revert

app.py

@@ -1,128 +1,128 @@
 import gradio as gr
+import spaces
+#import gradio.helpers
 import torch
 import os
 from glob import glob
+from pathlib import Path
 from typing import Optional
+
 from diffusers import StableVideoDiffusionPipeline
-from diffusers.utils import export_to_video
+from diffusers.utils import load_image, export_to_video
 from PIL import Image
+
+import uuid
 import random
-from moviepy import VideoFileClip, concatenate_videoclips
+from huggingface_hub import hf_hub_download
+
+#gradio.helpers.CACHED_FOLDER = '/data/cache'
 
-# Load the Stable Video Diffusion Pipeline
 pipe = StableVideoDiffusionPipeline.from_pretrained(
-    "stabilityai/stable-video-diffusion-img2vid-xt", 
-    torch_dtype=torch.float16, 
-    variant="fp16"
+    "stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16"
 )
 pipe.to("cuda")
+#pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+#pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True)
 
-# Maximum seed value
 max_64_bit_int = 2**63 - 1
 
-# Resize and crop image to desired resolution
+@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)):
-    target_aspect = output_size[0] / output_size[1]
-    image_aspect = image.width / image.height
+    # 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
 
+    # 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
-        top, bottom = 0, output_size[1]
+        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)
-        left, right = 0, output_size[0]
+        # Calculate coordinates for cropping
+        left = 0
         top = (new_height - output_size[1]) / 2
+        right = output_size[0]
         bottom = (new_height + output_size[1]) / 2
 
-    return resized_image.crop((left, top, right, bottom))
-
-# Combine multiple video snippets into a single video
-def combine_videos(video_paths, output_path="outputs/final_long_video.mp4"):
-    os.makedirs("outputs", exist_ok=True)
-    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
-
-# Generate a video snippet from an input image
-def generate_snippet(
-    init_image: Image, seed: int, motion_bucket_id: int, fps_id: int, decoding_t: int, output_folder: str
-):
-    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")
-
-    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]
-    export_to_video(frames, video_path, fps=fps_id)
-
-    return frames[-1], video_path
-
-# Generate a long video composed of 5 short snippets
-def sample_long(
-    image: Image,
-    seed: Optional[int] = 42,
-    randomize_seed: bool = True,
-    motion_bucket_id: int = 127,
-    fps_id: int = 6,
-    decoding_t: int = 3,
-    output_folder: str = "outputs"
-):
-    if image.mode == "RGBA":
-        image = image.convert("RGB")
-    if randomize_seed:
-        seed = random.randint(0, max_64_bit_int)
-
-    snippet_paths = []
-    current_image = image
-    for _ in range(5):
-        current_image, snippet_path = generate_snippet(
-            init_image=current_image,
-            seed=seed,
-            motion_bucket_id=motion_bucket_id,
-            fps_id=fps_id,
-            decoding_t=decoding_t,
-            output_folder=output_folder
-        )
-        snippet_paths.append(snippet_path)
-
-    return combine_videos(snippet_paths), seed
+    # Crop the image
+    cropped_image = resized_image.crop((left, top, right, bottom))
+    return cropped_image
 
-# Build the Gradio interface
 with gr.Blocks() as demo:
-    gr.Markdown("### Stable Video Diffusion - Generate a Long Video")
-
-    with gr.Row():
-        with gr.Column():
-            image = gr.Image(label="Upload an image", type="pil")
-            generate_btn = gr.Button("Generate Long Video")
-        video_output = gr.Video()
-
-    with gr.Accordion("Advanced Options", open=False):
-        seed = gr.Slider(0, max_64_bit_int, value=42, step=1, label="Seed")
-        randomize_seed = gr.Checkbox(value=True, label="Randomize Seed")
-        motion_bucket_id = gr.Slider(1, 255, value=127, step=1, label="Motion Bucket ID")
-        fps_id = gr.Slider(5, 30, value=6, step=1, label="Frames Per Second")
-
-    generate_btn.click(
-        sample_long,
-        inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id],
-        outputs=[video_output, seed]
-    )
+  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",
+  )
 
 if __name__ == "__main__":
-    demo.launch(share=True)
+    #demo.queue(max_size=20, api_open=False)
+    demo.launch(share=True, show_api=False)