disk space fix test

app.py

@@ -3,6 +3,7 @@ import spaces
 #import gradio.helpers
 import torch
 import os
+import shutil
 from glob import glob
 from pathlib import Path
 from typing import Optional
@@ -17,19 +18,38 @@ from huggingface_hub import hf_hub_download
 
 #gradio.helpers.CACHED_FOLDER = '/data/cache'
 
+# OPTIONAL: Clear caches at startup to free space (comment out if you prefer not to)
+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)
+
 # 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 the environment variable
+    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)
+# Uncomment these lines only if you want experimental compilation (and if your environment supports it)
+# 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)
 def sample(
     image: Image,
@@ -55,62 +75,79 @@ def sample(
     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]
+    # 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]
+    
     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]  # Aspect ratio of the desired size
-    image_aspect = image.width / image.height  # Aspect ratio of the original image
+    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
 
 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.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"
+    )
+
+# REMOVED: gr.Examples(...) that referenced local images like "blink_meme.png" which don't exist
 
 if __name__ == "__main__":
-    #demo.queue(max_size=20, api_open=False)
-    demo.launch(share=True, show_api=False)
+    # remove `share=True` because it is not supported on Hugging Face Spaces
+    demo.launch(show_api=False)
+