.

app.py

@@ -3,7 +3,6 @@ import spaces
 #import gradio.helpers
 import torch
 import os
-import shutil
 from glob import glob
 from pathlib import Path
 from typing import Optional
@@ -18,40 +17,19 @@ from huggingface_hub import hf_hub_download
 
 #gradio.helpers.CACHED_FOLDER = '/data/cache'
 
-# OPTIONAL: Clear caches at startup to free space
-hf_cache = os.path.expanduser("~/.cache/huggingface")
-torch_cache = os.path.expanduser("~/.cache/torch")
-if os.path.exists(hf_cache):
-    shutil.rmtree(hf_cache)
-if os.path.exists(torch_cache):
-    shutil.rmtree(torch_cache)
-
-# Configure ZeroGPU to use memory instead of disk
-from spaces.zero.config import Config
-Config.zerogpu_offload_dir = None  # Disable disk offloading to prevent disk space issues
-
 # Load the pipeline with authentication token
 pipe = StableVideoDiffusionPipeline.from_pretrained(
     "stabilityai/stable-video-diffusion-img2vid-xt",
     torch_dtype=torch.float16,
     variant="fp16",
-    use_auth_token=os.getenv("HUGGINGFACE_TOKEN")  # Fetch the token from environment if set
 )
 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)
 
 max_64_bit_int = 2**63 - 1
 
-def clean_outputs(output_folder: str, keep: int = 1):
-    """
-    Remove old video files to prevent using all disk space.
-    Keeps the most recent <keep> files.
-    """
-    files = sorted(glob(os.path.join(output_folder, "*.mp4")), key=os.path.getmtime)
-    if len(files) > keep:
-        for old_file in files[:-keep]:
-            os.remove(old_file)
-
-@spaces.GPU(duration=250)
+@spaces.GPU(duration=120)
 def sample(
     image: Image,
     seed: Optional[int] = 42,
@@ -76,75 +54,62 @@ def sample(
     base_count = len(glob(os.path.join(output_folder, "*.mp4")))
     video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")
 
-    # Reduce num_frames from 25 to 10 to consume less space
-    frames = pipe(
-        image, 
-        decode_chunk_size=decoding_t, 
-        generator=generator, 
-        motion_bucket_id=motion_bucket_id, 
-        noise_aug_strength=0.1, 
-        num_frames=10  # reduced from 25
-    ).frames[0]
-    
+    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)
-
-    # Clean up old videos to prevent filling disk
-    clean_outputs(output_folder, keep=2)
     
     return video_path, seed
 
 def resize_image(image, output_size=(1024, 576)):
     # Calculate aspect ratios
-    target_aspect = output_size[0] / output_size[1]
-    image_aspect = image.width / image.height
+    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))
     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 (`10 frames` at `6 fps`). This demo uses [🧨 diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/svd) for low VRAM usage.
-    ''')
-    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 num_frames/fps", value=6, minimum=5, maximum=30)
-
-    # Resize on upload
-    image.upload(fn=resize_image, inputs=image, outputs=image, queue=False)
-
-    # Generate with sample() function
-    generate_btn.click(
-        fn=sample,
-        inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id],
-        outputs=[video, seed],
-        api_name="video"
-    )
+  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")
+  
 
 if __name__ == "__main__":
-    demo.launch(show_api=False)
+    #demo.queue(max_size=20, api_open=False)
+    demo.launch(share=True, show_api=False)