Update app.py

app.py

@@ -12,80 +12,99 @@ import random
 from huggingface_hub import hf_hub_download
 import spaces
 
+
+MAX_64_BIT_INT = 2**63 - 1
+DEFAULT_SEED = 42
+DEFAULT_OUTPUT_FOLDER = "outputs"
+
+
 pipe = StableVideoDiffusionPipeline.from_pretrained("vdo/stable-video-diffusion-img2vid-xt-1-1", torch_dtype=torch.float16, variant="fp16")
 pipe.to("cpu")
-max_64_bit_int = 2**63 - 1
-@spaces.GPU(duration=120)
 
+
+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
+
+
+def generate_video(image, seed, motion_bucket_id, fps_id):
+    if image.mode == "RGBA":
+        image = image.convert("RGB")
+
+    generator = torch.manual_seed(seed)
+    frames = pipe(image, decode_chunk_size=3, generator=generator, motion_bucket_id=motion_bucket_id, noise_aug_strength=0.1, num_frames=25).frames[0]
+    return frames
+
+
+def export_video(frames, video_path, fps_id):
+    export_to_video(frames, video_path, fps=fps_id)
+
+
+@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",
+    image,
+    seed=DEFAULT_SEED,
+    randomize_seed=True,
+    motion_bucket_id=127,
+    fps_id=6,
+    version="svd_xt",
+    cond_aug=0.02,
+    decoding_t=3,
+    device="cuda",
+    output_folder=DEFAULT_OUTPUT_FOLDER,
 ):
-if image.mode == "RGBA":
-image = image.convert("RGB")
-
-if(randomize_seed):
-seed = random.randint(0, max_64_bit_int)
-generator = torch.manual_seed(seed)
+    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")
+    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)
+    frames = generate_video(image, seed, motion_bucket_id, fps_id)
+    export_video(frames, video_path, fps_id)
+    torch.manual_seed(seed)
 
-return video_path, frames, seed
+    return video_path, frames, seed
 
-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")
+    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=DEFAULT_SEED, 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)
+    demo.launch(share=True, show_api=False)