Add video of denoising steps

.gitignore

@@ -1,3 +1,7 @@
+.gradio
+.vscode
+output
+
 # Created by https://www.toptal.com/developers/gitignore/api/linux,macos,dotenv,python,windows,intellij,visualstudio,visualstudiocode
 # Edit at https://www.toptal.com/developers/gitignore?templates=linux,macos,dotenv,python,windows,intellij,visualstudio,visualstudiocode
 

app.py

@@ -1,13 +1,14 @@
-# from pathlib import Path
-
+import shutil
+import time
 from pathlib import Path
 
+import cv2
 import gradio as gr
 import PIL.Image
 import torch
 from diffusers import (
     DiffusionPipeline,  # type: ignore
-    QwenImageEditPlusPipeline,
+    QwenImageEditPlusPipeline,  # type: ignore
 )
 
 # from diffusers.utils import load_image
@@ -20,49 +21,18 @@ RANK = 128
 TRANSFORMER_ID = f"nunchaku-tech/nunchaku-qwen-image-edit-2509/svdq-{get_precision()}_r{RANK}-qwen-image-edit-2509.safetensors"
 PIPELINE_ID = "Qwen/Qwen-Image-Edit-2509"
 
-
-def callback(
-    pipeline: DiffusionPipeline,
-    step: int,
-    timestep: int,
-    callback_kwargs: dict,
-):
-    print(f"Step {step}, Timestep {timestep}, Kwargs: {callback_kwargs.keys()}")
-    latents = callback_kwargs.get("latents", None)
-
-    height = callback_kwargs.get("height", 800)
-    width = callback_kwargs.get("width", 512)
-
-    if latents is not None:
-        print(f"Latents shape: {latents.shape}, dtype: {latents.dtype}")
-
-        latents = pipeline._unpack_latents(
-            latents, height, width, pipeline.vae_scale_factor
-        )
-        latents = latents.to(pipeline.vae.dtype)
-        latents_mean = (
-            torch.tensor(pipeline.vae.config.latents_mean)
-            .view(1, pipeline.vae.config.z_dim, 1, 1, 1)
-            .to(latents.device, latents.dtype)
-        )
-        latents_std = 1.0 / torch.tensor(pipeline.vae.config.latents_std).view(
-            1, pipeline.vae.config.z_dim, 1, 1, 1
-        ).to(latents.device, latents.dtype)
-        latents = latents / latents_std + latents_mean
-        image = pipeline.vae.decode(latents, return_dict=False)[0][:, :, 0]
-        image = pipeline.image_processor.postprocess(image, output_type="pil")
-        image = image[0]
-
-        image_filename = f"step_{step:03d}_t{timestep}.png"
-        output_dir = Path(__file__).parent / "output"
-        output_dir.mkdir(parents=True, exist_ok=True)
-        image.save(output_dir / image_filename)
-
-    return {}
+OUTPUT_DIR = Path(__file__).parent / "output"
+IMAGES_DIR = OUTPUT_DIR / "images"
+IMAGES_DIR.mkdir(parents=True, exist_ok=True)
+VIDEO_PATH = OUTPUT_DIR / "video.mp4"
 
 
 class Model:
     def __init__(self):
+        self.progress = gr.Progress()
+        self.num_inference_steps = 50
+        self.current_inference_step = 0
+
         transformer = NunchakuQwenImageTransformer2DModel.from_pretrained(
             TRANSFORMER_ID
         )
@@ -95,7 +65,14 @@ class Model:
         num_inference_steps: int = 40,
         image_width: int = 512,
         image_height: int = 512,
-    ) -> PIL.Image.Image:
+    ) -> tuple[PIL.Image.Image, Path]:
+        self.num_inference_steps = num_inference_steps
+        self.current_inference_step = 0
+        self.progress((self.current_inference_step, self.num_inference_steps))
+
+        shutil.rmtree(IMAGES_DIR, ignore_errors=True)
+        IMAGES_DIR.mkdir(parents=True, exist_ok=True)
+
         # Validate inputs
         if not images:
             raise gr.Error("No images provided. Please upload at least one image.")
@@ -116,49 +93,81 @@ class Model:
             width=image_width,
             height=image_height,
             generator=torch.manual_seed(0),
-            callback_on_step_end=callback,
+            callback_on_step_end=self.callback,
             # output_type="latent"
         )
         output = self.pipeline(**inputs)
         output_image = output.images[0]
-        return output_image
 
+        # Create video from saved images
 
-model = Model()
+        print(list(IMAGES_DIR.glob("*.png")))
 
+        # Get all PNG files and sort them
+        image_files = sorted(IMAGES_DIR.glob("step_*.png"))
 
-def process_images(
-    images,
-    prompt,
-    negative_prompt,
-    true_cfg_scale,
-    num_inference_steps,
-    image_width,
-    image_height,
-):
-    """Wrapper function to handle errors gracefully"""
+        if image_files:
+            # Read first image to get dimensions
+            first_img = cv2.imread(str(image_files[0]))
+            height, width, _ = first_img.shape
 
-    pil_images = []
+            # Create video writer
+            fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+            fps = 10  # Adjust frame rate as needed
+            video_writer = cv2.VideoWriter(
+                str(VIDEO_PATH.absolute()), fourcc, fps, (width, height)
+            )
 
-    for contents in images:
-        for content in contents:
-            if isinstance(content, PIL.Image.Image):
-                pil_images.append(content)
-                break
+            # Add each image to video
+            for img_path in image_files:
+                img = cv2.imread(str(img_path))
+                video_writer.write(img)
 
-    try:
-        return model.compute(
-            pil_images,
-            prompt,
-            negative_prompt,
-            true_cfg_scale,
-            num_inference_steps,
-            image_width,
-            image_height,
-        )
-    except Exception as e:
-        print(f"Error processing images: {e}")
-        raise gr.Error(f"Failed to process images: {str(e)}")
+            video_writer.release()
+            print(f"Video saved to: {VIDEO_PATH}")
+
+            time.sleep(3)
+
+        return output_image, VIDEO_PATH
+
+    def callback(
+        self,
+        pipeline: DiffusionPipeline,
+        step: int,
+        timestep: int,
+        callback_kwargs: dict,
+    ):
+        latents = callback_kwargs.get("latents", None)
+
+        height = callback_kwargs.get("height", 800)
+        width = callback_kwargs.get("width", 512)
+
+        if latents is not None:
+            print(f"Latents shape: {latents.shape}, dtype: {latents.dtype}")
+
+            latents = pipeline._unpack_latents(
+                latents, height, width, pipeline.vae_scale_factor
+            )
+            latents = latents.to(pipeline.vae.dtype)
+            latents_mean = (
+                torch.tensor(pipeline.vae.config.latents_mean)
+                .view(1, pipeline.vae.config.z_dim, 1, 1, 1)
+                .to(latents.device, latents.dtype)
+            )
+            latents_std = 1.0 / torch.tensor(pipeline.vae.config.latents_std).view(
+                1, pipeline.vae.config.z_dim, 1, 1, 1
+            ).to(latents.device, latents.dtype)
+            latents = latents / latents_std + latents_mean
+            image = pipeline.vae.decode(latents, return_dict=False)[0][:, :, 0]
+            image = pipeline.image_processor.postprocess(image, output_type="pil")
+            image = image[0]
+
+            image.save(IMAGES_DIR / f"step_{step:03d}.png")
+
+            self.current_inference_step += 1
+            self.progress((self.current_inference_step, self.num_inference_steps))
+
+        return {}
 
 
 with gr.Blocks() as demo:
@@ -189,6 +198,18 @@ with gr.Blocks() as demo:
                 format="png",
             )
 
+        with gr.Column():
+            gr.Markdown("## Output Video")
+
+            video_output = gr.Video(
+                label="Output Video",
+                format="mp4",
+                show_download_button=True,
+                streaming=True,
+                autoplay=True,
+                loop=False,
+            )
+
     with gr.Row():
         with gr.Column():
             gr.Markdown("## Prompts")
@@ -210,8 +231,8 @@ with gr.Blocks() as demo:
 
             num_inference_steps = gr.Slider(
                 1,
-                100,
-                value=40,
+                300,
+                value=50,
                 step=1,
                 interactive=True,
                 label="Number of denoising steps:",
@@ -238,6 +259,41 @@ with gr.Blocks() as demo:
     with gr.Row():
         run_button = gr.Button("Run")
 
+    model = Model()
+
+    def process_images(
+        images,
+        prompt,
+        negative_prompt,
+        true_cfg_scale,
+        num_inference_steps,
+        image_width,
+        image_height,
+    ):
+        """Wrapper function to handle errors gracefully"""
+
+        pil_images = []
+
+        for contents in images:
+            for content in contents:
+                if isinstance(content, PIL.Image.Image):
+                    pil_images.append(content)
+                    break
+
+        try:
+            return model.compute(
+                pil_images,
+                prompt,
+                negative_prompt,
+                true_cfg_scale,
+                num_inference_steps,
+                image_width,
+                image_height,
+            )
+        except Exception as e:
+            print(f"Error processing images: {e}")
+            raise gr.Error(f"Failed to process images: {str(e)}")
+
     # Connect the button to the detection function
     run_button.click(
         fn=process_images,
@@ -252,9 +308,13 @@ with gr.Blocks() as demo:
         ],
         outputs=[
             image_output,
+            video_output,
         ],
     )
 
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(
+        allowed_paths=["output/video.mp4"],
+        share=True,
+    )

pyproject.toml

@@ -7,7 +7,8 @@ dependencies = [
     "nunchaku",
     "torch~=2.7.0",
     "transformers[torch]>=4.57.0",
-    "gradio~=5.49.1"
+    "gradio~=5.49.1",
+    "opencv-python-headless>=4.12.0.88",
 ]
 
 [tool.uv.sources]

requirements.txt

@@ -101,11 +101,12 @@ mpmath==1.3.0
     # via sympy
 networkx==3.5
     # via torch
-numpy==2.3.3
+numpy==2.2.6
     # via
     #   accelerate
     #   diffusers
     #   gradio
+    #   opencv-python-headless
     #   pandas
     #   peft
     #   torchvision
@@ -149,6 +150,8 @@ nvidia-nvjitlink-cu12==12.6.85
     #   torch
 nvidia-nvtx-cu12==12.6.77
     # via torch
+opencv-python-headless==4.12.0.88
+    # via nunchaku-qwen-image-edit-2509 (pyproject.toml)
 orjson==3.11.3
     # via gradio
 packaging==25.0

uv.lock

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-version = "2.3.3"
+version = "2.2.6"
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 ]
 
 [[package]]
@@ -531,6 +530,7 @@ dependencies = [
     { name = "diffusers" },
     { name = "gradio" },
     { name = "nunchaku" },
+    { name = "opencv-python-headless" },
     { name = "torch" },
     { name = "transformers", extra = ["torch"] },
 ]
@@ -540,6 +540,7 @@ requires-dist = [
     { name = "diffusers", git = "https://github.com/huggingface/diffusers?rev=a519272d97f011332588e1aaa73d32952d80f3f5" },
     { name = "gradio", specifier = "~=5.49.1" },
     { name = "nunchaku", url = "https://github.com/nunchaku-tech/nunchaku/releases/download/v1.0.1/nunchaku-1.0.1+torch2.7-cp312-cp312-linux_x86_64.whl" },
+    { name = "opencv-python-headless", specifier = ">=4.12.0.88" },
     { name = "torch", specifier = "~=2.7.0" },
     { name = "transformers", extras = ["torch"], specifier = ">=4.57.0" },
 ]
@@ -677,6 +678,23 @@ wheels = [
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 ]
 
+[[package]]
+name = "opencv-python-headless"
+version = "4.12.0.88"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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