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import gradio as gr
import torch
import os
import spaces
import uuid
from diffusers import AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler
from diffusers.utils import export_to_video
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from PIL import Image
# Constants
base = "frankjoshua/toonyou_beta6"
repo = "ByteDance/AnimateDiff-Lightning"
checkpoints = {
"1-Step" : ["animatediff_lightning_1step_diffusers.safetensors", 1],
"2-Step" : ["animatediff_lightning_2step_diffusers.safetensors", 2],
"4-Step" : ["animatediff_lightning_4step_diffusers.safetensors", 4],
"8-Step" : ["animatediff_lightning_8step_diffusers.safetensors", 8],
}
loaded = None
# Ensure model and scheduler are initialized in GPU-enabled function
if torch.cuda.is_available():
device = "cuda"
dtype = torch.float16
adapter = MotionAdapter().to(device, dtype)
pipe = AnimateDiffPipeline.from_pretrained(base, motion_adapter=adapter, torch_dtype=dtype).to(device)
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", beta_schedule="linear")
else:
raise NotImplementedError("No GPU detected!")
# Function
@spaces.GPU(enable_queue=True)
def generate_image(prompt, ckpt):
global loaded
print(prompt, ckpt)
checkpoint = checkpoints[ckpt][0]
num_inference_steps = checkpoints[ckpt][1]
if loaded != num_inference_steps:
pipe.unet.load_state_dict(load_file(hf_hub_download(repo, checkpoint), device=device), strict=False)
loaded = num_inference_steps
output = pipe(prompt=prompt, guidance_scale=1.0, num_inference_steps=num_inference_steps)
name = str(uuid.uuid4()).replace("-", "")
path = f"/tmp/{name}.mp4"
export_to_video(output.frames[0], path, fps=10)
return path
# Gradio Interface
with gr.Blocks(css="style.css") as demo:
gr.HTML("<h1><center>AnimateDiff-Lightning ⚡</center></h1>")
gr.HTML("<p><center>Lightning-fast text-to-video generation</center></p><p><center><a href='https://huggingface.co/ByteDance/AnimateDiff-Lightning'>https://huggingface.co/ByteDance/AnimateDiff-Lightning</a></center></p>")
with gr.Group():
with gr.Row():
prompt = gr.Textbox(label='Enter your prompt (English)', scale=8)
ckpt = gr.Dropdown(label='Select inference steps',choices=['1-Step', '2-Step', '4-Step', '8-Step'], value='4-Step', interactive=True)
submit = gr.Button(scale=1, variant='primary')
video = gr.Video(label='AnimateDiff-Lightning Generated Image')
prompt.submit(
fn=generate_image,
inputs=[prompt, ckpt],
outputs=video,
)
submit.click(
fn=generate_image,
inputs=[prompt, ckpt],
outputs=video,
)
demo.queue().launch() |