Merge 0d4f4d0972 into b08debceca

Signed-off-by: bigcat88 <bigcat88@icloud.com>

.github/workflows/test-ci.yml

@@ -22,11 +22,10 @@ jobs:
       fail-fast: false
       matrix:
         # os: [macos, linux, windows]
-        # os: [macos, linux]
         os: [linux]
-        python_version: ["3.10", "3.11", "3.12"]
-        cuda_version: ["12.1"]
-        torch_version: ["stable"]
+        python_version: ["3.14", "3.13", "3.12", "3.11"]
+        cuda_version: ["13.2"]
+        torch_version: ["specific"]
         include:
           # - os: macos
           #   runner_label: [self-hosted, macOS]
@@ -44,33 +43,18 @@ jobs:
         with:
           os: ${{ matrix.os }}
           python_version: ${{ matrix.python_version }}
+          cuda_version: ${{ matrix.cuda_version }}
           torch_version: ${{ matrix.torch_version }}
+          # Pin PyTorch 2.12 on CUDA 13.2 (cu132 wheel index). torchaudio is omitted
+          # on purpose: cu132 has no torchaudio build for these Python versions
+          # (it stops at 2.2.0/cp312). requirements.txt still lists torchaudio, so
+          # validate that leg once a runner exists — it may need to be made optional.
+          # NOTE: requires comfy-action to honor `torch_version: specific` on Linux
+          # (today macOS-only). Companion PR in comfy-org/comfy-action is required.
+          specific_torch_install: "pip install torch==2.12.* torchvision --index-url https://download.pytorch.org/whl/cu132"
           google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
           comfyui_flags: ${{ matrix.flags }}
 
-  # test-win-nightly:
-  #   strategy:
-  #     fail-fast: true
-  #     matrix:
-  #       os: [windows]
-  #       python_version: ["3.9", "3.10", "3.11", "3.12"]
-  #       cuda_version: ["12.1"]
-  #       torch_version: ["nightly"]
-  #       include:
-  #         - os: windows
-  #           runner_label: [self-hosted, Windows]
-  #           flags: ""
-  #   runs-on: ${{ matrix.runner_label }}
-  #   steps:
-  #     - name: Test Workflows
-  #       uses: comfy-org/comfy-action@main
-  #       with:
-  #         os: ${{ matrix.os }}
-  #         python_version: ${{ matrix.python_version }}
-  #         torch_version: ${{ matrix.torch_version }}
-  #         google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
-  #         comfyui_flags: ${{ matrix.flags }}
-
   test-unix-nightly:
     strategy:
       fail-fast: false
@@ -78,7 +62,7 @@ jobs:
         # os: [macos, linux]
         os: [linux]
         python_version: ["3.11"]
-        cuda_version: ["12.1"]
+        cuda_version: ["13.2"]
         torch_version: ["nightly"]
         include:
           # - os: macos
@@ -94,6 +78,7 @@ jobs:
         with:
           os: ${{ matrix.os }}
           python_version: ${{ matrix.python_version }}
+          cuda_version: ${{ matrix.cuda_version }}
           torch_version: ${{ matrix.torch_version }}
           google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
           comfyui_flags: ${{ matrix.flags }}

comfy/text_encoders/llama.py

@@ -937,22 +937,41 @@ class BaseGenerate:
             return torch.argmax(logits, dim=-1, keepdim=True)
 
         # Sampling mode
-        if repetition_penalty != 1.0:
-            for i in range(logits.shape[0]):
-                for token_id in set(token_history):
-                    logits[i, token_id] *= repetition_penalty if logits[i, token_id] < 0 else 1/repetition_penalty
-
-        if presence_penalty is not None and presence_penalty != 0.0:
-            for i in range(logits.shape[0]):
-                for token_id in set(token_history):
-                    logits[i, token_id] -= presence_penalty
+        if len(token_history) > 0 and (repetition_penalty != 1.0 or (presence_penalty is not None and presence_penalty != 0.0)):
+            token_ids = torch.tensor(list(set(token_history)), device=logits.device)
+            token_logits = logits[:, token_ids]
+            if repetition_penalty != 1.0:
+                token_logits = torch.where(token_logits < 0, token_logits * repetition_penalty, token_logits / repetition_penalty)
+            if presence_penalty is not None and presence_penalty != 0.0:
+                token_logits = token_logits - presence_penalty
+            logits[:, token_ids] = token_logits
 
         if temperature != 1.0:
             logits = logits / temperature
 
         if top_k > 0:
-            indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
-            logits[indices_to_remove] = torch.finfo(logits.dtype).min
+            top_k = min(top_k, logits.shape[-1])
+            logits, top_indices = torch.topk(logits, top_k)
+
+            if min_p > 0.0:
+                probs_before_filter = torch.nn.functional.softmax(logits, dim=-1)
+                top_probs, _ = probs_before_filter.max(dim=-1, keepdim=True)
+                min_threshold = min_p * top_probs
+                indices_to_remove = probs_before_filter < min_threshold
+                logits[indices_to_remove] = torch.finfo(logits.dtype).min
+
+            if top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+                cumulative_probs = torch.cumsum(torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[..., 0] = False
+                indices_to_remove = torch.zeros_like(logits, dtype=torch.bool)
+                indices_to_remove.scatter_(1, sorted_indices, sorted_indices_to_remove)
+                logits[indices_to_remove] = torch.finfo(logits.dtype).min
+
+            probs = torch.nn.functional.softmax(logits, dim=-1)
+            next_token = torch.multinomial(probs, num_samples=1, generator=generator)
+            return top_indices.gather(1, next_token)
 
         if min_p > 0.0:
             probs_before_filter = torch.nn.functional.softmax(logits, dim=-1)

comfy_api_nodes/util/request_logger.py

@@ -9,6 +9,7 @@ from typing import Any
 import folder_paths
 
 logger = logging.getLogger(__name__)
+_SENSITIVE_HEADERS = {"authorization", "x-api-key"}
 
 
 def get_log_directory():
@@ -73,6 +74,10 @@ def _format_data_for_logging(data: Any) -> str:
     return str(data)
 
 
+def _redact_headers(headers: dict) -> dict:
+    return {k: ("***" if k.lower() in _SENSITIVE_HEADERS else v) for k, v in headers.items()}
+
+
 def log_request_response(
     operation_id: str,
     request_method: str,
@@ -101,7 +106,7 @@ def log_request_response(
         log_content.append(f"Method: {request_method}")
         log_content.append(f"URL: {request_url}")
         if request_headers:
-            log_content.append(f"Headers:\n{_format_data_for_logging(request_headers)}")
+            log_content.append(f"Headers:\n{_format_data_for_logging(_redact_headers(request_headers))}")
         if request_params:
             log_content.append(f"Params:\n{_format_data_for_logging(request_params)}")
         if request_data is not None:

comfy_extras/nodes_color.py

@@ -16,23 +16,30 @@ class ColorToRGBInt(io.ComfyNode):
             ],
             outputs=[
                 io.Int.Output(display_name="rgb_int"),
-                io.Color.Output(display_name="hex")
+                io.Color.Output(display_name="hex"),
+                io.Float.Output(display_name="alpha"),
             ],
         )
 
     @classmethod
     def execute(cls, color: str) -> io.NodeOutput:
-        # expect format #RRGGBB
-        if len(color) != 7 or color[0] != "#":
-            raise ValueError("Color must be in format #RRGGBB")
+        # expect format #RRGGBB or #RRGGBBAA
+        if len(color) not in (7, 9) or color[0] != "#":
+            raise ValueError("Color must be in format #RRGGBB or #RRGGBBAA")
         try:
             int(color[1:], 16)
         except ValueError:
-            raise ValueError("Color must be in format #RRGGBB") from None
+            raise ValueError("Color must be in format #RRGGBB or #RRGGBBAA") from None
+
+        alpha = 1.0
+        if len(color) == 9:
+            alpha = int(color[7:9], 16) / 255.0
+            color = color[:7]
+
         r, g, b = hex_to_rgb(color)
 
         rgb_int = r * 256 * 256 + g * 256 + b
-        return io.NodeOutput(rgb_int, color)
+        return io.NodeOutput(rgb_int, color, alpha)
 
 
 class ColorExtension(ComfyExtension):

requirements.txt

@@ -1,6 +1,6 @@
 comfyui-frontend-package==1.45.20
 comfyui-workflow-templates==0.11.2
-comfyui-embedded-docs==0.5.6
+comfyui-embedded-docs==0.5.7
 torch
 torchsde
 torchvision