Create app.py

app.py

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+import gradio as gr
+import torch
+import numpy as np
+import modin.pandas as pd
+from PIL import Image
+from diffusers import DiffusionPipeline
+from huggingface_hub import login
+import os
+from glob import glob
+from pathlib import Path
+from typing import Optional
+import uuid
+import random
+
+token = os.environ['HF_TOKEN']
+login(token=token)
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+torch.cuda.max_memory_allocated(device=device)
+torch.cuda.empty_cache()
+pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt-1-1")
+#pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+
+pipe.enable_xformers_memory_efficient_attention()
+pipe = pipe.to(device)
+torch.cuda.empty_cache()
+
+max_64_bit_int = 2**63 - 1
+
+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_1-1",
+    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",):
+        
+    if image.mode == "RGBA":
+        image = image.convert("RGB")
+        
+    if(randomize_seed):
+        seed = random.randint(0, max_64_bit_int)
+    generator = torch.manual_seed(seed)
+    torch.cuda.empty_cache()
+    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)
+    torch.cuda.empty_cache()
+    return video_path, seed
+
+def resize_image(image, output_size=(1024, 578)):
+    # 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
+        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))
+    torch.cuda.empty_cache()
+    return cropped_image
+
+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",)# inputs=image, outputs=[video, seed], fn=sample, cache_examples=True,)
+
+if __name__ == "__main__":
+    demo.queue(max_size=20, api_open=False)
+    demo.launch(show_api=False)