Create app.py

app.py

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+import torch
+import gradio as gr
+from diffusers import AnimateDiffSparseControlNetPipeline, AutoencoderKL, MotionAdapter, SparseControlNetModel
+from diffusers.schedulers import DPMSolverMultistepScheduler
+from diffusers.utils import export_to_gif, load_image
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+def generate_video(prompt, negative_prompt, num_inference_steps, conditioning_frame_indices, controlnet_conditioning_scale):
+    motion_adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-3", torch_dtype=torch.float16).to(device)
+    controlnet = SparseControlNetModel.from_pretrained("guoyww/animatediff-sparsectrl-scribble", torch_dtype=torch.float16).to(device)
+    vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16).to(device)
+    scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, beta_schedule="linear", algorithm_type="dpmsolver++", use_karras_sigmas=True)
+    
+    pipe = AnimateDiffSparseControlNetPipeline.from_pretrained(
+        "SG161222/Realistic_Vision_V5.1_noVAE",
+        motion_adapter=motion_adapter,
+        controlnet=controlnet,
+        vae=vae,
+        scheduler=scheduler,
+        torch_dtype=torch.float16,
+    ).to(device)
+    
+    pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, beta_schedule="linear", algorithm_type="dpmsolver++", use_karras_sigmas=True)
+    pipe.load_lora_weights("guoyww/animatediff-motion-lora-v1-5-3", adapter_name="motion_lora")
+    pipe.fuse_lora(lora_scale=1.0)
+    
+    image_files = [
+        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-scribble-1.png",
+        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-scribble-2.png",
+        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-scribble-3.png"
+    ]
+    conditioning_frames = [load_image(img_file) for img_file in image_files]
+
+    video = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=num_inference_steps,
+        conditioning_frames=conditioning_frames,
+        controlnet_conditioning_scale=controlnet_conditioning_scale,
+        controlnet_frame_indices=conditioning_frame_indices,
+        generator=torch.Generator().manual_seed(1337),
+    ).frames[0]
+    
+    export_to_gif(video, "output.gif")
+    return "output.gif"
+
+demo = gr.Interface(
+    fn=generate_video,
+    inputs=[
+        gr.Textbox(label="Prompt", default="an aerial view of a cyberpunk city, night time, neon lights, masterpiece, high quality"),
+        gr.Textbox(label="Negative Prompt", default="low quality, worst quality, letterboxed"),
+        gr.Slider(label="Number of Inference Steps", minimum=1, maximum=100, step=1, default=25),
+        gr.Textbox(label="Conditioning Frame Indices", default="[0, 8, 15]"),
+        gr.Slider(label="ControlNet Conditioning Scale", minimum=0.1, maximum=2.0, step=0.1, default=1.0)
+    ],
+    outputs=gr.Image(label="Generated Video"),
+    title="Generate Video with AnimateDiff",
+    description="Generate a video using the AnimateDiffSparseControlNetPipeline."
+)
+
+demo.launch(server_name="0.0.0.0", server_port=7910)