Merge ca6da3d9fb into c4a14df9a3

For LTX Audio VAE, remove normalization of audio during MEL spectrogram creation.
This aligs inference with training and prevents loud audio from being attenuated.

README.md

@@ -69,6 +69,8 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
    - [Hunyuan Image 2.1](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_image/)
    - [Flux 2](https://comfyanonymous.github.io/ComfyUI_examples/flux2/)
    - [Z Image](https://comfyanonymous.github.io/ComfyUI_examples/z_image/)
+   - [Chroma](https://comfyanonymous.github.io/ComfyUI_examples/chroma/)
+   - [Ovis Image](https://docs.comfy.org/tutorials/image/ovis/ovis-image)
 - Image Editing Models
    - [Omnigen 2](https://comfyanonymous.github.io/ComfyUI_examples/omnigen/)
    - [Flux Kontext](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-kontext-image-editing-model)
@@ -82,6 +84,9 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
    - [Wan 2.1](https://comfyanonymous.github.io/ComfyUI_examples/wan/)
    - [Wan 2.2](https://comfyanonymous.github.io/ComfyUI_examples/wan22/)
    - [Hunyuan Video 1.5](https://docs.comfy.org/tutorials/video/hunyuan/hunyuan-video-1-5)
+   - [Kandinsky 5.0](https://docs.comfy.org/tutorials/video/kandinsky/kandinsky-5)
+   - [Nvidia Cosmos](https://comfyanonymous.github.io/ComfyUI_examples/cosmos)
+   - [Nvidia Cosmos Predict2](https://comfyanonymous.github.io/ComfyUI_examples/cosmos_predict2)
 - Audio Models
    - [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
    - [ACE Step](https://comfyanonymous.github.io/ComfyUI_examples/audio/)

comfy/ldm/lightricks/vae/audio_vae.py

@@ -103,20 +103,10 @@ class AudioPreprocessor:
             return waveform
         return torchaudio.functional.resample(waveform, source_rate, self.target_sample_rate)
 
-    @staticmethod
-    def normalize_amplitude(
-        waveform: torch.Tensor, max_amplitude: float = 0.5, eps: float = 1e-5
-    ) -> torch.Tensor:
-        waveform = waveform - waveform.mean(dim=2, keepdim=True)
-        peak = torch.max(torch.abs(waveform)) + eps
-        scale = peak.clamp(max=max_amplitude) / peak
-        return waveform * scale
-
     def waveform_to_mel(
         self, waveform: torch.Tensor, waveform_sample_rate: int, device
     ) -> torch.Tensor:
         waveform = self.resample(waveform, waveform_sample_rate)
-        waveform = self.normalize_amplitude(waveform)
 
         mel_transform = torchaudio.transforms.MelSpectrogram(
             sample_rate=self.target_sample_rate,

comfy/model_detection.py

@@ -253,7 +253,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
                 dit_config["image_model"] = "chroma_radiance"
                 dit_config["in_channels"] = 3
                 dit_config["out_channels"] = 3
-                dit_config["patch_size"] = 16
+                dit_config["patch_size"] = state_dict.get('{}img_in_patch.weight'.format(key_prefix)).size(dim=-1)
                 dit_config["nerf_hidden_size"] = 64
                 dit_config["nerf_mlp_ratio"] = 4
                 dit_config["nerf_depth"] = 4

comfy_api_nodes/apis/wavespeed.py

@@ -0,0 +1,35 @@
+from pydantic import BaseModel, Field
+
+
+class SeedVR2ImageRequest(BaseModel):
+    image: str = Field(...)
+    target_resolution: str = Field(...)
+    output_format: str = Field("png")
+    enable_sync_mode: bool = Field(False)
+
+
+class FlashVSRRequest(BaseModel):
+    target_resolution: str = Field(...)
+    video: str = Field(...)
+    duration: float = Field(...)
+
+
+class TaskCreatedDataResponse(BaseModel):
+    id: str = Field(...)
+
+
+class TaskCreatedResponse(BaseModel):
+    code: int = Field(...)
+    message: str = Field(...)
+    data: TaskCreatedDataResponse | None = Field(None)
+
+
+class TaskResultDataResponse(BaseModel):
+    status: str = Field(...)
+    outputs: list[str] = Field([])
+
+
+class TaskResultResponse(BaseModel):
+    code: int = Field(...)
+    message: str = Field(...)
+    data: TaskResultDataResponse | None = Field(None)

comfy_api_nodes/nodes_wavespeed.py

@@ -0,0 +1,178 @@
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.wavespeed import (
+    FlashVSRRequest,
+    TaskCreatedResponse,
+    TaskResultResponse,
+    SeedVR2ImageRequest,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_video_output,
+    poll_op,
+    sync_op,
+    upload_video_to_comfyapi,
+    validate_container_format_is_mp4,
+    validate_video_duration,
+    upload_images_to_comfyapi,
+    get_number_of_images,
+    download_url_to_image_tensor,
+)
+
+
+class WavespeedFlashVSRNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="WavespeedFlashVSRNode",
+            display_name="FlashVSR Video Upscale",
+            category="api node/video/WaveSpeed",
+            description="Fast, high-quality video upscaler that "
+            "boosts resolution and restores clarity for low-resolution or blurry footage.",
+            inputs=[
+                IO.Video.Input("video"),
+                IO.Combo.Input("target_resolution", options=["720p", "1080p", "2K", "4K"]),
+            ],
+            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,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["target_resolution"]),
+                expr="""
+                (
+                  $price_for_1sec := {"720p": 0.012, "1080p": 0.018, "2k": 0.024, "4k": 0.032};
+                  {
+                    "type":"usd",
+                    "usd": $lookup($price_for_1sec, widgets.target_resolution),
+                    "format":{"suffix": "/second", "approximate": true}
+                  }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        target_resolution: str,
+    ) -> IO.NodeOutput:
+        validate_container_format_is_mp4(video)
+        validate_video_duration(video, min_duration=5, max_duration=60 * 10)
+        initial_res = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/wavespeed/api/v3/wavespeed-ai/flashvsr", method="POST"),
+            response_model=TaskCreatedResponse,
+            data=FlashVSRRequest(
+                target_resolution=target_resolution.lower(),
+                video=await upload_video_to_comfyapi(cls, video),
+                duration=video.get_duration(),
+            ),
+        )
+        if initial_res.code != 200:
+            raise ValueError(f"Task creation fails with code={initial_res.code} and message={initial_res.message}")
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wavespeed/api/v3/predictions/{initial_res.data.id}/result"),
+            response_model=TaskResultResponse,
+            status_extractor=lambda x: "failed" if x.data is None else x.data.status,
+            poll_interval=10.0,
+            max_poll_attempts=480,
+        )
+        if final_response.code != 200:
+            raise ValueError(
+                f"Task processing failed with code={final_response.code} and message={final_response.message}"
+            )
+        return IO.NodeOutput(await download_url_to_video_output(final_response.data.outputs[0]))
+
+
+class WavespeedImageUpscaleNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="WavespeedImageUpscaleNode",
+            display_name="WaveSpeed Image Upscale",
+            category="api node/image/WaveSpeed",
+            description="Boost image resolution and quality, upscaling photos to 4K or 8K for sharp, detailed results.",
+            inputs=[
+                IO.Combo.Input("model", options=["SeedVR2", "Ultimate"]),
+                IO.Image.Input("image"),
+                IO.Combo.Input("target_resolution", options=["2K", "4K", "8K"]),
+            ],
+            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,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+                expr="""
+                (
+                  $prices := {"seedvr2": 0.01, "ultimate": 0.06};
+                  {"type":"usd", "usd": $lookup($prices, widgets.model)}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        image: Input.Image,
+        target_resolution: str,
+    ) -> IO.NodeOutput:
+        if get_number_of_images(image) != 1:
+            raise ValueError("Exactly one input image is required.")
+        if model == "SeedVR2":
+            model_path = "seedvr2/image"
+        else:
+            model_path = "ultimate-image-upscaler"
+        initial_res = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wavespeed/api/v3/wavespeed-ai/{model_path}", method="POST"),
+            response_model=TaskCreatedResponse,
+            data=SeedVR2ImageRequest(
+                target_resolution=target_resolution.lower(),
+                image=(await upload_images_to_comfyapi(cls, image, max_images=1))[0],
+            ),
+        )
+        if initial_res.code != 200:
+            raise ValueError(f"Task creation fails with code={initial_res.code} and message={initial_res.message}")
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wavespeed/api/v3/predictions/{initial_res.data.id}/result"),
+            response_model=TaskResultResponse,
+            status_extractor=lambda x: "failed" if x.data is None else x.data.status,
+            poll_interval=10.0,
+            max_poll_attempts=480,
+        )
+        if final_response.code != 200:
+            raise ValueError(
+                f"Task processing failed with code={final_response.code} and message={final_response.message}"
+            )
+        return IO.NodeOutput(await download_url_to_image_tensor(final_response.data.outputs[0]))
+
+
+class WavespeedExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            WavespeedFlashVSRNode,
+            WavespeedImageUpscaleNode,
+        ]
+
+
+async def comfy_entrypoint() -> WavespeedExtension:
+    return WavespeedExtension()