video concat test

app.py

@@ -15,6 +15,10 @@ import uuid
 import random
 from huggingface_hub import hf_hub_download
 
+# NEW CODE HERE:
+# If moviepy is not installed by default, you need to ensure your Space installs it (e.g. in requirements.txt).
+from moviepy.editor import VideoFileClip, concatenate_videoclips
+
 #gradio.helpers.CACHED_FOLDER = '/data/cache'
 
 pipe = StableVideoDiffusionPipeline.from_pretrained(
@@ -26,103 +30,205 @@ pipe.to("cuda")
 
 max_64_bit_int = 2**63 - 1
 
-@spaces.GPU(duration=120)
-def sample(
-    image: Image,
-    seed: Optional[int] = 42,
-    randomize_seed: bool = True,
-    motion_bucket_id: int = 127,
-    fps_id: int = 6,
-    version: str = "svd_xt",
-    cond_aug: float = 0.02,
-    decoding_t: int = 3,  # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
-    device: str = "cuda",
-    output_folder: str = "outputs",
-    progress=gr.Progress(track_tqdm=True)
-):
-    if image.mode == "RGBA":
-        image = image.convert("RGB")
-        
-    if(randomize_seed):
-        seed = random.randint(0, max_64_bit_int)
-    generator = torch.manual_seed(seed)
-    
-    os.makedirs(output_folder, exist_ok=True)
-    base_count = len(glob(os.path.join(output_folder, "*.mp4")))
-    video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")
-
-    frames = pipe(image, decode_chunk_size=decoding_t, generator=generator, motion_bucket_id=motion_bucket_id, noise_aug_strength=0.1, num_frames=25).frames[0]
-    export_to_video(frames, video_path, fps=fps_id)
-    torch.manual_seed(seed)
-    
-    return video_path, seed
-
 def resize_image(image, output_size=(1024, 576)):
-    # Calculate aspect ratios
-    target_aspect = output_size[0] / output_size[1]  # Aspect ratio of the desired size
-    image_aspect = image.width / image.height  # Aspect ratio of the original image
+    """
+    Resizes/crops the image to match a target resolution without
+    distorting aspect ratio.
+    """
+    target_aspect = output_size[0] / output_size[1]
+    image_aspect = image.width / image.height
 
-    # Resize then crop if the original image is larger
     if image_aspect > target_aspect:
-        # Resize the image to match the target height, maintaining aspect ratio
         new_height = output_size[1]
         new_width = int(new_height * image_aspect)
         resized_image = image.resize((new_width, new_height), Image.LANCZOS)
-        # Calculate coordinates for cropping
         left = (new_width - output_size[0]) / 2
         top = 0
         right = (new_width + output_size[0]) / 2
         bottom = output_size[1]
     else:
-        # Resize the image to match the target width, maintaining aspect ratio
         new_width = output_size[0]
         new_height = int(new_width / image_aspect)
         resized_image = image.resize((new_width, new_height), Image.LANCZOS)
-        # Calculate coordinates for cropping
         left = 0
         top = (new_height - output_size[1]) / 2
         right = output_size[0]
         bottom = (new_height + output_size[1]) / 2
 
-    # Crop the image
     cropped_image = resized_image.crop((left, top, right, bottom))
     return cropped_image
 
+# NEW CODE HERE:
+def combine_videos(video_paths, output_path="outputs/final_long_video.mp4"):
+    """
+    Concatenate a list of MP4 videos into one MP4.
+    """
+    clips = [VideoFileClip(vp) for vp in video_paths]
+    final_clip = concatenate_videoclips(clips, method="compose")
+    final_clip.write_videofile(output_path, codec="libx264", fps=clips[0].fps, audio=False)
+    return output_path
+
+# NEW CODE HERE:
+# We create a helper function that returns both the frames and the snippet path
+def generate_snippet(
+    init_image: Image,
+    seed: int,
+    motion_bucket_id: int,
+    fps_id: int,
+    decoding_t: int = 3,
+    device: str = "cuda",
+    output_folder: str = "outputs"
+):
+    """
+    Generate a short snippet from `init_image` using the pipeline.
+    Returns: (frames, video_path)
+    """
+    generator = torch.manual_seed(seed)
+    os.makedirs(output_folder, exist_ok=True)
+    base_count = len(glob(os.path.join(output_folder, "*.mp4")))
+    video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")
+
+    # Generate frames
+    result = pipe(
+        init_image,
+        decode_chunk_size=decoding_t,
+        generator=generator,
+        motion_bucket_id=motion_bucket_id,
+        noise_aug_strength=0.1,
+        num_frames=25
+    )
+    frames = result.frames[0]  # a list of PIL images
+
+    # Save snippet
+    export_to_video(frames, video_path, fps=fps_id)
+
+    return frames, video_path
+
+@spaces.GPU(duration=120)
+def sample_long(
+    image: Image,
+    seed: Optional[int] = 42,
+    randomize_seed: bool = True,
+    motion_bucket_id: int = 127,
+    fps_id: int = 6,
+    cond_aug: float = 0.02,
+    decoding_t: int = 3,  # Number of frames decoded at a time! This can be lowered if VRAM is an issue.
+    device: str = "cuda",
+    output_folder: str = "outputs",
+    progress=gr.Progress(track_tqdm=True)
+):
+    """
+    Generate 5 snippets in a row. Each new snippet starts from the last frame of the previous snippet.
+    Return the path to the final, concatenated MP4.
+    """
+    if image.mode == "RGBA":
+        image = image.convert("RGB")
+
+    if randomize_seed:
+        seed = random.randint(0, max_64_bit_int)
+    torch.manual_seed(seed)
+
+    snippet_paths = []
+    current_image = image
+
+    for i in range(5):
+        frames, snippet_path = generate_snippet(
+            init_image=current_image,
+            seed=seed,
+            motion_bucket_id=motion_bucket_id,
+            fps_id=fps_id,
+            decoding_t=decoding_t,
+            device=device,
+            output_folder=output_folder
+        )
+        snippet_paths.append(snippet_path)
+
+        # Get the last frame for the next snippet
+        last_frame = frames[-1]  # PIL image
+        current_image = last_frame
+
+        # Optional: re-seed each time if you like randomness in every snippet
+        # Otherwise, keep the same seed for a more cohesive “style”
+        # If you want random seeds each snippet, uncomment:
+        # seed = random.randint(0, max_64_bit_int)
+
+    # Concatenate all snippets
+    final_video_path = os.path.join(output_folder, "final_long_video.mp4")
+    final_video_path = combine_videos(snippet_paths, output_path=final_video_path)
+
+    return final_video_path, seed
+
+
 with gr.Blocks() as demo:
-  gr.Markdown('''# Community demo for Stable Video Diffusion - Img2Vid - XT ([model](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt), [paper](https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets), [stability's ui waitlist](https://stability.ai/contact))
-#### Research release ([_non-commercial_](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/blob/main/LICENSE)): generate `4s` vid from a single image at (`25 frames` at `6 fps`). this demo uses [🧨 diffusers for low VRAM and fast generation](https://huggingface.co/docs/diffusers/main/en/using-diffusers/svd).
-  ''')
-  with gr.Row():
-    with gr.Column():
-        image = gr.Image(label="Upload your image", type="pil")
-        generate_btn = gr.Button("Generate")
-    video = gr.Video()
-  with gr.Accordion("Advanced options", open=False):
-      seed = gr.Slider(label="Seed", value=42, randomize=True, minimum=0, maximum=max_64_bit_int, step=1)
-      randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-      motion_bucket_id = gr.Slider(label="Motion bucket id", info="Controls how much motion to add/remove from the image", value=127, minimum=1, maximum=255)
-      fps_id = gr.Slider(label="Frames per second", info="The length of your video in seconds will be 25/fps", value=6, minimum=5, maximum=30)
-      
-  image.upload(fn=resize_image, inputs=image, outputs=image, queue=False)
-  generate_btn.click(fn=sample, inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id], outputs=[video, seed], api_name="video")
-  gr.Examples(
-    examples=[
-        "images/blink_meme.png",
-        "images/confused2_meme.png",
-        "images/disaster_meme.png",
-        "images/distracted_meme.png",
-        "images/hide_meme.png",
-        "images/nazare_meme.png",
-        "images/success_meme.png",
-        "images/willy_meme.png",
-        "images/wink_meme.png"
-    ],
-    inputs=image,
-    outputs=[video, seed],
-    fn=sample,
-    cache_examples="lazy",
-  )
+    gr.Markdown('''# Community demo for Stable Video Diffusion - Img2Vid - XT 
+    ([model](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt), 
+    [paper](https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets), 
+    [stability's ui waitlist](https://stability.ai/contact))
+    #### Research release ([_non-commercial_](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/blob/main/LICENSE)):
+    Generate a longer video by chaining together multiple short snippets.
+    ''')
+    
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(label="Upload your image", type="pil")
+            generate_btn = gr.Button("Generate Long Video (5 snippets)")
+        video = gr.Video()
+
+    with gr.Accordion("Advanced options", open=False):
+        seed = gr.Slider(
+            label="Seed",
+            value=42,
+            randomize=True,
+            minimum=0,
+            maximum=max_64_bit_int,
+            step=1
+        )
+        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        motion_bucket_id = gr.Slider(
+            label="Motion bucket id",
+            info="Controls how much motion to add/remove from the image",
+            value=127,
+            minimum=1,
+            maximum=255
+        )
+        fps_id = gr.Slider(
+            label="Frames per second",
+            info="The length of your video in seconds will be 25/fps",
+            value=6,
+            minimum=5,
+            maximum=30
+        )
+    
+    # Automatically resize on image upload
+    image.upload(fn=resize_image, inputs=image, outputs=image, queue=False)
+
+    # NEW: Generate a *long* video composed of 5 short snippets
+    generate_btn.click(
+        fn=sample_long, 
+        inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id], 
+        outputs=[video, seed], 
+        api_name="video"
+    )
+
+    # You can still provide examples as you did before, but now the 
+    # pipeline will chain 5 videos by default.
+    gr.Examples(
+        examples=[
+            "images/blink_meme.png",
+            "images/confused2_meme.png",
+            "images/disaster_meme.png",
+            "images/distracted_meme.png",
+            "images/hide_meme.png",
+            "images/nazare_meme.png",
+            "images/success_meme.png",
+            "images/willy_meme.png",
+            "images/wink_meme.png"
+        ],
+        inputs=image,
+        outputs=[video, seed],
+        fn=sample_long,
+        cache_examples="lazy",
+    )
 
 if __name__ == "__main__":
-    #demo.queue(max_size=20, api_open=False)
     demo.launch(share=True, show_api=False)