Initial commit

app.py

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+import gradio as gr
+import torch
+import numpy as np
+import torchvision.transforms.functional as TF
+from diffusers import AutoencoderKLWan, WanImageToVideoPipeline
+from diffusers.utils import export_to_video, load_image
+from transformers import CLIPVisionModel
+
+def generate_video(first_frame_url, last_frame_url, prompt):
+    model_id = "Wan-AI/Wan2.1-FLF2V-14B-720P-diffusers"
+    image_encoder = CLIPVisionModel.from_pretrained(model_id, subfolder="image_encoder", torch_dtype=torch.float32)
+    vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
+    pipe = WanImageToVideoPipeline.from_pretrained(
+        model_id, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16
+    )
+    pipe.to("cuda")
+
+    first_frame = load_image(first_frame_url)
+    last_frame = load_image(last_frame_url)
+
+    def aspect_ratio_resize(image, pipe, max_area=720 * 1280):
+        aspect_ratio = image.height / image.width
+        mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
+        height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
+        width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
+        image = image.resize((width, height))
+        return image, height, width
+
+    def center_crop_resize(image, height, width):
+        resize_ratio = max(width / image.width, height / image.height)
+        width = round(image.width * resize_ratio)
+        height = round(image.height * resize_ratio)
+        size = [width, height]
+        image = TF.center_crop(image, size)
+        return image, height, width
+
+    first_frame, height, width = aspect_ratio_resize(first_frame, pipe)
+    if last_frame.size != first_frame.size:
+        last_frame, _, _ = center_crop_resize(last_frame, height, width)
+
+    output = pipe(
+        image=first_frame, last_image=last_frame, prompt=prompt, height=height, width=width, guidance_scale=5.5
+    ).frames[0]
+    video_path = "wan_output.mp4"
+    export_to_video(output, video_path, fps=16)
+    return video_path
+
+iface = gr.Interface(
+    fn=generate_video,
+    inputs=[
+        gr.Textbox(label="First Frame URL"),
+        gr.Textbox(label="Last Frame URL"),
+        gr.Textbox(label="Prompt")
+    ],
+    outputs=gr.Video(label="Generated Video"),
+    title="Wan2.1 FLF2V Video Generator"
+)
+
+iface.launch()
+

requirements.txt

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+diffusers
+transformers
+torch
+torchvision
+gradio