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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)
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