Update app.py

app.py

@@ -1,4 +1,4 @@
-import gradio as gr
+ import gradio as gr
 import torch
 import os
 from glob import glob
@@ -10,100 +10,85 @@ from PIL import Image
 import uuid
 import random
 from huggingface_hub import hf_hub_download
-
-
-max_64_bit_int = 2**63 - 1
-
+import spaces
 
 pipe = StableVideoDiffusionPipeline.from_pretrained(
-    "vdo/stable-video-diffusion-img2vid-xt-1-1", torch_dtype=torch.float16, variant="fp16"
+"vdo/stable-video-diffusion-img2vid-xt-1-1", torch_dtype=torch.float16, variant="fp16"
 )
 pipe.to("cpu")
 
+max_64_bit_int = 2**63 - 1
 
-def resize_image(image, output_size=(1024, 576)):
-    target_aspect = output_size[0] / output_size[1]  
-    image_aspect = image.width / image.height  
-
-    if image_aspect > target_aspect:
-        new_height = output_size[1]
-        new_width = int(new_height * image_aspect)
-        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
-        left = (new_width - output_size[0]) / 2
-        top = 0
-        right = (new_width + output_size[0]) / 2
-        bottom = output_size[1]
-    else:
-        new_width = output_size[0]
-        new_height = int(new_width / image_aspect)
-        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
-        left = 0
-        top = (new_height - output_size[1]) / 2
-        right = output_size[0]
-        bottom = (new_height + output_size[1]) / 2
-
-    cropped_image = resized_image.crop((left, top, right, bottom))
-    return cropped_image
-
-
-@gr.Blocks()
-def demo():
-    with gr.Row():
-        with gr.Column():
-            image = gr.Image(label="Upload your image", type="pil")
-            resize_btn = gr.Button("Resize image")
-            resized_image = gr.Image(label="Resized image")
-            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("Animate")
-        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(image):
-        return resize_image(image)
-
-
-    def animate(resized_image, progress, seed, randomize_seed, motion_bucket_id, fps_id):
-        if resized_image.mode == "RGBA":
-            resized_image = resized_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(resized_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
-
-
-    resize_btn.click(
-        fn=resize,
-        inputs=image,
-        outputs=resized_image,
-    )
-    generate_btn.click(
-        fn=animate,
-        inputs=[resized_image, progress, seed, randomize_seed, motion_bucket_id, fps_id],
-        outputs=[video, gallery, progress],
-    )
-
+@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,
+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 = 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, frames, seed
 
-demo.launch()
+def resize_image(image, output_size=(1024, 576)):
+target_aspect = output_size[0] / output_size[1]
+image_aspect = image.width / image.height
+
+if image_aspect > target_aspect:
+new_height = output_size[1]
+new_width = int(new_height * image_aspect)
+resized_image = image.resize((new_width, new_height), Image.LANCZOS)
+left = (new_width - output_size[0]) / 2
+top = 0
+right = (new_width + output_size[0]) / 2
+bottom = output_size[1]
+else:
+new_width = output_size[0]
+new_height = int(new_width / image_aspect)
+resized_image = image.resize((new_width, new_height), Image.LANCZOS)
+left = 0
+top = (new_height - output_size[1]) / 2
+right = output_size[0]
+bottom = (new_height + output_size[1]) / 2
+
+cropped_image = resized_image.crop((left, top, right, bottom))
+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")
+
+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, gallery, seed], api_name="video")
+
+if __name__ == "__main__":
+demo.launch(share=True, show_api=False)