 class ControlLora(ControlNet):
     def __init__(self, control_weights, global_average_pooling=False, model_options={}): #TODO? model_options
diff --git a/comfy/model_management.py b/comfy/model_management.py
index 3e5b977d4..79c0dfdb4 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -1013,6 +1013,16 @@ if args.async_offload:
     NUM_STREAMS = 2
     logging.info("Using async weight offloading with {} streams".format(NUM_STREAMS))
 
+def current_stream(device):
+    if device is None:
+        return None
+    if is_device_cuda(device):
+        return torch.cuda.current_stream()
+    elif is_device_xpu(device):
+        return torch.xpu.current_stream()
+    else:
+        return None
+
 stream_counters = {}
 def get_offload_stream(device):
     stream_counter = stream_counters.get(device, 0)
@@ -1021,21 +1031,17 @@ def get_offload_stream(device):
 
     if device in STREAMS:
         ss = STREAMS[device]
-        s = ss[stream_counter]
+        #Sync the oldest stream in the queue with the current
+        ss[stream_counter].wait_stream(current_stream(device))
         stream_counter = (stream_counter + 1) % len(ss)
-        if is_device_cuda(device):
-            ss[stream_counter].wait_stream(torch.cuda.current_stream())
-        elif is_device_xpu(device):
-            ss[stream_counter].wait_stream(torch.xpu.current_stream())
         stream_counters[device] = stream_counter
-        return s
+        return ss[stream_counter]
     elif is_device_cuda(device):
         ss = []
         for k in range(NUM_STREAMS):
             ss.append(torch.cuda.Stream(device=device, priority=0))
         STREAMS[device] = ss
         s = ss[stream_counter]
-        stream_counter = (stream_counter + 1) % len(ss)
         stream_counters[device] = stream_counter
         return s
     elif is_device_xpu(device):
@@ -1044,18 +1050,14 @@ def get_offload_stream(device):
             ss.append(torch.xpu.Stream(device=device, priority=0))
         STREAMS[device] = ss
         s = ss[stream_counter]
-        stream_counter = (stream_counter + 1) % len(ss)
         stream_counters[device] = stream_counter
         return s
     return None
 
 def sync_stream(device, stream):
-    if stream is None:
+    if stream is None or current_stream(device) is None:
         return
-    if is_device_cuda(device):
-        torch.cuda.current_stream().wait_stream(stream)
-    elif is_device_xpu(device):
-        torch.xpu.current_stream().wait_stream(stream)
+    current_stream(device).wait_stream(stream)
 
 def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, stream=None):
     if device is None or weight.device == device:
diff --git a/comfy/ops.py b/comfy/ops.py
index 93731eedf..71ca7a2bd 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -70,8 +70,12 @@ cast_to = comfy.model_management.cast_to #TODO: remove once no more references
 def cast_to_input(weight, input, non_blocking=False, copy=True):
     return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy)
 
+
 @torch.compiler.disable()
-def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
+def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, offloadable=False):
+    # NOTE: offloadable=False is a a legacy and if you are a custom node author reading this please pass
+    # offloadable=True and call uncast_bias_weight() after your last usage of the weight/bias. This
+    # will add async-offload support to your cast and improve performance.
     if input is not None:
         if dtype is None:
             dtype = input.dtype
@@ -80,7 +84,11 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
         if device is None:
             device = input.device
 
-    offload_stream = comfy.model_management.get_offload_stream(device)
+    if offloadable:
+        offload_stream = comfy.model_management.get_offload_stream(device)
+    else:
+        offload_stream = None
+
     if offload_stream is not None:
         wf_context = offload_stream
     else:
@@ -105,7 +113,24 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
                 weight = f(weight)
 
     comfy.model_management.sync_stream(device, offload_stream)
-    return weight, bias
+    if offloadable:
+        return weight, bias, offload_stream
+    else:
+        #Legacy function signature
+        return weight, bias
+
+
+def uncast_bias_weight(s, weight, bias, offload_stream):
+    if offload_stream is None:
+        return
+    if weight is not None:
+        device = weight.device
+    else:
+        if bias is None:
+            return
+        device = bias.device
+    offload_stream.wait_stream(comfy.model_management.current_stream(device))
+
 
 class CastWeightBiasOp:
     comfy_cast_weights = False
@@ -118,8 +143,10 @@ class disable_weight_init:
             return None
 
         def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return torch.nn.functional.linear(input, weight, bias)
+            weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+            x = torch.nn.functional.linear(input, weight, bias)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
         def forward(self, *args, **kwargs):
             run_every_op()
@@ -133,8 +160,10 @@ class disable_weight_init:
             return None
 
         def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return self._conv_forward(input, weight, bias)
+            weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+            x = self._conv_forward(input, weight, bias)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
         def forward(self, *args, **kwargs):
             run_every_op()
@@ -148,8 +177,10 @@ class disable_weight_init:
             return None
 
         def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return self._conv_forward(input, weight, bias)
+            weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+            x = self._conv_forward(input, weight, bias)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
         def forward(self, *args, **kwargs):
             run_every_op()
@@ -172,8 +203,10 @@ class disable_weight_init:
                 return super()._conv_forward(input, weight, bias, *args, **kwargs)
 
         def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return self._conv_forward(input, weight, bias)
+            weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+            x = self._conv_forward(input, weight, bias)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
         def forward(self, *args, **kwargs):
             run_every_op()
@@ -187,8 +220,10 @@ class disable_weight_init:
             return None
 
         def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
+            weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+            x = torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
         def forward(self, *args, **kwargs):
             run_every_op()
@@ -203,11 +238,14 @@ class disable_weight_init:
 
         def forward_comfy_cast_weights(self, input):
             if self.weight is not None:
-                weight, bias = cast_bias_weight(self, input)
+                weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
             else:
                 weight = None
                 bias = None
-            return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
+                offload_stream = None
+            x = torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
         def forward(self, *args, **kwargs):
             run_every_op()
@@ -223,11 +261,15 @@ class disable_weight_init:
 
         def forward_comfy_cast_weights(self, input):
             if self.weight is not None:
-                weight, bias = cast_bias_weight(self, input)
+                weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
             else:
                 weight = None
-            return comfy.rmsnorm.rms_norm(input, weight, self.eps)  # TODO: switch to commented out line when old torch is deprecated
-            # return torch.nn.functional.rms_norm(input, self.normalized_shape, weight, self.eps)
+                bias = None
+                offload_stream = None
+            x = comfy.rmsnorm.rms_norm(input, weight, self.eps)  # TODO: switch to commented out line when old torch is deprecated
+            # x = torch.nn.functional.rms_norm(input, self.normalized_shape, weight, self.eps)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
         def forward(self, *args, **kwargs):
             run_every_op()
@@ -246,10 +288,12 @@ class disable_weight_init:
                 input, output_size, self.stride, self.padding, self.kernel_size,
                 num_spatial_dims, self.dilation)
 
-            weight, bias = cast_bias_weight(self, input)
-            return torch.nn.functional.conv_transpose2d(
+            weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+            x = torch.nn.functional.conv_transpose2d(
                 input, weight, bias, self.stride, self.padding,
                 output_padding, self.groups, self.dilation)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
         def forward(self, *args, **kwargs):
             run_every_op()
@@ -268,10 +312,12 @@ class disable_weight_init:
                 input, output_size, self.stride, self.padding, self.kernel_size,
                 num_spatial_dims, self.dilation)
 
-            weight, bias = cast_bias_weight(self, input)
-            return torch.nn.functional.conv_transpose1d(
+            weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+            x = torch.nn.functional.conv_transpose1d(
                 input, weight, bias, self.stride, self.padding,
                 output_padding, self.groups, self.dilation)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
         def forward(self, *args, **kwargs):
             run_every_op()
@@ -289,8 +335,11 @@ class disable_weight_init:
             output_dtype = out_dtype
             if self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16:
                 out_dtype = None
-            weight, bias = cast_bias_weight(self, device=input.device, dtype=out_dtype)
-            return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype)
+            weight, bias, offload_stream = cast_bias_weight(self, device=input.device, dtype=out_dtype, offloadable=True)
+            x = torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
+
 
         def forward(self, *args, **kwargs):
             run_every_op()
@@ -361,7 +410,7 @@ def fp8_linear(self, input):
     input_dtype = input.dtype
 
     if len(input.shape) == 3:
-        w, bias = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype)
+        w, bias, offload_stream = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype, offloadable=True)
 
         scale_weight = self.scale_weight
         scale_input = self.scale_input
@@ -382,6 +431,8 @@ def fp8_linear(self, input):
         quantized_input = QuantizedTensor.from_float(input.reshape(-1, input_shape[2]), TensorCoreFP8Layout, scale=scale_input, dtype=dtype)
         o = torch.nn.functional.linear(quantized_input, quantized_weight, bias)
 
+        uncast_bias_weight(self, w, bias, offload_stream)
+
         if tensor_2d:
             return o.reshape(input_shape[0], -1)
         return o.reshape((-1, input_shape[1], self.weight.shape[0]))
@@ -404,8 +455,10 @@ class fp8_ops(manual_cast):
                 except Exception as e:
                     logging.info("Exception during fp8 op: {}".format(e))
 
-            weight, bias = cast_bias_weight(self, input)
-            return torch.nn.functional.linear(input, weight, bias)
+            weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+            x = torch.nn.functional.linear(input, weight, bias)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
 def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None):
     logging.info("Using scaled fp8: fp8 matrix mult: {}, scale input: {}".format(fp8_matrix_mult, scale_input))
@@ -433,12 +486,14 @@ def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None
                     if out is not None:
                         return out
 
-                weight, bias = cast_bias_weight(self, input)
+                weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
 
                 if weight.numel() < input.numel(): #TODO: optimize
-                    return torch.nn.functional.linear(input, weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype), bias)
+                    x = torch.nn.functional.linear(input, weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype), bias)
                 else:
-                    return torch.nn.functional.linear(input * self.scale_weight.to(device=weight.device, dtype=weight.dtype), weight, bias)
+                    x = torch.nn.functional.linear(input * self.scale_weight.to(device=weight.device, dtype=weight.dtype), weight, bias)
+                uncast_bias_weight(self, weight, bias, offload_stream)
+                return x
 
             def convert_weight(self, weight, inplace=False, **kwargs):
                 if inplace:
@@ -577,8 +632,10 @@ class MixedPrecisionOps(disable_weight_init):
             return torch.nn.functional.linear(input, weight, bias)
 
         def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return self._forward(input, weight, bias)
+            weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+            x = self._forward(input, weight, bias)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
         def forward(self, input, *args, **kwargs):
             run_every_op()

From 25de7b1bfa22dd98922f047a1342cc97f8e46c5b Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Wed, 29 Oct 2025 14:20:27 -0700
Subject: [PATCH 20/56] Try to fix slow load issue on low ram hardware with
 pinned mem. (#10536)

---
 comfy/model_patcher.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 119119e96..74b9e48bc 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -658,6 +658,7 @@ class ModelPatcher:
             loading = self._load_list()
 
             load_completely = []
+            offloaded = []
             loading.sort(reverse=True)
             for x in loading:
                 n = x[1]
@@ -699,8 +700,7 @@ class ModelPatcher:
                             patch_counter += 1
 
                     cast_weight = True
-                    for param in params:
-                        self.pin_weight_to_device("{}.{}".format(n, param))
+                    offloaded.append((module_mem, n, m, params))
                 else:
                     if hasattr(m, "comfy_cast_weights"):
                         wipe_lowvram_weight(m)
@@ -741,6 +741,12 @@ class ModelPatcher:
             for x in load_completely:
                 x[2].to(device_to)
 
+            for x in offloaded:
+                n = x[1]
+                params = x[3]
+                for param in params:
+                    self.pin_weight_to_device("{}.{}".format(n, param))
+
             if lowvram_counter > 0:
                 logging.info("loaded partially {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), patch_counter))
                 self.model.model_lowvram = True

From 906c0899575a83ac69bb095e835fdec748891da4 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Wed, 29 Oct 2025 16:29:01 -0700
Subject: [PATCH 21/56] Fix small performance regression with fp8 fast and
 scaled fp8. (#10537)

---
 comfy/ops.py       | 6 +++++-
 comfy/quant_ops.py | 5 +++--
 2 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/comfy/ops.py b/comfy/ops.py
index 71ca7a2bd..18f6b804b 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -421,14 +421,18 @@ def fp8_linear(self, input):
 
         if scale_input is None:
             scale_input = torch.ones((), device=input.device, dtype=torch.float32)
+            input = torch.clamp(input, min=-448, max=448, out=input)
+            input = input.reshape(-1, input_shape[2]).to(dtype).contiguous()
+            layout_params_weight = {'scale': scale_input, 'orig_dtype': input_dtype}
+            quantized_input = QuantizedTensor(input.reshape(-1, input_shape[2]).to(dtype).contiguous(), TensorCoreFP8Layout, layout_params_weight)
         else:
             scale_input = scale_input.to(input.device)
+            quantized_input = QuantizedTensor.from_float(input.reshape(-1, input_shape[2]), TensorCoreFP8Layout, scale=scale_input, dtype=dtype)
 
         # Wrap weight in QuantizedTensor - this enables unified dispatch
         # Call F.linear - __torch_dispatch__ routes to fp8_linear handler in quant_ops.py!
         layout_params_weight = {'scale': scale_weight, 'orig_dtype': input_dtype}
         quantized_weight = QuantizedTensor(w, TensorCoreFP8Layout, layout_params_weight)
-        quantized_input = QuantizedTensor.from_float(input.reshape(-1, input_shape[2]), TensorCoreFP8Layout, scale=scale_input, dtype=dtype)
         o = torch.nn.functional.linear(quantized_input, quantized_weight, bias)
 
         uncast_bias_weight(self, w, bias, offload_stream)
diff --git a/comfy/quant_ops.py b/comfy/quant_ops.py
index fb35a0d40..c822fe53c 100644
--- a/comfy/quant_ops.py
+++ b/comfy/quant_ops.py
@@ -357,9 +357,10 @@ class TensorCoreFP8Layout(QuantizedLayout):
             scale = torch.tensor(scale)
         scale = scale.to(device=tensor.device, dtype=torch.float32)
 
-        lp_amax = torch.finfo(dtype).max
         tensor_scaled = tensor * (1.0 / scale).to(tensor.dtype)
-        torch.clamp(tensor_scaled, min=-lp_amax, max=lp_amax, out=tensor_scaled)
+        # TODO: uncomment this if it's actually needed because the clamp has a small performance penality'
+        # lp_amax = torch.finfo(dtype).max
+        # torch.clamp(tensor_scaled, min=-lp_amax, max=lp_amax, out=tensor_scaled)
         qdata = tensor_scaled.to(dtype, memory_format=torch.contiguous_format)
 
         layout_params = {

From 998bf60bebd03e57a55e106434657849342b733f Mon Sep 17 00:00:00 2001
From: Jedrzej Kosinski <kosinkadink1@gmail.com>
Date: Wed, 29 Oct 2025 16:37:06 -0700
Subject: [PATCH 22/56] Add units/info for the numbers displayed on 'load
 completely' and 'load partially' log messages (#10538)

---
 comfy/model_patcher.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 74b9e48bc..ed3f3f5cb 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -655,6 +655,7 @@ class ModelPatcher:
             mem_counter = 0
             patch_counter = 0
             lowvram_counter = 0
+            lowvram_mem_counter = 0
             loading = self._load_list()
 
             load_completely = []
@@ -675,6 +676,7 @@ class ModelPatcher:
                     if mem_counter + module_mem >= lowvram_model_memory:
                         lowvram_weight = True
                         lowvram_counter += 1
+                        lowvram_mem_counter += module_mem
                         if hasattr(m, "prev_comfy_cast_weights"): #Already lowvramed
                             continue
 
@@ -748,10 +750,10 @@ class ModelPatcher:
                     self.pin_weight_to_device("{}.{}".format(n, param))
 
             if lowvram_counter > 0:
-                logging.info("loaded partially {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), patch_counter))
+                logging.info("loaded partially; {:.2f} MB usable, {:.2f} MB loaded, {:.2f} MB offloaded, lowvram patches: {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), lowvram_mem_counter / (1024 * 1024), patch_counter))
                 self.model.model_lowvram = True
             else:
-                logging.info("loaded completely {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load))
+                logging.info("loaded completely; {:.2f} MB usable, {:.2f} MB loaded, full load: {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load))
                 self.model.model_lowvram = False
                 if full_load:
                     self.model.to(device_to)

From 163b629c70a349c7d1e91eebc5365713e770af8a Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Thu, 30 Oct 2025 08:49:03 +0200
Subject: [PATCH 23/56] use new API client in Pixverse and Ideogram nodes
 (#10543)

---
 comfy_api_nodes/apinode_utils.py         | 109 +------------
 comfy_api_nodes/nodes_bfl.py             |  43 +----
 comfy_api_nodes/nodes_bytedance.py       |  12 +-
 comfy_api_nodes/nodes_ideogram.py        | 137 ++++------------
 comfy_api_nodes/nodes_kling.py           |   2 +-
 comfy_api_nodes/nodes_pixverse.py        | 194 ++++++-----------------
 comfy_api_nodes/nodes_recraft.py         |   4 +-
 comfy_api_nodes/nodes_runway.py          |  10 +-
 comfy_api_nodes/nodes_vidu.py            |  12 +-
 comfy_api_nodes/util/__init__.py         |  10 +-
 comfy_api_nodes/util/conversions.py      |  21 +++
 comfy_api_nodes/util/validation_utils.py | 125 ++++++++++-----
 12 files changed, 220 insertions(+), 459 deletions(-)

diff --git a/comfy_api_nodes/apinode_utils.py b/comfy_api_nodes/apinode_utils.py
index 6a72b9d1d..ecd604ff8 100644
--- a/comfy_api_nodes/apinode_utils.py
+++ b/comfy_api_nodes/apinode_utils.py
@@ -1,15 +1,12 @@
 from __future__ import annotations
 import aiohttp
 import mimetypes
-from typing import Optional, Union
-from comfy.utils import common_upscale
+from typing import Union
 from server import PromptServer
-from comfy.cli_args import args
 
 import numpy as np
 from PIL import Image
 import torch
-import math
 import base64
 from io import BytesIO
 
@@ -60,85 +57,6 @@ async def validate_and_cast_response(
     return torch.stack(image_tensors, dim=0)
 
 
-def validate_aspect_ratio(
-    aspect_ratio: str,
-    minimum_ratio: float,
-    maximum_ratio: float,
-    minimum_ratio_str: str,
-    maximum_ratio_str: str,
-) -> float:
-    """Validates and casts an aspect ratio string to a float.
-
-    Args:
-        aspect_ratio: The aspect ratio string to validate.
-        minimum_ratio: The minimum aspect ratio.
-        maximum_ratio: The maximum aspect ratio.
-        minimum_ratio_str: The minimum aspect ratio string.
-        maximum_ratio_str: The maximum aspect ratio string.
-
-    Returns:
-        The validated and cast aspect ratio.
-
-    Raises:
-        Exception: If the aspect ratio is not valid.
-    """
-    # get ratio values
-    numbers = aspect_ratio.split(":")
-    if len(numbers) != 2:
-        raise TypeError(
-            f"Aspect ratio must be in the format X:Y, such as 16:9, but was {aspect_ratio}."
-        )
-    try:
-        numerator = int(numbers[0])
-        denominator = int(numbers[1])
-    except ValueError as exc:
-        raise TypeError(
-            f"Aspect ratio must contain numbers separated by ':', such as 16:9, but was {aspect_ratio}."
-        ) from exc
-    calculated_ratio = numerator / denominator
-    # if not close to minimum and maximum, check bounds
-    if not math.isclose(calculated_ratio, minimum_ratio) or not math.isclose(
-        calculated_ratio, maximum_ratio
-    ):
-        if calculated_ratio < minimum_ratio:
-            raise TypeError(
-                f"Aspect ratio cannot reduce to any less than {minimum_ratio_str} ({minimum_ratio}), but was {aspect_ratio} ({calculated_ratio})."
-            )
-        if calculated_ratio > maximum_ratio:
-            raise TypeError(
-                f"Aspect ratio cannot reduce to any greater than {maximum_ratio_str} ({maximum_ratio}), but was {aspect_ratio} ({calculated_ratio})."
-            )
-    return aspect_ratio
-
-
-async def download_url_to_bytesio(
-    url: str, timeout: int = None, auth_kwargs: Optional[dict[str, str]] = None
-) -> BytesIO:
-    """Downloads content from a URL using requests and returns it as BytesIO.
-
-    Args:
-        url: The URL to download.
-        timeout: Request timeout in seconds. Defaults to None (no timeout).
-
-    Returns:
-        BytesIO object containing the downloaded content.
-    """
-    headers = {}
-    if url.startswith("/proxy/"):
-        url = str(args.comfy_api_base).rstrip("/") + url
-        auth_token = auth_kwargs.get("auth_token")
-        comfy_api_key = auth_kwargs.get("comfy_api_key")
-        if auth_token:
-            headers["Authorization"] = f"Bearer {auth_token}"
-        elif comfy_api_key:
-            headers["X-API-KEY"] = comfy_api_key
-    timeout_cfg = aiohttp.ClientTimeout(total=timeout) if timeout else None
-    async with aiohttp.ClientSession(timeout=timeout_cfg) as session:
-        async with session.get(url, headers=headers) as resp:
-            resp.raise_for_status()  # Raises HTTPError for bad responses (4XX or 5XX)
-            return BytesIO(await resp.read())
-
-
 def text_filepath_to_base64_string(filepath: str) -> str:
     """Converts a text file to a base64 string."""
     with open(filepath, "rb") as f:
@@ -153,28 +71,3 @@ def text_filepath_to_data_uri(filepath: str) -> str:
     if mime_type is None:
         mime_type = "application/octet-stream"
     return f"data:{mime_type};base64,{base64_string}"
-
-
-def resize_mask_to_image(
-    mask: torch.Tensor,
-    image: torch.Tensor,
-    upscale_method="nearest-exact",
-    crop="disabled",
-    allow_gradient=True,
-    add_channel_dim=False,
-):
-    """
-    Resize mask to be the same dimensions as an image, while maintaining proper format for API calls.
-    """
-    _, H, W, _ = image.shape
-    mask = mask.unsqueeze(-1)
-    mask = mask.movedim(-1, 1)
-    mask = common_upscale(
-        mask, width=W, height=H, upscale_method=upscale_method, crop=crop
-    )
-    mask = mask.movedim(1, -1)
-    if not add_channel_dim:
-        mask = mask.squeeze(-1)
-    if not allow_gradient:
-        mask = (mask > 0.5).float()
-    return mask
diff --git a/comfy_api_nodes/nodes_bfl.py b/comfy_api_nodes/nodes_bfl.py
index baa74fd52..1740fb377 100644
--- a/comfy_api_nodes/nodes_bfl.py
+++ b/comfy_api_nodes/nodes_bfl.py
@@ -5,10 +5,6 @@ import torch
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension
-from comfy_api_nodes.apinode_utils import (
-    resize_mask_to_image,
-    validate_aspect_ratio,
-)
 from comfy_api_nodes.apis.bfl_api import (
     BFLFluxExpandImageRequest,
     BFLFluxFillImageRequest,
@@ -23,8 +19,10 @@ from comfy_api_nodes.util import (
     ApiEndpoint,
     download_url_to_image_tensor,
     poll_op,
+    resize_mask_to_image,
     sync_op,
     tensor_to_base64_string,
+    validate_aspect_ratio_string,
     validate_string,
 )
 
@@ -43,11 +41,6 @@ class FluxProUltraImageNode(IO.ComfyNode):
     Generates images using Flux Pro 1.1 Ultra via api based on prompt and resolution.
     """
 
-    MINIMUM_RATIO = 1 / 4
-    MAXIMUM_RATIO = 4 / 1
-    MINIMUM_RATIO_STR = "1:4"
-    MAXIMUM_RATIO_STR = "4:1"
-
     @classmethod
     def define_schema(cls) -> IO.Schema:
         return IO.Schema(
@@ -112,16 +105,7 @@ class FluxProUltraImageNode(IO.ComfyNode):
 
     @classmethod
     def validate_inputs(cls, aspect_ratio: str):
-        try:
-            validate_aspect_ratio(
-                aspect_ratio,
-                minimum_ratio=cls.MINIMUM_RATIO,
-                maximum_ratio=cls.MAXIMUM_RATIO,
-                minimum_ratio_str=cls.MINIMUM_RATIO_STR,
-                maximum_ratio_str=cls.MAXIMUM_RATIO_STR,
-            )
-        except Exception as e:
-            return str(e)
+        validate_aspect_ratio_string(aspect_ratio, (1, 4), (4, 1))
         return True
 
     @classmethod
@@ -145,13 +129,7 @@ class FluxProUltraImageNode(IO.ComfyNode):
                 prompt=prompt,
                 prompt_upsampling=prompt_upsampling,
                 seed=seed,
-                aspect_ratio=validate_aspect_ratio(
-                    aspect_ratio,
-                    minimum_ratio=cls.MINIMUM_RATIO,
-                    maximum_ratio=cls.MAXIMUM_RATIO,
-                    minimum_ratio_str=cls.MINIMUM_RATIO_STR,
-                    maximum_ratio_str=cls.MAXIMUM_RATIO_STR,
-                ),
+                aspect_ratio=aspect_ratio,
                 raw=raw,
                 image_prompt=(image_prompt if image_prompt is None else tensor_to_base64_string(image_prompt)),
                 image_prompt_strength=(None if image_prompt is None else round(image_prompt_strength, 2)),
@@ -180,11 +158,6 @@ class FluxKontextProImageNode(IO.ComfyNode):
     Edits images using Flux.1 Kontext [pro] via api based on prompt and aspect ratio.
     """
 
-    MINIMUM_RATIO = 1 / 4
-    MAXIMUM_RATIO = 4 / 1
-    MINIMUM_RATIO_STR = "1:4"
-    MAXIMUM_RATIO_STR = "4:1"
-
     @classmethod
     def define_schema(cls) -> IO.Schema:
         return IO.Schema(
@@ -261,13 +234,7 @@ class FluxKontextProImageNode(IO.ComfyNode):
         seed=0,
         prompt_upsampling=False,
     ) -> IO.NodeOutput:
-        aspect_ratio = validate_aspect_ratio(
-            aspect_ratio,
-            minimum_ratio=cls.MINIMUM_RATIO,
-            maximum_ratio=cls.MAXIMUM_RATIO,
-            minimum_ratio_str=cls.MINIMUM_RATIO_STR,
-            maximum_ratio_str=cls.MAXIMUM_RATIO_STR,
-        )
+        validate_aspect_ratio_string(aspect_ratio, (1, 4), (4, 1))
         if input_image is None:
             validate_string(prompt, strip_whitespace=False)
         initial_response = await sync_op(
diff --git a/comfy_api_nodes/nodes_bytedance.py b/comfy_api_nodes/nodes_bytedance.py
index 534af380d..caced471e 100644
--- a/comfy_api_nodes/nodes_bytedance.py
+++ b/comfy_api_nodes/nodes_bytedance.py
@@ -17,7 +17,7 @@ from comfy_api_nodes.util import (
     poll_op,
     sync_op,
     upload_images_to_comfyapi,
-    validate_image_aspect_ratio_range,
+    validate_image_aspect_ratio,
     validate_image_dimensions,
     validate_string,
 )
@@ -403,7 +403,7 @@ class ByteDanceImageEditNode(IO.ComfyNode):
         validate_string(prompt, strip_whitespace=True, min_length=1)
         if get_number_of_images(image) != 1:
             raise ValueError("Exactly one input image is required.")
-        validate_image_aspect_ratio_range(image, (1, 3), (3, 1))
+        validate_image_aspect_ratio(image, (1, 3), (3, 1))
         source_url = (await upload_images_to_comfyapi(cls, image, max_images=1, mime_type="image/png"))[0]
         payload = Image2ImageTaskCreationRequest(
             model=model,
@@ -565,7 +565,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
         reference_images_urls = []
         if n_input_images:
             for i in image:
-                validate_image_aspect_ratio_range(i, (1, 3), (3, 1))
+                validate_image_aspect_ratio(i, (1, 3), (3, 1))
             reference_images_urls = await upload_images_to_comfyapi(
                 cls,
                 image,
@@ -798,7 +798,7 @@ class ByteDanceImageToVideoNode(IO.ComfyNode):
         validate_string(prompt, strip_whitespace=True, min_length=1)
         raise_if_text_params(prompt, ["resolution", "ratio", "duration", "seed", "camerafixed", "watermark"])
         validate_image_dimensions(image, min_width=300, min_height=300, max_width=6000, max_height=6000)
-        validate_image_aspect_ratio_range(image, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
+        validate_image_aspect_ratio(image, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
 
         image_url = (await upload_images_to_comfyapi(cls, image, max_images=1))[0]
         prompt = (
@@ -923,7 +923,7 @@ class ByteDanceFirstLastFrameNode(IO.ComfyNode):
         raise_if_text_params(prompt, ["resolution", "ratio", "duration", "seed", "camerafixed", "watermark"])
         for i in (first_frame, last_frame):
             validate_image_dimensions(i, min_width=300, min_height=300, max_width=6000, max_height=6000)
-            validate_image_aspect_ratio_range(i, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
+            validate_image_aspect_ratio(i, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
 
         download_urls = await upload_images_to_comfyapi(
             cls,
@@ -1045,7 +1045,7 @@ class ByteDanceImageReferenceNode(IO.ComfyNode):
         raise_if_text_params(prompt, ["resolution", "ratio", "duration", "seed", "watermark"])
         for image in images:
             validate_image_dimensions(image, min_width=300, min_height=300, max_width=6000, max_height=6000)
-            validate_image_aspect_ratio_range(image, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
+            validate_image_aspect_ratio(image, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
 
         image_urls = await upload_images_to_comfyapi(cls, images, max_images=4, mime_type="image/png")
         prompt = (
diff --git a/comfy_api_nodes/nodes_ideogram.py b/comfy_api_nodes/nodes_ideogram.py
index d8fd3378b..48f94e612 100644
--- a/comfy_api_nodes/nodes_ideogram.py
+++ b/comfy_api_nodes/nodes_ideogram.py
@@ -1,6 +1,6 @@
 from io import BytesIO
 from typing_extensions import override
-from comfy_api.latest import ComfyExtension, IO
+from comfy_api.latest import IO, ComfyExtension
 from PIL import Image
 import numpy as np
 import torch
@@ -11,19 +11,13 @@ from comfy_api_nodes.apis import (
     IdeogramV3Request,
     IdeogramV3EditRequest,
 )
-
-from comfy_api_nodes.apis.client import (
+from comfy_api_nodes.util import (
     ApiEndpoint,
-    HttpMethod,
-    SynchronousOperation,
-)
-
-from comfy_api_nodes.apinode_utils import (
-    download_url_to_bytesio,
+    bytesio_to_image_tensor,
+    download_url_as_bytesio,
     resize_mask_to_image,
+    sync_op,
 )
-from comfy_api_nodes.util import bytesio_to_image_tensor
-from server import PromptServer
 
 V1_V1_RES_MAP = {
   "Auto":"AUTO",
@@ -220,7 +214,7 @@ async def download_and_process_images(image_urls):
 
     for image_url in image_urls:
         # Using functions from apinode_utils.py to handle downloading and processing
-        image_bytesio = await download_url_to_bytesio(image_url)  # Download image content to BytesIO
+        image_bytesio = await download_url_as_bytesio(image_url)  # Download image content to BytesIO
         img_tensor = bytesio_to_image_tensor(image_bytesio, mode="RGB")  # Convert to torch.Tensor with RGB mode
         image_tensors.append(img_tensor)
 
@@ -233,19 +227,6 @@ async def download_and_process_images(image_urls):
     return stacked_tensors
 
 
-def display_image_urls_on_node(image_urls, node_id):
-    if node_id and image_urls:
-        if len(image_urls) == 1:
-            PromptServer.instance.send_progress_text(
-                f"Generated Image URL:\n{image_urls[0]}", node_id
-            )
-        else:
-            urls_text = "Generated Image URLs:\n" + "\n".join(
-                f"{i+1}. {url}" for i, url in enumerate(image_urls)
-            )
-            PromptServer.instance.send_progress_text(urls_text, node_id)
-
-
 class IdeogramV1(IO.ComfyNode):
 
     @classmethod
@@ -334,44 +315,30 @@ class IdeogramV1(IO.ComfyNode):
         aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None)
         model = "V_1_TURBO" if turbo else "V_1"
 
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/ideogram/generate",
-                method=HttpMethod.POST,
-                request_model=IdeogramGenerateRequest,
-                response_model=IdeogramGenerateResponse,
-            ),
-            request=IdeogramGenerateRequest(
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/ideogram/generate", method="POST"),
+            response_model=IdeogramGenerateResponse,
+            data=IdeogramGenerateRequest(
                 image_request=ImageRequest(
                     prompt=prompt,
                     model=model,
                     num_images=num_images,
                     seed=seed,
                     aspect_ratio=aspect_ratio if aspect_ratio != "ASPECT_1_1" else None,
-                    magic_prompt_option=(
-                        magic_prompt_option if magic_prompt_option != "AUTO" else None
-                    ),
+                    magic_prompt_option=(magic_prompt_option if magic_prompt_option != "AUTO" else None),
                     negative_prompt=negative_prompt if negative_prompt else None,
                 )
             ),
-            auth_kwargs=auth,
+            max_retries=1,
         )
 
-        response = await operation.execute()
-
         if not response.data or len(response.data) == 0:
             raise Exception("No images were generated in the response")
 
         image_urls = [image_data.url for image_data in response.data if image_data.url]
-
         if not image_urls:
             raise Exception("No image URLs were generated in the response")
-
-        display_image_urls_on_node(image_urls, cls.hidden.unique_id)
         return IO.NodeOutput(await download_and_process_images(image_urls))
 
 
@@ -500,18 +467,11 @@ class IdeogramV2(IO.ComfyNode):
         else:
             final_aspect_ratio = aspect_ratio if aspect_ratio != "ASPECT_1_1" else None
 
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/ideogram/generate",
-                method=HttpMethod.POST,
-                request_model=IdeogramGenerateRequest,
-                response_model=IdeogramGenerateResponse,
-            ),
-            request=IdeogramGenerateRequest(
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/ideogram/generate", method="POST"),
+            response_model=IdeogramGenerateResponse,
+            data=IdeogramGenerateRequest(
                 image_request=ImageRequest(
                     prompt=prompt,
                     model=model,
@@ -519,28 +479,20 @@ class IdeogramV2(IO.ComfyNode):
                     seed=seed,
                     aspect_ratio=final_aspect_ratio,
                     resolution=final_resolution,
-                    magic_prompt_option=(
-                        magic_prompt_option if magic_prompt_option != "AUTO" else None
-                    ),
+                    magic_prompt_option=(magic_prompt_option if magic_prompt_option != "AUTO" else None),
                     style_type=style_type if style_type != "NONE" else None,
                     negative_prompt=negative_prompt if negative_prompt else None,
                     color_palette=color_palette if color_palette else None,
                 )
             ),
-            auth_kwargs=auth,
+            max_retries=1,
         )
-
-        response = await operation.execute()
-
         if not response.data or len(response.data) == 0:
             raise Exception("No images were generated in the response")
 
         image_urls = [image_data.url for image_data in response.data if image_data.url]
-
         if not image_urls:
             raise Exception("No image URLs were generated in the response")
-
-        display_image_urls_on_node(image_urls, cls.hidden.unique_id)
         return IO.NodeOutput(await download_and_process_images(image_urls))
 
 
@@ -656,10 +608,6 @@ class IdeogramV3(IO.ComfyNode):
         character_image=None,
         character_mask=None,
     ):
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
         if rendering_speed == "BALANCED":  # for backward compatibility
             rendering_speed = "DEFAULT"
 
@@ -694,9 +642,6 @@ class IdeogramV3(IO.ComfyNode):
 
         # Check if both image and mask are provided for editing mode
         if image is not None and mask is not None:
-            # Edit mode
-            path = "/proxy/ideogram/ideogram-v3/edit"
-
             # Process image and mask
             input_tensor = image.squeeze().cpu()
             # Resize mask to match image dimension
@@ -749,27 +694,20 @@ class IdeogramV3(IO.ComfyNode):
             if character_mask_binary:
                 files["character_mask_binary"] = character_mask_binary
 
-            # Execute the operation for edit mode
-            operation = SynchronousOperation(
-                endpoint=ApiEndpoint(
-                    path=path,
-                    method=HttpMethod.POST,
-                    request_model=IdeogramV3EditRequest,
-                    response_model=IdeogramGenerateResponse,
-                ),
-                request=edit_request,
+            response = await sync_op(
+                cls,
+                ApiEndpoint(path="/proxy/ideogram/ideogram-v3/edit", method="POST"),
+                response_model=IdeogramGenerateResponse,
+                data=edit_request,
                 files=files,
                 content_type="multipart/form-data",
-                auth_kwargs=auth,
+                max_retries=1,
             )
 
         elif image is not None or mask is not None:
             # If only one of image or mask is provided, raise an error
             raise Exception("Ideogram V3 image editing requires both an image AND a mask")
         else:
-            # Generation mode
-            path = "/proxy/ideogram/ideogram-v3/generate"
-
             # Create generation request
             gen_request = IdeogramV3Request(
                 prompt=prompt,
@@ -800,32 +738,22 @@ class IdeogramV3(IO.ComfyNode):
             if files:
                 gen_request.style_type = "AUTO"
 
-            # Execute the operation for generation mode
-            operation = SynchronousOperation(
-                endpoint=ApiEndpoint(
-                    path=path,
-                    method=HttpMethod.POST,
-                    request_model=IdeogramV3Request,
-                    response_model=IdeogramGenerateResponse,
-                ),
-                request=gen_request,
+            response = await sync_op(
+                cls,
+                endpoint=ApiEndpoint(path="/proxy/ideogram/ideogram-v3/generate", method="POST"),
+                response_model=IdeogramGenerateResponse,
+                data=gen_request,
                 files=files if files else None,
                 content_type="multipart/form-data",
-                auth_kwargs=auth,
+                max_retries=1,
             )
 
-        # Execute the operation and process response
-        response = await operation.execute()
-
         if not response.data or len(response.data) == 0:
             raise Exception("No images were generated in the response")
 
         image_urls = [image_data.url for image_data in response.data if image_data.url]
-
         if not image_urls:
             raise Exception("No image URLs were generated in the response")
-
-        display_image_urls_on_node(image_urls, cls.hidden.unique_id)
         return IO.NodeOutput(await download_and_process_images(image_urls))
 
 
@@ -838,5 +766,6 @@ class IdeogramExtension(ComfyExtension):
             IdeogramV3,
         ]
 
+
 async def comfy_entrypoint() -> IdeogramExtension:
     return IdeogramExtension()
diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py
index eea65c9ac..7b23e9cf9 100644
--- a/comfy_api_nodes/nodes_kling.py
+++ b/comfy_api_nodes/nodes_kling.py
@@ -282,7 +282,7 @@ def validate_input_image(image: torch.Tensor) -> None:
     See: https://app.klingai.com/global/dev/document-api/apiReference/model/imageToVideo
     """
     validate_image_dimensions(image, min_width=300, min_height=300)
-    validate_image_aspect_ratio(image, min_aspect_ratio=1 / 2.5, max_aspect_ratio=2.5)
+    validate_image_aspect_ratio(image, (1, 2.5), (2.5, 1))
 
 
 def get_video_from_response(response) -> KlingVideoResult:
diff --git a/comfy_api_nodes/nodes_pixverse.py b/comfy_api_nodes/nodes_pixverse.py
index b2b841be8..6e1686af0 100644
--- a/comfy_api_nodes/nodes_pixverse.py
+++ b/comfy_api_nodes/nodes_pixverse.py
@@ -1,7 +1,6 @@
-from inspect import cleandoc
-from typing import Optional
+import torch
 from typing_extensions import override
-from io import BytesIO
+from comfy_api.latest import IO, ComfyExtension
 from comfy_api_nodes.apis.pixverse_api import (
     PixverseTextVideoRequest,
     PixverseImageVideoRequest,
@@ -17,53 +16,30 @@ from comfy_api_nodes.apis.pixverse_api import (
     PixverseIO,
     pixverse_templates,
 )
-from comfy_api_nodes.apis.client import (
+from comfy_api_nodes.util import (
     ApiEndpoint,
-    HttpMethod,
-    SynchronousOperation,
-    PollingOperation,
-    EmptyRequest,
+    download_url_to_video_output,
+    poll_op,
+    sync_op,
+    tensor_to_bytesio,
+    validate_string,
 )
-from comfy_api_nodes.util import validate_string, tensor_to_bytesio
-from comfy_api.input_impl import VideoFromFile
-from comfy_api.latest import ComfyExtension, IO
-
-import torch
-import aiohttp
-
 
 AVERAGE_DURATION_T2V = 32
 AVERAGE_DURATION_I2V = 30
 AVERAGE_DURATION_T2T = 52
 
 
-def get_video_url_from_response(
-    response: PixverseGenerationStatusResponse,
-) -> Optional[str]:
-    if response.Resp is None or response.Resp.url is None:
-        return None
-    return str(response.Resp.url)
-
-
-async def upload_image_to_pixverse(image: torch.Tensor, auth_kwargs=None):
-    # first, upload image to Pixverse and get image id to use in actual generation call
-    operation = SynchronousOperation(
-        endpoint=ApiEndpoint(
-            path="/proxy/pixverse/image/upload",
-            method=HttpMethod.POST,
-            request_model=EmptyRequest,
-            response_model=PixverseImageUploadResponse,
-        ),
-        request=EmptyRequest(),
+async def upload_image_to_pixverse(cls: type[IO.ComfyNode], image: torch.Tensor):
+    response_upload = await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/pixverse/image/upload", method="POST"),
+        response_model=PixverseImageUploadResponse,
         files={"image": tensor_to_bytesio(image)},
         content_type="multipart/form-data",
-        auth_kwargs=auth_kwargs,
     )
-    response_upload: PixverseImageUploadResponse = await operation.execute()
-
     if response_upload.Resp is None:
         raise Exception(f"PixVerse image upload request failed: '{response_upload.ErrMsg}'")
-
     return response_upload.Resp.img_id
 
 
@@ -93,17 +69,13 @@ class PixverseTemplateNode(IO.ComfyNode):
 
 
 class PixverseTextToVideoNode(IO.ComfyNode):
-    """
-    Generates videos based on prompt and output_size.
-    """
-
     @classmethod
     def define_schema(cls) -> IO.Schema:
         return IO.Schema(
             node_id="PixverseTextToVideoNode",
             display_name="PixVerse Text to Video",
             category="api node/video/PixVerse",
-            description=cleandoc(cls.__doc__ or ""),
+            description="Generates videos based on prompt and output_size.",
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -170,7 +142,7 @@ class PixverseTextToVideoNode(IO.ComfyNode):
         negative_prompt: str = None,
         pixverse_template: int = None,
     ) -> IO.NodeOutput:
-        validate_string(prompt, strip_whitespace=False)
+        validate_string(prompt, strip_whitespace=False, min_length=1)
         # 1080p is limited to 5 seconds duration
         # only normal motion_mode supported for 1080p or for non-5 second duration
         if quality == PixverseQuality.res_1080p:
@@ -179,18 +151,11 @@ class PixverseTextToVideoNode(IO.ComfyNode):
         elif duration_seconds != PixverseDuration.dur_5:
             motion_mode = PixverseMotionMode.normal
 
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/pixverse/video/text/generate",
-                method=HttpMethod.POST,
-                request_model=PixverseTextVideoRequest,
-                response_model=PixverseVideoResponse,
-            ),
-            request=PixverseTextVideoRequest(
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/pixverse/video/text/generate", method="POST"),
+            response_model=PixverseVideoResponse,
+            data=PixverseTextVideoRequest(
                 prompt=prompt,
                 aspect_ratio=aspect_ratio,
                 quality=quality,
@@ -200,20 +165,14 @@ class PixverseTextToVideoNode(IO.ComfyNode):
                 template_id=pixverse_template,
                 seed=seed,
             ),
-            auth_kwargs=auth,
         )
-        response_api = await operation.execute()
-
         if response_api.Resp is None:
             raise Exception(f"PixVerse request failed: '{response_api.ErrMsg}'")
 
-        operation = PollingOperation(
-            poll_endpoint=ApiEndpoint(
-                path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}",
-                method=HttpMethod.GET,
-                request_model=EmptyRequest,
-                response_model=PixverseGenerationStatusResponse,
-            ),
+        response_poll = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}"),
+            response_model=PixverseGenerationStatusResponse,
             completed_statuses=[PixverseStatus.successful],
             failed_statuses=[
                 PixverseStatus.contents_moderation,
@@ -221,30 +180,19 @@ class PixverseTextToVideoNode(IO.ComfyNode):
                 PixverseStatus.deleted,
             ],
             status_extractor=lambda x: x.Resp.status,
-            auth_kwargs=auth,
-            node_id=cls.hidden.unique_id,
-            result_url_extractor=get_video_url_from_response,
             estimated_duration=AVERAGE_DURATION_T2V,
         )
-        response_poll = await operation.execute()
-
-        async with aiohttp.ClientSession() as session:
-            async with session.get(response_poll.Resp.url) as vid_response:
-                return IO.NodeOutput(VideoFromFile(BytesIO(await vid_response.content.read())))
+        return IO.NodeOutput(await download_url_to_video_output(response_poll.Resp.url))
 
 
 class PixverseImageToVideoNode(IO.ComfyNode):
-    """
-    Generates videos based on prompt and output_size.
-    """
-
     @classmethod
     def define_schema(cls) -> IO.Schema:
         return IO.Schema(
             node_id="PixverseImageToVideoNode",
             display_name="PixVerse Image to Video",
             category="api node/video/PixVerse",
-            description=cleandoc(cls.__doc__ or ""),
+            description="Generates videos based on prompt and output_size.",
             inputs=[
                 IO.Image.Input("image"),
                 IO.String.Input(
@@ -309,11 +257,7 @@ class PixverseImageToVideoNode(IO.ComfyNode):
         pixverse_template: int = None,
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=False)
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
-        img_id = await upload_image_to_pixverse(image, auth_kwargs=auth)
+        img_id = await upload_image_to_pixverse(cls, image)
 
         # 1080p is limited to 5 seconds duration
         # only normal motion_mode supported for 1080p or for non-5 second duration
@@ -323,14 +267,11 @@ class PixverseImageToVideoNode(IO.ComfyNode):
         elif duration_seconds != PixverseDuration.dur_5:
             motion_mode = PixverseMotionMode.normal
 
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/pixverse/video/img/generate",
-                method=HttpMethod.POST,
-                request_model=PixverseImageVideoRequest,
-                response_model=PixverseVideoResponse,
-            ),
-            request=PixverseImageVideoRequest(
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/pixverse/video/img/generate", method="POST"),
+            response_model=PixverseVideoResponse,
+            data=PixverseImageVideoRequest(
                 img_id=img_id,
                 prompt=prompt,
                 quality=quality,
@@ -340,20 +281,15 @@ class PixverseImageToVideoNode(IO.ComfyNode):
                 template_id=pixverse_template,
                 seed=seed,
             ),
-            auth_kwargs=auth,
         )
-        response_api = await operation.execute()
 
         if response_api.Resp is None:
             raise Exception(f"PixVerse request failed: '{response_api.ErrMsg}'")
 
-        operation = PollingOperation(
-            poll_endpoint=ApiEndpoint(
-                path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}",
-                method=HttpMethod.GET,
-                request_model=EmptyRequest,
-                response_model=PixverseGenerationStatusResponse,
-            ),
+        response_poll = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}"),
+            response_model=PixverseGenerationStatusResponse,
             completed_statuses=[PixverseStatus.successful],
             failed_statuses=[
                 PixverseStatus.contents_moderation,
@@ -361,30 +297,19 @@ class PixverseImageToVideoNode(IO.ComfyNode):
                 PixverseStatus.deleted,
             ],
             status_extractor=lambda x: x.Resp.status,
-            auth_kwargs=auth,
-            node_id=cls.hidden.unique_id,
-            result_url_extractor=get_video_url_from_response,
             estimated_duration=AVERAGE_DURATION_I2V,
         )
-        response_poll = await operation.execute()
-
-        async with aiohttp.ClientSession() as session:
-            async with session.get(response_poll.Resp.url) as vid_response:
-                return IO.NodeOutput(VideoFromFile(BytesIO(await vid_response.content.read())))
+        return IO.NodeOutput(await download_url_to_video_output(response_poll.Resp.url))
 
 
 class PixverseTransitionVideoNode(IO.ComfyNode):
-    """
-    Generates videos based on prompt and output_size.
-    """
-
     @classmethod
     def define_schema(cls) -> IO.Schema:
         return IO.Schema(
             node_id="PixverseTransitionVideoNode",
             display_name="PixVerse Transition Video",
             category="api node/video/PixVerse",
-            description=cleandoc(cls.__doc__ or ""),
+            description="Generates videos based on prompt and output_size.",
             inputs=[
                 IO.Image.Input("first_frame"),
                 IO.Image.Input("last_frame"),
@@ -445,12 +370,8 @@ class PixverseTransitionVideoNode(IO.ComfyNode):
         negative_prompt: str = None,
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=False)
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
-        first_frame_id = await upload_image_to_pixverse(first_frame, auth_kwargs=auth)
-        last_frame_id = await upload_image_to_pixverse(last_frame, auth_kwargs=auth)
+        first_frame_id = await upload_image_to_pixverse(cls, first_frame)
+        last_frame_id = await upload_image_to_pixverse(cls, last_frame)
 
         # 1080p is limited to 5 seconds duration
         # only normal motion_mode supported for 1080p or for non-5 second duration
@@ -460,14 +381,11 @@ class PixverseTransitionVideoNode(IO.ComfyNode):
         elif duration_seconds != PixverseDuration.dur_5:
             motion_mode = PixverseMotionMode.normal
 
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/pixverse/video/transition/generate",
-                method=HttpMethod.POST,
-                request_model=PixverseTransitionVideoRequest,
-                response_model=PixverseVideoResponse,
-            ),
-            request=PixverseTransitionVideoRequest(
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/pixverse/video/transition/generate", method="POST"),
+            response_model=PixverseVideoResponse,
+            data=PixverseTransitionVideoRequest(
                 first_frame_img=first_frame_id,
                 last_frame_img=last_frame_id,
                 prompt=prompt,
@@ -477,20 +395,15 @@ class PixverseTransitionVideoNode(IO.ComfyNode):
                 negative_prompt=negative_prompt if negative_prompt else None,
                 seed=seed,
             ),
-            auth_kwargs=auth,
         )
-        response_api = await operation.execute()
 
         if response_api.Resp is None:
             raise Exception(f"PixVerse request failed: '{response_api.ErrMsg}'")
 
-        operation = PollingOperation(
-            poll_endpoint=ApiEndpoint(
-                path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}",
-                method=HttpMethod.GET,
-                request_model=EmptyRequest,
-                response_model=PixverseGenerationStatusResponse,
-            ),
+        response_poll = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}"),
+            response_model=PixverseGenerationStatusResponse,
             completed_statuses=[PixverseStatus.successful],
             failed_statuses=[
                 PixverseStatus.contents_moderation,
@@ -498,16 +411,9 @@ class PixverseTransitionVideoNode(IO.ComfyNode):
                 PixverseStatus.deleted,
             ],
             status_extractor=lambda x: x.Resp.status,
-            auth_kwargs=auth,
-            node_id=cls.hidden.unique_id,
-            result_url_extractor=get_video_url_from_response,
             estimated_duration=AVERAGE_DURATION_T2V,
         )
-        response_poll = await operation.execute()
-
-        async with aiohttp.ClientSession() as session:
-            async with session.get(response_poll.Resp.url) as vid_response:
-                return IO.NodeOutput(VideoFromFile(BytesIO(await vid_response.content.read())))
+        return IO.NodeOutput(await download_url_to_video_output(response_poll.Resp.url))
 
 
 class PixVerseExtension(ComfyExtension):
diff --git a/comfy_api_nodes/nodes_recraft.py b/comfy_api_nodes/nodes_recraft.py
index dee186cd6..e3440b946 100644
--- a/comfy_api_nodes/nodes_recraft.py
+++ b/comfy_api_nodes/nodes_recraft.py
@@ -8,9 +8,6 @@ from typing_extensions import override
 
 from comfy.utils import ProgressBar
 from comfy_api.latest import IO, ComfyExtension
-from comfy_api_nodes.apinode_utils import (
-    resize_mask_to_image,
-)
 from comfy_api_nodes.apis.recraft_api import (
     RecraftColor,
     RecraftColorChain,
@@ -28,6 +25,7 @@ from comfy_api_nodes.util import (
     ApiEndpoint,
     bytesio_to_image_tensor,
     download_url_as_bytesio,
+    resize_mask_to_image,
     sync_op,
     tensor_to_bytesio,
     validate_string,
diff --git a/comfy_api_nodes/nodes_runway.py b/comfy_api_nodes/nodes_runway.py
index 0543d1d0e..2fdafbbfe 100644
--- a/comfy_api_nodes/nodes_runway.py
+++ b/comfy_api_nodes/nodes_runway.py
@@ -200,7 +200,7 @@ class RunwayImageToVideoNodeGen3a(IO.ComfyNode):
     ) -> IO.NodeOutput:
         validate_string(prompt, min_length=1)
         validate_image_dimensions(start_frame, max_width=7999, max_height=7999)
-        validate_image_aspect_ratio(start_frame, min_aspect_ratio=0.5, max_aspect_ratio=2.0)
+        validate_image_aspect_ratio(start_frame, (1, 2), (2, 1))
 
         download_urls = await upload_images_to_comfyapi(
             cls,
@@ -290,7 +290,7 @@ class RunwayImageToVideoNodeGen4(IO.ComfyNode):
     ) -> IO.NodeOutput:
         validate_string(prompt, min_length=1)
         validate_image_dimensions(start_frame, max_width=7999, max_height=7999)
-        validate_image_aspect_ratio(start_frame, min_aspect_ratio=0.5, max_aspect_ratio=2.0)
+        validate_image_aspect_ratio(start_frame, (1, 2), (2, 1))
 
         download_urls = await upload_images_to_comfyapi(
             cls,
@@ -390,8 +390,8 @@ class RunwayFirstLastFrameNode(IO.ComfyNode):
         validate_string(prompt, min_length=1)
         validate_image_dimensions(start_frame, max_width=7999, max_height=7999)
         validate_image_dimensions(end_frame, max_width=7999, max_height=7999)
-        validate_image_aspect_ratio(start_frame, min_aspect_ratio=0.5, max_aspect_ratio=2.0)
-        validate_image_aspect_ratio(end_frame, min_aspect_ratio=0.5, max_aspect_ratio=2.0)
+        validate_image_aspect_ratio(start_frame, (1, 2), (2, 1))
+        validate_image_aspect_ratio(end_frame, (1, 2), (2, 1))
 
         stacked_input_images = image_tensor_pair_to_batch(start_frame, end_frame)
         download_urls = await upload_images_to_comfyapi(
@@ -475,7 +475,7 @@ class RunwayTextToImageNode(IO.ComfyNode):
         reference_images = None
         if reference_image is not None:
             validate_image_dimensions(reference_image, max_width=7999, max_height=7999)
-            validate_image_aspect_ratio(reference_image, min_aspect_ratio=0.5, max_aspect_ratio=2.0)
+            validate_image_aspect_ratio(reference_image, (1, 2), (2, 1))
             download_urls = await upload_images_to_comfyapi(
                 cls,
                 reference_image,
diff --git a/comfy_api_nodes/nodes_vidu.py b/comfy_api_nodes/nodes_vidu.py
index 0e0572f8c..7a679f0d9 100644
--- a/comfy_api_nodes/nodes_vidu.py
+++ b/comfy_api_nodes/nodes_vidu.py
@@ -14,9 +14,9 @@ from comfy_api_nodes.util import (
     poll_op,
     sync_op,
     upload_images_to_comfyapi,
-    validate_aspect_ratio_closeness,
-    validate_image_aspect_ratio_range,
+    validate_image_aspect_ratio,
     validate_image_dimensions,
+    validate_images_aspect_ratio_closeness,
 )
 
 VIDU_TEXT_TO_VIDEO = "/proxy/vidu/text2video"
@@ -114,7 +114,7 @@ async def execute_task(
         cls,
         ApiEndpoint(path=VIDU_GET_GENERATION_STATUS % response.task_id),
         response_model=TaskStatusResponse,
-        status_extractor=lambda r: r.state.value,
+        status_extractor=lambda r: r.state,
         estimated_duration=estimated_duration,
     )
 
@@ -307,7 +307,7 @@ class ViduImageToVideoNode(IO.ComfyNode):
     ) -> IO.NodeOutput:
         if get_number_of_images(image) > 1:
             raise ValueError("Only one input image is allowed.")
-        validate_image_aspect_ratio_range(image, (1, 4), (4, 1))
+        validate_image_aspect_ratio(image, (1, 4), (4, 1))
         payload = TaskCreationRequest(
             model_name=model,
             prompt=prompt,
@@ -423,7 +423,7 @@ class ViduReferenceVideoNode(IO.ComfyNode):
         if a > 7:
             raise ValueError("Too many images, maximum allowed is 7.")
         for image in images:
-            validate_image_aspect_ratio_range(image, (1, 4), (4, 1))
+            validate_image_aspect_ratio(image, (1, 4), (4, 1))
             validate_image_dimensions(image, min_width=128, min_height=128)
         payload = TaskCreationRequest(
             model_name=model,
@@ -533,7 +533,7 @@ class ViduStartEndToVideoNode(IO.ComfyNode):
         resolution: str,
         movement_amplitude: str,
     ) -> IO.NodeOutput:
-        validate_aspect_ratio_closeness(first_frame, end_frame, min_rel=0.8, max_rel=1.25, strict=False)
+        validate_images_aspect_ratio_closeness(first_frame, end_frame, min_rel=0.8, max_rel=1.25, strict=False)
         payload = TaskCreationRequest(
             model_name=model,
             prompt=prompt,
diff --git a/comfy_api_nodes/util/__init__.py b/comfy_api_nodes/util/__init__.py
index 0cca2b59b..bbc71363a 100644
--- a/comfy_api_nodes/util/__init__.py
+++ b/comfy_api_nodes/util/__init__.py
@@ -14,6 +14,7 @@ from .conversions import (
     downscale_image_tensor,
     image_tensor_pair_to_batch,
     pil_to_bytesio,
+    resize_mask_to_image,
     tensor_to_base64_string,
     tensor_to_bytesio,
     tensor_to_pil,
@@ -34,12 +35,12 @@ from .upload_helpers import (
 )
 from .validation_utils import (
     get_number_of_images,
-    validate_aspect_ratio_closeness,
+    validate_aspect_ratio_string,
     validate_audio_duration,
     validate_container_format_is_mp4,
     validate_image_aspect_ratio,
-    validate_image_aspect_ratio_range,
     validate_image_dimensions,
+    validate_images_aspect_ratio_closeness,
     validate_string,
     validate_video_dimensions,
     validate_video_duration,
@@ -70,6 +71,7 @@ __all__ = [
     "downscale_image_tensor",
     "image_tensor_pair_to_batch",
     "pil_to_bytesio",
+    "resize_mask_to_image",
     "tensor_to_base64_string",
     "tensor_to_bytesio",
     "tensor_to_pil",
@@ -77,12 +79,12 @@ __all__ = [
     "video_to_base64_string",
     # Validation utilities
     "get_number_of_images",
-    "validate_aspect_ratio_closeness",
+    "validate_aspect_ratio_string",
     "validate_audio_duration",
     "validate_container_format_is_mp4",
     "validate_image_aspect_ratio",
-    "validate_image_aspect_ratio_range",
     "validate_image_dimensions",
+    "validate_images_aspect_ratio_closeness",
     "validate_string",
     "validate_video_dimensions",
     "validate_video_duration",
diff --git a/comfy_api_nodes/util/conversions.py b/comfy_api_nodes/util/conversions.py
index 9f4c90c5c..b59c2bd84 100644
--- a/comfy_api_nodes/util/conversions.py
+++ b/comfy_api_nodes/util/conversions.py
@@ -430,3 +430,24 @@ def audio_bytes_to_audio_input(audio_bytes: bytes) -> dict:
     wav = torch.cat(frames, dim=1)  # [C, T]
     wav = _f32_pcm(wav)
     return {"waveform": wav.unsqueeze(0).contiguous(), "sample_rate": out_sr}
+
+
+def resize_mask_to_image(
+    mask: torch.Tensor,
+    image: torch.Tensor,
+    upscale_method="nearest-exact",
+    crop="disabled",
+    allow_gradient=True,
+    add_channel_dim=False,
+):
+    """Resize mask to be the same dimensions as an image, while maintaining proper format for API calls."""
+    _, height, width, _ = image.shape
+    mask = mask.unsqueeze(-1)
+    mask = mask.movedim(-1, 1)
+    mask = common_upscale(mask, width=width, height=height, upscale_method=upscale_method, crop=crop)
+    mask = mask.movedim(1, -1)
+    if not add_channel_dim:
+        mask = mask.squeeze(-1)
+    if not allow_gradient:
+        mask = (mask > 0.5).float()
+    return mask
diff --git a/comfy_api_nodes/util/validation_utils.py b/comfy_api_nodes/util/validation_utils.py
index 22da05bc1..ec7006aed 100644
--- a/comfy_api_nodes/util/validation_utils.py
+++ b/comfy_api_nodes/util/validation_utils.py
@@ -37,63 +37,62 @@ def validate_image_dimensions(
 
 def validate_image_aspect_ratio(
     image: torch.Tensor,
-    min_aspect_ratio: Optional[float] = None,
-    max_aspect_ratio: Optional[float] = None,
-):
-    width, height = get_image_dimensions(image)
-    aspect_ratio = width / height
-
-    if min_aspect_ratio is not None and aspect_ratio < min_aspect_ratio:
-        raise ValueError(f"Image aspect ratio must be at least {min_aspect_ratio}, got {aspect_ratio}")
-    if max_aspect_ratio is not None and aspect_ratio > max_aspect_ratio:
-        raise ValueError(f"Image aspect ratio must be at most {max_aspect_ratio}, got {aspect_ratio}")
-
-
-def validate_image_aspect_ratio_range(
-    image: torch.Tensor,
-    min_ratio: tuple[float, float],  # e.g. (1, 4)
-    max_ratio: tuple[float, float],  # e.g. (4, 1)
+    min_ratio: Optional[tuple[float, float]] = None,  # e.g. (1, 4)
+    max_ratio: Optional[tuple[float, float]] = None,  # e.g. (4, 1)
     *,
     strict: bool = True,  # True -> (min, max); False -> [min, max]
 ) -> float:
-    a1, b1 = min_ratio
-    a2, b2 = max_ratio
-    if a1 <= 0 or b1 <= 0 or a2 <= 0 or b2 <= 0:
-        raise ValueError("Ratios must be positive, like (1, 4) or (4, 1).")
-    lo, hi = (a1 / b1), (a2 / b2)
-    if lo > hi:
-        lo, hi = hi, lo
-        a1, b1, a2, b2 = a2, b2, a1, b1  # swap only for error text
+    """Validates that image aspect ratio is within min and max. If a bound is None, that side is not checked."""
     w, h = get_image_dimensions(image)
     if w <= 0 or h <= 0:
         raise ValueError(f"Invalid image dimensions: {w}x{h}")
     ar = w / h
-    ok = (lo < ar < hi) if strict else (lo <= ar <= hi)
-    if not ok:
-        op = "<" if strict else "≤"
-        raise ValueError(f"Image aspect ratio {ar:.6g} is outside allowed range: {a1}:{b1} {op} ratio {op} {a2}:{b2}")
+    _assert_ratio_bounds(ar, min_ratio=min_ratio, max_ratio=max_ratio, strict=strict)
     return ar
 
 
-def validate_aspect_ratio_closeness(
-    start_img,
-    end_img,
-    min_rel: float,
-    max_rel: float,
+def validate_images_aspect_ratio_closeness(
+    first_image: torch.Tensor,
+    second_image: torch.Tensor,
+    min_rel: float,   # e.g. 0.8
+    max_rel: float,   # e.g. 1.25
     *,
-    strict: bool = False,  # True => exclusive, False => inclusive
-) -> None:
-    w1, h1 = get_image_dimensions(start_img)
-    w2, h2 = get_image_dimensions(end_img)
+    strict: bool = False,  # True -> (min, max); False -> [min, max]
+) -> float:
+    """
+    Validates that the two images' aspect ratios are 'close'.
+    The closeness factor is C = max(ar1, ar2) / min(ar1, ar2)  (C >= 1).
+    We require C <= limit, where limit = max(max_rel, 1.0 / min_rel).
+
+    Returns the computed closeness factor C.
+    """
+    w1, h1 = get_image_dimensions(first_image)
+    w2, h2 = get_image_dimensions(second_image)
     if min(w1, h1, w2, h2) <= 0:
         raise ValueError("Invalid image dimensions")
     ar1 = w1 / h1
     ar2 = w2 / h2
-    # Normalize so it is symmetric (no need to check both ar1/ar2 and ar2/ar1)
     closeness = max(ar1, ar2) / min(ar1, ar2)
-    limit = max(max_rel, 1.0 / min_rel)  # for 0.8..1.25 this is 1.25
+    limit = max(max_rel, 1.0 / min_rel)
     if (closeness >= limit) if strict else (closeness > limit):
-        raise ValueError(f"Aspect ratios must be close: start/end={ar1/ar2:.4f}, allowed range {min_rel}–{max_rel}.")
+        raise ValueError(
+            f"Aspect ratios must be close: ar1/ar2={ar1/ar2:.2g}, "
+            f"allowed range {min_rel}–{max_rel} (limit {limit:.2g})."
+        )
+    return closeness
+
+
+def validate_aspect_ratio_string(
+    aspect_ratio: str,
+    min_ratio: Optional[tuple[float, float]] = None,  # e.g. (1, 4)
+    max_ratio: Optional[tuple[float, float]] = None,  # e.g. (4, 1)
+    *,
+    strict: bool = False,  # True -> (min, max); False -> [min, max]
+) -> float:
+    """Parses 'X:Y' and validates it against optional bounds. Returns the numeric ratio."""
+    ar = _parse_aspect_ratio_string(aspect_ratio)
+    _assert_ratio_bounds(ar, min_ratio=min_ratio, max_ratio=max_ratio, strict=strict)
+    return ar
 
 
 def validate_video_dimensions(
@@ -183,3 +182,49 @@ def validate_container_format_is_mp4(video: VideoInput) -> None:
     container_format = video.get_container_format()
     if container_format not in ["mp4", "mov,mp4,m4a,3gp,3g2,mj2"]:
         raise ValueError(f"Only MP4 container format supported. Got: {container_format}")
+
+
+def _ratio_from_tuple(r: tuple[float, float]) -> float:
+    a, b = r
+    if a <= 0 or b <= 0:
+        raise ValueError(f"Ratios must be positive, got {a}:{b}.")
+    return a / b
+
+
+def _assert_ratio_bounds(
+    ar: float,
+    *,
+    min_ratio: Optional[tuple[float, float]] = None,
+    max_ratio: Optional[tuple[float, float]] = None,
+    strict: bool = True,
+) -> None:
+    """Validate a numeric aspect ratio against optional min/max ratio bounds."""
+    lo = _ratio_from_tuple(min_ratio) if min_ratio is not None else None
+    hi = _ratio_from_tuple(max_ratio) if max_ratio is not None else None
+
+    if lo is not None and hi is not None and lo > hi:
+        lo, hi = hi, lo  # normalize order if caller swapped them
+
+    if lo is not None:
+        if (ar <= lo) if strict else (ar < lo):
+            op = "<" if strict else "≤"
+            raise ValueError(f"Aspect ratio `{ar:.2g}` must be {op} {lo:.2g}.")
+    if hi is not None:
+        if (ar >= hi) if strict else (ar > hi):
+            op = "<" if strict else "≤"
+            raise ValueError(f"Aspect ratio `{ar:.2g}` must be {op} {hi:.2g}.")
+
+
+def _parse_aspect_ratio_string(ar_str: str) -> float:
+    """Parse 'X:Y' with integer parts into a positive float ratio X/Y."""
+    parts = ar_str.split(":")
+    if len(parts) != 2:
+        raise ValueError(f"Aspect ratio must be 'X:Y' (e.g., 16:9), got '{ar_str}'.")
+    try:
+        a = int(parts[0].strip())
+        b = int(parts[1].strip())
+    except ValueError as exc:
+        raise ValueError(f"Aspect ratio must contain integers separated by ':', got '{ar_str}'.") from exc
+    if a <= 0 or b <= 0:
+        raise ValueError(f"Aspect ratio parts must be positive integers, got {a}:{b}.")
+    return a / b

From dfac94695be95076d8028d04005a744f3ec0de8d Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Thu, 30 Oct 2025 19:22:35 +0200
Subject: [PATCH 24/56] fix img2img operation in Dall2 node (#10552)

---
 comfy_api_nodes/nodes_openai.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy_api_nodes/nodes_openai.py b/comfy_api_nodes/nodes_openai.py
index c467e840c..b4568fc85 100644
--- a/comfy_api_nodes/nodes_openai.py
+++ b/comfy_api_nodes/nodes_openai.py
@@ -225,7 +225,7 @@ class OpenAIDalle2(ComfyNodeABC):
             ),
             files=(
                 {
-                    "image": img_binary,
+                    "image": ("image.png", img_binary, "image/png"),
                 }
                 if img_binary
                 else None

From 513b0c46fba3bf40191d684ff81207ad935f1717 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Fri, 31 Oct 2025 07:39:02 +1000
Subject: [PATCH 25/56] Add RAM Pressure cache mode (#10454)

* execution: Roll the UI cache into the outputs

Currently the UI cache is parallel to the output cache with
expectations of being a content superset of the output cache.
At the same time the UI and output cache are maintained completely
seperately, making it awkward to free the output cache content without
changing the behaviour of the UI cache.

There are two actual users (getters) of the UI cache. The first is
the case of a direct content hit on the output cache when executing a
node. This case is very naturally handled by merging the UI and outputs
cache.

The second case is the history JSON generation at the end of the prompt.
This currently works by asking the cache for all_node_ids and then
pulling the cache contents for those nodes. all_node_ids is the nodes
of the dynamic prompt.

So fold the UI cache into the output cache. The current UI cache setter
now writes to a prompt-scope dict. When the output cache is set, just
get this value from the dict and tuple up with the outputs.

When generating the history, simply iterate prompt-scope dict.

This prepares support for more complex caching strategies (like RAM
pressure caching) where less than 1 workflow will be cached and it
will be desirable to keep the UI cache and output cache in sync.

* sd: Implement RAM getter for VAE

* model_patcher: Implement RAM getter for ModelPatcher

* sd: Implement RAM getter for CLIP

* Implement RAM Pressure cache

Implement a cache sensitive to RAM pressure. When RAM headroom drops
down below a certain threshold, evict RAM-expensive nodes from the
cache.

Models and tensors are measured directly for RAM usage. An OOM score
is then computed based on the RAM usage of the node.

Note the due to indirection through shared objects (like a model
patcher), multiple nodes can account the same RAM as their individual
usage. The intent is this will free chains of nodes particularly
model loaders and associate loras as they all score similar and are
sorted in close to each other.

Has a bias towards unloading model nodes mid flow while being able
to keep results like text encodings and VAE.

* execution: Convert the cache entry to NamedTuple

As commented in review.

Convert this to a named tuple and abstract away the tuple type
completely from graph.py.
---
 comfy/cli_args.py          |  1 +
 comfy/model_patcher.py     |  3 ++
 comfy/sd.py                | 14 +++++++
 comfy_execution/caching.py | 83 ++++++++++++++++++++++++++++++++++++++
 comfy_execution/graph.py   |  9 ++++-
 execution.py               | 81 +++++++++++++++++++++----------------
 main.py                    |  4 +-
 7 files changed, 157 insertions(+), 38 deletions(-)

diff --git a/comfy/cli_args.py b/comfy/cli_args.py
index 001abd843..3d5bc7c90 100644
--- a/comfy/cli_args.py
+++ b/comfy/cli_args.py
@@ -105,6 +105,7 @@ cache_group = parser.add_mutually_exclusive_group()
 cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.")
 cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.")
 cache_group.add_argument("--cache-none", action="store_true", help="Reduced RAM/VRAM usage at the expense of executing every node for each run.")
+cache_group.add_argument("--cache-ram", nargs='?', const=4.0, type=float, default=0, help="Use RAM pressure caching with the specified headroom threshold. If available RAM drops below the threhold the cache remove large items to free RAM. Default 4GB")
 
 attn_group = parser.add_mutually_exclusive_group()
 attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.")
diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index ed3f3f5cb..674a214ca 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -276,6 +276,9 @@ class ModelPatcher:
         self.size = comfy.model_management.module_size(self.model)
         return self.size
 
+    def get_ram_usage(self):
+        return self.model_size()
+
     def loaded_size(self):
         return self.model.model_loaded_weight_memory
 
diff --git a/comfy/sd.py b/comfy/sd.py
index de4eee96e..9e5ebbf15 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -143,6 +143,9 @@ class CLIP:
         n.apply_hooks_to_conds = self.apply_hooks_to_conds
         return n
 
+    def get_ram_usage(self):
+        return self.patcher.get_ram_usage()
+
     def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
         return self.patcher.add_patches(patches, strength_patch, strength_model)
 
@@ -293,6 +296,7 @@ class VAE:
         self.working_dtypes = [torch.bfloat16, torch.float32]
         self.disable_offload = False
         self.not_video = False
+        self.size = None
 
         self.downscale_index_formula = None
         self.upscale_index_formula = None
@@ -595,6 +599,16 @@ class VAE:
 
         self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device)
         logging.info("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype))
+        self.model_size()
+
+    def model_size(self):
+        if self.size is not None:
+            return self.size
+        self.size = comfy.model_management.module_size(self.first_stage_model)
+        return self.size
+
+    def get_ram_usage(self):
+        return self.model_size()
 
     def throw_exception_if_invalid(self):
         if self.first_stage_model is None:
diff --git a/comfy_execution/caching.py b/comfy_execution/caching.py
index 566bc3f9c..b498f43e7 100644
--- a/comfy_execution/caching.py
+++ b/comfy_execution/caching.py
@@ -1,4 +1,9 @@
+import bisect
+import gc
 import itertools
+import psutil
+import time
+import torch
 from typing import Sequence, Mapping, Dict
 from comfy_execution.graph import DynamicPrompt
 from abc import ABC, abstractmethod
@@ -188,6 +193,9 @@ class BasicCache:
         self._clean_cache()
         self._clean_subcaches()
 
+    def poll(self, **kwargs):
+        pass
+
     def _set_immediate(self, node_id, value):
         assert self.initialized
         cache_key = self.cache_key_set.get_data_key(node_id)
@@ -276,6 +284,9 @@ class NullCache:
     def clean_unused(self):
         pass
 
+    def poll(self, **kwargs):
+        pass
+
     def get(self, node_id):
         return None
 
@@ -336,3 +347,75 @@ class LRUCache(BasicCache):
             self._mark_used(child_id)
             self.children[cache_key].append(self.cache_key_set.get_data_key(child_id))
         return self
+
+
+#Iterating the cache for usage analysis might be expensive, so if we trigger make sure
+#to take a chunk out to give breathing space on high-node / low-ram-per-node flows.
+
+RAM_CACHE_HYSTERESIS = 1.1
+
+#This is kinda in GB but not really. It needs to be non-zero for the below heuristic
+#and as long as Multi GB models dwarf this it will approximate OOM scoring OK
+
+RAM_CACHE_DEFAULT_RAM_USAGE = 0.1
+
+#Exponential bias towards evicting older workflows so garbage will be taken out
+#in constantly changing setups.
+
+RAM_CACHE_OLD_WORKFLOW_OOM_MULTIPLIER = 1.3
+
+class RAMPressureCache(LRUCache):
+
+    def __init__(self, key_class):
+        super().__init__(key_class, 0)
+        self.timestamps = {}
+
+    def clean_unused(self):
+        self._clean_subcaches()
+
+    def set(self, node_id, value):
+        self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time()
+        super().set(node_id, value)
+
+    def get(self, node_id):
+        self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time()
+        return super().get(node_id)
+
+    def poll(self, ram_headroom):
+        def _ram_gb():
+            return psutil.virtual_memory().available / (1024**3)
+
+        if _ram_gb() > ram_headroom:
+            return
+        gc.collect()
+        if _ram_gb() > ram_headroom:
+            return
+
+        clean_list = []
+
+        for key, (outputs, _), in self.cache.items():
+            oom_score =  RAM_CACHE_OLD_WORKFLOW_OOM_MULTIPLIER ** (self.generation - self.used_generation[key])
+
+            ram_usage = RAM_CACHE_DEFAULT_RAM_USAGE
+            def scan_list_for_ram_usage(outputs):
+                nonlocal ram_usage
+                for output in outputs:
+                    if isinstance(output, list):
+                        scan_list_for_ram_usage(output)
+                    elif isinstance(output, torch.Tensor) and output.device.type == 'cpu':
+                        #score Tensors at a 50% discount for RAM usage as they are likely to
+                        #be high value intermediates
+                        ram_usage += (output.numel() * output.element_size()) * 0.5
+                    elif hasattr(output, "get_ram_usage"):
+                        ram_usage += output.get_ram_usage()
+            scan_list_for_ram_usage(outputs)
+
+            oom_score *= ram_usage
+            #In the case where we have no information on the node ram usage at all,
+            #break OOM score ties on the last touch timestamp (pure LRU)
+            bisect.insort(clean_list, (oom_score, self.timestamps[key], key))
+
+        while _ram_gb() < ram_headroom * RAM_CACHE_HYSTERESIS and clean_list:
+            _, _, key = clean_list.pop()
+            del self.cache[key]
+            gc.collect()
diff --git a/comfy_execution/graph.py b/comfy_execution/graph.py
index 341c9735d..0d811e354 100644
--- a/comfy_execution/graph.py
+++ b/comfy_execution/graph.py
@@ -209,10 +209,15 @@ class ExecutionList(TopologicalSort):
             self.execution_cache_listeners[from_node_id] = set()
         self.execution_cache_listeners[from_node_id].add(to_node_id)
 
-    def get_output_cache(self, from_node_id, to_node_id):
+    def get_cache(self, from_node_id, to_node_id):
         if not to_node_id in self.execution_cache:
             return None
-        return self.execution_cache[to_node_id].get(from_node_id)
+        value = self.execution_cache[to_node_id].get(from_node_id)
+        if value is None:
+            return None
+        #Write back to the main cache on touch.
+        self.output_cache.set(from_node_id, value)
+        return value
 
     def cache_update(self, node_id, value):
         if node_id in self.execution_cache_listeners:
diff --git a/execution.py b/execution.py
index 20e106213..17c77beab 100644
--- a/execution.py
+++ b/execution.py
@@ -21,6 +21,7 @@ from comfy_execution.caching import (
     NullCache,
     HierarchicalCache,
     LRUCache,
+    RAMPressureCache,
 )
 from comfy_execution.graph import (
     DynamicPrompt,
@@ -88,49 +89,56 @@ class IsChangedCache:
         return self.is_changed[node_id]
 
 
+class CacheEntry(NamedTuple):
+    ui: dict
+    outputs: list
+
+
 class CacheType(Enum):
     CLASSIC = 0
     LRU = 1
     NONE = 2
+    RAM_PRESSURE = 3
 
 
 class CacheSet:
-    def __init__(self, cache_type=None, cache_size=None):
+    def __init__(self, cache_type=None, cache_args={}):
         if cache_type == CacheType.NONE:
             self.init_null_cache()
             logging.info("Disabling intermediate node cache.")
+        elif cache_type == CacheType.RAM_PRESSURE:
+            cache_ram = cache_args.get("ram", 16.0)
+            self.init_ram_cache(cache_ram)
+            logging.info("Using RAM pressure cache.")
         elif cache_type == CacheType.LRU:
-            if cache_size is None:
-                cache_size = 0
+            cache_size = cache_args.get("lru", 0)
             self.init_lru_cache(cache_size)
             logging.info("Using LRU cache")
         else:
             self.init_classic_cache()
 
-        self.all = [self.outputs, self.ui, self.objects]
+        self.all = [self.outputs, self.objects]
 
     # Performs like the old cache -- dump data ASAP
     def init_classic_cache(self):
         self.outputs = HierarchicalCache(CacheKeySetInputSignature)
-        self.ui = HierarchicalCache(CacheKeySetInputSignature)
         self.objects = HierarchicalCache(CacheKeySetID)
 
     def init_lru_cache(self, cache_size):
         self.outputs = LRUCache(CacheKeySetInputSignature, max_size=cache_size)
-        self.ui = LRUCache(CacheKeySetInputSignature, max_size=cache_size)
+        self.objects = HierarchicalCache(CacheKeySetID)
+
+    def init_ram_cache(self, min_headroom):
+        self.outputs = RAMPressureCache(CacheKeySetInputSignature)
         self.objects = HierarchicalCache(CacheKeySetID)
 
     def init_null_cache(self):
         self.outputs = NullCache()
-        #The UI cache is expected to be iterable at the end of each workflow
-        #so it must cache at least a full workflow. Use Heirachical
-        self.ui = HierarchicalCache(CacheKeySetInputSignature)
         self.objects = NullCache()
 
     def recursive_debug_dump(self):
         result = {
             "outputs": self.outputs.recursive_debug_dump(),
-            "ui": self.ui.recursive_debug_dump(),
         }
         return result
 
@@ -157,14 +165,14 @@ def get_input_data(inputs, class_def, unique_id, execution_list=None, dynprompt=
             if execution_list is None:
                 mark_missing()
                 continue # This might be a lazily-evaluated input
-            cached_output = execution_list.get_output_cache(input_unique_id, unique_id)
-            if cached_output is None:
+            cached = execution_list.get_cache(input_unique_id, unique_id)
+            if cached is None or cached.outputs is None:
                 mark_missing()
                 continue
-            if output_index >= len(cached_output):
+            if output_index >= len(cached.outputs):
                 mark_missing()
                 continue
-            obj = cached_output[output_index]
+            obj = cached.outputs[output_index]
             input_data_all[x] = obj
         elif input_category is not None:
             input_data_all[x] = [input_data]
@@ -393,7 +401,7 @@ def format_value(x):
     else:
         return str(x)
 
-async def execute(server, dynprompt, caches, current_item, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes):
+async def execute(server, dynprompt, caches, current_item, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes, ui_outputs):
     unique_id = current_item
     real_node_id = dynprompt.get_real_node_id(unique_id)
     display_node_id = dynprompt.get_display_node_id(unique_id)
@@ -401,12 +409,15 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
     inputs = dynprompt.get_node(unique_id)['inputs']
     class_type = dynprompt.get_node(unique_id)['class_type']
     class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
-    if caches.outputs.get(unique_id) is not None:
+    cached = caches.outputs.get(unique_id)
+    if cached is not None:
         if server.client_id is not None:
-            cached_output = caches.ui.get(unique_id) or {}
-            server.send_sync("executed", { "node": unique_id, "display_node": display_node_id, "output": cached_output.get("output",None), "prompt_id": prompt_id }, server.client_id)
+            cached_ui = cached.ui or {}
+            server.send_sync("executed", { "node": unique_id, "display_node": display_node_id, "output": cached_ui.get("output",None), "prompt_id": prompt_id }, server.client_id)
+            if cached.ui is not None:
+                ui_outputs[unique_id] = cached.ui
         get_progress_state().finish_progress(unique_id)
-        execution_list.cache_update(unique_id, caches.outputs.get(unique_id))
+        execution_list.cache_update(unique_id, cached)
         return (ExecutionResult.SUCCESS, None, None)
 
     input_data_all = None
@@ -436,8 +447,8 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
                     for r in result:
                         if is_link(r):
                             source_node, source_output = r[0], r[1]
-                            node_output = execution_list.get_output_cache(source_node, unique_id)[source_output]
-                            for o in node_output:
+                            node_cached = execution_list.get_cache(source_node, unique_id)
+                            for o in node_cached.outputs[source_output]:
                                 resolved_output.append(o)
 
                         else:
@@ -507,7 +518,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
                 asyncio.create_task(await_completion())
                 return (ExecutionResult.PENDING, None, None)
         if len(output_ui) > 0:
-            caches.ui.set(unique_id, {
+            ui_outputs[unique_id] = {
                 "meta": {
                     "node_id": unique_id,
                     "display_node": display_node_id,
@@ -515,7 +526,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
                     "real_node_id": real_node_id,
                 },
                 "output": output_ui
-            })
+            }
             if server.client_id is not None:
                 server.send_sync("executed", { "node": unique_id, "display_node": display_node_id, "output": output_ui, "prompt_id": prompt_id }, server.client_id)
         if has_subgraph:
@@ -554,8 +565,9 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
             pending_subgraph_results[unique_id] = cached_outputs
             return (ExecutionResult.PENDING, None, None)
 
-        caches.outputs.set(unique_id, output_data)
-        execution_list.cache_update(unique_id, output_data)
+        cache_entry = CacheEntry(ui=ui_outputs.get(unique_id), outputs=output_data)
+        execution_list.cache_update(unique_id, cache_entry)
+        caches.outputs.set(unique_id, cache_entry)
 
     except comfy.model_management.InterruptProcessingException as iex:
         logging.info("Processing interrupted")
@@ -600,14 +612,14 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
     return (ExecutionResult.SUCCESS, None, None)
 
 class PromptExecutor:
-    def __init__(self, server, cache_type=False, cache_size=None):
-        self.cache_size = cache_size
+    def __init__(self, server, cache_type=False, cache_args=None):
+        self.cache_args = cache_args
         self.cache_type = cache_type
         self.server = server
         self.reset()
 
     def reset(self):
-        self.caches = CacheSet(cache_type=self.cache_type, cache_size=self.cache_size)
+        self.caches = CacheSet(cache_type=self.cache_type, cache_args=self.cache_args)
         self.status_messages = []
         self.success = True
 
@@ -682,6 +694,7 @@ class PromptExecutor:
                           broadcast=False)
             pending_subgraph_results = {}
             pending_async_nodes = {} # TODO - Unify this with pending_subgraph_results
+            ui_node_outputs = {}
             executed = set()
             execution_list = ExecutionList(dynamic_prompt, self.caches.outputs)
             current_outputs = self.caches.outputs.all_node_ids()
@@ -695,7 +708,7 @@ class PromptExecutor:
                     break
 
                 assert node_id is not None, "Node ID should not be None at this point"
-                result, error, ex = await execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes)
+                result, error, ex = await execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes, ui_node_outputs)
                 self.success = result != ExecutionResult.FAILURE
                 if result == ExecutionResult.FAILURE:
                     self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
@@ -704,18 +717,16 @@ class PromptExecutor:
                     execution_list.unstage_node_execution()
                 else: # result == ExecutionResult.SUCCESS:
                     execution_list.complete_node_execution()
+                self.caches.outputs.poll(ram_headroom=self.cache_args["ram"])
             else:
                 # Only execute when the while-loop ends without break
                 self.add_message("execution_success", { "prompt_id": prompt_id }, broadcast=False)
 
             ui_outputs = {}
             meta_outputs = {}
-            all_node_ids = self.caches.ui.all_node_ids()
-            for node_id in all_node_ids:
-                ui_info = self.caches.ui.get(node_id)
-                if ui_info is not None:
-                    ui_outputs[node_id] = ui_info["output"]
-                    meta_outputs[node_id] = ui_info["meta"]
+            for node_id, ui_info in ui_node_outputs.items():
+                ui_outputs[node_id] = ui_info["output"]
+                meta_outputs[node_id] = ui_info["meta"]
             self.history_result = {
                 "outputs": ui_outputs,
                 "meta": meta_outputs,
diff --git a/main.py b/main.py
index 8d466d2eb..e1b0f1620 100644
--- a/main.py
+++ b/main.py
@@ -172,10 +172,12 @@ def prompt_worker(q, server_instance):
     cache_type = execution.CacheType.CLASSIC
     if args.cache_lru > 0:
         cache_type = execution.CacheType.LRU
+    elif args.cache_ram > 0:
+        cache_type = execution.CacheType.RAM_PRESSURE
     elif args.cache_none:
         cache_type = execution.CacheType.NONE
 
-    e = execution.PromptExecutor(server_instance, cache_type=cache_type, cache_size=args.cache_lru)
+    e = execution.PromptExecutor(server_instance, cache_type=cache_type, cache_args={ "lru" : args.cache_lru, "ram" : args.cache_ram } )
     last_gc_collect = 0
     need_gc = False
     gc_collect_interval = 10.0

From 614cf9805e1056216487a2d1b1a07206d77f87e7 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Thu, 30 Oct 2025 19:11:38 -0700
Subject: [PATCH 26/56] Add a ScaleROPE node. Currently only works on WAN
 models. (#10559)

---
 comfy/ldm/wan/model.py     | 20 ++++++++++++----
 comfy/model_patcher.py     | 13 +++++++++++
 comfy_extras/nodes_rope.py | 47 ++++++++++++++++++++++++++++++++++++++
 nodes.py                   |  1 +
 4 files changed, 77 insertions(+), 4 deletions(-)
 create mode 100644 comfy_extras/nodes_rope.py

diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py
index 90c347d3d..77876c2e7 100644
--- a/comfy/ldm/wan/model.py
+++ b/comfy/ldm/wan/model.py
@@ -588,7 +588,7 @@ class WanModel(torch.nn.Module):
         x = self.unpatchify(x, grid_sizes)
         return x
 
-    def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None):
+    def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, transformer_options={}):
         patch_size = self.patch_size
         t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
         h_len = ((h + (patch_size[1] // 2)) // patch_size[1])
@@ -601,10 +601,22 @@ class WanModel(torch.nn.Module):
         if steps_w is None:
             steps_w = w_len
 
+        h_start = 0
+        w_start = 0
+        rope_options = transformer_options.get("rope_options", None)
+        if rope_options is not None:
+            t_len = t_len * rope_options.get("scale_t", 1.0)
+            h_len = h_len * rope_options.get("scale_y", 1.0)
+            w_len = w_len * rope_options.get("scale_x", 1.0)
+
+            t_start += rope_options.get("shift_t", 0.0)
+            h_start += rope_options.get("shift_y", 0.0)
+            w_start += rope_options.get("shift_x", 0.0)
+
         img_ids = torch.zeros((steps_t, steps_h, steps_w, 3), device=device, dtype=dtype)
         img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(t_start, t_start + (t_len - 1), steps=steps_t, device=device, dtype=dtype).reshape(-1, 1, 1)
-        img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=steps_h, device=device, dtype=dtype).reshape(1, -1, 1)
-        img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=steps_w, device=device, dtype=dtype).reshape(1, 1, -1)
+        img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(h_start, h_start + (h_len - 1), steps=steps_h, device=device, dtype=dtype).reshape(1, -1, 1)
+        img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(w_start, w_start + (w_len - 1), steps=steps_w, device=device, dtype=dtype).reshape(1, 1, -1)
         img_ids = img_ids.reshape(1, -1, img_ids.shape[-1])
 
         freqs = self.rope_embedder(img_ids).movedim(1, 2)
@@ -630,7 +642,7 @@ class WanModel(torch.nn.Module):
         if self.ref_conv is not None and "reference_latent" in kwargs:
             t_len += 1
 
-        freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype)
+        freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options)
         return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options, **kwargs)[:, :, :t, :h, :w]
 
     def unpatchify(self, x, grid_sizes):
diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 674a214ca..3e8799983 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -454,6 +454,19 @@ class ModelPatcher:
     def set_model_post_input_patch(self, patch):
         self.set_model_patch(patch, "post_input")
 
+    def set_model_rope_options(self, scale_x, shift_x, scale_y, shift_y, scale_t, shift_t, **kwargs):
+        rope_options = self.model_options["transformer_options"].get("rope_options", {})
+        rope_options["scale_x"] = scale_x
+        rope_options["scale_y"] = scale_y
+        rope_options["scale_t"] = scale_t
+
+        rope_options["shift_x"] = shift_x
+        rope_options["shift_y"] = shift_y
+        rope_options["shift_t"] = shift_t
+
+        self.model_options["transformer_options"]["rope_options"] = rope_options
+
+
     def add_object_patch(self, name, obj):
         self.object_patches[name] = obj
 
diff --git a/comfy_extras/nodes_rope.py b/comfy_extras/nodes_rope.py
new file mode 100644
index 000000000..d1feb031e
--- /dev/null
+++ b/comfy_extras/nodes_rope.py
@@ -0,0 +1,47 @@
+from comfy_api.latest import ComfyExtension, io
+from typing_extensions import override
+
+
+class ScaleROPE(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ScaleROPE",
+            category="advanced/model_patches",
+            description="Scale and shift the ROPE of the model.",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("scale_x", default=1.0, min=0.0, max=100.0, step=0.1),
+                io.Float.Input("shift_x", default=0.0, min=-256.0, max=256.0, step=0.1),
+
+                io.Float.Input("scale_y", default=1.0, min=0.0, max=100.0, step=0.1),
+                io.Float.Input("shift_y", default=0.0, min=-256.0, max=256.0, step=0.1),
+
+                io.Float.Input("scale_t", default=1.0, min=0.0, max=100.0, step=0.1),
+                io.Float.Input("shift_t", default=0.0, min=-256.0, max=256.0, step=0.1),
+
+
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, scale_x, shift_x, scale_y, shift_y, scale_t, shift_t) -> io.NodeOutput:
+        m = model.clone()
+        m.set_model_rope_options(scale_x, shift_x, scale_y, shift_y, scale_t, shift_t)
+        return io.NodeOutput(m)
+
+
+class RopeExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            ScaleROPE
+        ]
+
+
+async def comfy_entrypoint() -> RopeExtension:
+    return RopeExtension()
diff --git a/nodes.py b/nodes.py
index 12e365ca9..5689f6fe1 100644
--- a/nodes.py
+++ b/nodes.py
@@ -2329,6 +2329,7 @@ async def init_builtin_extra_nodes():
         "nodes_model_patch.py",
         "nodes_easycache.py",
         "nodes_audio_encoder.py",
+        "nodes_rope.py",
     ]
 
     import_failed = []

From 27d1bd882925e3bbdffb405cea098ac52bb20ac5 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Thu, 30 Oct 2025 19:51:58 -0700
Subject: [PATCH 27/56] Fix rope scaling. (#10560)

---
 comfy/ldm/wan/model.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py
index 77876c2e7..5ec1511ce 100644
--- a/comfy/ldm/wan/model.py
+++ b/comfy/ldm/wan/model.py
@@ -605,9 +605,9 @@ class WanModel(torch.nn.Module):
         w_start = 0
         rope_options = transformer_options.get("rope_options", None)
         if rope_options is not None:
-            t_len = t_len * rope_options.get("scale_t", 1.0)
-            h_len = h_len * rope_options.get("scale_y", 1.0)
-            w_len = w_len * rope_options.get("scale_x", 1.0)
+            t_len = (t_len - 1.0) * rope_options.get("scale_t", 1.0) + 1.0
+            h_len = (h_len - 1.0) * rope_options.get("scale_y", 1.0) + 1.0
+            w_len = (w_len - 1.0) * rope_options.get("scale_x", 1.0) + 1.0
 
             t_start += rope_options.get("shift_t", 0.0)
             h_start += rope_options.get("shift_y", 0.0)

From 7f374e42c833c69c71605507b90f79cc26d14a71 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Fri, 31 Oct 2025 12:41:40 -0700
Subject: [PATCH 28/56] ScaleROPE now works on Lumina models. (#10578)

---
 comfy/ldm/lumina/model.py | 20 ++++++++++++++++----
 1 file changed, 16 insertions(+), 4 deletions(-)

diff --git a/comfy/ldm/lumina/model.py b/comfy/ldm/lumina/model.py
index f87d98ac0..b4494a51d 100644
--- a/comfy/ldm/lumina/model.py
+++ b/comfy/ldm/lumina/model.py
@@ -522,7 +522,7 @@ class NextDiT(nn.Module):
         max_cap_len = max(l_effective_cap_len)
         max_img_len = max(l_effective_img_len)
 
-        position_ids = torch.zeros(bsz, max_seq_len, 3, dtype=torch.int32, device=device)
+        position_ids = torch.zeros(bsz, max_seq_len, 3, dtype=torch.float32, device=device)
 
         for i in range(bsz):
             cap_len = l_effective_cap_len[i]
@@ -531,10 +531,22 @@ class NextDiT(nn.Module):
             H_tokens, W_tokens = H // pH, W // pW
             assert H_tokens * W_tokens == img_len
 
-            position_ids[i, :cap_len, 0] = torch.arange(cap_len, dtype=torch.int32, device=device)
+            rope_options = transformer_options.get("rope_options", None)
+            h_scale = 1.0
+            w_scale = 1.0
+            h_start = 0
+            w_start = 0
+            if rope_options is not None:
+                h_scale = rope_options.get("scale_y", 1.0)
+                w_scale = rope_options.get("scale_x", 1.0)
+
+                h_start = rope_options.get("shift_y", 0.0)
+                w_start = rope_options.get("shift_x", 0.0)
+
+            position_ids[i, :cap_len, 0] = torch.arange(cap_len, dtype=torch.float32, device=device)
             position_ids[i, cap_len:cap_len+img_len, 0] = cap_len
-            row_ids = torch.arange(H_tokens, dtype=torch.int32, device=device).view(-1, 1).repeat(1, W_tokens).flatten()
-            col_ids = torch.arange(W_tokens, dtype=torch.int32, device=device).view(1, -1).repeat(H_tokens, 1).flatten()
+            row_ids = (torch.arange(H_tokens, dtype=torch.float32, device=device) * h_scale + h_start).view(-1, 1).repeat(1, W_tokens).flatten()
+            col_ids = (torch.arange(W_tokens, dtype=torch.float32, device=device) * w_scale + w_start).view(1, -1).repeat(H_tokens, 1).flatten()
             position_ids[i, cap_len:cap_len+img_len, 1] = row_ids
             position_ids[i, cap_len:cap_len+img_len, 2] = col_ids
 

From c58c13b2bad6df0de93cc0cf107e96522a3cb5b3 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Fri, 31 Oct 2025 21:25:17 -0700
Subject: [PATCH 29/56] Fix torch compile regression on fp8 ops. (#10580)

---
 comfy/ops.py                                  | 24 +++++------------
 comfy/quant_ops.py                            | 27 +++++++++++++++----
 .../comfy_quant/test_mixed_precision.py       |  8 +++---
 tests-unit/comfy_quant/test_quant_registry.py | 20 +++++++-------
 4 files changed, 43 insertions(+), 36 deletions(-)

diff --git a/comfy/ops.py b/comfy/ops.py
index 18f6b804b..279f6b1a7 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -401,15 +401,9 @@ def fp8_linear(self, input):
     if dtype not in [torch.float8_e4m3fn]:
         return None
 
-    tensor_2d = False
-    if len(input.shape) == 2:
-        tensor_2d = True
-        input = input.unsqueeze(1)
-
-    input_shape = input.shape
     input_dtype = input.dtype
 
-    if len(input.shape) == 3:
+    if input.ndim == 3 or input.ndim == 2:
         w, bias, offload_stream = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype, offloadable=True)
 
         scale_weight = self.scale_weight
@@ -422,24 +416,20 @@ def fp8_linear(self, input):
         if scale_input is None:
             scale_input = torch.ones((), device=input.device, dtype=torch.float32)
             input = torch.clamp(input, min=-448, max=448, out=input)
-            input = input.reshape(-1, input_shape[2]).to(dtype).contiguous()
             layout_params_weight = {'scale': scale_input, 'orig_dtype': input_dtype}
-            quantized_input = QuantizedTensor(input.reshape(-1, input_shape[2]).to(dtype).contiguous(), TensorCoreFP8Layout, layout_params_weight)
+            quantized_input = QuantizedTensor(input.to(dtype).contiguous(), "TensorCoreFP8Layout", layout_params_weight)
         else:
             scale_input = scale_input.to(input.device)
-            quantized_input = QuantizedTensor.from_float(input.reshape(-1, input_shape[2]), TensorCoreFP8Layout, scale=scale_input, dtype=dtype)
+            quantized_input = QuantizedTensor.from_float(input, "TensorCoreFP8Layout", scale=scale_input, dtype=dtype)
 
         # Wrap weight in QuantizedTensor - this enables unified dispatch
         # Call F.linear - __torch_dispatch__ routes to fp8_linear handler in quant_ops.py!
         layout_params_weight = {'scale': scale_weight, 'orig_dtype': input_dtype}
-        quantized_weight = QuantizedTensor(w, TensorCoreFP8Layout, layout_params_weight)
+        quantized_weight = QuantizedTensor(w, "TensorCoreFP8Layout", layout_params_weight)
         o = torch.nn.functional.linear(quantized_input, quantized_weight, bias)
 
         uncast_bias_weight(self, w, bias, offload_stream)
-
-        if tensor_2d:
-            return o.reshape(input_shape[0], -1)
-        return o.reshape((-1, input_shape[1], self.weight.shape[0]))
+        return o
 
     return None
 
@@ -540,12 +530,12 @@ if CUBLAS_IS_AVAILABLE:
 # ==============================================================================
 # Mixed Precision Operations
 # ==============================================================================
-from .quant_ops import QuantizedTensor, TensorCoreFP8Layout
+from .quant_ops import QuantizedTensor
 
 QUANT_FORMAT_MIXINS = {
     "float8_e4m3fn": {
         "dtype": torch.float8_e4m3fn,
-        "layout_type": TensorCoreFP8Layout,
+        "layout_type": "TensorCoreFP8Layout",
         "parameters": {
             "weight_scale": torch.nn.Parameter(torch.zeros((), dtype=torch.float32), requires_grad=False),
             "input_scale": torch.nn.Parameter(torch.zeros((), dtype=torch.float32), requires_grad=False),
diff --git a/comfy/quant_ops.py b/comfy/quant_ops.py
index c822fe53c..873f173ed 100644
--- a/comfy/quant_ops.py
+++ b/comfy/quant_ops.py
@@ -123,7 +123,7 @@ class QuantizedTensor(torch.Tensor):
             layout_type: Layout class (subclass of QuantizedLayout)
             layout_params: Dict with layout-specific parameters
         """
-        return torch.Tensor._make_subclass(cls, qdata, require_grad=False)
+        return torch.Tensor._make_wrapper_subclass(cls, qdata.shape, device=qdata.device, dtype=qdata.dtype, requires_grad=False)
 
     def __init__(self, qdata, layout_type, layout_params):
         self._qdata = qdata.contiguous()
@@ -183,11 +183,11 @@ class QuantizedTensor(torch.Tensor):
 
     @classmethod
     def from_float(cls, tensor, layout_type, **quantize_kwargs) -> 'QuantizedTensor':
-        qdata, layout_params = layout_type.quantize(tensor, **quantize_kwargs)
+        qdata, layout_params = LAYOUTS[layout_type].quantize(tensor, **quantize_kwargs)
         return cls(qdata, layout_type, layout_params)
 
     def dequantize(self) -> torch.Tensor:
-        return self._layout_type.dequantize(self._qdata, **self._layout_params)
+        return LAYOUTS[self._layout_type].dequantize(self._qdata, **self._layout_params)
 
     @classmethod
     def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
@@ -379,7 +379,12 @@ class TensorCoreFP8Layout(QuantizedLayout):
         return qtensor._qdata, qtensor._layout_params['scale']
 
 
-@register_layout_op(torch.ops.aten.linear.default, TensorCoreFP8Layout)
+LAYOUTS = {
+    "TensorCoreFP8Layout": TensorCoreFP8Layout,
+}
+
+
+@register_layout_op(torch.ops.aten.linear.default, "TensorCoreFP8Layout")
 def fp8_linear(func, args, kwargs):
     input_tensor = args[0]
     weight = args[1]
@@ -422,7 +427,7 @@ def fp8_linear(func, args, kwargs):
                     'scale': output_scale,
                     'orig_dtype': input_tensor._layout_params['orig_dtype']
                 }
-                return QuantizedTensor(output, TensorCoreFP8Layout, output_params)
+                return QuantizedTensor(output, "TensorCoreFP8Layout", output_params)
             else:
                 return output
 
@@ -436,3 +441,15 @@ def fp8_linear(func, args, kwargs):
         input_tensor = input_tensor.dequantize()
 
     return torch.nn.functional.linear(input_tensor, weight, bias)
+
+
+@register_layout_op(torch.ops.aten.view.default, "TensorCoreFP8Layout")
+@register_layout_op(torch.ops.aten.t.default, "TensorCoreFP8Layout")
+def fp8_func(func, args, kwargs):
+    input_tensor = args[0]
+    if isinstance(input_tensor, QuantizedTensor):
+        plain_input, scale_a = TensorCoreFP8Layout.get_plain_tensors(input_tensor)
+        ar = list(args)
+        ar[0] = plain_input
+        return QuantizedTensor(func(*ar, **kwargs), "TensorCoreFP8Layout", input_tensor._layout_params)
+    return func(*args, **kwargs)
diff --git a/tests-unit/comfy_quant/test_mixed_precision.py b/tests-unit/comfy_quant/test_mixed_precision.py
index 267bc177b..f8d1fd04e 100644
--- a/tests-unit/comfy_quant/test_mixed_precision.py
+++ b/tests-unit/comfy_quant/test_mixed_precision.py
@@ -14,7 +14,7 @@ if not has_gpu():
     args.cpu = True
 
 from comfy import ops
-from comfy.quant_ops import QuantizedTensor, TensorCoreFP8Layout
+from comfy.quant_ops import QuantizedTensor
 
 
 class SimpleModel(torch.nn.Module):
@@ -104,14 +104,14 @@ class TestMixedPrecisionOps(unittest.TestCase):
 
         # Verify weights are wrapped in QuantizedTensor
         self.assertIsInstance(model.layer1.weight, QuantizedTensor)
-        self.assertEqual(model.layer1.weight._layout_type, TensorCoreFP8Layout)
+        self.assertEqual(model.layer1.weight._layout_type, "TensorCoreFP8Layout")
 
         # Layer 2 should NOT be quantized
         self.assertNotIsInstance(model.layer2.weight, QuantizedTensor)
 
         # Layer 3 should be quantized
         self.assertIsInstance(model.layer3.weight, QuantizedTensor)
-        self.assertEqual(model.layer3.weight._layout_type, TensorCoreFP8Layout)
+        self.assertEqual(model.layer3.weight._layout_type, "TensorCoreFP8Layout")
 
         # Verify scales were loaded
         self.assertEqual(model.layer1.weight._layout_params['scale'].item(), 2.0)
@@ -155,7 +155,7 @@ class TestMixedPrecisionOps(unittest.TestCase):
         # Verify layer1.weight is a QuantizedTensor with scale preserved
         self.assertIsInstance(state_dict2["layer1.weight"], QuantizedTensor)
         self.assertEqual(state_dict2["layer1.weight"]._layout_params['scale'].item(), 3.0)
-        self.assertEqual(state_dict2["layer1.weight"]._layout_type, TensorCoreFP8Layout)
+        self.assertEqual(state_dict2["layer1.weight"]._layout_type, "TensorCoreFP8Layout")
 
         # Verify non-quantized layers are standard tensors
         self.assertNotIsInstance(state_dict2["layer2.weight"], QuantizedTensor)
diff --git a/tests-unit/comfy_quant/test_quant_registry.py b/tests-unit/comfy_quant/test_quant_registry.py
index 477811029..9cb54ede8 100644
--- a/tests-unit/comfy_quant/test_quant_registry.py
+++ b/tests-unit/comfy_quant/test_quant_registry.py
@@ -25,14 +25,14 @@ class TestQuantizedTensor(unittest.TestCase):
         scale = torch.tensor(2.0)
         layout_params = {'scale': scale, 'orig_dtype': torch.bfloat16}
 
-        qt = QuantizedTensor(fp8_data, TensorCoreFP8Layout, layout_params)
+        qt = QuantizedTensor(fp8_data, "TensorCoreFP8Layout", layout_params)
 
         self.assertIsInstance(qt, QuantizedTensor)
         self.assertEqual(qt.shape, (256, 128))
         self.assertEqual(qt.dtype, torch.float8_e4m3fn)
         self.assertEqual(qt._layout_params['scale'], scale)
         self.assertEqual(qt._layout_params['orig_dtype'], torch.bfloat16)
-        self.assertEqual(qt._layout_type, TensorCoreFP8Layout)
+        self.assertEqual(qt._layout_type, "TensorCoreFP8Layout")
 
     def test_dequantize(self):
         """Test explicit dequantization"""
@@ -41,7 +41,7 @@ class TestQuantizedTensor(unittest.TestCase):
         scale = torch.tensor(3.0)
         layout_params = {'scale': scale, 'orig_dtype': torch.float32}
 
-        qt = QuantizedTensor(fp8_data, TensorCoreFP8Layout, layout_params)
+        qt = QuantizedTensor(fp8_data, "TensorCoreFP8Layout", layout_params)
         dequantized = qt.dequantize()
 
         self.assertEqual(dequantized.dtype, torch.float32)
@@ -54,7 +54,7 @@ class TestQuantizedTensor(unittest.TestCase):
 
         qt = QuantizedTensor.from_float(
             float_tensor,
-            TensorCoreFP8Layout,
+            "TensorCoreFP8Layout",
             scale=scale,
             dtype=torch.float8_e4m3fn
         )
@@ -77,28 +77,28 @@ class TestGenericUtilities(unittest.TestCase):
         fp8_data = torch.randn(10, 20, dtype=torch.float32).to(torch.float8_e4m3fn)
         scale = torch.tensor(1.5)
         layout_params = {'scale': scale, 'orig_dtype': torch.float32}
-        qt = QuantizedTensor(fp8_data, TensorCoreFP8Layout, layout_params)
+        qt = QuantizedTensor(fp8_data, "TensorCoreFP8Layout", layout_params)
 
         # Detach should return a new QuantizedTensor
         qt_detached = qt.detach()
 
         self.assertIsInstance(qt_detached, QuantizedTensor)
         self.assertEqual(qt_detached.shape, qt.shape)
-        self.assertEqual(qt_detached._layout_type, TensorCoreFP8Layout)
+        self.assertEqual(qt_detached._layout_type, "TensorCoreFP8Layout")
 
     def test_clone(self):
         """Test clone operation on quantized tensor"""
         fp8_data = torch.randn(10, 20, dtype=torch.float32).to(torch.float8_e4m3fn)
         scale = torch.tensor(1.5)
         layout_params = {'scale': scale, 'orig_dtype': torch.float32}
-        qt = QuantizedTensor(fp8_data, TensorCoreFP8Layout, layout_params)
+        qt = QuantizedTensor(fp8_data, "TensorCoreFP8Layout", layout_params)
 
         # Clone should return a new QuantizedTensor
         qt_cloned = qt.clone()
 
         self.assertIsInstance(qt_cloned, QuantizedTensor)
         self.assertEqual(qt_cloned.shape, qt.shape)
-        self.assertEqual(qt_cloned._layout_type, TensorCoreFP8Layout)
+        self.assertEqual(qt_cloned._layout_type, "TensorCoreFP8Layout")
 
         # Verify it's a deep copy
         self.assertIsNot(qt_cloned._qdata, qt._qdata)
@@ -109,7 +109,7 @@ class TestGenericUtilities(unittest.TestCase):
         fp8_data = torch.randn(10, 20, dtype=torch.float32).to(torch.float8_e4m3fn)
         scale = torch.tensor(1.5)
         layout_params = {'scale': scale, 'orig_dtype': torch.float32}
-        qt = QuantizedTensor(fp8_data, TensorCoreFP8Layout, layout_params)
+        qt = QuantizedTensor(fp8_data, "TensorCoreFP8Layout", layout_params)
 
         # Moving to same device should work (CPU to CPU)
         qt_cpu = qt.to('cpu')
@@ -169,7 +169,7 @@ class TestFallbackMechanism(unittest.TestCase):
         scale = torch.tensor(1.0)
         a_q = QuantizedTensor.from_float(
             a_fp32,
-            TensorCoreFP8Layout,
+            "TensorCoreFP8Layout",
             scale=scale,
             dtype=torch.float8_e4m3fn
         )

From 5f109fe6a06a3462b31a066bcfd650de67d66102 Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Sat, 1 Nov 2025 21:13:39 +0200
Subject: [PATCH 30/56] added 12s-20s as available output durations for the
 LTXV API nodes (#10570)

---
 comfy_api_nodes/nodes_ltxv.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/comfy_api_nodes/nodes_ltxv.py b/comfy_api_nodes/nodes_ltxv.py
index e6ad6e27a..0b757a62b 100644
--- a/comfy_api_nodes/nodes_ltxv.py
+++ b/comfy_api_nodes/nodes_ltxv.py
@@ -46,7 +46,7 @@ class TextToVideoNode(IO.ComfyNode):
                     multiline=True,
                     default="",
                 ),
-                IO.Combo.Input("duration", options=[6, 8, 10], default=8),
+                IO.Combo.Input("duration", options=[6, 8, 10, 12, 14, 16, 18, 20], default=8),
                 IO.Combo.Input(
                     "resolution",
                     options=[
@@ -85,6 +85,10 @@ class TextToVideoNode(IO.ComfyNode):
         generate_audio: bool = False,
     ) -> IO.NodeOutput:
         validate_string(prompt, min_length=1, max_length=10000)
+        if duration > 10 and (model != "LTX-2 (Fast)" or resolution != "1920x1080" or fps != 25):
+            raise ValueError(
+                "Durations over 10s are only available for the Fast model at 1920x1080 resolution and 25 FPS."
+            )
         response = await sync_op_raw(
             cls,
             ApiEndpoint("/proxy/ltx/v1/text-to-video", "POST"),
@@ -118,7 +122,7 @@ class ImageToVideoNode(IO.ComfyNode):
                     multiline=True,
                     default="",
                 ),
-                IO.Combo.Input("duration", options=[6, 8, 10], default=8),
+                IO.Combo.Input("duration", options=[6, 8, 10, 12, 14, 16, 18, 20], default=8),
                 IO.Combo.Input(
                     "resolution",
                     options=[
@@ -158,6 +162,10 @@ class ImageToVideoNode(IO.ComfyNode):
         generate_audio: bool = False,
     ) -> IO.NodeOutput:
         validate_string(prompt, min_length=1, max_length=10000)
+        if duration > 10 and (model != "LTX-2 (Fast)" or resolution != "1920x1080" or fps != 25):
+            raise ValueError(
+                "Durations over 10s are only available for the Fast model at 1920x1080 resolution and 25 FPS."
+            )
         if get_number_of_images(image) != 1:
             raise ValueError("Currently only one input image is supported.")
         response = await sync_op_raw(

From 20182a393f43ab1fdf798f8da6aac0ef6116e7e6 Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Sat, 1 Nov 2025 21:14:06 +0200
Subject: [PATCH 31/56] convert StabilityAI to use new API client (#10582)

---
 comfy_api_nodes/nodes_stability.py | 179 ++++++++---------------------
 comfy_api_nodes/util/client.py     |   4 +-
 2 files changed, 48 insertions(+), 135 deletions(-)

diff --git a/comfy_api_nodes/nodes_stability.py b/comfy_api_nodes/nodes_stability.py
index 783666ddf..bb7ceed78 100644
--- a/comfy_api_nodes/nodes_stability.py
+++ b/comfy_api_nodes/nodes_stability.py
@@ -20,13 +20,6 @@ from comfy_api_nodes.apis.stability_api import (
     StabilityAudioInpaintRequest,
     StabilityAudioResponse,
 )
-from comfy_api_nodes.apis.client import (
-    ApiEndpoint,
-    HttpMethod,
-    SynchronousOperation,
-    PollingOperation,
-    EmptyRequest,
-)
 from comfy_api_nodes.util import (
     validate_audio_duration,
     validate_string,
@@ -34,6 +27,9 @@ from comfy_api_nodes.util import (
     bytesio_to_image_tensor,
     tensor_to_bytesio,
     audio_bytes_to_audio_input,
+    sync_op,
+    poll_op,
+    ApiEndpoint,
 )
 
 import torch
@@ -161,19 +157,11 @@ class StabilityStableImageUltraNode(IO.ComfyNode):
             "image": image_binary
         }
 
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
-
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/stability/v2beta/stable-image/generate/ultra",
-                method=HttpMethod.POST,
-                request_model=StabilityStableUltraRequest,
-                response_model=StabilityStableUltraResponse,
-            ),
-            request=StabilityStableUltraRequest(
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/stability/v2beta/stable-image/generate/ultra", method="POST"),
+            response_model=StabilityStableUltraResponse,
+            data=StabilityStableUltraRequest(
                 prompt=prompt,
                 negative_prompt=negative_prompt,
                 aspect_ratio=aspect_ratio,
@@ -183,9 +171,7 @@ class StabilityStableImageUltraNode(IO.ComfyNode):
             ),
             files=files,
             content_type="multipart/form-data",
-            auth_kwargs=auth,
         )
-        response_api = await operation.execute()
 
         if response_api.finish_reason != "SUCCESS":
             raise Exception(f"Stable Image Ultra generation failed: {response_api.finish_reason}.")
@@ -313,19 +299,11 @@ class StabilityStableImageSD_3_5Node(IO.ComfyNode):
             "image": image_binary
         }
 
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
-
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/stability/v2beta/stable-image/generate/sd3",
-                method=HttpMethod.POST,
-                request_model=StabilityStable3_5Request,
-                response_model=StabilityStableUltraResponse,
-            ),
-            request=StabilityStable3_5Request(
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/stability/v2beta/stable-image/generate/sd3", method="POST"),
+            response_model=StabilityStableUltraResponse,
+            data=StabilityStable3_5Request(
                 prompt=prompt,
                 negative_prompt=negative_prompt,
                 aspect_ratio=aspect_ratio,
@@ -338,9 +316,7 @@ class StabilityStableImageSD_3_5Node(IO.ComfyNode):
             ),
             files=files,
             content_type="multipart/form-data",
-            auth_kwargs=auth,
         )
-        response_api = await operation.execute()
 
         if response_api.finish_reason != "SUCCESS":
             raise Exception(f"Stable Diffusion 3.5 Image generation failed: {response_api.finish_reason}.")
@@ -427,19 +403,11 @@ class StabilityUpscaleConservativeNode(IO.ComfyNode):
             "image": image_binary
         }
 
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
-
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/stability/v2beta/stable-image/upscale/conservative",
-                method=HttpMethod.POST,
-                request_model=StabilityUpscaleConservativeRequest,
-                response_model=StabilityStableUltraResponse,
-            ),
-            request=StabilityUpscaleConservativeRequest(
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/stability/v2beta/stable-image/upscale/conservative", method="POST"),
+            response_model=StabilityStableUltraResponse,
+            data=StabilityUpscaleConservativeRequest(
                 prompt=prompt,
                 negative_prompt=negative_prompt,
                 creativity=round(creativity,2),
@@ -447,9 +415,7 @@ class StabilityUpscaleConservativeNode(IO.ComfyNode):
             ),
             files=files,
             content_type="multipart/form-data",
-            auth_kwargs=auth,
         )
-        response_api = await operation.execute()
 
         if response_api.finish_reason != "SUCCESS":
             raise Exception(f"Stability Upscale Conservative generation failed: {response_api.finish_reason}.")
@@ -544,19 +510,11 @@ class StabilityUpscaleCreativeNode(IO.ComfyNode):
             "image": image_binary
         }
 
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
-
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/stability/v2beta/stable-image/upscale/creative",
-                method=HttpMethod.POST,
-                request_model=StabilityUpscaleCreativeRequest,
-                response_model=StabilityAsyncResponse,
-            ),
-            request=StabilityUpscaleCreativeRequest(
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/stability/v2beta/stable-image/upscale/creative", method="POST"),
+            response_model=StabilityAsyncResponse,
+            data=StabilityUpscaleCreativeRequest(
                 prompt=prompt,
                 negative_prompt=negative_prompt,
                 creativity=round(creativity,2),
@@ -565,25 +523,15 @@ class StabilityUpscaleCreativeNode(IO.ComfyNode):
             ),
             files=files,
             content_type="multipart/form-data",
-            auth_kwargs=auth,
         )
-        response_api = await operation.execute()
 
-        operation = PollingOperation(
-            poll_endpoint=ApiEndpoint(
-                path=f"/proxy/stability/v2beta/results/{response_api.id}",
-                method=HttpMethod.GET,
-                request_model=EmptyRequest,
-                response_model=StabilityResultsGetResponse,
-            ),
+        response_poll = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/stability/v2beta/results/{response_api.id}"),
+            response_model=StabilityResultsGetResponse,
             poll_interval=3,
-            completed_statuses=[StabilityPollStatus.finished],
-            failed_statuses=[StabilityPollStatus.failed],
             status_extractor=lambda x: get_async_dummy_status(x),
-            auth_kwargs=auth,
-            node_id=cls.hidden.unique_id,
         )
-        response_poll: StabilityResultsGetResponse = await operation.execute()
 
         if response_poll.finish_reason != "SUCCESS":
             raise Exception(f"Stability Upscale Creative generation failed: {response_poll.finish_reason}.")
@@ -628,24 +576,13 @@ class StabilityUpscaleFastNode(IO.ComfyNode):
             "image": image_binary
         }
 
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
-
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/stability/v2beta/stable-image/upscale/fast",
-                method=HttpMethod.POST,
-                request_model=EmptyRequest,
-                response_model=StabilityStableUltraResponse,
-            ),
-            request=EmptyRequest(),
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/stability/v2beta/stable-image/upscale/fast", method="POST"),
+            response_model=StabilityStableUltraResponse,
             files=files,
             content_type="multipart/form-data",
-            auth_kwargs=auth,
         )
-        response_api = await operation.execute()
 
         if response_api.finish_reason != "SUCCESS":
             raise Exception(f"Stability Upscale Fast failed: {response_api.finish_reason}.")
@@ -717,21 +654,13 @@ class StabilityTextToAudio(IO.ComfyNode):
     async def execute(cls, model: str, prompt: str, duration: int, seed: int, steps: int) -> IO.NodeOutput:
         validate_string(prompt, max_length=10000)
         payload = StabilityTextToAudioRequest(prompt=prompt, model=model, duration=duration, seed=seed, steps=steps)
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/stability/v2beta/audio/stable-audio-2/text-to-audio",
-                method=HttpMethod.POST,
-                request_model=StabilityTextToAudioRequest,
-                response_model=StabilityAudioResponse,
-            ),
-            request=payload,
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/stability/v2beta/audio/stable-audio-2/text-to-audio", method="POST"),
+            response_model=StabilityAudioResponse,
+            data=payload,
             content_type="multipart/form-data",
-            auth_kwargs= {
-                "auth_token": cls.hidden.auth_token_comfy_org,
-                "comfy_api_key": cls.hidden.api_key_comfy_org,
-            },
         )
-        response_api = await operation.execute()
         if not response_api.audio:
             raise ValueError("No audio file was received in response.")
         return IO.NodeOutput(audio_bytes_to_audio_input(base64.b64decode(response_api.audio)))
@@ -814,22 +743,14 @@ class StabilityAudioToAudio(IO.ComfyNode):
         payload = StabilityAudioToAudioRequest(
             prompt=prompt, model=model, duration=duration, seed=seed, steps=steps, strength=strength
         )
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/stability/v2beta/audio/stable-audio-2/audio-to-audio",
-                method=HttpMethod.POST,
-                request_model=StabilityAudioToAudioRequest,
-                response_model=StabilityAudioResponse,
-            ),
-            request=payload,
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/stability/v2beta/audio/stable-audio-2/audio-to-audio", method="POST"),
+            response_model=StabilityAudioResponse,
+            data=payload,
             content_type="multipart/form-data",
             files={"audio": audio_input_to_mp3(audio)},
-            auth_kwargs= {
-                "auth_token": cls.hidden.auth_token_comfy_org,
-                "comfy_api_key": cls.hidden.api_key_comfy_org,
-            },
         )
-        response_api = await operation.execute()
         if not response_api.audio:
             raise ValueError("No audio file was received in response.")
         return IO.NodeOutput(audio_bytes_to_audio_input(base64.b64decode(response_api.audio)))
@@ -935,22 +856,14 @@ class StabilityAudioInpaint(IO.ComfyNode):
             mask_start=mask_start,
             mask_end=mask_end,
         )
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/stability/v2beta/audio/stable-audio-2/inpaint",
-                method=HttpMethod.POST,
-                request_model=StabilityAudioInpaintRequest,
-                response_model=StabilityAudioResponse,
-            ),
-            request=payload,
+        response_api = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/stability/v2beta/audio/stable-audio-2/inpaint", method="POST"),
+            response_model=StabilityAudioResponse,
+            data=payload,
             content_type="multipart/form-data",
             files={"audio": audio_input_to_mp3(audio)},
-            auth_kwargs={
-                "auth_token": cls.hidden.auth_token_comfy_org,
-                "comfy_api_key": cls.hidden.api_key_comfy_org,
-            },
         )
-        response_api = await operation.execute()
         if not response_api.audio:
             raise ValueError("No audio file was received in response.")
         return IO.NodeOutput(audio_bytes_to_audio_input(base64.b64decode(response_api.audio)))
diff --git a/comfy_api_nodes/util/client.py b/comfy_api_nodes/util/client.py
index 9ae512fe5..65bb35f0f 100644
--- a/comfy_api_nodes/util/client.py
+++ b/comfy_api_nodes/util/client.py
@@ -77,7 +77,7 @@ class _PollUIState:
 
 
 _RETRY_STATUS = {408, 429, 500, 502, 503, 504}
-COMPLETED_STATUSES = ["succeeded", "succeed", "success", "completed"]
+COMPLETED_STATUSES = ["succeeded", "succeed", "success", "completed", "finished"]
 FAILED_STATUSES = ["cancelled", "canceled", "fail", "failed", "error"]
 QUEUED_STATUSES = ["created", "queued", "queueing", "submitted"]
 
@@ -589,7 +589,7 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
         operation_id = _generate_operation_id(method, cfg.endpoint.path, attempt)
         logging.debug("[DEBUG] HTTP %s %s (attempt %d)", method, url, attempt)
 
-        payload_headers = {"Accept": "*/*"}
+        payload_headers = {"Accept": "*/*"} if expect_binary else {"Accept": "application/json"}
         if not parsed_url.scheme and not parsed_url.netloc:  # is URL relative?
             payload_headers.update(get_auth_header(cfg.node_cls))
         if cfg.endpoint.headers:

From 44869ff786dc90b36172fd766c9a110e4c40c04b Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Sat, 1 Nov 2025 14:25:59 -0700
Subject: [PATCH 32/56] Fix issue with pinned memory. (#10597)

---
 comfy/model_patcher.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 3e8799983..5a31a8734 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -298,6 +298,7 @@ class ModelPatcher:
         n.backup = self.backup
         n.object_patches_backup = self.object_patches_backup
         n.parent = self
+        n.pinned = self.pinned
 
         n.force_cast_weights = self.force_cast_weights
 

From 135fa49ec23320834f774cf3def9e51ad3773f86 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Sun, 2 Nov 2025 08:48:53 +1000
Subject: [PATCH 33/56] Small speed improvements to --async-offload (#10593)

* ops: dont take an offload stream if you dont need one

* ops: prioritize mem transfer

The async offload streams reason for existence is to transfer from
RAM to GPU. The post processing compute steps are a bonus on the side
stream, but if the compute stream is running a long kernel, it can
stall the side stream, as it wait to type-cast the bias before
transferring the weight. So do a pure xfer of the weight straight up,
then do everything bias, then go back to fix the weight type and do
weight patches.
---
 comfy/ops.py | 23 ++++++++++++++---------
 1 file changed, 14 insertions(+), 9 deletions(-)

diff --git a/comfy/ops.py b/comfy/ops.py
index 279f6b1a7..0c8f23848 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -84,7 +84,8 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
         if device is None:
             device = input.device
 
-    if offloadable:
+    if offloadable and (device != s.weight.device or
+                        (s.bias is not None and device != s.bias.device)):
         offload_stream = comfy.model_management.get_offload_stream(device)
     else:
         offload_stream = None
@@ -94,20 +95,24 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
     else:
         wf_context = contextlib.nullcontext()
 
-    bias = None
     non_blocking = comfy.model_management.device_supports_non_blocking(device)
-    if s.bias is not None:
-        has_function = len(s.bias_function) > 0
-        bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function, stream=offload_stream)
 
-        if has_function:
+    weight_has_function = len(s.weight_function) > 0
+    bias_has_function = len(s.bias_function) > 0
+
+    weight = comfy.model_management.cast_to(s.weight, None, device, non_blocking=non_blocking, copy=weight_has_function, stream=offload_stream)
+
+    bias = None
+    if s.bias is not None:
+        bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=bias_has_function, stream=offload_stream)
+
+        if bias_has_function:
             with wf_context:
                 for f in s.bias_function:
                     bias = f(bias)
 
-    has_function = len(s.weight_function) > 0
-    weight = comfy.model_management.cast_to(s.weight, dtype, device, non_blocking=non_blocking, copy=has_function, stream=offload_stream)
-    if has_function:
+    weight = weight.to(dtype=dtype)
+    if weight_has_function:
         with wf_context:
             for f in s.weight_function:
                 weight = f(weight)

From 97ff9fae7e728cffdfc3aee6d72aa1e0d0b78702 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Sun, 2 Nov 2025 10:14:04 -0800
Subject: [PATCH 34/56] Clarify help text for --fast argument (#10609)

Updated help text for the --fast argument to clarify potential risks.
---
 comfy/cli_args.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/cli_args.py b/comfy/cli_args.py
index 3d5bc7c90..3947e62a8 100644
--- a/comfy/cli_args.py
+++ b/comfy/cli_args.py
@@ -147,7 +147,7 @@ class PerformanceFeature(enum.Enum):
     AutoTune = "autotune"
     PinnedMem = "pinned_memory"
 
-parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: {}".format(" ".join(map(lambda c: c.value, PerformanceFeature))))
+parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. This is used to test new features so using it might crash your comfyui. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: {}".format(" ".join(map(lambda c: c.value, PerformanceFeature))))
 
 parser.add_argument("--mmap-torch-files", action="store_true", help="Use mmap when loading ckpt/pt files.")
 parser.add_argument("--disable-mmap", action="store_true", help="Don't use mmap when loading safetensors.")

From 6d6a18b0b730351416556eeb0990ab219ffec189 Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Mon, 3 Nov 2025 10:04:56 +0200
Subject: [PATCH 35/56] fix(api-nodes-cloud): stop using sub-folder and
 absolute path for output of Rodin3D nodes (#10556)

---
 comfy_api_nodes/nodes_rodin.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/comfy_api_nodes/nodes_rodin.py b/comfy_api_nodes/nodes_rodin.py
index cf2172bd6..ad4029236 100644
--- a/comfy_api_nodes/nodes_rodin.py
+++ b/comfy_api_nodes/nodes_rodin.py
@@ -225,21 +225,20 @@ async def get_rodin_download_list(uuid, auth_kwargs: Optional[dict[str, str]] =
 
 
 async def download_files(url_list, task_uuid):
-    save_path = os.path.join(comfy_paths.get_output_directory(), f"Rodin3D_{task_uuid}")
+    result_folder_name = f"Rodin3D_{task_uuid}"
+    save_path = os.path.join(comfy_paths.get_output_directory(), result_folder_name)
     os.makedirs(save_path, exist_ok=True)
     model_file_path = None
     async with aiohttp.ClientSession() as session:
         for i in url_list.list:
-            url = i.url
-            file_name = i.name
-            file_path = os.path.join(save_path, file_name)
+            file_path = os.path.join(save_path, i.name)
             if file_path.endswith(".glb"):
-                model_file_path = file_path
+                model_file_path = os.path.join(result_folder_name, i.name)
             logging.info("[ Rodin3D API - download_files ] Downloading file: %s", file_path)
             max_retries = 5
             for attempt in range(max_retries):
                 try:
-                    async with session.get(url) as resp:
+                    async with session.get(i.url) as resp:
                         resp.raise_for_status()
                         with open(file_path, "wb") as f:
                             async for chunk in resp.content.iter_chunked(32 * 1024):

From 88df172790b8ed7b2e6ea7c0f0bd63ca3553921b Mon Sep 17 00:00:00 2001
From: EverNebula <sfpxxwhb@qq.com>
Date: Mon, 3 Nov 2025 16:16:40 +0800
Subject: [PATCH 36/56] fix(caching): treat bytes as hashable (#10567)

---
 comfy_execution/caching.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy_execution/caching.py b/comfy_execution/caching.py
index b498f43e7..e077f78b0 100644
--- a/comfy_execution/caching.py
+++ b/comfy_execution/caching.py
@@ -53,7 +53,7 @@ class Unhashable:
 def to_hashable(obj):
     # So that we don't infinitely recurse since frozenset and tuples
     # are Sequences.
-    if isinstance(obj, (int, float, str, bool, type(None))):
+    if isinstance(obj, (int, float, str, bool, bytes, type(None))):
         return obj
     elif isinstance(obj, Mapping):
         return frozenset([(to_hashable(k), to_hashable(v)) for k, v in sorted(obj.items())])

From 1f3f7a2823017ad193e060d9221fb6a52f2eba3a Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Mon, 3 Nov 2025 10:21:47 +0200
Subject: [PATCH 37/56] convert nodes_hypernetwork.py to V3 schema (#10583)

---
 comfy_extras/nodes_hypernetwork.py | 48 ++++++++++++++++++++----------
 1 file changed, 33 insertions(+), 15 deletions(-)

diff --git a/comfy_extras/nodes_hypernetwork.py b/comfy_extras/nodes_hypernetwork.py
index 665632292..2a6a87a81 100644
--- a/comfy_extras/nodes_hypernetwork.py
+++ b/comfy_extras/nodes_hypernetwork.py
@@ -2,6 +2,9 @@ import comfy.utils
 import folder_paths
 import torch
 import logging
+from comfy_api.latest import IO, ComfyExtension
+from typing_extensions import override
+
 
 def load_hypernetwork_patch(path, strength):
     sd = comfy.utils.load_torch_file(path, safe_load=True)
@@ -94,27 +97,42 @@ def load_hypernetwork_patch(path, strength):
 
     return hypernetwork_patch(out, strength)
 
-class HypernetworkLoader:
+class HypernetworkLoader(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model": ("MODEL",),
-                              "hypernetwork_name": (folder_paths.get_filename_list("hypernetworks"), ),
-                              "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
-                              }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "load_hypernetwork"
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="HypernetworkLoader",
+            category="loaders",
+            inputs=[
+                IO.Model.Input("model"),
+                IO.Combo.Input("hypernetwork_name", options=folder_paths.get_filename_list("hypernetworks")),
+                IO.Float.Input("strength", default=1.0, min=-10.0, max=10.0, step=0.01),
+            ],
+            outputs=[
+                IO.Model.Output(),
+            ],
+        )
 
-    CATEGORY = "loaders"
-
-    def load_hypernetwork(self, model, hypernetwork_name, strength):
+    @classmethod
+    def execute(cls, model, hypernetwork_name, strength) -> IO.NodeOutput:
         hypernetwork_path = folder_paths.get_full_path_or_raise("hypernetworks", hypernetwork_name)
         model_hypernetwork = model.clone()
         patch = load_hypernetwork_patch(hypernetwork_path, strength)
         if patch is not None:
             model_hypernetwork.set_model_attn1_patch(patch)
             model_hypernetwork.set_model_attn2_patch(patch)
-        return (model_hypernetwork,)
+        return IO.NodeOutput(model_hypernetwork)
 
-NODE_CLASS_MAPPINGS = {
-    "HypernetworkLoader": HypernetworkLoader
-}
+    load_hypernetwork = execute  # TODO: remove
+
+
+class HyperNetworkExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            HypernetworkLoader,
+        ]
+
+
+async def comfy_entrypoint() -> HyperNetworkExtension:
+    return HyperNetworkExtension()

From e617cddf244e4b789afba4b4ece01661b12fdde5 Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Mon, 3 Nov 2025 10:28:13 +0200
Subject: [PATCH 38/56] convert nodes_openai.py to V3 schema (#10604)

---
 comfy_api_nodes/apinode_utils.py    |   73 --
 comfy_api_nodes/nodes_openai.py     | 1094 +++++++++++----------------
 comfy_api_nodes/util/__init__.py    |    4 +
 comfy_api_nodes/util/conversions.py |   19 +-
 4 files changed, 482 insertions(+), 708 deletions(-)
 delete mode 100644 comfy_api_nodes/apinode_utils.py

diff --git a/comfy_api_nodes/apinode_utils.py b/comfy_api_nodes/apinode_utils.py
deleted file mode 100644
index ecd604ff8..000000000
--- a/comfy_api_nodes/apinode_utils.py
+++ /dev/null
@@ -1,73 +0,0 @@
-from __future__ import annotations
-import aiohttp
-import mimetypes
-from typing import Union
-from server import PromptServer
-
-import numpy as np
-from PIL import Image
-import torch
-import base64
-from io import BytesIO
-
-
-async def validate_and_cast_response(
-    response, timeout: int = None, node_id: Union[str, None] = None
-) -> torch.Tensor:
-    """Validates and casts a response to a torch.Tensor.
-
-    Args:
-        response: The response to validate and cast.
-        timeout: Request timeout in seconds. Defaults to None (no timeout).
-
-    Returns:
-        A torch.Tensor representing the image (1, H, W, C).
-
-    Raises:
-        ValueError: If the response is not valid.
-    """
-    # validate raw JSON response
-    data = response.data
-    if not data or len(data) == 0:
-        raise ValueError("No images returned from API endpoint")
-
-    # Initialize list to store image tensors
-    image_tensors: list[torch.Tensor] = []
-
-    # Process each image in the data array
-    async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=timeout)) as session:
-        for img_data in data:
-            img_bytes: bytes
-            if img_data.b64_json:
-                img_bytes = base64.b64decode(img_data.b64_json)
-            elif img_data.url:
-                if node_id:
-                    PromptServer.instance.send_progress_text(f"Result URL: {img_data.url}", node_id)
-                async with session.get(img_data.url) as resp:
-                    if resp.status != 200:
-                        raise ValueError("Failed to download generated image")
-                    img_bytes = await resp.read()
-            else:
-                raise ValueError("Invalid image payload – neither URL nor base64 data present.")
-
-            pil_img = Image.open(BytesIO(img_bytes)).convert("RGBA")
-            arr = np.asarray(pil_img).astype(np.float32) / 255.0
-            image_tensors.append(torch.from_numpy(arr))
-
-    return torch.stack(image_tensors, dim=0)
-
-
-def text_filepath_to_base64_string(filepath: str) -> str:
-    """Converts a text file to a base64 string."""
-    with open(filepath, "rb") as f:
-        file_content = f.read()
-    return base64.b64encode(file_content).decode("utf-8")
-
-
-def text_filepath_to_data_uri(filepath: str) -> str:
-    """Converts a text file to a data URI."""
-    base64_string = text_filepath_to_base64_string(filepath)
-    mime_type, _ = mimetypes.guess_type(filepath)
-    if mime_type is None:
-        mime_type = "application/octet-stream"
-    return f"data:{mime_type};base64,{base64_string}"
diff --git a/comfy_api_nodes/nodes_openai.py b/comfy_api_nodes/nodes_openai.py
index b4568fc85..acf35d276 100644
--- a/comfy_api_nodes/nodes_openai.py
+++ b/comfy_api_nodes/nodes_openai.py
@@ -1,18 +1,19 @@
-import io
-from typing import TypedDict, Optional
+from io import BytesIO
+from typing import Optional, Union
 import json
 import os
 import time
-import re
 import uuid
 from enum import Enum
 from inspect import cleandoc
 import numpy as np
 import torch
 from PIL import Image
-from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict
 from server import PromptServer
 import folder_paths
+import base64
+from comfy_api.latest import IO, ComfyExtension
+from typing_extensions import override
 
 
 from comfy_api_nodes.apis import (
@@ -23,7 +24,6 @@ from comfy_api_nodes.apis import (
     OpenAIResponse,
     CreateModelResponseProperties,
     Item,
-    Includable,
     OutputContent,
     InputImageContent,
     Detail,
@@ -34,41 +34,22 @@ from comfy_api_nodes.apis import (
     InputFileContent,
 )
 
-from comfy_api_nodes.apis.client import (
+from comfy_api_nodes.util import (
+    downscale_image_tensor,
+    download_url_to_bytesio,
+    validate_string,
+    tensor_to_base64_string,
     ApiEndpoint,
-    HttpMethod,
-    SynchronousOperation,
-    PollingOperation,
-    EmptyRequest,
-)
-
-from comfy_api_nodes.apinode_utils import (
-    validate_and_cast_response,
+    sync_op,
+    poll_op,
     text_filepath_to_data_uri,
 )
-from comfy_api_nodes.mapper_utils import model_field_to_node_input
-from comfy_api_nodes.util import downscale_image_tensor, validate_string, tensor_to_base64_string
 
 
 RESPONSES_ENDPOINT = "/proxy/openai/v1/responses"
 STARTING_POINT_ID_PATTERN = r"<starting_point_id:(.*)>"
 
 
-class HistoryEntry(TypedDict):
-    """Type definition for a single history entry in the chat."""
-
-    prompt: str
-    response: str
-    response_id: str
-    timestamp: float
-
-
-class ChatHistory(TypedDict):
-    """Type definition for the chat history dictionary."""
-
-    __annotations__: dict[str, list[HistoryEntry]]
-
-
 class SupportedOpenAIModel(str, Enum):
     o4_mini = "o4-mini"
     o1 = "o1"
@@ -83,98 +64,123 @@ class SupportedOpenAIModel(str, Enum):
     gpt_5_nano = "gpt-5-nano"
 
 
-class OpenAIDalle2(ComfyNodeABC):
+async def validate_and_cast_response(response, timeout: int = None) -> torch.Tensor:
+    """Validates and casts a response to a torch.Tensor.
+
+    Args:
+        response: The response to validate and cast.
+        timeout: Request timeout in seconds. Defaults to None (no timeout).
+
+    Returns:
+        A torch.Tensor representing the image (1, H, W, C).
+
+    Raises:
+        ValueError: If the response is not valid.
+    """
+    # validate raw JSON response
+    data = response.data
+    if not data or len(data) == 0:
+        raise ValueError("No images returned from API endpoint")
+
+    # Initialize list to store image tensors
+    image_tensors: list[torch.Tensor] = []
+
+    # Process each image in the data array
+    for img_data in data:
+        if img_data.b64_json:
+            img_io = BytesIO(base64.b64decode(img_data.b64_json))
+        elif img_data.url:
+            img_io = BytesIO()
+            await download_url_to_bytesio(img_data.url, img_io, timeout=timeout)
+        else:
+            raise ValueError("Invalid image payload – neither URL nor base64 data present.")
+
+        pil_img = Image.open(img_io).convert("RGBA")
+        arr = np.asarray(pil_img).astype(np.float32) / 255.0
+        image_tensors.append(torch.from_numpy(arr))
+
+    return torch.stack(image_tensors, dim=0)
+
+
+class OpenAIDalle2(IO.ComfyNode):
     """
     Generates images synchronously via OpenAI's DALL·E 2 endpoint.
     """
 
-    def __init__(self):
-        pass
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="OpenAIDalle2",
+            display_name="OpenAI DALL·E 2",
+            category="api node/image/OpenAI",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    default="",
+                    multiline=True,
+                    tooltip="Text prompt for DALL·E",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2**31 - 1,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="not implemented yet in backend",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "size",
+                    default="1024x1024",
+                    options=["256x256", "512x512", "1024x1024"],
+                    tooltip="Image size",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "n",
+                    default=1,
+                    min=1,
+                    max=8,
+                    step=1,
+                    tooltip="How many images to generate",
+                    display_mode=IO.NumberDisplay.number,
+                    optional=True,
+                ),
+                IO.Image.Input(
+                    "image",
+                    tooltip="Optional reference image for image editing.",
+                    optional=True,
+                ),
+                IO.Mask.Input(
+                    "mask",
+                    tooltip="Optional mask for inpainting (white areas will be replaced)",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(cls) -> InputTypeDict:
-        return {
-            "required": {
-                "prompt": (
-                    IO.STRING,
-                    {
-                        "multiline": True,
-                        "default": "",
-                        "tooltip": "Text prompt for DALL·E",
-                    },
-                ),
-            },
-            "optional": {
-                "seed": (
-                    IO.INT,
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "max": 2**31 - 1,
-                        "step": 1,
-                        "display": "number",
-                        "control_after_generate": True,
-                        "tooltip": "not implemented yet in backend",
-                    },
-                ),
-                "size": (
-                    IO.COMBO,
-                    {
-                        "options": ["256x256", "512x512", "1024x1024"],
-                        "default": "1024x1024",
-                        "tooltip": "Image size",
-                    },
-                ),
-                "n": (
-                    IO.INT,
-                    {
-                        "default": 1,
-                        "min": 1,
-                        "max": 8,
-                        "step": 1,
-                        "display": "number",
-                        "tooltip": "How many images to generate",
-                    },
-                ),
-                "image": (
-                    IO.IMAGE,
-                    {
-                        "default": None,
-                        "tooltip": "Optional reference image for image editing.",
-                    },
-                ),
-                "mask": (
-                    IO.MASK,
-                    {
-                        "default": None,
-                        "tooltip": "Optional mask for inpainting (white areas will be replaced)",
-                    },
-                ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-                "unique_id": "UNIQUE_ID",
-            },
-        }
-
-    RETURN_TYPES = (IO.IMAGE,)
-    FUNCTION = "api_call"
-    CATEGORY = "api node/image/OpenAI"
-    DESCRIPTION = cleandoc(__doc__ or "")
-    API_NODE = True
-
-    async def api_call(
-        self,
+    async def execute(
+        cls,
         prompt,
         seed=0,
         image=None,
         mask=None,
         n=1,
         size="1024x1024",
-        unique_id=None,
-        **kwargs,
-    ):
+    ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=False)
         model = "dall-e-2"
         path = "/proxy/openai/images/generations"
@@ -200,7 +206,7 @@ class OpenAIDalle2(ComfyNodeABC):
 
             image_np = (rgba_tensor.numpy() * 255).astype(np.uint8)
             img = Image.fromarray(image_np)
-            img_byte_arr = io.BytesIO()
+            img_byte_arr = BytesIO()
             img.save(img_byte_arr, format="PNG")
             img_byte_arr.seek(0)
             img_binary = img_byte_arr  # .getvalue()
@@ -208,15 +214,11 @@ class OpenAIDalle2(ComfyNodeABC):
         elif image is not None or mask is not None:
             raise Exception("Dall-E 2 image editing requires an image AND a mask")
 
-        # Build the operation
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path=path,
-                method=HttpMethod.POST,
-                request_model=request_class,
-                response_model=OpenAIImageGenerationResponse,
-            ),
-            request=request_class(
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=path, method="POST"),
+            response_model=OpenAIImageGenerationResponse,
+            data=request_class(
                 model=model,
                 prompt=prompt,
                 n=n,
@@ -231,109 +233,92 @@ class OpenAIDalle2(ComfyNodeABC):
                 else None
             ),
             content_type=content_type,
-            auth_kwargs=kwargs,
         )
 
-        response = await operation.execute()
-
-        img_tensor = await validate_and_cast_response(response, node_id=unique_id)
-        return (img_tensor,)
+        return IO.NodeOutput(await validate_and_cast_response(response))
 
 
-class OpenAIDalle3(ComfyNodeABC):
+class OpenAIDalle3(IO.ComfyNode):
     """
     Generates images synchronously via OpenAI's DALL·E 3 endpoint.
     """
 
-    def __init__(self):
-        pass
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="OpenAIDalle3",
+            display_name="OpenAI DALL·E 3",
+            category="api node/image/OpenAI",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    default="",
+                    multiline=True,
+                    tooltip="Text prompt for DALL·E",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2 ** 31 - 1,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="not implemented yet in backend",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "quality",
+                    default="standard",
+                    options=["standard", "hd"],
+                    tooltip="Image quality",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "style",
+                    default="natural",
+                    options=["natural", "vivid"],
+                    tooltip="Vivid causes the model to lean towards generating hyper-real and dramatic images. Natural causes the model to produce more natural, less hyper-real looking images.",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "size",
+                    default="1024x1024",
+                    options=["1024x1024", "1024x1792", "1792x1024"],
+                    tooltip="Image size",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(cls) -> InputTypeDict:
-        return {
-            "required": {
-                "prompt": (
-                    IO.STRING,
-                    {
-                        "multiline": True,
-                        "default": "",
-                        "tooltip": "Text prompt for DALL·E",
-                    },
-                ),
-            },
-            "optional": {
-                "seed": (
-                    IO.INT,
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "max": 2**31 - 1,
-                        "step": 1,
-                        "display": "number",
-                        "control_after_generate": True,
-                        "tooltip": "not implemented yet in backend",
-                    },
-                ),
-                "quality": (
-                    IO.COMBO,
-                    {
-                        "options": ["standard", "hd"],
-                        "default": "standard",
-                        "tooltip": "Image quality",
-                    },
-                ),
-                "style": (
-                    IO.COMBO,
-                    {
-                        "options": ["natural", "vivid"],
-                        "default": "natural",
-                        "tooltip": "Vivid causes the model to lean towards generating hyper-real and dramatic images. Natural causes the model to produce more natural, less hyper-real looking images.",
-                    },
-                ),
-                "size": (
-                    IO.COMBO,
-                    {
-                        "options": ["1024x1024", "1024x1792", "1792x1024"],
-                        "default": "1024x1024",
-                        "tooltip": "Image size",
-                    },
-                ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-                "unique_id": "UNIQUE_ID",
-            },
-        }
-
-    RETURN_TYPES = (IO.IMAGE,)
-    FUNCTION = "api_call"
-    CATEGORY = "api node/image/OpenAI"
-    DESCRIPTION = cleandoc(__doc__ or "")
-    API_NODE = True
-
-    async def api_call(
-        self,
+    async def execute(
+        cls,
         prompt,
         seed=0,
         style="natural",
         quality="standard",
         size="1024x1024",
-        unique_id=None,
-        **kwargs,
-    ):
+    ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=False)
         model = "dall-e-3"
 
         # build the operation
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/openai/images/generations",
-                method=HttpMethod.POST,
-                request_model=OpenAIImageGenerationRequest,
-                response_model=OpenAIImageGenerationResponse,
-            ),
-            request=OpenAIImageGenerationRequest(
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/openai/images/generations", method="POST"),
+            response_model=OpenAIImageGenerationResponse,
+            data=OpenAIImageGenerationRequest(
                 model=model,
                 prompt=prompt,
                 quality=quality,
@@ -341,114 +326,97 @@ class OpenAIDalle3(ComfyNodeABC):
                 style=style,
                 seed=seed,
             ),
-            auth_kwargs=kwargs,
         )
 
-        response = await operation.execute()
-
-        img_tensor = await validate_and_cast_response(response, node_id=unique_id)
-        return (img_tensor,)
+        return IO.NodeOutput(await validate_and_cast_response(response))
 
 
-class OpenAIGPTImage1(ComfyNodeABC):
+class OpenAIGPTImage1(IO.ComfyNode):
     """
     Generates images synchronously via OpenAI's GPT Image 1 endpoint.
     """
 
-    def __init__(self):
-        pass
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="OpenAIGPTImage1",
+            display_name="OpenAI GPT Image 1",
+            category="api node/image/OpenAI",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    default="",
+                    multiline=True,
+                    tooltip="Text prompt for GPT Image 1",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2 ** 31 - 1,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="not implemented yet in backend",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "quality",
+                    default="low",
+                    options=["low", "medium", "high"],
+                    tooltip="Image quality, affects cost and generation time.",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "background",
+                    default="opaque",
+                    options=["opaque", "transparent"],
+                    tooltip="Return image with or without background",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "size",
+                    default="auto",
+                    options=["auto", "1024x1024", "1024x1536", "1536x1024"],
+                    tooltip="Image size",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "n",
+                    default=1,
+                    min=1,
+                    max=8,
+                    step=1,
+                    tooltip="How many images to generate",
+                    display_mode=IO.NumberDisplay.number,
+                    optional=True,
+                ),
+                IO.Image.Input(
+                    "image",
+                    tooltip="Optional reference image for image editing.",
+                    optional=True,
+                ),
+                IO.Mask.Input(
+                    "mask",
+                    tooltip="Optional mask for inpainting (white areas will be replaced)",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(cls) -> InputTypeDict:
-        return {
-            "required": {
-                "prompt": (
-                    IO.STRING,
-                    {
-                        "multiline": True,
-                        "default": "",
-                        "tooltip": "Text prompt for GPT Image 1",
-                    },
-                ),
-            },
-            "optional": {
-                "seed": (
-                    IO.INT,
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "max": 2**31 - 1,
-                        "step": 1,
-                        "display": "number",
-                        "control_after_generate": True,
-                        "tooltip": "not implemented yet in backend",
-                    },
-                ),
-                "quality": (
-                    IO.COMBO,
-                    {
-                        "options": ["low", "medium", "high"],
-                        "default": "low",
-                        "tooltip": "Image quality, affects cost and generation time.",
-                    },
-                ),
-                "background": (
-                    IO.COMBO,
-                    {
-                        "options": ["opaque", "transparent"],
-                        "default": "opaque",
-                        "tooltip": "Return image with or without background",
-                    },
-                ),
-                "size": (
-                    IO.COMBO,
-                    {
-                        "options": ["auto", "1024x1024", "1024x1536", "1536x1024"],
-                        "default": "auto",
-                        "tooltip": "Image size",
-                    },
-                ),
-                "n": (
-                    IO.INT,
-                    {
-                        "default": 1,
-                        "min": 1,
-                        "max": 8,
-                        "step": 1,
-                        "display": "number",
-                        "tooltip": "How many images to generate",
-                    },
-                ),
-                "image": (
-                    IO.IMAGE,
-                    {
-                        "default": None,
-                        "tooltip": "Optional reference image for image editing.",
-                    },
-                ),
-                "mask": (
-                    IO.MASK,
-                    {
-                        "default": None,
-                        "tooltip": "Optional mask for inpainting (white areas will be replaced)",
-                    },
-                ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-                "unique_id": "UNIQUE_ID",
-            },
-        }
-
-    RETURN_TYPES = (IO.IMAGE,)
-    FUNCTION = "api_call"
-    CATEGORY = "api node/image/OpenAI"
-    DESCRIPTION = cleandoc(__doc__ or "")
-    API_NODE = True
-
-    async def api_call(
-        self,
+    async def execute(
+        cls,
         prompt,
         seed=0,
         quality="low",
@@ -457,9 +425,7 @@ class OpenAIGPTImage1(ComfyNodeABC):
         mask=None,
         n=1,
         size="1024x1024",
-        unique_id=None,
-        **kwargs,
-    ):
+    ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=False)
         model = "gpt-image-1"
         path = "/proxy/openai/images/generations"
@@ -480,7 +446,7 @@ class OpenAIGPTImage1(ComfyNodeABC):
 
                 image_np = (scaled_image.numpy() * 255).astype(np.uint8)
                 img = Image.fromarray(image_np)
-                img_byte_arr = io.BytesIO()
+                img_byte_arr = BytesIO()
                 img.save(img_byte_arr, format="PNG")
                 img_byte_arr.seek(0)
 
@@ -504,20 +470,17 @@ class OpenAIGPTImage1(ComfyNodeABC):
 
             mask_np = (scaled_mask.numpy() * 255).astype(np.uint8)
             mask_img = Image.fromarray(mask_np)
-            mask_img_byte_arr = io.BytesIO()
+            mask_img_byte_arr = BytesIO()
             mask_img.save(mask_img_byte_arr, format="PNG")
             mask_img_byte_arr.seek(0)
             files.append(("mask", ("mask.png", mask_img_byte_arr, "image/png")))
 
         # Build the operation
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path=path,
-                method=HttpMethod.POST,
-                request_model=request_class,
-                response_model=OpenAIImageGenerationResponse,
-            ),
-            request=request_class(
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=path, method="POST"),
+            response_model=OpenAIImageGenerationResponse,
+            data=request_class(
                 model=model,
                 prompt=prompt,
                 quality=quality,
@@ -528,127 +491,70 @@ class OpenAIGPTImage1(ComfyNodeABC):
             ),
             files=files if files else None,
             content_type=content_type,
-            auth_kwargs=kwargs,
         )
 
-        response = await operation.execute()
-
-        img_tensor = await validate_and_cast_response(response, node_id=unique_id)
-        return (img_tensor,)
+        return IO.NodeOutput(await validate_and_cast_response(response))
 
 
-class OpenAITextNode(ComfyNodeABC):
-    """
-    Base class for OpenAI text generation nodes.
-    """
-
-    RETURN_TYPES = (IO.STRING,)
-    FUNCTION = "api_call"
-    CATEGORY = "api node/text/OpenAI"
-    API_NODE = True
-
-
-class OpenAIChatNode(OpenAITextNode):
+class OpenAIChatNode(IO.ComfyNode):
     """
     Node to generate text responses from an OpenAI model.
     """
 
-    def __init__(self) -> None:
-        """Initialize the chat node with a new session ID and empty history."""
-        self.current_session_id: str = str(uuid.uuid4())
-        self.history: dict[str, list[HistoryEntry]] = {}
-        self.previous_response_id: Optional[str] = None
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="OpenAIChatNode",
+            display_name="OpenAI ChatGPT",
+            category="api node/text/OpenAI",
+            description="Generate text responses from an OpenAI model.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    default="",
+                    multiline=True,
+                    tooltip="Text inputs to the model, used to generate a response.",
+                ),
+                IO.Boolean.Input(
+                    "persist_context",
+                    default=False,
+                    tooltip="This parameter is deprecated and has no effect.",
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=SupportedOpenAIModel,
+                    tooltip="The model used to generate the response",
+                ),
+                IO.Image.Input(
+                    "images",
+                    tooltip="Optional image(s) to use as context for the model. To include multiple images, you can use the Batch Images node.",
+                    optional=True,
+                ),
+                IO.Custom("OPENAI_INPUT_FILES").Input(
+                    "files",
+                    optional=True,
+                    tooltip="Optional file(s) to use as context for the model. Accepts inputs from the OpenAI Chat Input Files node.",
+                ),
+                IO.Custom("OPENAI_CHAT_CONFIG").Input(
+                    "advanced_options",
+                    optional=True,
+                    tooltip="Optional configuration for the model. Accepts inputs from the OpenAI Chat Advanced Options node.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(cls) -> InputTypeDict:
-        return {
-            "required": {
-                "prompt": (
-                    IO.STRING,
-                    {
-                        "multiline": True,
-                        "default": "",
-                        "tooltip": "Text inputs to the model, used to generate a response.",
-                    },
-                ),
-                "persist_context": (
-                    IO.BOOLEAN,
-                    {
-                        "default": True,
-                        "tooltip": "Persist chat context between calls (multi-turn conversation)",
-                    },
-                ),
-                "model": model_field_to_node_input(
-                    IO.COMBO,
-                    OpenAICreateResponse,
-                    "model",
-                    enum_type=SupportedOpenAIModel,
-                ),
-            },
-            "optional": {
-                "images": (
-                    IO.IMAGE,
-                    {
-                        "default": None,
-                        "tooltip": "Optional image(s) to use as context for the model. To include multiple images, you can use the Batch Images node.",
-                    },
-                ),
-                "files": (
-                    "OPENAI_INPUT_FILES",
-                    {
-                        "default": None,
-                        "tooltip": "Optional file(s) to use as context for the model. Accepts inputs from the OpenAI Chat Input Files node.",
-                    },
-                ),
-                "advanced_options": (
-                    "OPENAI_CHAT_CONFIG",
-                    {
-                        "default": None,
-                        "tooltip": "Optional configuration for the model. Accepts inputs from the OpenAI Chat Advanced Options node.",
-                    },
-                ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-                "unique_id": "UNIQUE_ID",
-            },
-        }
-
-    DESCRIPTION = "Generate text responses from an OpenAI model."
-
-    async def get_result_response(
-        self,
-        response_id: str,
-        include: Optional[list[Includable]] = None,
-        auth_kwargs: Optional[dict[str, str]] = None,
-    ) -> OpenAIResponse:
-        """
-        Retrieve a model response with the given ID from the OpenAI API.
-
-        Args:
-            response_id (str): The ID of the response to retrieve.
-            include (Optional[List[Includable]]): Additional fields to include
-                in the response. See the `include` parameter for Response
-                creation above for more information.
-
-        """
-        return await PollingOperation(
-            poll_endpoint=ApiEndpoint(
-                path=f"{RESPONSES_ENDPOINT}/{response_id}",
-                method=HttpMethod.GET,
-                request_model=EmptyRequest,
-                response_model=OpenAIResponse,
-                query_params={"include": include},
-            ),
-            completed_statuses=["completed"],
-            failed_statuses=["failed"],
-            status_extractor=lambda response: response.status,
-            auth_kwargs=auth_kwargs,
-        ).execute()
-
     def get_message_content_from_response(
-        self, response: OpenAIResponse
+        cls, response: OpenAIResponse
     ) -> list[OutputContent]:
         """Extract message content from the API response."""
         for output in response.output:
@@ -656,8 +562,9 @@ class OpenAIChatNode(OpenAITextNode):
                 return output.root.content
         raise TypeError("No output message found in response")
 
+    @classmethod
     def get_text_from_message_content(
-        self, message_content: list[OutputContent]
+        cls, message_content: list[OutputContent]
     ) -> str:
         """Extract text content from message content."""
         for content_item in message_content:
@@ -665,58 +572,9 @@ class OpenAIChatNode(OpenAITextNode):
                 return str(content_item.root.text)
         return "No text output found in response"
 
-    def get_history_text(self, session_id: str) -> str:
-        """Convert the entire history for a given session to JSON string."""
-        return json.dumps(self.history[session_id])
-
-    def display_history_on_node(self, session_id: str, node_id: str) -> None:
-        """Display formatted chat history on the node UI."""
-        render_spec = {
-            "node_id": node_id,
-            "component": "ChatHistoryWidget",
-            "props": {
-                "history": self.get_history_text(session_id),
-            },
-        }
-        PromptServer.instance.send_sync(
-            "display_component",
-            render_spec,
-        )
-
-    def add_to_history(
-        self, session_id: str, prompt: str, output_text: str, response_id: str
-    ) -> None:
-        """Add a new entry to the chat history."""
-        if session_id not in self.history:
-            self.history[session_id] = []
-        self.history[session_id].append(
-            {
-                "prompt": prompt,
-                "response": output_text,
-                "response_id": response_id,
-                "timestamp": time.time(),
-            }
-        )
-
-    def parse_output_text_from_response(self, response: OpenAIResponse) -> str:
-        """Extract text output from the API response."""
-        message_contents = self.get_message_content_from_response(response)
-        return self.get_text_from_message_content(message_contents)
-
-    def generate_new_session_id(self) -> str:
-        """Generate a new unique session ID."""
-        return str(uuid.uuid4())
-
-    def get_session_id(self, persist_context: bool) -> str:
-        """Get the current or generate a new session ID based on context persistence."""
-        return (
-            self.current_session_id
-            if persist_context
-            else self.generate_new_session_id()
-        )
-
+    @classmethod
     def tensor_to_input_image_content(
-        self, image: torch.Tensor, detail_level: Detail = "auto"
+        cls, image: torch.Tensor, detail_level: Detail = "auto"
     ) -> InputImageContent:
         """Convert a tensor to an input image content object."""
         return InputImageContent(
@@ -725,21 +583,27 @@ class OpenAIChatNode(OpenAITextNode):
             type="input_image",
         )
 
+    @classmethod
     def create_input_message_contents(
-        self,
+        cls,
         prompt: str,
         image: Optional[torch.Tensor] = None,
         files: Optional[list[InputFileContent]] = None,
     ) -> InputMessageContentList:
         """Create a list of input message contents from prompt and optional image."""
-        content_list: list[InputContent] = [
+        content_list: list[Union[InputContent, InputTextContent, InputImageContent, InputFileContent]] = [
             InputTextContent(text=prompt, type="input_text"),
         ]
         if image is not None:
             for i in range(image.shape[0]):
                 content_list.append(
-                    self.tensor_to_input_image_content(image[i].unsqueeze(0))
+                    InputImageContent(
+                        detail="auto",
+                        image_url=f"data:image/png;base64,{tensor_to_base64_string(image[i].unsqueeze(0))}",
+                        type="input_image",
+                    )
                 )
+
         if files is not None:
             content_list.extend(files)
 
@@ -747,80 +611,28 @@ class OpenAIChatNode(OpenAITextNode):
             root=content_list,
         )
 
-    def parse_response_id_from_prompt(self, prompt: str) -> Optional[str]:
-        """Extract response ID from prompt if it exists."""
-        parsed_id = re.search(STARTING_POINT_ID_PATTERN, prompt)
-        return parsed_id.group(1) if parsed_id else None
-
-    def strip_response_tag_from_prompt(self, prompt: str) -> str:
-        """Remove the response ID tag from the prompt."""
-        return re.sub(STARTING_POINT_ID_PATTERN, "", prompt.strip())
-
-    def delete_history_after_response_id(
-        self, new_start_id: str, session_id: str
-    ) -> None:
-        """Delete history entries after a specific response ID."""
-        if session_id not in self.history:
-            return
-
-        new_history = []
-        i = 0
-        while (
-            i < len(self.history[session_id])
-            and self.history[session_id][i]["response_id"] != new_start_id
-        ):
-            new_history.append(self.history[session_id][i])
-            i += 1
-
-        # Since it's the new starting point (not the response being edited), we include it as well
-        if i < len(self.history[session_id]):
-            new_history.append(self.history[session_id][i])
-
-        self.history[session_id] = new_history
-
-    async def api_call(
-        self,
+    @classmethod
+    async def execute(
+        cls,
         prompt: str,
-        persist_context: bool,
-        model: SupportedOpenAIModel,
-        unique_id: Optional[str] = None,
+        persist_context: bool = False,
+        model: SupportedOpenAIModel = SupportedOpenAIModel.gpt_5.value,
         images: Optional[torch.Tensor] = None,
         files: Optional[list[InputFileContent]] = None,
         advanced_options: Optional[CreateModelResponseProperties] = None,
-        **kwargs,
-    ) -> tuple[str]:
-        # Validate inputs
+    ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=False)
 
-        session_id = self.get_session_id(persist_context)
-        response_id_override = self.parse_response_id_from_prompt(prompt)
-        if response_id_override:
-            is_starting_from_beginning = response_id_override == "start"
-            if is_starting_from_beginning:
-                self.history[session_id] = []
-                previous_response_id = None
-            else:
-                previous_response_id = response_id_override
-                self.delete_history_after_response_id(response_id_override, session_id)
-            prompt = self.strip_response_tag_from_prompt(prompt)
-        elif persist_context:
-            previous_response_id = self.previous_response_id
-        else:
-            previous_response_id = None
-
         # Create response
-        create_response = await SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path=RESPONSES_ENDPOINT,
-                method=HttpMethod.POST,
-                request_model=OpenAICreateResponse,
-                response_model=OpenAIResponse,
-            ),
-            request=OpenAICreateResponse(
+        create_response = await sync_op(
+            cls,
+            ApiEndpoint(path=RESPONSES_ENDPOINT, method="POST"),
+            response_model=OpenAIResponse,
+            data=OpenAICreateResponse(
                 input=[
                     Item(
                         root=InputMessage(
-                            content=self.create_input_message_contents(
+                            content=cls.create_input_message_contents(
                                 prompt, images, files
                             ),
                             role="user",
@@ -830,36 +642,57 @@ class OpenAIChatNode(OpenAITextNode):
                 store=True,
                 stream=False,
                 model=model,
-                previous_response_id=previous_response_id,
+                previous_response_id=None,
                 **(
                     advanced_options.model_dump(exclude_none=True)
                     if advanced_options
                     else {}
                 ),
             ),
-            auth_kwargs=kwargs,
-        ).execute()
+        )
         response_id = create_response.id
 
         # Get result output
-        result_response = await self.get_result_response(response_id, auth_kwargs=kwargs)
-        output_text = self.parse_output_text_from_response(result_response)
+        result_response = await poll_op(
+                cls,
+                ApiEndpoint(path=f"{RESPONSES_ENDPOINT}/{response_id}"),
+                response_model=OpenAIResponse,
+                status_extractor=lambda response: response.status,
+                completed_statuses=["incomplete", "completed"]
+            )
+        output_text = cls.get_text_from_message_content(cls.get_message_content_from_response(result_response))
 
         # Update history
-        self.add_to_history(session_id, prompt, output_text, response_id)
-        self.display_history_on_node(session_id, unique_id)
-        self.previous_response_id = response_id
-
-        return (output_text,)
+        render_spec = {
+            "node_id": cls.hidden.unique_id,
+            "component": "ChatHistoryWidget",
+            "props": {
+                "history": json.dumps(
+                    [
+                        {
+                            "prompt": prompt,
+                            "response": output_text,
+                            "response_id": str(uuid.uuid4()),
+                            "timestamp": time.time(),
+                        }
+                    ]
+                ),
+            },
+        }
+        PromptServer.instance.send_sync(
+            "display_component",
+            render_spec,
+        )
+        return IO.NodeOutput(output_text)
 
 
-class OpenAIInputFiles(ComfyNodeABC):
+class OpenAIInputFiles(IO.ComfyNode):
     """
     Loads and formats input files for OpenAI API.
     """
 
     @classmethod
-    def INPUT_TYPES(cls) -> InputTypeDict:
+    def define_schema(cls):
         """
         For details about the supported file input types, see:
         https://platform.openai.com/docs/guides/pdf-files?api-mode=responses
@@ -874,97 +707,92 @@ class OpenAIInputFiles(ComfyNodeABC):
         ]
         input_files = sorted(input_files, key=lambda x: x.name)
         input_files = [f.name for f in input_files]
-        return {
-            "required": {
-                "file": (
-                    IO.COMBO,
-                    {
-                        "tooltip": "Input files to include as context for the model. Only accepts text (.txt) and PDF (.pdf) files for now.",
-                        "options": input_files,
-                        "default": input_files[0] if input_files else None,
-                    },
+        return IO.Schema(
+            node_id="OpenAIInputFiles",
+            display_name="OpenAI ChatGPT Input Files",
+            category="api node/text/OpenAI",
+            description="Loads and prepares input files (text, pdf, etc.) to include as inputs for the OpenAI Chat Node. The files will be read by the OpenAI model when generating a response. 🛈 TIP: Can be chained together with other OpenAI Input File nodes.",
+            inputs=[
+                IO.Combo.Input(
+                    "file",
+                    options=input_files,
+                    default=input_files[0] if input_files else None,
+                    tooltip="Input files to include as context for the model. Only accepts text (.txt) and PDF (.pdf) files for now.",
                 ),
-            },
-            "optional": {
-                "OPENAI_INPUT_FILES": (
+                IO.Custom("OPENAI_INPUT_FILES").Input(
                     "OPENAI_INPUT_FILES",
-                    {
-                        "tooltip": "An optional additional file(s) to batch together with the file loaded from this node. Allows chaining of input files so that a single message can include multiple input files.",
-                        "default": None,
-                    },
+                    tooltip="An optional additional file(s) to batch together with the file loaded from this node. Allows chaining of input files so that a single message can include multiple input files.",
+                    optional=True,
                 ),
-            },
-        }
+            ],
+            outputs=[
+                IO.Custom("OPENAI_INPUT_FILES").Output(),
+            ],
+        )
 
-    DESCRIPTION = "Loads and prepares input files (text, pdf, etc.) to include as inputs for the OpenAI Chat Node. The files will be read by the OpenAI model when generating a response. 🛈 TIP: Can be chained together with other OpenAI Input File nodes."
-    RETURN_TYPES = ("OPENAI_INPUT_FILES",)
-    FUNCTION = "prepare_files"
-    CATEGORY = "api node/text/OpenAI"
-
-    def create_input_file_content(self, file_path: str) -> InputFileContent:
+    @classmethod
+    def create_input_file_content(cls, file_path: str) -> InputFileContent:
         return InputFileContent(
             file_data=text_filepath_to_data_uri(file_path),
             filename=os.path.basename(file_path),
             type="input_file",
         )
 
-    def prepare_files(
-        self, file: str, OPENAI_INPUT_FILES: list[InputFileContent] = []
-    ) -> tuple[list[InputFileContent]]:
+    @classmethod
+    def execute(cls, file: str, OPENAI_INPUT_FILES: list[InputFileContent] = []) -> IO.NodeOutput:
         """
         Loads and formats input files for OpenAI API.
         """
         file_path = folder_paths.get_annotated_filepath(file)
-        input_file_content = self.create_input_file_content(file_path)
+        input_file_content = cls.create_input_file_content(file_path)
         files = [input_file_content] + OPENAI_INPUT_FILES
-        return (files,)
+        return IO.NodeOutput(files)
 
 
-class OpenAIChatConfig(ComfyNodeABC):
+class OpenAIChatConfig(IO.ComfyNode):
     """Allows setting additional configuration for the OpenAI Chat Node."""
 
-    RETURN_TYPES = ("OPENAI_CHAT_CONFIG",)
-    FUNCTION = "configure"
-    DESCRIPTION = (
-        "Allows specifying advanced configuration options for the OpenAI Chat Nodes."
-    )
-    CATEGORY = "api node/text/OpenAI"
-
     @classmethod
-    def INPUT_TYPES(cls) -> InputTypeDict:
-        return {
-            "required": {
-                "truncation": (
-                    IO.COMBO,
-                    {
-                        "options": ["auto", "disabled"],
-                        "default": "auto",
-                        "tooltip": "The truncation strategy to use for the model response. auto: If the context of this response and previous ones exceeds the model's context window size, the model will truncate the response to fit the context window by dropping input items in the middle of the conversation.disabled: If a model response will exceed the context window size for a model, the request will fail with a 400 error",
-                    },
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="OpenAIChatConfig",
+            display_name="OpenAI ChatGPT Advanced Options",
+            category="api node/text/OpenAI",
+            description="Allows specifying advanced configuration options for the OpenAI Chat Nodes.",
+            inputs=[
+                IO.Combo.Input(
+                    "truncation",
+                    options=["auto", "disabled"],
+                    default="auto",
+                    tooltip="The truncation strategy to use for the model response. auto: If the context of this response and previous ones exceeds the model's context window size, the model will truncate the response to fit the context window by dropping input items in the middle of the conversation.disabled: If a model response will exceed the context window size for a model, the request will fail with a 400 error",
                 ),
-            },
-            "optional": {
-                "max_output_tokens": model_field_to_node_input(
-                    IO.INT,
-                    OpenAICreateResponse,
+                IO.Int.Input(
                     "max_output_tokens",
                     min=16,
                     default=4096,
                     max=16384,
                     tooltip="An upper bound for the number of tokens that can be generated for a response, including visible output tokens",
+                    optional=True,
                 ),
-                "instructions": model_field_to_node_input(
-                    IO.STRING, OpenAICreateResponse, "instructions", multiline=True
+                IO.String.Input(
+                    "instructions",
+                    multiline=True,
+                    optional=True,
+                    tooltip="Instructions for the model on how to generate the response",
                 ),
-            },
-        }
+            ],
+            outputs=[
+                IO.Custom("OPENAI_CHAT_CONFIG").Output(),
+            ],
+        )
 
-    def configure(
-        self,
+    @classmethod
+    def execute(
+        cls,
         truncation: bool,
         instructions: Optional[str] = None,
         max_output_tokens: Optional[int] = None,
-    ) -> tuple[CreateModelResponseProperties]:
+    ) -> IO.NodeOutput:
         """
         Configure advanced options for the OpenAI Chat Node.
 
@@ -974,29 +802,27 @@ class OpenAIChatConfig(ComfyNodeABC):
             They are not exposed as inputs at all to avoid having to manually
             remove depending on model choice.
         """
-        return (
+        return IO.NodeOutput(
             CreateModelResponseProperties(
                 instructions=instructions,
                 truncation=truncation,
                 max_output_tokens=max_output_tokens,
-            ),
+            )
         )
 
 
-NODE_CLASS_MAPPINGS = {
-    "OpenAIDalle2": OpenAIDalle2,
-    "OpenAIDalle3": OpenAIDalle3,
-    "OpenAIGPTImage1": OpenAIGPTImage1,
-    "OpenAIChatNode": OpenAIChatNode,
-    "OpenAIInputFiles": OpenAIInputFiles,
-    "OpenAIChatConfig": OpenAIChatConfig,
-}
+class OpenAIExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            OpenAIDalle2,
+            OpenAIDalle3,
+            OpenAIGPTImage1,
+            OpenAIChatNode,
+            OpenAIInputFiles,
+            OpenAIChatConfig,
+        ]
 
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "OpenAIDalle2": "OpenAI DALL·E 2",
-    "OpenAIDalle3": "OpenAI DALL·E 3",
-    "OpenAIGPTImage1": "OpenAI GPT Image 1",
-    "OpenAIChatNode": "OpenAI ChatGPT",
-    "OpenAIInputFiles": "OpenAI ChatGPT Input Files",
-    "OpenAIChatConfig": "OpenAI ChatGPT Advanced Options",
-}
+
+async def comfy_entrypoint() -> OpenAIExtension:
+    return OpenAIExtension()
diff --git a/comfy_api_nodes/util/__init__.py b/comfy_api_nodes/util/__init__.py
index bbc71363a..21013b591 100644
--- a/comfy_api_nodes/util/__init__.py
+++ b/comfy_api_nodes/util/__init__.py
@@ -18,6 +18,8 @@ from .conversions import (
     tensor_to_base64_string,
     tensor_to_bytesio,
     tensor_to_pil,
+    text_filepath_to_base64_string,
+    text_filepath_to_data_uri,
     trim_video,
     video_to_base64_string,
 )
@@ -75,6 +77,8 @@ __all__ = [
     "tensor_to_base64_string",
     "tensor_to_bytesio",
     "tensor_to_pil",
+    "text_filepath_to_base64_string",
+    "text_filepath_to_data_uri",
     "trim_video",
     "video_to_base64_string",
     # Validation utilities
diff --git a/comfy_api_nodes/util/conversions.py b/comfy_api_nodes/util/conversions.py
index b59c2bd84..971dc57de 100644
--- a/comfy_api_nodes/util/conversions.py
+++ b/comfy_api_nodes/util/conversions.py
@@ -1,6 +1,7 @@
 import base64
 import logging
 import math
+import mimetypes
 import uuid
 from io import BytesIO
 from typing import Optional
@@ -12,7 +13,7 @@ from PIL import Image
 
 from comfy.utils import common_upscale
 from comfy_api.latest import Input, InputImpl
-from comfy_api.util import VideoContainer, VideoCodec
+from comfy_api.util import VideoCodec, VideoContainer
 
 from ._helpers import mimetype_to_extension
 
@@ -451,3 +452,19 @@ def resize_mask_to_image(
     if not allow_gradient:
         mask = (mask > 0.5).float()
     return mask
+
+
+def text_filepath_to_base64_string(filepath: str) -> str:
+    """Converts a text file to a base64 string."""
+    with open(filepath, "rb") as f:
+        file_content = f.read()
+    return base64.b64encode(file_content).decode("utf-8")
+
+
+def text_filepath_to_data_uri(filepath: str) -> str:
+    """Converts a text file to a data URI."""
+    base64_string = text_filepath_to_base64_string(filepath)
+    mime_type, _ = mimetypes.guess_type(filepath)
+    if mime_type is None:
+        mime_type = "application/octet-stream"
+    return f"data:{mime_type};base64,{base64_string}"

From 4e2110c794b187c9326d604e7c0b0a4fad81148a Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Mon, 3 Nov 2025 10:29:08 +0200
Subject: [PATCH 39/56] feat(Pika-API-nodes): use new API client (#10608)

---
 .../apis/{pika_defs.py => pika_api.py}        |   0
 comfy_api_nodes/nodes_pika.py                 | 181 ++++++------------
 2 files changed, 57 insertions(+), 124 deletions(-)
 rename comfy_api_nodes/apis/{pika_defs.py => pika_api.py} (100%)

diff --git a/comfy_api_nodes/apis/pika_defs.py b/comfy_api_nodes/apis/pika_api.py
similarity index 100%
rename from comfy_api_nodes/apis/pika_defs.py
rename to comfy_api_nodes/apis/pika_api.py
diff --git a/comfy_api_nodes/nodes_pika.py b/comfy_api_nodes/nodes_pika.py
index 5bb406a3b..51148211b 100644
--- a/comfy_api_nodes/nodes_pika.py
+++ b/comfy_api_nodes/nodes_pika.py
@@ -7,24 +7,23 @@ from __future__ import annotations
 
 from io import BytesIO
 import logging
-from typing import Optional, TypeVar
+from typing import Optional
 
 import torch
 
 from typing_extensions import override
 from comfy_api.latest import ComfyExtension, IO
 from comfy_api.input_impl.video_types import VideoCodec, VideoContainer, VideoInput
-from comfy_api_nodes.apis import pika_defs
-from comfy_api_nodes.apis.client import (
+from comfy_api_nodes.apis import pika_api as pika_defs
+from comfy_api_nodes.util import (
+    validate_string,
+    download_url_to_video_output,
+    tensor_to_bytesio,
     ApiEndpoint,
-    EmptyRequest,
-    HttpMethod,
-    PollingOperation,
-    SynchronousOperation,
+    sync_op,
+    poll_op,
 )
-from comfy_api_nodes.util import validate_string, download_url_to_video_output, tensor_to_bytesio
 
-R = TypeVar("R")
 
 PATH_PIKADDITIONS = "/proxy/pika/generate/pikadditions"
 PATH_PIKASWAPS = "/proxy/pika/generate/pikaswaps"
@@ -40,28 +39,18 @@ PATH_VIDEO_GET = "/proxy/pika/videos"
 
 
 async def execute_task(
-    initial_operation: SynchronousOperation[R, pika_defs.PikaGenerateResponse],
-    auth_kwargs: Optional[dict[str, str]] = None,
-    node_id: Optional[str] = None,
+    task_id: str,
+    cls: type[IO.ComfyNode],
 ) -> IO.NodeOutput:
-    task_id = (await initial_operation.execute()).video_id
-    final_response: pika_defs.PikaVideoResponse = await PollingOperation(
-        poll_endpoint=ApiEndpoint(
-            path=f"{PATH_VIDEO_GET}/{task_id}",
-            method=HttpMethod.GET,
-            request_model=EmptyRequest,
-            response_model=pika_defs.PikaVideoResponse,
-        ),
-        completed_statuses=["finished"],
-        failed_statuses=["failed", "cancelled"],
+    final_response: pika_defs.PikaVideoResponse = await poll_op(
+        cls,
+        ApiEndpoint(path=f"{PATH_VIDEO_GET}/{task_id}"),
+        response_model=pika_defs.PikaVideoResponse,
         status_extractor=lambda response: (response.status.value if response.status else None),
         progress_extractor=lambda response: (response.progress if hasattr(response, "progress") else None),
-        auth_kwargs=auth_kwargs,
-        result_url_extractor=lambda response: (response.url if hasattr(response, "url") else None),
-        node_id=node_id,
         estimated_duration=60,
         max_poll_attempts=240,
-    ).execute()
+    )
     if not final_response.url:
         error_msg = f"Pika task {task_id} succeeded but no video data found in response:\n{final_response}"
         logging.error(error_msg)
@@ -124,23 +113,15 @@ class PikaImageToVideo(IO.ComfyNode):
             resolution=resolution,
             duration=duration,
         )
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
-        initial_operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path=PATH_IMAGE_TO_VIDEO,
-                method=HttpMethod.POST,
-                request_model=pika_defs.PikaBodyGenerate22I2vGenerate22I2vPost,
-                response_model=pika_defs.PikaGenerateResponse,
-            ),
-            request=pika_request_data,
+        initial_operation = await sync_op(
+            cls,
+            ApiEndpoint(path=PATH_IMAGE_TO_VIDEO, method="POST"),
+            response_model=pika_defs.PikaGenerateResponse,
+            data=pika_request_data,
             files=pika_files,
             content_type="multipart/form-data",
-            auth_kwargs=auth,
         )
-        return await execute_task(initial_operation, auth_kwargs=auth, node_id=cls.hidden.unique_id)
+        return await execute_task(initial_operation.video_id, cls)
 
 
 class PikaTextToVideoNode(IO.ComfyNode):
@@ -183,18 +164,11 @@ class PikaTextToVideoNode(IO.ComfyNode):
         duration: int,
         aspect_ratio: float,
     ) -> IO.NodeOutput:
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
-        initial_operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path=PATH_TEXT_TO_VIDEO,
-                method=HttpMethod.POST,
-                request_model=pika_defs.PikaBodyGenerate22T2vGenerate22T2vPost,
-                response_model=pika_defs.PikaGenerateResponse,
-            ),
-            request=pika_defs.PikaBodyGenerate22T2vGenerate22T2vPost(
+        initial_operation = await sync_op(
+            cls,
+            ApiEndpoint(path=PATH_TEXT_TO_VIDEO, method="POST"),
+            response_model=pika_defs.PikaGenerateResponse,
+            data=pika_defs.PikaBodyGenerate22T2vGenerate22T2vPost(
                 promptText=prompt_text,
                 negativePrompt=negative_prompt,
                 seed=seed,
@@ -202,10 +176,9 @@ class PikaTextToVideoNode(IO.ComfyNode):
                 duration=duration,
                 aspectRatio=aspect_ratio,
             ),
-            auth_kwargs=auth,
             content_type="application/x-www-form-urlencoded",
         )
-        return await execute_task(initial_operation, auth_kwargs=auth, node_id=cls.hidden.unique_id)
+        return await execute_task(initial_operation.video_id, cls)
 
 
 class PikaScenes(IO.ComfyNode):
@@ -309,24 +282,16 @@ class PikaScenes(IO.ComfyNode):
             duration=duration,
             aspectRatio=aspect_ratio,
         )
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
-        initial_operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path=PATH_PIKASCENES,
-                method=HttpMethod.POST,
-                request_model=pika_defs.PikaBodyGenerate22C2vGenerate22PikascenesPost,
-                response_model=pika_defs.PikaGenerateResponse,
-            ),
-            request=pika_request_data,
+        initial_operation = await sync_op(
+            cls,
+            ApiEndpoint(path=PATH_PIKASCENES, method="POST"),
+            response_model=pika_defs.PikaGenerateResponse,
+            data=pika_request_data,
             files=pika_files,
             content_type="multipart/form-data",
-            auth_kwargs=auth,
         )
 
-        return await execute_task(initial_operation, auth_kwargs=auth, node_id=cls.hidden.unique_id)
+        return await execute_task(initial_operation.video_id, cls)
 
 
 class PikAdditionsNode(IO.ComfyNode):
@@ -383,24 +348,16 @@ class PikAdditionsNode(IO.ComfyNode):
             negativePrompt=negative_prompt,
             seed=seed,
         )
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
-        initial_operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path=PATH_PIKADDITIONS,
-                method=HttpMethod.POST,
-                request_model=pika_defs.PikaBodyGeneratePikadditionsGeneratePikadditionsPost,
-                response_model=pika_defs.PikaGenerateResponse,
-            ),
-            request=pika_request_data,
+        initial_operation = await sync_op(
+            cls,
+            ApiEndpoint(path=PATH_PIKADDITIONS, method="POST"),
+            response_model=pika_defs.PikaGenerateResponse,
+            data=pika_request_data,
             files=pika_files,
             content_type="multipart/form-data",
-            auth_kwargs=auth,
         )
 
-        return await execute_task(initial_operation, auth_kwargs=auth, node_id=cls.hidden.unique_id)
+        return await execute_task(initial_operation.video_id, cls)
 
 
 class PikaSwapsNode(IO.ComfyNode):
@@ -472,23 +429,15 @@ class PikaSwapsNode(IO.ComfyNode):
             seed=seed,
             modifyRegionRoi=region_to_modify if region_to_modify else None,
         )
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
-        initial_operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path=PATH_PIKASWAPS,
-                method=HttpMethod.POST,
-                request_model=pika_defs.PikaBodyGeneratePikaswapsGeneratePikaswapsPost,
-                response_model=pika_defs.PikaGenerateResponse,
-            ),
-            request=pika_request_data,
+        initial_operation = await sync_op(
+            cls,
+            ApiEndpoint(path=PATH_PIKASWAPS, method="POST"),
+            response_model=pika_defs.PikaGenerateResponse,
+            data=pika_request_data,
             files=pika_files,
             content_type="multipart/form-data",
-            auth_kwargs=auth,
         )
-        return await execute_task(initial_operation, auth_kwargs=auth, node_id=cls.hidden.unique_id)
+        return await execute_task(initial_operation.video_id, cls)
 
 
 class PikaffectsNode(IO.ComfyNode):
@@ -528,18 +477,11 @@ class PikaffectsNode(IO.ComfyNode):
         negative_prompt: str,
         seed: int,
     ) -> IO.NodeOutput:
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
-        initial_operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path=PATH_PIKAFFECTS,
-                method=HttpMethod.POST,
-                request_model=pika_defs.PikaBodyGeneratePikaffectsGeneratePikaffectsPost,
-                response_model=pika_defs.PikaGenerateResponse,
-            ),
-            request=pika_defs.PikaBodyGeneratePikaffectsGeneratePikaffectsPost(
+        initial_operation = await sync_op(
+            cls,
+            ApiEndpoint(path=PATH_PIKAFFECTS, method="POST"),
+            response_model=pika_defs.PikaGenerateResponse,
+            data=pika_defs.PikaBodyGeneratePikaffectsGeneratePikaffectsPost(
                 pikaffect=pikaffect,
                 promptText=prompt_text,
                 negativePrompt=negative_prompt,
@@ -547,9 +489,8 @@ class PikaffectsNode(IO.ComfyNode):
             ),
             files={"image": ("image.png", tensor_to_bytesio(image), "image/png")},
             content_type="multipart/form-data",
-            auth_kwargs=auth,
         )
-        return await execute_task(initial_operation, auth_kwargs=auth, node_id=cls.hidden.unique_id)
+        return await execute_task(initial_operation.video_id, cls)
 
 
 class PikaStartEndFrameNode(IO.ComfyNode):
@@ -592,18 +533,11 @@ class PikaStartEndFrameNode(IO.ComfyNode):
             ("keyFrames", ("image_start.png", tensor_to_bytesio(image_start), "image/png")),
             ("keyFrames", ("image_end.png", tensor_to_bytesio(image_end), "image/png")),
         ]
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
-        initial_operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path=PATH_PIKAFRAMES,
-                method=HttpMethod.POST,
-                request_model=pika_defs.PikaBodyGenerate22KeyframeGenerate22PikaframesPost,
-                response_model=pika_defs.PikaGenerateResponse,
-            ),
-            request=pika_defs.PikaBodyGenerate22KeyframeGenerate22PikaframesPost(
+        initial_operation = await sync_op(
+            cls,
+            ApiEndpoint(path=PATH_PIKAFRAMES, method="POST"),
+            response_model=pika_defs.PikaGenerateResponse,
+            data=pika_defs.PikaBodyGenerate22KeyframeGenerate22PikaframesPost(
                 promptText=prompt_text,
                 negativePrompt=negative_prompt,
                 seed=seed,
@@ -612,9 +546,8 @@ class PikaStartEndFrameNode(IO.ComfyNode):
             ),
             files=pika_files,
             content_type="multipart/form-data",
-            auth_kwargs=auth,
         )
-        return await execute_task(initial_operation, auth_kwargs=auth, node_id=cls.hidden.unique_id)
+        return await execute_task(initial_operation.video_id, cls)
 
 
 class PikaApiNodesExtension(ComfyExtension):

From e974e554ca23be505b72bc9c1614f4285c1db6e3 Mon Sep 17 00:00:00 2001
From: ComfyUI Wiki <contact@comfyui-wiki.com>
Date: Tue, 4 Nov 2025 02:59:44 +0800
Subject: [PATCH 40/56] chore: update embedded docs to v0.3.1 (#10614)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 4d84b0d3e..856e373de 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,6 @@
 comfyui-frontend-package==1.28.8
 comfyui-workflow-templates==0.2.4
-comfyui-embedded-docs==0.3.0
+comfyui-embedded-docs==0.3.1
 torch
 torchsde
 torchvision

From 958a17199ac519504e390ea0d53295ceb8cbd2c1 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Mon, 3 Nov 2025 14:08:30 -0800
Subject: [PATCH 41/56] People should update their pytorch versions. (#10618)

---
 comfy/quant_ops.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/comfy/quant_ops.py b/comfy/quant_ops.py
index 873f173ed..5af6f118e 100644
--- a/comfy/quant_ops.py
+++ b/comfy/quant_ops.py
@@ -418,6 +418,10 @@ def fp8_linear(func, args, kwargs):
                 scale_b=scale_b,
                 out_dtype=out_dtype,
             )
+
+            if isinstance(output, tuple):  # TODO: remove when we drop support for torch 2.4
+                output = output[0]
+
             if not tensor_2d:
                 output = output.reshape((-1, input_shape[1], weight.shape[0]))
 

From 0652cb8e2d343f68e38285755835c77bda7f6389 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Mon, 3 Nov 2025 14:37:12 -0800
Subject: [PATCH 42/56] Speed up torch.compile (#10620)

---
 comfy/ops.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/comfy/ops.py b/comfy/ops.py
index 0c8f23848..afe498caa 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -71,7 +71,6 @@ def cast_to_input(weight, input, non_blocking=False, copy=True):
     return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy)
 
 
-@torch.compiler.disable()
 def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, offloadable=False):
     # NOTE: offloadable=False is a a legacy and if you are a custom node author reading this please pass
     # offloadable=True and call uncast_bias_weight() after your last usage of the weight/bias. This

From e199c8cc6758d388792fd66b99e8de832814ff91 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Mon, 3 Nov 2025 14:58:24 -0800
Subject: [PATCH 43/56] Fixes (#10621)

---
 comfy/quant_ops.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfy/quant_ops.py b/comfy/quant_ops.py
index 5af6f118e..835fc4b8d 100644
--- a/comfy/quant_ops.py
+++ b/comfy/quant_ops.py
@@ -131,7 +131,7 @@ class QuantizedTensor(torch.Tensor):
         self._layout_params = layout_params
 
     def __repr__(self):
-        layout_name = self._layout_type.__name__
+        layout_name = self._layout_type
         param_str = ", ".join(f"{k}={v}" for k, v in list(self._layout_params.items())[:2])
         return f"QuantizedTensor(shape={self.shape}, layout={layout_name}, {param_str})"
 
@@ -179,7 +179,7 @@ class QuantizedTensor(torch.Tensor):
             attr_name = f"_layout_param_{key}"
             layout_params[key] = inner_tensors[attr_name]
 
-        return QuantizedTensor(inner_tensors["_q_data"], layout_type, layout_params)
+        return QuantizedTensor(inner_tensors["_qdata"], layout_type, layout_params)
 
     @classmethod
     def from_float(cls, tensor, layout_type, **quantize_kwargs) -> 'QuantizedTensor':

From 6b88478f9fe0874c0e17468c9fca3a0a84e6c781 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Mon, 3 Nov 2025 16:22:10 -0800
Subject: [PATCH 44/56] Bring back fp8 torch compile performance to what it
 should be. (#10622)

---
 comfy/quant_ops.py | 41 +++++++++++++++++++++++++++++++++++++++--
 1 file changed, 39 insertions(+), 2 deletions(-)

diff --git a/comfy/quant_ops.py b/comfy/quant_ops.py
index 835fc4b8d..ed7b29963 100644
--- a/comfy/quant_ops.py
+++ b/comfy/quant_ops.py
@@ -126,7 +126,7 @@ class QuantizedTensor(torch.Tensor):
         return torch.Tensor._make_wrapper_subclass(cls, qdata.shape, device=qdata.device, dtype=qdata.dtype, requires_grad=False)
 
     def __init__(self, qdata, layout_type, layout_params):
-        self._qdata = qdata.contiguous()
+        self._qdata = qdata
         self._layout_type = layout_type
         self._layout_params = layout_params
 
@@ -411,7 +411,7 @@ def fp8_linear(func, args, kwargs):
 
         try:
             output = torch._scaled_mm(
-                plain_input.reshape(-1, input_shape[2]),
+                plain_input.reshape(-1, input_shape[2]).contiguous(),
                 weight_t,
                 bias=bias,
                 scale_a=scale_a,
@@ -447,6 +447,43 @@ def fp8_linear(func, args, kwargs):
     return torch.nn.functional.linear(input_tensor, weight, bias)
 
 
+@register_layout_op(torch.ops.aten.addmm.default, "TensorCoreFP8Layout")
+def fp8_addmm(func, args, kwargs):
+    input_tensor = args[1]
+    weight = args[2]
+    bias = args[0]
+
+    if isinstance(input_tensor, QuantizedTensor) and isinstance(weight, QuantizedTensor):
+        out_dtype = kwargs.get("out_dtype")
+        if out_dtype is None:
+            out_dtype = input_tensor._layout_params['orig_dtype']
+
+        plain_input, scale_a = TensorCoreFP8Layout.get_plain_tensors(input_tensor)
+        plain_weight, scale_b = TensorCoreFP8Layout.get_plain_tensors(weight)
+
+        output = torch._scaled_mm(
+            plain_input.contiguous(),
+            plain_weight,
+            bias=bias,
+            scale_a=scale_a,
+            scale_b=scale_b,
+            out_dtype=out_dtype,
+        )
+
+        if isinstance(output, tuple):  # TODO: remove when we drop support for torch 2.4
+            output = output[0]
+        return output
+
+    a = list(args)
+    if isinstance(args[0], QuantizedTensor):
+        a[0] = args[0].dequantize()
+    if isinstance(args[1], QuantizedTensor):
+        a[1] = args[1].dequantize()
+    if isinstance(args[2], QuantizedTensor):
+        a[2] = args[2].dequantize()
+
+    return func(*a, **kwargs)
+
 @register_layout_op(torch.ops.aten.view.default, "TensorCoreFP8Layout")
 @register_layout_op(torch.ops.aten.t.default, "TensorCoreFP8Layout")
 def fp8_func(func, args, kwargs):

From 0f4ef3afa0772ad11d6d72ad21fb1e089c2fcf5f Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Mon, 3 Nov 2025 18:47:14 -0800
Subject: [PATCH 45/56] This seems to slow things down slightly on Linux.
 (#10624)

---
 comfy/ops.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/ops.py b/comfy/ops.py
index afe498caa..733bff99d 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -35,7 +35,7 @@ def scaled_dot_product_attention(q, k, v, *args, **kwargs):
 
 
 try:
-    if torch.cuda.is_available():
+    if torch.cuda.is_available() and comfy.model_management.WINDOWS:
         from torch.nn.attention import SDPBackend, sdpa_kernel
         import inspect
         if "set_priority" in inspect.signature(sdpa_kernel).parameters:

From af4b7b5edb339a15aa443e32aefbceac1810baa0 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Mon, 3 Nov 2025 19:14:20 -0800
Subject: [PATCH 46/56] More fp8 torch.compile regressions fixed. (#10625)

---
 comfy/quant_ops.py | 54 ++++++++++++++++++++++++++++++----------------
 1 file changed, 35 insertions(+), 19 deletions(-)

diff --git a/comfy/quant_ops.py b/comfy/quant_ops.py
index ed7b29963..c56e32a73 100644
--- a/comfy/quant_ops.py
+++ b/comfy/quant_ops.py
@@ -446,6 +446,25 @@ def fp8_linear(func, args, kwargs):
 
     return torch.nn.functional.linear(input_tensor, weight, bias)
 
+def fp8_mm_(input_tensor, weight, bias=None, out_dtype=None):
+    if out_dtype is None:
+        out_dtype = input_tensor._layout_params['orig_dtype']
+
+    plain_input, scale_a = TensorCoreFP8Layout.get_plain_tensors(input_tensor)
+    plain_weight, scale_b = TensorCoreFP8Layout.get_plain_tensors(weight)
+
+    output = torch._scaled_mm(
+        plain_input.contiguous(),
+        plain_weight,
+        bias=bias,
+        scale_a=scale_a,
+        scale_b=scale_b,
+        out_dtype=out_dtype,
+    )
+
+    if isinstance(output, tuple):  # TODO: remove when we drop support for torch 2.4
+        output = output[0]
+    return output
 
 @register_layout_op(torch.ops.aten.addmm.default, "TensorCoreFP8Layout")
 def fp8_addmm(func, args, kwargs):
@@ -454,25 +473,7 @@ def fp8_addmm(func, args, kwargs):
     bias = args[0]
 
     if isinstance(input_tensor, QuantizedTensor) and isinstance(weight, QuantizedTensor):
-        out_dtype = kwargs.get("out_dtype")
-        if out_dtype is None:
-            out_dtype = input_tensor._layout_params['orig_dtype']
-
-        plain_input, scale_a = TensorCoreFP8Layout.get_plain_tensors(input_tensor)
-        plain_weight, scale_b = TensorCoreFP8Layout.get_plain_tensors(weight)
-
-        output = torch._scaled_mm(
-            plain_input.contiguous(),
-            plain_weight,
-            bias=bias,
-            scale_a=scale_a,
-            scale_b=scale_b,
-            out_dtype=out_dtype,
-        )
-
-        if isinstance(output, tuple):  # TODO: remove when we drop support for torch 2.4
-            output = output[0]
-        return output
+        return fp8_mm_(input_tensor, weight, bias=bias, out_dtype=kwargs.get("out_dtype", None))
 
     a = list(args)
     if isinstance(args[0], QuantizedTensor):
@@ -484,6 +485,21 @@ def fp8_addmm(func, args, kwargs):
 
     return func(*a, **kwargs)
 
+@register_layout_op(torch.ops.aten.mm.default, "TensorCoreFP8Layout")
+def fp8_mm(func, args, kwargs):
+    input_tensor = args[0]
+    weight = args[1]
+
+    if isinstance(input_tensor, QuantizedTensor) and isinstance(weight, QuantizedTensor):
+        return fp8_mm_(input_tensor, weight, bias=None, out_dtype=kwargs.get("out_dtype", None))
+
+    a = list(args)
+    if isinstance(args[0], QuantizedTensor):
+        a[0] = args[0].dequantize()
+    if isinstance(args[1], QuantizedTensor):
+        a[1] = args[1].dequantize()
+    return func(*a, **kwargs)
+
 @register_layout_op(torch.ops.aten.view.default, "TensorCoreFP8Layout")
 @register_layout_op(torch.ops.aten.t.default, "TensorCoreFP8Layout")
 def fp8_func(func, args, kwargs):

From 9c71a667904a049975531f2a7dd55f4a8fc92652 Mon Sep 17 00:00:00 2001
From: ComfyUI Wiki <contact@comfyui-wiki.com>
Date: Wed, 5 Nov 2025 02:51:53 +0800
Subject: [PATCH 47/56] chore: update workflow templates to v0.2.11 (#10634)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 856e373de..249c36dee 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.28.8
-comfyui-workflow-templates==0.2.4
+comfyui-workflow-templates==0.2.11
 comfyui-embedded-docs==0.3.1
 torch
 torchsde

From a389ee01bb7ba5174729906a7f85bd08b5c2cb87 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Wed, 5 Nov 2025 08:14:10 +1000
Subject: [PATCH 48/56] caching: Handle None outputs tuple case (#10637)

---
 comfy_execution/caching.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/comfy_execution/caching.py b/comfy_execution/caching.py
index e077f78b0..326a279fc 100644
--- a/comfy_execution/caching.py
+++ b/comfy_execution/caching.py
@@ -399,6 +399,8 @@ class RAMPressureCache(LRUCache):
             ram_usage = RAM_CACHE_DEFAULT_RAM_USAGE
             def scan_list_for_ram_usage(outputs):
                 nonlocal ram_usage
+                if outputs is None:
+                    return
                 for output in outputs:
                     if isinstance(output, list):
                         scan_list_for_ram_usage(output)

From 7f3e4d486cd77c3ad30eb4714ec18bdaf29e2b5c Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Tue, 4 Nov 2025 14:37:50 -0800
Subject: [PATCH 49/56] Limit amount of pinned memory on windows to prevent
 issues. (#10638)

---
 comfy/model_management.py | 32 ++++++++++++++++++++++++++++----
 1 file changed, 28 insertions(+), 4 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 79c0dfdb4..0d040e55e 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -1082,8 +1082,20 @@ def cast_to_device(tensor, device, dtype, copy=False):
     non_blocking = device_supports_non_blocking(device)
     return cast_to(tensor, dtype=dtype, device=device, non_blocking=non_blocking, copy=copy)
 
+
+PINNED_MEMORY = {}
+TOTAL_PINNED_MEMORY = 0
+if PerformanceFeature.PinnedMem in args.fast:
+    if WINDOWS:
+        MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.45  # Windows limit is apparently 50%
+    else:
+        MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.95
+else:
+    MAX_PINNED_MEMORY = -1
+
 def pin_memory(tensor):
-    if PerformanceFeature.PinnedMem not in args.fast:
+    global TOTAL_PINNED_MEMORY
+    if MAX_PINNED_MEMORY <= 0:
         return False
 
     if not is_nvidia():
@@ -1092,13 +1104,21 @@ def pin_memory(tensor):
     if not is_device_cpu(tensor.device):
         return False
 
-    if torch.cuda.cudart().cudaHostRegister(tensor.data_ptr(), tensor.numel() * tensor.element_size(), 1) == 0:
+    size = tensor.numel() * tensor.element_size()
+    if (TOTAL_PINNED_MEMORY + size) > MAX_PINNED_MEMORY:
+        return False
+
+    ptr = tensor.data_ptr()
+    if torch.cuda.cudart().cudaHostRegister(ptr, size, 1) == 0:
+        PINNED_MEMORY[ptr] = size
+        TOTAL_PINNED_MEMORY += size
         return True
 
     return False
 
 def unpin_memory(tensor):
-    if PerformanceFeature.PinnedMem not in args.fast:
+    global TOTAL_PINNED_MEMORY
+    if MAX_PINNED_MEMORY <= 0:
         return False
 
     if not is_nvidia():
@@ -1107,7 +1127,11 @@ def unpin_memory(tensor):
     if not is_device_cpu(tensor.device):
         return False
 
-    if torch.cuda.cudart().cudaHostUnregister(tensor.data_ptr()) == 0:
+    ptr = tensor.data_ptr()
+    if torch.cuda.cudart().cudaHostUnregister(ptr) == 0:
+        TOTAL_PINNED_MEMORY -= PINNED_MEMORY.pop(ptr)
+        if len(PINNED_MEMORY) == 0:
+            TOTAL_PINNED_MEMORY = 0
         return True
 
     return False

From 265adad858e1f31b66cd3523a02b16f5d34ced52 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 4 Nov 2025 19:42:23 -0500
Subject: [PATCH 50/56] ComfyUI version v0.3.68

---
 comfyui_version.py | 2 +-
 pyproject.toml     | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfyui_version.py b/comfyui_version.py
index db48b05c4..25d1a4157 100644
--- a/comfyui_version.py
+++ b/comfyui_version.py
@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.3.67"
+__version__ = "0.3.68"
diff --git a/pyproject.toml b/pyproject.toml
index ab054355c..79ff3f74a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.3.67"
+version = "0.3.68"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"

From 4cd881866bad0cde70273cc123d725693c1f2759 Mon Sep 17 00:00:00 2001
From: contentis <lspindler@nvidia.com>
Date: Wed, 5 Nov 2025 02:10:11 +0100
Subject: [PATCH 51/56] Use single apply_rope function across models (#10547)

---
 comfy/ldm/flux/layers.py      |  4 +-
 comfy/ldm/flux/math.py        | 10 +---
 comfy/ldm/lightricks/model.py | 88 ++++++++++++++---------------------
 comfy/ldm/qwen_image/model.py | 36 +++++++-------
 comfy/ldm/wan/model.py        |  1 +
 5 files changed, 59 insertions(+), 80 deletions(-)

diff --git a/comfy/ldm/flux/layers.py b/comfy/ldm/flux/layers.py
index ef21b416b..a3eab0470 100644
--- a/comfy/ldm/flux/layers.py
+++ b/comfy/ldm/flux/layers.py
@@ -195,8 +195,8 @@ class DoubleStreamBlock(nn.Module):
             txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:]
 
         # calculate the img bloks
-        img = img + apply_mod(self.img_attn.proj(img_attn), img_mod1.gate, None, modulation_dims_img)
-        img = img + apply_mod(self.img_mlp(apply_mod(self.img_norm2(img), (1 + img_mod2.scale), img_mod2.shift, modulation_dims_img)), img_mod2.gate, None, modulation_dims_img)
+        img += apply_mod(self.img_attn.proj(img_attn), img_mod1.gate, None, modulation_dims_img)
+        img += apply_mod(self.img_mlp(apply_mod(self.img_norm2(img), (1 + img_mod2.scale), img_mod2.shift, modulation_dims_img)), img_mod2.gate, None, modulation_dims_img)
 
         # calculate the txt bloks
         txt += apply_mod(self.txt_attn.proj(txt_attn), txt_mod1.gate, None, modulation_dims_txt)
diff --git a/comfy/ldm/flux/math.py b/comfy/ldm/flux/math.py
index 8deda0d4a..158420290 100644
--- a/comfy/ldm/flux/math.py
+++ b/comfy/ldm/flux/math.py
@@ -7,15 +7,7 @@ import comfy.model_management
 
 
 def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transformer_options={}) -> Tensor:
-    q_shape = q.shape
-    k_shape = k.shape
-
-    if pe is not None:
-        q = q.to(dtype=pe.dtype).reshape(*q.shape[:-1], -1, 1, 2)
-        k = k.to(dtype=pe.dtype).reshape(*k.shape[:-1], -1, 1, 2)
-        q = (pe[..., 0] * q[..., 0] + pe[..., 1] * q[..., 1]).reshape(*q_shape).type_as(v)
-        k = (pe[..., 0] * k[..., 0] + pe[..., 1] * k[..., 1]).reshape(*k_shape).type_as(v)
-
+    q, k = apply_rope(q, k, pe)
     heads = q.shape[1]
     x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask, transformer_options=transformer_options)
     return x
diff --git a/comfy/ldm/lightricks/model.py b/comfy/ldm/lightricks/model.py
index def365ba7..5bcba998b 100644
--- a/comfy/ldm/lightricks/model.py
+++ b/comfy/ldm/lightricks/model.py
@@ -3,12 +3,11 @@ from torch import nn
 import comfy.patcher_extension
 import comfy.ldm.modules.attention
 import comfy.ldm.common_dit
-from einops import rearrange
 import math
 from typing import Dict, Optional, Tuple
 
 from .symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords
-
+from comfy.ldm.flux.math import apply_rope1
 
 def get_timestep_embedding(
     timesteps: torch.Tensor,
@@ -238,20 +237,6 @@ class FeedForward(nn.Module):
         return self.net(x)
 
 
-def apply_rotary_emb(input_tensor, freqs_cis): #TODO: remove duplicate funcs and pick the best/fastest one
-    cos_freqs = freqs_cis[0]
-    sin_freqs = freqs_cis[1]
-
-    t_dup = rearrange(input_tensor, "... (d r) -> ... d r", r=2)
-    t1, t2 = t_dup.unbind(dim=-1)
-    t_dup = torch.stack((-t2, t1), dim=-1)
-    input_tensor_rot = rearrange(t_dup, "... d r -> ... (d r)")
-
-    out = input_tensor * cos_freqs + input_tensor_rot * sin_freqs
-
-    return out
-
-
 class CrossAttention(nn.Module):
     def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=None):
         super().__init__()
@@ -281,8 +266,8 @@ class CrossAttention(nn.Module):
         k = self.k_norm(k)
 
         if pe is not None:
-            q = apply_rotary_emb(q, pe)
-            k = apply_rotary_emb(k, pe)
+            q = apply_rope1(q.unsqueeze(1), pe).squeeze(1)
+            k = apply_rope1(k.unsqueeze(1), pe).squeeze(1)
 
         if mask is None:
             out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options)
@@ -306,12 +291,17 @@ class BasicTransformerBlock(nn.Module):
     def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None, transformer_options={}):
         shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)).unbind(dim=2)
 
-        x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe, transformer_options=transformer_options) * gate_msa
+        norm_x = comfy.ldm.common_dit.rms_norm(x)
+        attn1_input = torch.addcmul(norm_x, norm_x, scale_msa).add_(shift_msa)
+        attn1_result = self.attn1(attn1_input, pe=pe, transformer_options=transformer_options)
+        x.addcmul_(attn1_result, gate_msa)
 
         x += self.attn2(x, context=context, mask=attention_mask, transformer_options=transformer_options)
 
-        y = comfy.ldm.common_dit.rms_norm(x) * (1 + scale_mlp) + shift_mlp
-        x += self.ff(y) * gate_mlp
+        norm_x = comfy.ldm.common_dit.rms_norm(x)
+        y = torch.addcmul(norm_x, norm_x, scale_mlp).add_(shift_mlp)
+        ff_result = self.ff(y)
+        x.addcmul_(ff_result, gate_mlp)
 
         return x
 
@@ -327,41 +317,35 @@ def get_fractional_positions(indices_grid, max_pos):
 
 
 def precompute_freqs_cis(indices_grid, dim, out_dtype, theta=10000.0, max_pos=[20, 2048, 2048]):
-    dtype = torch.float32 #self.dtype
+    dtype = torch.float32
+    device = indices_grid.device
 
+    # Get fractional positions and compute frequency indices
     fractional_positions = get_fractional_positions(indices_grid, max_pos)
+    indices = theta ** torch.linspace(0, 1, dim // 6, device=device, dtype=dtype) * math.pi / 2
 
-    start = 1
-    end = theta
-    device = fractional_positions.device
+    # Compute frequencies and apply cos/sin
+    freqs = (indices * (fractional_positions.unsqueeze(-1) * 2 - 1)).transpose(-1, -2).flatten(2)
+    cos_vals = freqs.cos().repeat_interleave(2, dim=-1)
+    sin_vals = freqs.sin().repeat_interleave(2, dim=-1)
 
-    indices = theta ** (
-        torch.linspace(
-            math.log(start, theta),
-            math.log(end, theta),
-            dim // 6,
-            device=device,
-            dtype=dtype,
-        )
-    )
-    indices = indices.to(dtype=dtype)
-
-    indices = indices * math.pi / 2
-
-    freqs = (
-        (indices * (fractional_positions.unsqueeze(-1) * 2 - 1))
-        .transpose(-1, -2)
-        .flatten(2)
-    )
-
-    cos_freq = freqs.cos().repeat_interleave(2, dim=-1)
-    sin_freq = freqs.sin().repeat_interleave(2, dim=-1)
+    # Pad if dim is not divisible by 6
     if dim % 6 != 0:
-        cos_padding = torch.ones_like(cos_freq[:, :, : dim % 6])
-        sin_padding = torch.zeros_like(cos_freq[:, :, : dim % 6])
-        cos_freq = torch.cat([cos_padding, cos_freq], dim=-1)
-        sin_freq = torch.cat([sin_padding, sin_freq], dim=-1)
-    return cos_freq.to(out_dtype), sin_freq.to(out_dtype)
+        padding_size = dim % 6
+        cos_vals = torch.cat([torch.ones_like(cos_vals[:, :, :padding_size]), cos_vals], dim=-1)
+        sin_vals = torch.cat([torch.zeros_like(sin_vals[:, :, :padding_size]), sin_vals], dim=-1)
+
+    # Reshape and extract one value per pair (since repeat_interleave duplicates each value)
+    cos_vals = cos_vals.reshape(*cos_vals.shape[:2], -1, 2)[..., 0]  # [B, N, dim//2]
+    sin_vals = sin_vals.reshape(*sin_vals.shape[:2], -1, 2)[..., 0]  # [B, N, dim//2]
+
+    # Build rotation matrix [[cos, -sin], [sin, cos]] and add heads dimension
+    freqs_cis = torch.stack([
+        torch.stack([cos_vals, -sin_vals], dim=-1),
+        torch.stack([sin_vals, cos_vals], dim=-1)
+    ], dim=-2).unsqueeze(1)  # [B, 1, N, dim//2, 2, 2]
+
+    return freqs_cis.to(out_dtype)
 
 
 class LTXVModel(torch.nn.Module):
@@ -501,7 +485,7 @@ class LTXVModel(torch.nn.Module):
         shift, scale = scale_shift_values[:, :, 0], scale_shift_values[:, :, 1]
         x = self.norm_out(x)
         # Modulation
-        x = x * (1 + scale) + shift
+        x = torch.addcmul(x, x, scale).add_(shift)
         x = self.proj_out(x)
 
         x = self.patchifier.unpatchify(
diff --git a/comfy/ldm/qwen_image/model.py b/comfy/ldm/qwen_image/model.py
index b9f60c2b7..81d3ee7c0 100644
--- a/comfy/ldm/qwen_image/model.py
+++ b/comfy/ldm/qwen_image/model.py
@@ -10,6 +10,7 @@ from comfy.ldm.modules.attention import optimized_attention_masked
 from comfy.ldm.flux.layers import EmbedND
 import comfy.ldm.common_dit
 import comfy.patcher_extension
+from comfy.ldm.flux.math import apply_rope1
 
 class GELU(nn.Module):
     def __init__(self, dim_in: int, dim_out: int, approximate: str = "none", bias: bool = True, dtype=None, device=None, operations=None):
@@ -134,33 +135,34 @@ class Attention(nn.Module):
         image_rotary_emb: Optional[torch.Tensor] = None,
         transformer_options={},
     ) -> Tuple[torch.Tensor, torch.Tensor]:
+        batch_size = hidden_states.shape[0]
+        seq_img = hidden_states.shape[1]
         seq_txt = encoder_hidden_states.shape[1]
 
-        img_query = self.to_q(hidden_states).unflatten(-1, (self.heads, -1))
-        img_key = self.to_k(hidden_states).unflatten(-1, (self.heads, -1))
-        img_value = self.to_v(hidden_states).unflatten(-1, (self.heads, -1))
+        # Project and reshape to BHND format (batch, heads, seq, dim)
+        img_query = self.to_q(hidden_states).view(batch_size, seq_img, self.heads, -1).transpose(1, 2).contiguous()
+        img_key = self.to_k(hidden_states).view(batch_size, seq_img, self.heads, -1).transpose(1, 2).contiguous()
+        img_value = self.to_v(hidden_states).view(batch_size, seq_img, self.heads, -1).transpose(1, 2)
 
-        txt_query = self.add_q_proj(encoder_hidden_states).unflatten(-1, (self.heads, -1))
-        txt_key = self.add_k_proj(encoder_hidden_states).unflatten(-1, (self.heads, -1))
-        txt_value = self.add_v_proj(encoder_hidden_states).unflatten(-1, (self.heads, -1))
+        txt_query = self.add_q_proj(encoder_hidden_states).view(batch_size, seq_txt, self.heads, -1).transpose(1, 2).contiguous()
+        txt_key = self.add_k_proj(encoder_hidden_states).view(batch_size, seq_txt, self.heads, -1).transpose(1, 2).contiguous()
+        txt_value = self.add_v_proj(encoder_hidden_states).view(batch_size, seq_txt, self.heads, -1).transpose(1, 2)
 
         img_query = self.norm_q(img_query)
         img_key = self.norm_k(img_key)
         txt_query = self.norm_added_q(txt_query)
         txt_key = self.norm_added_k(txt_key)
 
-        joint_query = torch.cat([txt_query, img_query], dim=1)
-        joint_key = torch.cat([txt_key, img_key], dim=1)
-        joint_value = torch.cat([txt_value, img_value], dim=1)
+        joint_query = torch.cat([txt_query, img_query], dim=2)
+        joint_key = torch.cat([txt_key, img_key], dim=2)
+        joint_value = torch.cat([txt_value, img_value], dim=2)
 
-        joint_query = apply_rotary_emb(joint_query, image_rotary_emb)
-        joint_key = apply_rotary_emb(joint_key, image_rotary_emb)
+        joint_query = apply_rope1(joint_query, image_rotary_emb)
+        joint_key = apply_rope1(joint_key, image_rotary_emb)
 
-        joint_query = joint_query.flatten(start_dim=2)
-        joint_key = joint_key.flatten(start_dim=2)
-        joint_value = joint_value.flatten(start_dim=2)
-
-        joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads, attention_mask, transformer_options=transformer_options)
+        joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads,
+                                                         attention_mask, transformer_options=transformer_options,
+                                                         skip_reshape=True)
 
         txt_attn_output = joint_hidden_states[:, :seq_txt, :]
         img_attn_output = joint_hidden_states[:, seq_txt:, :]
@@ -413,7 +415,7 @@ class QwenImageTransformer2DModel(nn.Module):
         txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2))
         txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3)
         ids = torch.cat((txt_ids, img_ids), dim=1)
-        image_rotary_emb = self.pe_embedder(ids).squeeze(1).unsqueeze(2).to(x.dtype)
+        image_rotary_emb = self.pe_embedder(ids).to(torch.float32).contiguous()
         del ids, txt_ids, img_ids
 
         hidden_states = self.img_in(hidden_states)
diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py
index 5ec1511ce..a9d5e10d9 100644
--- a/comfy/ldm/wan/model.py
+++ b/comfy/ldm/wan/model.py
@@ -232,6 +232,7 @@ class WanAttentionBlock(nn.Module):
         # assert e[0].dtype == torch.float32
 
         # self-attention
+        x = x.contiguous() # otherwise implicit in LayerNorm
         y = self.self_attn(
             torch.addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)),
             freqs, transformer_options=transformer_options)

From c4a6b389de1014471a75a46ee57d2fdac4f8df93 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Tue, 4 Nov 2025 19:47:35 -0800
Subject: [PATCH 52/56] Lower ltxv mem usage to what it was before previous pr.
 (#10643)

Bring back qwen behavior to what it was before previous pr.
---
 comfy/ldm/lightricks/model.py | 22 +++++++++++-----------
 comfy/ldm/qwen_image/model.py |  2 +-
 2 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/comfy/ldm/lightricks/model.py b/comfy/ldm/lightricks/model.py
index 5bcba998b..593f7940f 100644
--- a/comfy/ldm/lightricks/model.py
+++ b/comfy/ldm/lightricks/model.py
@@ -291,17 +291,17 @@ class BasicTransformerBlock(nn.Module):
     def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None, transformer_options={}):
         shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)).unbind(dim=2)
 
-        norm_x = comfy.ldm.common_dit.rms_norm(x)
-        attn1_input = torch.addcmul(norm_x, norm_x, scale_msa).add_(shift_msa)
-        attn1_result = self.attn1(attn1_input, pe=pe, transformer_options=transformer_options)
-        x.addcmul_(attn1_result, gate_msa)
+        attn1_input = comfy.ldm.common_dit.rms_norm(x)
+        attn1_input = torch.addcmul(attn1_input, attn1_input, scale_msa).add_(shift_msa)
+        attn1_input = self.attn1(attn1_input, pe=pe, transformer_options=transformer_options)
+        x.addcmul_(attn1_input, gate_msa)
+        del attn1_input
 
         x += self.attn2(x, context=context, mask=attention_mask, transformer_options=transformer_options)
 
-        norm_x = comfy.ldm.common_dit.rms_norm(x)
-        y = torch.addcmul(norm_x, norm_x, scale_mlp).add_(shift_mlp)
-        ff_result = self.ff(y)
-        x.addcmul_(ff_result, gate_mlp)
+        y = comfy.ldm.common_dit.rms_norm(x)
+        y = torch.addcmul(y, y, scale_mlp).add_(shift_mlp)
+        x.addcmul_(self.ff(y), gate_mlp)
 
         return x
 
@@ -336,8 +336,8 @@ def precompute_freqs_cis(indices_grid, dim, out_dtype, theta=10000.0, max_pos=[2
         sin_vals = torch.cat([torch.zeros_like(sin_vals[:, :, :padding_size]), sin_vals], dim=-1)
 
     # Reshape and extract one value per pair (since repeat_interleave duplicates each value)
-    cos_vals = cos_vals.reshape(*cos_vals.shape[:2], -1, 2)[..., 0]  # [B, N, dim//2]
-    sin_vals = sin_vals.reshape(*sin_vals.shape[:2], -1, 2)[..., 0]  # [B, N, dim//2]
+    cos_vals = cos_vals.reshape(*cos_vals.shape[:2], -1, 2)[..., 0].to(out_dtype)  # [B, N, dim//2]
+    sin_vals = sin_vals.reshape(*sin_vals.shape[:2], -1, 2)[..., 0].to(out_dtype)  # [B, N, dim//2]
 
     # Build rotation matrix [[cos, -sin], [sin, cos]] and add heads dimension
     freqs_cis = torch.stack([
@@ -345,7 +345,7 @@ def precompute_freqs_cis(indices_grid, dim, out_dtype, theta=10000.0, max_pos=[2
         torch.stack([sin_vals, cos_vals], dim=-1)
     ], dim=-2).unsqueeze(1)  # [B, 1, N, dim//2, 2, 2]
 
-    return freqs_cis.to(out_dtype)
+    return freqs_cis
 
 
 class LTXVModel(torch.nn.Module):
diff --git a/comfy/ldm/qwen_image/model.py b/comfy/ldm/qwen_image/model.py
index 81d3ee7c0..e5d0d17c1 100644
--- a/comfy/ldm/qwen_image/model.py
+++ b/comfy/ldm/qwen_image/model.py
@@ -415,7 +415,7 @@ class QwenImageTransformer2DModel(nn.Module):
         txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2))
         txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3)
         ids = torch.cat((txt_ids, img_ids), dim=1)
-        image_rotary_emb = self.pe_embedder(ids).to(torch.float32).contiguous()
+        image_rotary_emb = self.pe_embedder(ids).to(x.dtype).contiguous()
         del ids, txt_ids, img_ids
 
         hidden_states = self.img_in(hidden_states)

From bda0eb2448135797d5a72f7236ce26d07e555baf Mon Sep 17 00:00:00 2001
From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Date: Wed, 5 Nov 2025 12:16:00 +0200
Subject: [PATCH 53/56] feat(API-nodes): move Rodin3D nodes to new client;
 removed old api client.py (#10645)

---
 comfy_api_nodes/apis/PixverseController.py    |  17 -
 comfy_api_nodes/apis/PixverseDto.py           |  57 -
 comfy_api_nodes/apis/client.py                | 981 ------------------
 comfy_api_nodes/nodes_rodin.py                | 196 ++--
 comfy_api_nodes/util/client.py                |   4 +-
 comfy_api_nodes/util/download_helpers.py      |   2 +-
 .../{apis => util}/request_logger.py          |   4 +-
 comfy_api_nodes/util/upload_helpers.py        |   2 +-
 8 files changed, 75 insertions(+), 1188 deletions(-)
 delete mode 100644 comfy_api_nodes/apis/PixverseController.py
 delete mode 100644 comfy_api_nodes/apis/PixverseDto.py
 delete mode 100644 comfy_api_nodes/apis/client.py
 rename comfy_api_nodes/{apis => util}/request_logger.py (100%)

diff --git a/comfy_api_nodes/apis/PixverseController.py b/comfy_api_nodes/apis/PixverseController.py
deleted file mode 100644
index 310c0f546..000000000
--- a/comfy_api_nodes/apis/PixverseController.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# generated by datamodel-codegen:
-#   filename:  filtered-openapi.yaml
-#   timestamp: 2025-04-29T23:44:54+00:00
-
-from __future__ import annotations
-
-from typing import Optional
-
-from pydantic import BaseModel
-
-from . import PixverseDto
-
-
-class ResponseData(BaseModel):
-    ErrCode: Optional[int] = None
-    ErrMsg: Optional[str] = None
-    Resp: Optional[PixverseDto.V2OpenAPII2VResp] = None
diff --git a/comfy_api_nodes/apis/PixverseDto.py b/comfy_api_nodes/apis/PixverseDto.py
deleted file mode 100644
index 323c38e96..000000000
--- a/comfy_api_nodes/apis/PixverseDto.py
+++ /dev/null
@@ -1,57 +0,0 @@
-# generated by datamodel-codegen:
-#   filename:  filtered-openapi.yaml
-#   timestamp: 2025-04-29T23:44:54+00:00
-
-from __future__ import annotations
-
-from typing import Optional
-
-from pydantic import BaseModel, Field
-
-
-class V2OpenAPII2VResp(BaseModel):
-    video_id: Optional[int] = Field(None, description='Video_id')
-
-
-class V2OpenAPIT2VReq(BaseModel):
-    aspect_ratio: str = Field(
-        ..., description='Aspect ratio (16:9, 4:3, 1:1, 3:4, 9:16)', examples=['16:9']
-    )
-    duration: int = Field(
-        ...,
-        description='Video duration (5, 8 seconds, --model=v3.5 only allows 5,8; --quality=1080p does not support 8s)',
-        examples=[5],
-    )
-    model: str = Field(
-        ..., description='Model version (only supports v3.5)', examples=['v3.5']
-    )
-    motion_mode: Optional[str] = Field(
-        'normal',
-        description='Motion mode (normal, fast, --fast only available when duration=5; --quality=1080p does not support fast)',
-        examples=['normal'],
-    )
-    negative_prompt: Optional[str] = Field(
-        None, description='Negative prompt\n', max_length=2048
-    )
-    prompt: str = Field(..., description='Prompt', max_length=2048)
-    quality: str = Field(
-        ...,
-        description='Video quality ("360p"(Turbo model), "540p", "720p", "1080p")',
-        examples=['540p'],
-    )
-    seed: Optional[int] = Field(None, description='Random seed, range: 0 - 2147483647')
-    style: Optional[str] = Field(
-        None,
-        description='Style (effective when model=v3.5, "anime", "3d_animation", "clay", "comic", "cyberpunk") Do not include style parameter unless needed',
-        examples=['anime'],
-    )
-    template_id: Optional[int] = Field(
-        None,
-        description='Template ID (template_id must be activated before use)',
-        examples=[302325299692608],
-    )
-    water_mark: Optional[bool] = Field(
-        False,
-        description='Watermark (true: add watermark, false: no watermark)',
-        examples=[False],
-    )
diff --git a/comfy_api_nodes/apis/client.py b/comfy_api_nodes/apis/client.py
deleted file mode 100644
index bdaddcc88..000000000
--- a/comfy_api_nodes/apis/client.py
+++ /dev/null
@@ -1,981 +0,0 @@
-"""
-API Client Framework for api.comfy.org.
-
-This module provides a flexible framework for making API requests from ComfyUI nodes.
-It supports both synchronous and asynchronous API operations with proper type validation.
-
-Key Components:
---------------
-1. ApiClient - Handles HTTP requests with authentication and error handling
-2. ApiEndpoint - Defines a single HTTP endpoint with its request/response models
-3. ApiOperation - Executes a single synchronous API operation
-
-Usage Examples:
---------------
-
-# Example 1: Synchronous API Operation
-# ------------------------------------
-# For a simple API call that returns the result immediately:
-
-# 1. Create the API client
-api_client = ApiClient(
-    base_url="https://api.example.com",
-    auth_token="your_auth_token_here",
-    comfy_api_key="your_comfy_api_key_here",
-    timeout=30.0,
-    verify_ssl=True
-)
-
-# 2. Define the endpoint
-user_info_endpoint = ApiEndpoint(
-    path="/v1/users/me",
-    method=HttpMethod.GET,
-    request_model=EmptyRequest,  # No request body needed
-    response_model=UserProfile,   # Pydantic model for the response
-    query_params=None
-)
-
-# 3. Create the request object
-request = EmptyRequest()
-
-# 4. Create and execute the operation
-operation = ApiOperation(
-    endpoint=user_info_endpoint,
-    request=request
-)
-user_profile = await operation.execute(client=api_client)  # Returns immediately with the result
-
-
-# Example 2: Asynchronous API Operation with Polling
-# -------------------------------------------------
-# For an API that starts a task and requires polling for completion:
-
-# 1. Define the endpoints (initial request and polling)
-generate_image_endpoint = ApiEndpoint(
-    path="/v1/images/generate",
-    method=HttpMethod.POST,
-    request_model=ImageGenerationRequest,
-    response_model=TaskCreatedResponse,
-    query_params=None
-)
-
-check_task_endpoint = ApiEndpoint(
-    path="/v1/tasks/{task_id}",
-    method=HttpMethod.GET,
-    request_model=EmptyRequest,
-    response_model=ImageGenerationResult,
-    query_params=None
-)
-
-# 2. Create the request object
-request = ImageGenerationRequest(
-    prompt="a beautiful sunset over mountains",
-    width=1024,
-    height=1024,
-    num_images=1
-)
-
-# 3. Create and execute the polling operation
-operation = PollingOperation(
-    initial_endpoint=generate_image_endpoint,
-    initial_request=request,
-    poll_endpoint=check_task_endpoint,
-    task_id_field="task_id",
-    status_field="status",
-    completed_statuses=["completed"],
-    failed_statuses=["failed", "error"]
-)
-
-# This will make the initial request and then poll until completion
-result = await operation.execute(client=api_client)  # Returns the final ImageGenerationResult when done
-"""
-
-from __future__ import annotations
-import aiohttp
-import asyncio
-import logging
-import io
-import os
-import socket
-from aiohttp.client_exceptions import ClientError, ClientResponseError
-from typing import Type, Optional, Any, TypeVar, Generic, Callable
-from enum import Enum
-import json
-from urllib.parse import urljoin, urlparse
-from pydantic import BaseModel, Field
-import uuid # For generating unique operation IDs
-
-from server import PromptServer
-from comfy.cli_args import args
-from comfy import utils
-from . import request_logger
-
-T = TypeVar("T", bound=BaseModel)
-R = TypeVar("R", bound=BaseModel)
-P = TypeVar("P", bound=BaseModel)  # For poll response
-
-PROGRESS_BAR_MAX = 100
-
-
-class NetworkError(Exception):
-    """Base exception for network-related errors with diagnostic information."""
-    pass
-
-
-class LocalNetworkError(NetworkError):
-    """Exception raised when local network connectivity issues are detected."""
-    pass
-
-
-class ApiServerError(NetworkError):
-    """Exception raised when the API server is unreachable but internet is working."""
-    pass
-
-
-class EmptyRequest(BaseModel):
-    """Base class for empty request bodies.
-    For GET requests, fields will be sent as query parameters."""
-
-    pass
-
-
-class UploadRequest(BaseModel):
-    file_name: str = Field(..., description="Filename to upload")
-    content_type: Optional[str] = Field(
-        None,
-        description="Mime type of the file. For example: image/png, image/jpeg, video/mp4, etc.",
-    )
-
-
-class UploadResponse(BaseModel):
-    download_url: str = Field(..., description="URL to GET uploaded file")
-    upload_url: str = Field(..., description="URL to PUT file to upload")
-
-
-class HttpMethod(str, Enum):
-    GET = "GET"
-    POST = "POST"
-    PUT = "PUT"
-    DELETE = "DELETE"
-    PATCH = "PATCH"
-
-
-class ApiClient:
-    """
-    Client for making HTTP requests to an API with authentication, error handling, and retry logic.
-    """
-
-    def __init__(
-        self,
-        base_url: str,
-        auth_token: Optional[str] = None,
-        comfy_api_key: Optional[str] = None,
-        timeout: float = 3600.0,
-        verify_ssl: bool = True,
-        max_retries: int = 3,
-        retry_delay: float = 1.0,
-        retry_backoff_factor: float = 2.0,
-        retry_status_codes: Optional[tuple[int, ...]] = None,
-        session: Optional[aiohttp.ClientSession] = None,
-    ):
-        self.base_url = base_url
-        self.auth_token = auth_token
-        self.comfy_api_key = comfy_api_key
-        self.timeout = timeout
-        self.verify_ssl = verify_ssl
-        self.max_retries = max_retries
-        self.retry_delay = retry_delay
-        self.retry_backoff_factor = retry_backoff_factor
-        # Default retry status codes: 408 (Request Timeout), 429 (Too Many Requests),
-        # 500, 502, 503, 504 (Server Errors)
-        self.retry_status_codes = retry_status_codes or (408, 429, 500, 502, 503, 504)
-        self._session: Optional[aiohttp.ClientSession] = session
-        self._owns_session = session is None  # Track if we have to close it
-
-    @staticmethod
-    def _generate_operation_id(path: str) -> str:
-        """Generates a unique operation ID for logging."""
-        return f"{path.strip('/').replace('/', '_')}_{uuid.uuid4().hex[:8]}"
-
-    @staticmethod
-    def _create_json_payload_args(
-        data: Optional[dict[str, Any]] = None,
-        headers: Optional[dict[str, str]] = None,
-    ) -> dict[str, Any]:
-        return {
-            "json": data,
-            "headers": headers,
-        }
-
-    def _create_form_data_args(
-        self,
-        data: dict[str, Any] | None,
-        files: dict[str, Any] | None,
-        headers: Optional[dict[str, str]] = None,
-        multipart_parser: Callable | None = None,
-    ) -> dict[str, Any]:
-        if headers and "Content-Type" in headers:
-            del headers["Content-Type"]
-
-        if multipart_parser and data:
-            data = multipart_parser(data)
-
-        if isinstance(data, aiohttp.FormData):
-            form = data  # If the parser already returned a FormData, pass it through
-        else:
-            form = aiohttp.FormData(default_to_multipart=True)
-            if data:  # regular text fields
-                for k, v in data.items():
-                    if v is None:
-                        continue  # aiohttp fails to serialize "None" values
-                    # aiohttp expects strings or bytes; convert enums etc.
-                    form.add_field(k, str(v) if not isinstance(v, (bytes, bytearray)) else v)
-
-        if files:
-            file_iter = files if isinstance(files, list) else files.items()
-            for field_name, file_obj in file_iter:
-                if file_obj is None:
-                    continue  # aiohttp fails to serialize "None" values
-                # file_obj can be (filename, bytes/io.BytesIO, content_type) tuple
-                if isinstance(file_obj, tuple):
-                    filename, file_value, content_type = self._unpack_tuple(file_obj)
-                else:
-                    file_value = file_obj
-                    filename = getattr(file_obj, "name", field_name)
-                    content_type = "application/octet-stream"
-
-                form.add_field(
-                    name=field_name,
-                    value=file_value,
-                    filename=filename,
-                    content_type=content_type,
-                )
-        return {"data": form, "headers": headers or {}}
-
-    @staticmethod
-    def _create_urlencoded_form_data_args(
-        data: dict[str, Any],
-        headers: Optional[dict[str, str]] = None,
-    ) -> dict[str, Any]:
-        headers = headers or {}
-        headers["Content-Type"] = "application/x-www-form-urlencoded"
-        return {
-            "data": data,
-            "headers": headers,
-        }
-
-    def get_headers(self) -> dict[str, str]:
-        """Get headers for API requests, including authentication if available"""
-        headers = {"Content-Type": "application/json", "Accept": "application/json"}
-
-        if self.auth_token:
-            headers["Authorization"] = f"Bearer {self.auth_token}"
-        elif self.comfy_api_key:
-            headers["X-API-KEY"] = self.comfy_api_key
-
-        return headers
-
-    async def _check_connectivity(self, target_url: str) -> dict[str, bool]:
-        """
-        Check connectivity to determine if network issues are local or server-related.
-
-        Args:
-            target_url: URL to check connectivity to
-
-        Returns:
-            Dictionary with connectivity status details
-        """
-        results = {
-            "internet_accessible": False,
-            "api_accessible": False,
-            "is_local_issue": False,
-            "is_api_issue": False,
-        }
-        timeout = aiohttp.ClientTimeout(total=5.0)
-        async with aiohttp.ClientSession(timeout=timeout) as session:
-            try:
-                async with session.get("https://www.google.com", ssl=self.verify_ssl) as resp:
-                    results["internet_accessible"] = resp.status < 500
-            except (ClientError, asyncio.TimeoutError, socket.gaierror):
-                results["is_local_issue"] = True
-                return results  # cannot reach the internet – early exit
-
-            # Now check API health endpoint
-            parsed = urlparse(target_url)
-            health_url = f"{parsed.scheme}://{parsed.netloc}/health"
-            try:
-                async with session.get(health_url, ssl=self.verify_ssl) as resp:
-                    results["api_accessible"] = resp.status < 500
-            except ClientError:
-                pass  # leave as False
-
-        results["is_api_issue"] = results["internet_accessible"] and not results["api_accessible"]
-        return results
-
-    async def request(
-        self,
-        method: str,
-        path: str,
-        params: Optional[dict[str, Any]] = None,
-        data: Optional[dict[str, Any]] = None,
-        files: Optional[dict[str, Any] | list[tuple[str, Any]]] = None,
-        headers: Optional[dict[str, str]] = None,
-        content_type: str = "application/json",
-        multipart_parser: Callable | None = None,
-        retry_count: int = 0,  # Used internally for tracking retries
-    ) -> dict[str, Any]:
-        """
-        Make an HTTP request to the API with automatic retries for transient errors.
-
-        Args:
-            method: HTTP method (GET, POST, etc.)
-            path: API endpoint path (will be joined with base_url)
-            params: Query parameters
-            data: body data
-            files: Files to upload
-            headers: Additional headers
-            content_type: Content type of the request. Defaults to application/json.
-            retry_count: Internal parameter for tracking retries, do not set manually
-
-        Returns:
-            Parsed JSON response
-
-        Raises:
-            LocalNetworkError: If local network connectivity issues are detected
-            ApiServerError: If the API server is unreachable but internet is working
-            Exception: For other request failures
-        """
-
-        # Build full URL and merge headers
-        relative_path = path.lstrip("/")
-        url = urljoin(self.base_url, relative_path)
-        self._check_auth(self.auth_token, self.comfy_api_key)
-
-        request_headers = self.get_headers()
-        if headers:
-            request_headers.update(headers)
-        if files:
-            request_headers.pop("Content-Type", None)
-        if params:
-            params = {k: v for k, v in params.items() if v is not None}  # aiohttp fails to serialize None values
-
-        logging.debug("[DEBUG] Request Headers: %s", request_headers)
-        logging.debug("[DEBUG] Files: %s", files)
-        logging.debug("[DEBUG] Params: %s", params)
-        logging.debug("[DEBUG] Data: %s", data)
-
-        if content_type == "application/x-www-form-urlencoded":
-            payload_args = self._create_urlencoded_form_data_args(data or {}, request_headers)
-        elif content_type == "multipart/form-data":
-            payload_args = self._create_form_data_args(data, files, request_headers, multipart_parser)
-        else:
-            payload_args = self._create_json_payload_args(data, request_headers)
-
-        operation_id = self._generate_operation_id(path)
-        request_logger.log_request_response(
-            operation_id=operation_id,
-            request_method=method,
-            request_url=url,
-            request_headers=request_headers,
-            request_params=params,
-            request_data=data if content_type == "application/json" else "[form-data or other]",
-        )
-
-        session = await self._get_session()
-        try:
-            async with session.request(
-                method,
-                url,
-                params=params,
-                ssl=self.verify_ssl,
-                **payload_args,
-            ) as resp:
-                if resp.status >= 400:
-                    try:
-                        error_data = await resp.json()
-                    except (aiohttp.ContentTypeError, json.JSONDecodeError):
-                        error_data = await resp.text()
-
-                    return await self._handle_http_error(
-                        ClientResponseError(resp.request_info, resp.history, status=resp.status, message=error_data),
-                        operation_id,
-                        method,
-                        url,
-                        params,
-                        data,
-                        files,
-                        headers,
-                        content_type,
-                        multipart_parser,
-                        retry_count=retry_count,
-                        response_content=error_data,
-                    )
-
-                # Success – parse JSON (safely) and log
-                try:
-                    payload = await resp.json()
-                    response_content_to_log = payload
-                except (aiohttp.ContentTypeError, json.JSONDecodeError):
-                    payload = {}
-                    response_content_to_log = await resp.text()
-
-                request_logger.log_request_response(
-                    operation_id=operation_id,
-                    request_method=method,
-                    request_url=url,
-                    response_status_code=resp.status,
-                    response_headers=dict(resp.headers),
-                    response_content=response_content_to_log,
-                )
-                return payload
-
-        except (ClientError, asyncio.TimeoutError, socket.gaierror) as e:
-            # Treat as *connection* problem – optionally retry, else escalate
-            if retry_count < self.max_retries:
-                delay = self.retry_delay * (self.retry_backoff_factor ** retry_count)
-                logging.warning("Connection error. Retrying in %.2fs (%s/%s): %s", delay, retry_count + 1,
-                                self.max_retries, str(e))
-                await asyncio.sleep(delay)
-                return await self.request(
-                    method,
-                    path,
-                    params=params,
-                    data=data,
-                    files=files,
-                    headers=headers,
-                    content_type=content_type,
-                    multipart_parser=multipart_parser,
-                    retry_count=retry_count + 1,
-                )
-            # One final connectivity check for diagnostics
-            connectivity = await self._check_connectivity(self.base_url)
-            if connectivity["is_local_issue"]:
-                raise LocalNetworkError(
-                    "Unable to connect to the API server due to local network issues. "
-                    "Please check your internet connection and try again."
-                ) from e
-            raise ApiServerError(
-                f"The API server at {self.base_url} is currently unreachable. "
-                f"The service may be experiencing issues. Please try again later."
-            ) from e
-
-    @staticmethod
-    def _check_auth(auth_token, comfy_api_key):
-        """Verify that an auth token is present or comfy_api_key is present"""
-        if auth_token is None and comfy_api_key is None:
-            raise Exception("Unauthorized: Please login first to use this node.")
-        return auth_token or comfy_api_key
-
-    @staticmethod
-    async def upload_file(
-        upload_url: str,
-        file: io.BytesIO | str,
-        content_type: str | None = None,
-        max_retries: int = 3,
-        retry_delay: float = 1.0,
-        retry_backoff_factor: float = 2.0,
-    ) -> aiohttp.ClientResponse:
-        """Upload a file to the API with retry logic.
-
-        Args:
-            upload_url: The URL to upload to
-            file: Either a file path string, BytesIO object, or tuple of (file_path, filename)
-            content_type: Optional mime type to set for the upload
-            max_retries: Maximum number of retry attempts
-            retry_delay: Initial delay between retries in seconds
-            retry_backoff_factor: Multiplier for the delay after each retry
-        """
-        headers: dict[str, str] = {}
-        skip_auto_headers: set[str] = set()
-        if content_type:
-            headers["Content-Type"] = content_type
-        else:
-            # tell aiohttp not to add Content-Type that will break the request signature and result in a 403 status.
-            skip_auto_headers.add("Content-Type")
-
-        # Extract file bytes
-        if isinstance(file, io.BytesIO):
-            file.seek(0)
-            data = file.read()
-        elif isinstance(file, str):
-            with open(file, "rb") as f:
-                data = f.read()
-        else:
-            raise ValueError("File must be BytesIO or str path")
-
-        parsed = urlparse(upload_url)
-        basename = os.path.basename(parsed.path) or parsed.netloc or "upload"
-        operation_id = f"upload_{basename}_{uuid.uuid4().hex[:8]}"
-        request_logger.log_request_response(
-            operation_id=operation_id,
-            request_method="PUT",
-            request_url=upload_url,
-            request_headers=headers,
-            request_data=f"[File data {len(data)} bytes]",
-        )
-
-        delay = retry_delay
-        for attempt in range(max_retries + 1):
-            try:
-                timeout = aiohttp.ClientTimeout(total=None)  # honour server side timeouts
-                async with aiohttp.ClientSession(timeout=timeout) as session:
-                    async with session.put(
-                        upload_url, data=data, headers=headers, skip_auto_headers=skip_auto_headers,
-                    ) as resp:
-                        resp.raise_for_status()
-                        request_logger.log_request_response(
-                            operation_id=operation_id,
-                            request_method="PUT",
-                            request_url=upload_url,
-                            response_status_code=resp.status,
-                            response_headers=dict(resp.headers),
-                            response_content="File uploaded successfully.",
-                        )
-                        return resp
-            except (ClientError, asyncio.TimeoutError) as e:
-                request_logger.log_request_response(
-                    operation_id=operation_id,
-                    request_method="PUT",
-                    request_url=upload_url,
-                    response_status_code=e.status if hasattr(e, "status") else None,
-                    response_headers=dict(e.headers) if hasattr(e, "headers") else None,
-                    response_content=None,
-                    error_message=f"{type(e).__name__}: {str(e)}",
-                )
-                if attempt < max_retries:
-                    logging.warning(
-                        "Upload failed (%s/%s). Retrying in %.2fs. %s", attempt + 1, max_retries, delay, str(e)
-                    )
-                    await asyncio.sleep(delay)
-                    delay *= retry_backoff_factor
-                else:
-                    raise NetworkError(f"Failed to upload file after {max_retries + 1} attempts: {e}") from e
-
-    async def _handle_http_error(
-        self,
-        exc: ClientResponseError,
-        operation_id: str,
-        *req_meta,
-        retry_count: int,
-        response_content: dict | str = "",
-    ) -> dict[str, Any]:
-        status_code = exc.status
-        if status_code == 401:
-            user_friendly = "Unauthorized: Please login first to use this node."
-        elif status_code == 402:
-            user_friendly = "Payment Required: Please add credits to your account to use this node."
-        elif status_code == 409:
-            user_friendly = "There is a problem with your account. Please contact support@comfy.org."
-        elif status_code == 429:
-            user_friendly = "Rate Limit Exceeded: Please try again later."
-        else:
-            if isinstance(response_content, dict):
-                if "error" in response_content and "message" in response_content["error"]:
-                    user_friendly = f"API Error: {response_content['error']['message']}"
-                    if "type" in response_content["error"]:
-                        user_friendly += f" (Type: {response_content['error']['type']})"
-                else: # Handle cases where error is just a JSON dict with unknown format
-                    user_friendly = f"API Error: {json.dumps(response_content)}"
-            else:
-                if len(response_content) < 200:  # Arbitrary limit for display
-                    user_friendly = f"API Error (raw): {response_content}"
-                else:
-                    user_friendly = f"API Error (raw, status {response_content})"
-
-        request_logger.log_request_response(
-            operation_id=operation_id,
-            request_method=req_meta[0],
-            request_url=req_meta[1],
-            response_status_code=exc.status,
-            response_headers=dict(req_meta[5]) if req_meta[5] else None,
-            response_content=response_content,
-            error_message=f"HTTP Error {exc.status}",
-        )
-
-        logging.debug("[DEBUG] API Error: %s (Status: %s)", user_friendly, status_code)
-        if response_content:
-            logging.debug("[DEBUG] Response content: %s", response_content)
-
-        # Retry if eligible
-        if status_code in self.retry_status_codes and retry_count < self.max_retries:
-            delay = self.retry_delay * (self.retry_backoff_factor ** retry_count)
-            logging.warning(
-                "HTTP error %s. Retrying in %.2fs (%s/%s)",
-                status_code,
-                delay,
-                retry_count + 1,
-                self.max_retries,
-            )
-            await asyncio.sleep(delay)
-            return await self.request(
-                req_meta[0],  # method
-                req_meta[1].replace(self.base_url, ""),  # path
-                params=req_meta[2],
-                data=req_meta[3],
-                files=req_meta[4],
-                headers=req_meta[5],
-                content_type=req_meta[6],
-                multipart_parser=req_meta[7],
-                retry_count=retry_count + 1,
-            )
-
-        raise Exception(user_friendly) from exc
-
-    @staticmethod
-    def _unpack_tuple(t):
-        """Helper to normalise (filename, file, content_type) tuples."""
-        if len(t) == 3:
-            return t
-        elif len(t) == 2:
-            return t[0], t[1], "application/octet-stream"
-        else:
-            raise ValueError("files tuple must be (filename, file[, content_type])")
-
-    async def _get_session(self) -> aiohttp.ClientSession:
-        if self._session is None or self._session.closed:
-            timeout = aiohttp.ClientTimeout(total=self.timeout)
-            self._session = aiohttp.ClientSession(timeout=timeout)
-            self._owns_session = True
-        return self._session
-
-    async def close(self) -> None:
-        if self._owns_session and self._session and not self._session.closed:
-            await self._session.close()
-
-    async def __aenter__(self) -> "ApiClient":
-        """Allow usage as async‑context‑manager – ensures clean teardown"""
-        return self
-
-    async def __aexit__(self, exc_type, exc, tb):
-        await self.close()
-
-
-class ApiEndpoint(Generic[T, R]):
-    """Defines an API endpoint with its request and response types"""
-
-    def __init__(
-        self,
-        path: str,
-        method: HttpMethod,
-        request_model: Type[T],
-        response_model: Type[R],
-        query_params: Optional[dict[str, Any]] = None,
-    ):
-        """Initialize an API endpoint definition.
-
-        Args:
-            path: The URL path for this endpoint, can include placeholders like {id}
-            method: The HTTP method to use (GET, POST, etc.)
-            request_model: Pydantic model class that defines the structure and validation rules for API requests to this endpoint
-            response_model: Pydantic model class that defines the structure and validation rules for API responses from this endpoint
-            query_params: Optional dictionary of query parameters to include in the request
-        """
-        self.path = path
-        self.method = method
-        self.request_model = request_model
-        self.response_model = response_model
-        self.query_params = query_params or {}
-
-
-class SynchronousOperation(Generic[T, R]):
-    """Represents a single synchronous API operation."""
-
-    def __init__(
-        self,
-        endpoint: ApiEndpoint[T, R],
-        request: T,
-        files: Optional[dict[str, Any] | list[tuple[str, Any]]] = None,
-        api_base: str | None = None,
-        auth_token: Optional[str] = None,
-        comfy_api_key: Optional[str] = None,
-        auth_kwargs: Optional[dict[str, str]] = None,
-        timeout: float = 7200.0,
-        verify_ssl: bool = True,
-        content_type: str = "application/json",
-        multipart_parser: Callable | None = None,
-        max_retries: int = 3,
-        retry_delay: float = 1.0,
-        retry_backoff_factor: float = 2.0,
-    ) -> None:
-        self.endpoint = endpoint
-        self.request = request
-        self.files = files
-        self.api_base: str = api_base or args.comfy_api_base
-        self.auth_token = auth_token
-        self.comfy_api_key = comfy_api_key
-        if auth_kwargs is not None:
-            self.auth_token = auth_kwargs.get("auth_token", self.auth_token)
-            self.comfy_api_key = auth_kwargs.get("comfy_api_key", self.comfy_api_key)
-        self.timeout = timeout
-        self.verify_ssl = verify_ssl
-        self.content_type = content_type
-        self.multipart_parser = multipart_parser
-        self.max_retries = max_retries
-        self.retry_delay = retry_delay
-        self.retry_backoff_factor = retry_backoff_factor
-
-    async def execute(self, client: Optional[ApiClient] = None) -> R:
-        owns_client = client is None
-        if owns_client:
-            client = ApiClient(
-                base_url=self.api_base,
-                auth_token=self.auth_token,
-                comfy_api_key=self.comfy_api_key,
-                timeout=self.timeout,
-                verify_ssl=self.verify_ssl,
-                max_retries=self.max_retries,
-                retry_delay=self.retry_delay,
-                retry_backoff_factor=self.retry_backoff_factor,
-            )
-
-        try:
-            request_dict: Optional[dict[str, Any]]
-            if isinstance(self.request, EmptyRequest):
-                request_dict = None
-            else:
-                request_dict = self.request.model_dump(exclude_none=True)
-                for k, v in list(request_dict.items()):
-                    if isinstance(v, Enum):
-                        request_dict[k] = v.value
-
-            logging.debug("[DEBUG] API Request: %s %s", self.endpoint.method.value, self.endpoint.path)
-            logging.debug("[DEBUG] Request Data: %s", json.dumps(request_dict, indent=2))
-            logging.debug("[DEBUG] Query Params: %s", self.endpoint.query_params)
-
-            response_json = await client.request(
-                self.endpoint.method.value,
-                self.endpoint.path,
-                params=self.endpoint.query_params,
-                data=request_dict,
-                files=self.files,
-                content_type=self.content_type,
-                multipart_parser=self.multipart_parser,
-            )
-
-            logging.debug("=" * 50)
-            logging.debug("[DEBUG] RESPONSE DETAILS:")
-            logging.debug("[DEBUG] Status Code: 200 (Success)")
-            logging.debug("[DEBUG] Response Body: %s", json.dumps(response_json, indent=2))
-            logging.debug("=" * 50)
-
-            parsed_response = self.endpoint.response_model.model_validate(response_json)
-            logging.debug("[DEBUG] Parsed Response: %s", parsed_response)
-            return parsed_response
-        finally:
-            if owns_client:
-                await client.close()
-
-
-class TaskStatus(str, Enum):
-    """Enum for task status values"""
-
-    COMPLETED = "completed"
-    FAILED = "failed"
-    PENDING = "pending"
-
-
-class PollingOperation(Generic[T, R]):
-    """Represents an asynchronous API operation that requires polling for completion."""
-
-    def __init__(
-        self,
-        poll_endpoint: ApiEndpoint[EmptyRequest, R],
-        completed_statuses: list[str],
-        failed_statuses: list[str],
-        *,
-        status_extractor: Callable[[R], Optional[str]],
-        progress_extractor: Callable[[R], Optional[float]] | None = None,
-        result_url_extractor: Callable[[R], Optional[str]] | None = None,
-        price_extractor: Callable[[R], Optional[float]] | None = None,
-        request: Optional[T] = None,
-        api_base: str | None = None,
-        auth_token: Optional[str] = None,
-        comfy_api_key: Optional[str] = None,
-        auth_kwargs: Optional[dict[str, str]] = None,
-        poll_interval: float = 5.0,
-        max_poll_attempts: int = 120,  # Default max polling attempts (10 minutes with 5s interval)
-        max_retries: int = 3,  # Max retries per individual API call
-        retry_delay: float = 1.0,
-        retry_backoff_factor: float = 2.0,
-        estimated_duration: Optional[float] = None,
-        node_id: Optional[str] = None,
-    ) -> None:
-        self.poll_endpoint = poll_endpoint
-        self.request = request
-        self.api_base: str = api_base or args.comfy_api_base
-        self.auth_token = auth_token
-        self.comfy_api_key = comfy_api_key
-        if auth_kwargs is not None:
-            self.auth_token = auth_kwargs.get("auth_token", self.auth_token)
-            self.comfy_api_key = auth_kwargs.get("comfy_api_key", self.comfy_api_key)
-        self.poll_interval = poll_interval
-        self.max_poll_attempts = max_poll_attempts
-        self.max_retries = max_retries
-        self.retry_delay = retry_delay
-        self.retry_backoff_factor = retry_backoff_factor
-        self.estimated_duration = estimated_duration
-        self.status_extractor = status_extractor or (lambda x: getattr(x, "status", None))
-        self.progress_extractor = progress_extractor
-        self.result_url_extractor = result_url_extractor
-        self.price_extractor = price_extractor
-        self.node_id = node_id
-        self.completed_statuses = completed_statuses
-        self.failed_statuses = failed_statuses
-        self.final_response: Optional[R] = None
-        self.extracted_price: Optional[float] = None
-
-    async def execute(self, client: Optional[ApiClient] = None) -> R:
-        owns_client = client is None
-        if owns_client:
-            client = ApiClient(
-                base_url=self.api_base,
-                auth_token=self.auth_token,
-                comfy_api_key=self.comfy_api_key,
-                max_retries=self.max_retries,
-                retry_delay=self.retry_delay,
-                retry_backoff_factor=self.retry_backoff_factor,
-            )
-        try:
-            return await self._poll_until_complete(client)
-        finally:
-            if owns_client:
-                await client.close()
-
-    def _display_text_on_node(self, text: str):
-        if not self.node_id:
-            return
-        if self.extracted_price is not None:
-            text = f"Price: ${self.extracted_price}\n{text}"
-        PromptServer.instance.send_progress_text(text, self.node_id)
-
-    def _display_time_progress_on_node(self, time_completed: int | float):
-        if not self.node_id:
-            return
-        if self.estimated_duration is not None:
-            remaining = max(0, int(self.estimated_duration) - time_completed)
-            message = f"Task in progress: {time_completed}s (~{remaining}s remaining)"
-        else:
-            message = f"Task in progress: {time_completed}s"
-        self._display_text_on_node(message)
-
-    def _check_task_status(self, response: R) -> TaskStatus:
-        try:
-            status = self.status_extractor(response)
-            if status in self.completed_statuses:
-                return TaskStatus.COMPLETED
-            if status in self.failed_statuses:
-                return TaskStatus.FAILED
-            return TaskStatus.PENDING
-        except Exception as e:
-            logging.error("Error extracting status: %s", e)
-            return TaskStatus.PENDING
-
-    async def _poll_until_complete(self, client: ApiClient) -> R:
-        """Poll until the task is complete"""
-        consecutive_errors = 0
-        max_consecutive_errors = min(5, self.max_retries * 2)  # Limit consecutive errors
-
-        if self.progress_extractor:
-            progress = utils.ProgressBar(PROGRESS_BAR_MAX)
-
-        status = TaskStatus.PENDING
-        for poll_count in range(1, self.max_poll_attempts + 1):
-            try:
-                logging.debug("[DEBUG] Polling attempt #%s", poll_count)
-
-                request_dict = None if self.request is None else self.request.model_dump(exclude_none=True)
-
-                if poll_count == 1:
-                    logging.debug(
-                        "[DEBUG] Poll Request: %s %s",
-                        self.poll_endpoint.method.value,
-                        self.poll_endpoint.path,
-                    )
-                    logging.debug(
-                        "[DEBUG] Poll Request Data: %s",
-                        json.dumps(request_dict, indent=2) if request_dict else "None",
-                    )
-
-                # Query task status
-                resp = await client.request(
-                    self.poll_endpoint.method.value,
-                    self.poll_endpoint.path,
-                    params=self.poll_endpoint.query_params,
-                    data=request_dict,
-                )
-                consecutive_errors = 0  # reset on success
-                response_obj: R = self.poll_endpoint.response_model.model_validate(resp)
-
-                # Check if task is complete
-                status = self._check_task_status(response_obj)
-                logging.debug("[DEBUG] Task Status: %s", status)
-
-                # If progress extractor is provided, extract progress
-                if self.progress_extractor:
-                    new_progress = self.progress_extractor(response_obj)
-                    if new_progress is not None:
-                        progress.update_absolute(new_progress, total=PROGRESS_BAR_MAX)
-
-                if self.price_extractor:
-                    price = self.price_extractor(response_obj)
-                    if price is not None:
-                        self.extracted_price = price
-
-                if status == TaskStatus.COMPLETED:
-                    message = "Task completed successfully"
-                    if self.result_url_extractor:
-                        result_url = self.result_url_extractor(response_obj)
-                        if result_url:
-                            message = f"Result URL: {result_url}"
-                    logging.debug("[DEBUG] %s", message)
-                    self._display_text_on_node(message)
-                    self.final_response = response_obj
-                    if self.progress_extractor:
-                        progress.update(100)
-                    return self.final_response
-                if status == TaskStatus.FAILED:
-                    message = f"Task failed: {json.dumps(resp)}"
-                    logging.error("[DEBUG] %s", message)
-                    raise Exception(message)
-                logging.debug("[DEBUG] Task still pending, continuing to poll...")
-                # Task pending – wait
-                for i in range(int(self.poll_interval)):
-                    self._display_time_progress_on_node((poll_count - 1) * self.poll_interval + i)
-                    await asyncio.sleep(1)
-
-            except (LocalNetworkError, ApiServerError, NetworkError) as e:
-                consecutive_errors += 1
-                if consecutive_errors >= max_consecutive_errors:
-                    raise Exception(
-                        f"Polling aborted after {consecutive_errors} network errors: {str(e)}"
-                    ) from e
-                logging.warning(
-                    "Network error (%s/%s): %s",
-                    consecutive_errors,
-                    max_consecutive_errors,
-                    str(e),
-                )
-                await asyncio.sleep(self.poll_interval)
-            except Exception as e:
-                # For other errors, increment count and potentially abort
-                consecutive_errors += 1
-                if consecutive_errors >= max_consecutive_errors or status == TaskStatus.FAILED:
-                    raise Exception(
-                        f"Polling aborted after {consecutive_errors} consecutive errors: {str(e)}"
-                    ) from e
-
-                logging.error("[DEBUG] Polling error: %s", str(e))
-                logging.warning(
-                    "Error during polling (attempt %s/%s): %s. Will retry in %s seconds.",
-                    poll_count,
-                    self.max_poll_attempts,
-                    str(e),
-                    self.poll_interval,
-                )
-                await asyncio.sleep(self.poll_interval)
-
-        # If we've exhausted all polling attempts
-        raise Exception(
-            f"Polling timed out after {self.max_poll_attempts} attempts (" f"{self.max_poll_attempts * self.poll_interval} seconds). "
-            "The operation may still be running on the server but is taking longer than expected."
-        )
diff --git a/comfy_api_nodes/nodes_rodin.py b/comfy_api_nodes/nodes_rodin.py
index ad4029236..e60e7a6d6 100644
--- a/comfy_api_nodes/nodes_rodin.py
+++ b/comfy_api_nodes/nodes_rodin.py
@@ -5,12 +5,9 @@ Rodin API docs: https://developer.hyper3d.ai/
 
 """
 
-from __future__ import annotations
 from inspect import cleandoc
 import folder_paths as comfy_paths
-import aiohttp
 import os
-import asyncio
 import logging
 import math
 from typing import Optional
@@ -26,11 +23,11 @@ from comfy_api_nodes.apis.rodin_api import (
     Rodin3DDownloadResponse,
     JobStatus,
 )
-from comfy_api_nodes.apis.client import (
+from comfy_api_nodes.util import (
+    sync_op,
+    poll_op,
     ApiEndpoint,
-    HttpMethod,
-    SynchronousOperation,
-    PollingOperation,
+    download_url_to_bytesio,
 )
 from comfy_api.latest import ComfyExtension, IO
 
@@ -121,35 +118,31 @@ def tensor_to_filelike(tensor, max_pixels: int = 2048*2048):
 
 
 async def create_generate_task(
+    cls: type[IO.ComfyNode],
     images=None,
     seed=1,
     material="PBR",
     quality_override=18000,
     tier="Regular",
     mesh_mode="Quad",
-    TAPose = False,
-    auth_kwargs: Optional[dict[str, str]] = None,
+    ta_pose: bool = False,
 ):
     if images is None:
         raise Exception("Rodin 3D generate requires at least 1 image.")
     if len(images) > 5:
         raise Exception("Rodin 3D generate requires up to 5 image.")
 
-    path = "/proxy/rodin/api/v2/rodin"
-    operation = SynchronousOperation(
-        endpoint=ApiEndpoint(
-            path=path,
-            method=HttpMethod.POST,
-            request_model=Rodin3DGenerateRequest,
-            response_model=Rodin3DGenerateResponse,
-        ),
-        request=Rodin3DGenerateRequest(
+    response = await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/rodin/api/v2/rodin", method="POST"),
+        response_model=Rodin3DGenerateResponse,
+        data=Rodin3DGenerateRequest(
             seed=seed,
             tier=tier,
             material=material,
             quality_override=quality_override,
             mesh_mode=mesh_mode,
-            TAPose=TAPose,
+            TAPose=ta_pose,
         ),
         files=[
             (
@@ -159,11 +152,8 @@ async def create_generate_task(
             for image in images if image is not None
         ],
         content_type="multipart/form-data",
-        auth_kwargs=auth_kwargs,
     )
 
-    response = await operation.execute()
-
     if hasattr(response, "error"):
         error_message = f"Rodin3D Create 3D generate Task Failed. Message: {response.message}, error: {response.error}"
         logging.error(error_message)
@@ -187,74 +177,46 @@ def check_rodin_status(response: Rodin3DCheckStatusResponse) -> str:
         return "DONE"
     return "Generating"
 
+def extract_progress(response: Rodin3DCheckStatusResponse) -> Optional[int]:
+    if not response.jobs:
+        return None
+    completed_count = sum(1 for job in response.jobs if job.status == JobStatus.Done)
+    return int((completed_count / len(response.jobs)) * 100)
 
-async def poll_for_task_status(
-    subscription_key, auth_kwargs: Optional[dict[str, str]] = None,
-) -> Rodin3DCheckStatusResponse:
-    poll_operation = PollingOperation(
-        poll_endpoint=ApiEndpoint(
-            path="/proxy/rodin/api/v2/status",
-            method=HttpMethod.POST,
-            request_model=Rodin3DCheckStatusRequest,
-            response_model=Rodin3DCheckStatusResponse,
-        ),
-        request=Rodin3DCheckStatusRequest(subscription_key=subscription_key),
-        completed_statuses=["DONE"],
-        failed_statuses=["FAILED"],
-        status_extractor=check_rodin_status,
-        poll_interval=3.0,
-        auth_kwargs=auth_kwargs,
-    )
+
+async def poll_for_task_status(subscription_key: str, cls: type[IO.ComfyNode]) -> Rodin3DCheckStatusResponse:
     logging.info("[ Rodin3D API - CheckStatus ] Generate Start!")
-    return await poll_operation.execute()
-
-
-async def get_rodin_download_list(uuid, auth_kwargs: Optional[dict[str, str]] = None) -> Rodin3DDownloadResponse:
-    logging.info("[ Rodin3D API - Downloading ] Generate Successfully!")
-    operation = SynchronousOperation(
-        endpoint=ApiEndpoint(
-            path="/proxy/rodin/api/v2/download",
-            method=HttpMethod.POST,
-            request_model=Rodin3DDownloadRequest,
-            response_model=Rodin3DDownloadResponse,
-        ),
-        request=Rodin3DDownloadRequest(task_uuid=uuid),
-        auth_kwargs=auth_kwargs,
+    return await poll_op(
+        cls,
+        ApiEndpoint(path="/proxy/rodin/api/v2/status", method="POST"),
+        response_model=Rodin3DCheckStatusResponse,
+        data=Rodin3DCheckStatusRequest(subscription_key=subscription_key),
+        status_extractor=check_rodin_status,
+        progress_extractor=extract_progress,
     )
-    return await operation.execute()
 
 
-async def download_files(url_list, task_uuid):
+async def get_rodin_download_list(uuid: str, cls: type[IO.ComfyNode]) -> Rodin3DDownloadResponse:
+    logging.info("[ Rodin3D API - Downloading ] Generate Successfully!")
+    return await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/rodin/api/v2/download", method="POST"),
+        response_model=Rodin3DDownloadResponse,
+        data=Rodin3DDownloadRequest(task_uuid=uuid),
+        monitor_progress=False,
+    )
+
+
+async def download_files(url_list, task_uuid: str):
     result_folder_name = f"Rodin3D_{task_uuid}"
     save_path = os.path.join(comfy_paths.get_output_directory(), result_folder_name)
     os.makedirs(save_path, exist_ok=True)
     model_file_path = None
-    async with aiohttp.ClientSession() as session:
-        for i in url_list.list:
-            file_path = os.path.join(save_path, i.name)
-            if file_path.endswith(".glb"):
-                model_file_path = os.path.join(result_folder_name, i.name)
-            logging.info("[ Rodin3D API - download_files ] Downloading file: %s", file_path)
-            max_retries = 5
-            for attempt in range(max_retries):
-                try:
-                    async with session.get(i.url) as resp:
-                        resp.raise_for_status()
-                        with open(file_path, "wb") as f:
-                            async for chunk in resp.content.iter_chunked(32 * 1024):
-                                f.write(chunk)
-                    break
-                except Exception as e:
-                    logging.info("[ Rodin3D API - download_files ] Error downloading %s:%s", file_path, str(e))
-                    if attempt < max_retries - 1:
-                        logging.info("Retrying...")
-                        await asyncio.sleep(2)
-                    else:
-                        logging.info(
-                            "[ Rodin3D API - download_files ] Failed to download %s after %s attempts.",
-                            file_path,
-                            max_retries,
-                        )
+    for i in url_list.list:
+        file_path = os.path.join(save_path, i.name)
+        if file_path.endswith(".glb"):
+            model_file_path = os.path.join(result_folder_name, i.name)
+        await download_url_to_bytesio(i.url, file_path)
     return model_file_path
 
 
@@ -276,6 +238,7 @@ class Rodin3D_Regular(IO.ComfyNode):
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
                 IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
             ],
             is_api_node=True,
         )
@@ -294,21 +257,17 @@ class Rodin3D_Regular(IO.ComfyNode):
         for i in range(num_images):
             m_images.append(Images[i])
         mesh_mode, quality_override = get_quality_mode(Polygon_count)
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
         task_uuid, subscription_key = await create_generate_task(
+            cls,
             images=m_images,
             seed=Seed,
             material=Material_Type,
             quality_override=quality_override,
             tier=tier,
             mesh_mode=mesh_mode,
-            auth_kwargs=auth,
         )
-        await poll_for_task_status(subscription_key, auth_kwargs=auth)
-        download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth)
+        await poll_for_task_status(subscription_key, cls)
+        download_list = await get_rodin_download_list(task_uuid, cls)
         model = await download_files(download_list, task_uuid)
 
         return IO.NodeOutput(model)
@@ -332,6 +291,7 @@ class Rodin3D_Detail(IO.ComfyNode):
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
                 IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
             ],
             is_api_node=True,
         )
@@ -350,21 +310,17 @@ class Rodin3D_Detail(IO.ComfyNode):
         for i in range(num_images):
             m_images.append(Images[i])
         mesh_mode, quality_override = get_quality_mode(Polygon_count)
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
         task_uuid, subscription_key = await create_generate_task(
+            cls,
             images=m_images,
             seed=Seed,
             material=Material_Type,
             quality_override=quality_override,
             tier=tier,
             mesh_mode=mesh_mode,
-            auth_kwargs=auth,
         )
-        await poll_for_task_status(subscription_key, auth_kwargs=auth)
-        download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth)
+        await poll_for_task_status(subscription_key, cls)
+        download_list = await get_rodin_download_list(task_uuid, cls)
         model = await download_files(download_list, task_uuid)
 
         return IO.NodeOutput(model)
@@ -388,6 +344,7 @@ class Rodin3D_Smooth(IO.ComfyNode):
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
                 IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
             ],
             is_api_node=True,
         )
@@ -400,27 +357,22 @@ class Rodin3D_Smooth(IO.ComfyNode):
         Material_Type,
         Polygon_count,
     ) -> IO.NodeOutput:
-        tier = "Smooth"
         num_images = Images.shape[0]
         m_images = []
         for i in range(num_images):
             m_images.append(Images[i])
         mesh_mode, quality_override = get_quality_mode(Polygon_count)
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
         task_uuid, subscription_key = await create_generate_task(
+            cls,
             images=m_images,
             seed=Seed,
             material=Material_Type,
             quality_override=quality_override,
-            tier=tier,
+            tier="Smooth",
             mesh_mode=mesh_mode,
-            auth_kwargs=auth,
         )
-        await poll_for_task_status(subscription_key, auth_kwargs=auth)
-        download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth)
+        await poll_for_task_status(subscription_key, cls)
+        download_list = await get_rodin_download_list(task_uuid, cls)
         model = await download_files(download_list, task_uuid)
 
         return IO.NodeOutput(model)
@@ -451,6 +403,7 @@ class Rodin3D_Sketch(IO.ComfyNode):
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
                 IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
             ],
             is_api_node=True,
         )
@@ -461,29 +414,21 @@ class Rodin3D_Sketch(IO.ComfyNode):
         Images,
         Seed,
     ) -> IO.NodeOutput:
-        tier = "Sketch"
         num_images = Images.shape[0]
         m_images = []
         for i in range(num_images):
             m_images.append(Images[i])
-        material_type = "PBR"
-        quality_override = 18000
-        mesh_mode = "Quad"
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
         task_uuid, subscription_key = await create_generate_task(
+            cls,
             images=m_images,
             seed=Seed,
-            material=material_type,
-            quality_override=quality_override,
-            tier=tier,
-            mesh_mode=mesh_mode,
-            auth_kwargs=auth,
+            material="PBR",
+            quality_override=18000,
+            tier="Sketch",
+            mesh_mode="Quad",
         )
-        await poll_for_task_status(subscription_key, auth_kwargs=auth)
-        download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth)
+        await poll_for_task_status(subscription_key, cls)
+        download_list = await get_rodin_download_list(task_uuid, cls)
         model = await download_files(download_list, task_uuid)
 
         return IO.NodeOutput(model)
@@ -522,6 +467,7 @@ class Rodin3D_Gen2(IO.ComfyNode):
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
                 IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
             ],
             is_api_node=True,
         )
@@ -541,22 +487,18 @@ class Rodin3D_Gen2(IO.ComfyNode):
         for i in range(num_images):
             m_images.append(Images[i])
         mesh_mode, quality_override = get_quality_mode(Polygon_count)
-        auth = {
-            "auth_token": cls.hidden.auth_token_comfy_org,
-            "comfy_api_key": cls.hidden.api_key_comfy_org,
-        }
         task_uuid, subscription_key = await create_generate_task(
+            cls,
             images=m_images,
             seed=Seed,
             material=Material_Type,
             quality_override=quality_override,
             tier=tier,
             mesh_mode=mesh_mode,
-            TAPose=TAPose,
-            auth_kwargs=auth,
+            ta_pose=TAPose,
         )
-        await poll_for_task_status(subscription_key, auth_kwargs=auth)
-        download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth)
+        await poll_for_task_status(subscription_key, cls)
+        download_list = await get_rodin_download_list(task_uuid, cls)
         model = await download_files(download_list, task_uuid)
 
         return IO.NodeOutput(model)
diff --git a/comfy_api_nodes/util/client.py b/comfy_api_nodes/util/client.py
index 65bb35f0f..2d5dcd648 100644
--- a/comfy_api_nodes/util/client.py
+++ b/comfy_api_nodes/util/client.py
@@ -16,9 +16,9 @@ from pydantic import BaseModel
 
 from comfy import utils
 from comfy_api.latest import IO
-from comfy_api_nodes.apis import request_logger
 from server import PromptServer
 
+from . import request_logger
 from ._helpers import (
     default_base_url,
     get_auth_header,
@@ -77,7 +77,7 @@ class _PollUIState:
 
 
 _RETRY_STATUS = {408, 429, 500, 502, 503, 504}
-COMPLETED_STATUSES = ["succeeded", "succeed", "success", "completed", "finished"]
+COMPLETED_STATUSES = ["succeeded", "succeed", "success", "completed", "finished", "done"]
 FAILED_STATUSES = ["cancelled", "canceled", "fail", "failed", "error"]
 QUEUED_STATUSES = ["created", "queued", "queueing", "submitted"]
 
diff --git a/comfy_api_nodes/util/download_helpers.py b/comfy_api_nodes/util/download_helpers.py
index 364874bed..14207dc68 100644
--- a/comfy_api_nodes/util/download_helpers.py
+++ b/comfy_api_nodes/util/download_helpers.py
@@ -12,8 +12,8 @@ from aiohttp.client_exceptions import ClientError, ContentTypeError
 
 from comfy_api.input_impl import VideoFromFile
 from comfy_api.latest import IO as COMFY_IO
-from comfy_api_nodes.apis import request_logger
 
+from . import request_logger
 from ._helpers import (
     default_base_url,
     get_auth_header,
diff --git a/comfy_api_nodes/apis/request_logger.py b/comfy_api_nodes/util/request_logger.py
similarity index 100%
rename from comfy_api_nodes/apis/request_logger.py
rename to comfy_api_nodes/util/request_logger.py
index c6974d35c..ac52e2eab 100644
--- a/comfy_api_nodes/apis/request_logger.py
+++ b/comfy_api_nodes/util/request_logger.py
@@ -1,11 +1,11 @@
 from __future__ import annotations
 
-import os
 import datetime
+import hashlib
 import json
 import logging
+import os
 import re
-import hashlib
 from typing import Any
 
 import folder_paths
diff --git a/comfy_api_nodes/util/upload_helpers.py b/comfy_api_nodes/util/upload_helpers.py
index 7bfc61704..632450d9b 100644
--- a/comfy_api_nodes/util/upload_helpers.py
+++ b/comfy_api_nodes/util/upload_helpers.py
@@ -13,8 +13,8 @@ from pydantic import BaseModel, Field
 
 from comfy_api.latest import IO, Input
 from comfy_api.util import VideoCodec, VideoContainer
-from comfy_api_nodes.apis import request_logger
 
+from . import request_logger
 from ._helpers import is_processing_interrupted, sleep_with_interrupt
 from .client import (
     ApiEndpoint,

From 97f198e4215680a83749ba95849f3cdcfa7aa64a Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Wed, 5 Nov 2025 15:07:35 -0800
Subject: [PATCH 54/56] Fix qwen controlnet regression. (#10657)

---
 comfy/ldm/qwen_image/controlnet.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/ldm/qwen_image/controlnet.py b/comfy/ldm/qwen_image/controlnet.py
index 92ac3cf0a..a6d408104 100644
--- a/comfy/ldm/qwen_image/controlnet.py
+++ b/comfy/ldm/qwen_image/controlnet.py
@@ -44,7 +44,7 @@ class QwenImageControlNetModel(QwenImageTransformer2DModel):
         txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2))
         txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3)
         ids = torch.cat((txt_ids, img_ids), dim=1)
-        image_rotary_emb = self.pe_embedder(ids).squeeze(1).unsqueeze(2).to(x.dtype)
+        image_rotary_emb = self.pe_embedder(ids).to(x.dtype).contiguous()
         del ids, txt_ids, img_ids
 
         hidden_states = self.img_in(hidden_states) + self.controlnet_x_embedder(hint)

From 1d69245981f9fb3861018613246042296d887dd3 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Wed, 5 Nov 2025 15:08:13 -0800
Subject: [PATCH 55/56] Enable pinned memory by default on Nvidia. (#10656)

Removed the --fast pinned_memory flag.

You can use --disable-pinned-memory to disable it. Please report if it
causes any issues.
---
 comfy/cli_args.py         |  3 ++-
 comfy/model_management.py | 22 +++++++++-------------
 2 files changed, 11 insertions(+), 14 deletions(-)

diff --git a/comfy/cli_args.py b/comfy/cli_args.py
index 3947e62a8..2f30b72d2 100644
--- a/comfy/cli_args.py
+++ b/comfy/cli_args.py
@@ -145,10 +145,11 @@ class PerformanceFeature(enum.Enum):
     Fp8MatrixMultiplication = "fp8_matrix_mult"
     CublasOps = "cublas_ops"
     AutoTune = "autotune"
-    PinnedMem = "pinned_memory"
 
 parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. This is used to test new features so using it might crash your comfyui. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: {}".format(" ".join(map(lambda c: c.value, PerformanceFeature))))
 
+parser.add_argument("--disable-pinned-memory", action="store_true", help="Disable pinned memory use.")
+
 parser.add_argument("--mmap-torch-files", action="store_true", help="Use mmap when loading ckpt/pt files.")
 parser.add_argument("--disable-mmap", action="store_true", help="Don't use mmap when loading safetensors.")
 
diff --git a/comfy/model_management.py b/comfy/model_management.py
index 0d040e55e..4d13c52c1 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -1085,22 +1085,21 @@ def cast_to_device(tensor, device, dtype, copy=False):
 
 PINNED_MEMORY = {}
 TOTAL_PINNED_MEMORY = 0
-if PerformanceFeature.PinnedMem in args.fast:
-    if WINDOWS:
-        MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.45  # Windows limit is apparently 50%
-    else:
-        MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.95
-else:
-    MAX_PINNED_MEMORY = -1
+MAX_PINNED_MEMORY = -1
+if not args.disable_pinned_memory:
+    if is_nvidia():
+        if WINDOWS:
+            MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.45  # Windows limit is apparently 50%
+        else:
+            MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.95
+        logging.info("Enabled pinned memory {}".format(MAX_PINNED_MEMORY // (1024 * 1024)))
+
 
 def pin_memory(tensor):
     global TOTAL_PINNED_MEMORY
     if MAX_PINNED_MEMORY <= 0:
         return False
 
-    if not is_nvidia():
-        return False
-
     if not is_device_cpu(tensor.device):
         return False
 
@@ -1121,9 +1120,6 @@ def unpin_memory(tensor):
     if MAX_PINNED_MEMORY <= 0:
         return False
 
-    if not is_nvidia():
-        return False
-
     if not is_device_cpu(tensor.device):
         return False
 

From 09dc24c8a982776abd5cb2f71e3d041139e1d5b2 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Wed, 5 Nov 2025 16:11:15 -0800
Subject: [PATCH 56/56] Pinned mem also seems to work on AMD. (#10658)

---
 comfy/model_management.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 4d13c52c1..7a30c4bec 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -1087,7 +1087,7 @@ PINNED_MEMORY = {}
 TOTAL_PINNED_MEMORY = 0
 MAX_PINNED_MEMORY = -1
 if not args.disable_pinned_memory:
-    if is_nvidia():
+    if is_nvidia() or is_amd():
         if WINDOWS:
             MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.45  # Windows limit is apparently 50%
         else: