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import torch
import gradio as gr
from diffusers import AnimateDiffSparseControlNetPipeline, AutoencoderKL, MotionAdapter, SparseControlNetModel, AnimateDiffPipeline, EulerAncestralDiscreteScheduler
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)
pipe = AnimateDiffSparseControlNetPipeline.from_pretrained(
"SG161222/Realistic_Vision_V5.1_noVAE",
motion_adapter=motion_adapter,
controlnet=controlnet,
vae=vae,
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=[int(x) for x in conditioning_frame_indices.split(",")],
generator=torch.Generator().manual_seed(1337),
).frames
output_file = "output.gif"
export_to_gif(video, output_file)
return output_file
def generate_simple_video(prompt):
adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16).to(device)
pipe = AnimateDiffPipeline.from_pretrained("SG161222/Realistic_Vision_V6.0_B1_noVAE", motion_adapter=adapter, torch_dtype=torch.float16).to(device)
pipe.scheduler = EulerAncestralDiscreteScheduler(
beta_schedule="linear",
beta_start=0.00085,
beta_end=0.012,
)
pipe.enable_free_noise()
pipe.vae.enable_slicing()
pipe.enable_model_cpu_offload()
frames = pipe(
prompt,
num_frames=64,
num_inference_steps=20,
guidance_scale=7.0,
decode_chunk_size=2,
).frames
output_file = "simple_output.gif"
export_to_gif(frames, output_file)
return output_file
demo1 = gr.Interface(
fn=generate_video,
inputs=[
gr.Textbox(label="Prompt", value="an aerial view of a cyberpunk city, night time, neon lights, masterpiece, high quality"),
gr.Textbox(label="Negative Prompt", value="low quality, worst quality, letterboxed"),
gr.Slider(label="Number of Inference Steps", minimum=1, maximum=100, step=1, value=25),
gr.Textbox(label="Conditioning Frame Indices", value="0, 8, 15"),
gr.Slider(label="ControlNet Conditioning Scale", minimum=0.1, maximum=2.0, step=0.1, value=1.0)
],
outputs=gr.Video(label="Generated Video"),
title="Generate Video with AnimateDiffSparseControlNetPipeline",
description="Generate a video using the AnimateDiffSparseControlNetPipeline."
)
demo2 = gr.Interface(
fn=generate_simple_video,
inputs=gr.Textbox(label="Prompt", value="An astronaut riding a horse on Mars."),
outputs=gr.Video(label="Generated Simple Video"),
title="Generate Simple Video with AnimateDiff",
description="Generate a simple video using the AnimateDiffPipeline."
)
demo = gr.TabbedInterface([demo1, demo2], ["Advanced Video Generation", "Simple Video Generation"])
demo.launch()
#demo.launch(server_name="0.0.0.0", server_port=7910)
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