Update app.py

app.py

@@ -10,49 +10,15 @@ from PIL import Image
 import uuid
 import random
 from huggingface_hub import hf_hub_download
-import spaces
-from tqdm import tqdm
+
 
 max_64_bit_int = 2**63 - 1
 
-pipe = StableVideoDiffusionPipeline.from_pretrained("vdo/stable-video-diffusion-img2vid-xt-1-1", torch_dtype=torch.float16, variant="fp16")
+
+pipe = StableVideoDiffusionPipeline.from_pretrained(
+    "vdo/stable-video-diffusion-img2vid-xt-1-1", torch_dtype=torch.float16, variant="fp16"
+)
 pipe.to("cpu")
-@spaces.GPU(duration=120)
-
-def sample(
-    progress: gr.Progress,
-    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,  
-    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)
-    
-    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 = []
-    for i in tqdm(range(25), desc="Generando frames"):
-        frame = pipe(image, decode_chunk_size=decoding_t, generator=generator, motion_bucket_id=motion_bucket_id, noise_aug_strength=0.1, num_frames=1).frames[0]
-        frames.extend(frame)
-        progress.update(i/25)
-
-    export_to_video(frames, video_path, fps=fps_id)
-    torch.manual_seed(seed)
-    
-    return video_path, frames, seed
 
 
 def resize_image(image, output_size=(1024, 576)):
@@ -80,24 +46,55 @@ def resize_image(image, output_size=(1024, 576)):
     return cropped_image
 
 
-with gr.Blocks() as demo:
-  with gr.Row():
-      with gr.Column():
-          image = gr.Image(label="Upload your image", type="pil")
-          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)
-          generate_btn = gr.Button(value="Animate", variant="primary")
-      with gr.Column():
-          video = gr.Video(label="Generated video")
-          gallery = gr.Gallery(label="Generated frames")
-          progress = gr.Progress(label="Progress")
-      
-  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, "svd_xt", 0.02, 3, "cuda", "outputs", progress], outputs=[video, gallery, seed, progress], api_name="video")
-
-
-if __name__ == "__main__":
-    demo.launch(share=True, show_api=False)
+@gr.Blocks()
+def demo():
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(label="Upload your image", type="pil")
+            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)
+            generate_btn = gr.Button(value="Animate", variant="primary")
+        with gr.Column():
+            video = gr.Video(label="Generated video")
+            gallery = gr.Gallery(label="Generated frames")
+            progress_label = gr.Label("Progress")
+            progress = gr.Progress()
+
+
+    def resize_and_animate(image, progress, seed, randomize_seed, motion_bucket_id, fps_id):
+        image = resize_image(image)
+        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("outputs", exist_ok=True)
+        base_count = len(glob(os.path.join("outputs", "*.mp4")))
+        video_path = os.path.join("outputs", f"{base_count:06d}.mp4")
+
+        frames = []
+        for i in range(25):
+            frame = pipe(image, decode_chunk_size=3, generator=generator, motion_bucket_id=motion_bucket_id, noise_aug_strength=0.1, num_frames=1).frames[0]
+            frames.extend(frame)
+            progress.update((i+1)/25)
+
+        export_to_video(frames, video_path, fps=fps_id)
+        torch.manual_seed(seed)
+        
+        return video_path, frames, seed
+
+
+    image.upload(fn=resize_image, inputs=image, outputs=image, queue=False)
+    generate_btn.click(
+        fn=resize_and_animate,
+        inputs=[image, progress, seed, randomize_seed, motion_bucket_id, fps_id],
+        outputs=[video, gallery, progress],
+    )
+
+
+demo.launch()