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from diffusers import DiffusionPipeline,UniPCMultistepScheduler
import gradio as gr
import torch
import gc
from style_trsfer import style_transfer_method
def generate(style_image,text, negative_prompts,steps,guidance_scale):
pipeline = DiffusionPipeline.from_pretrained("./CCLAP")
pipeline.scheduler = UniPCMultistepScheduler.from_config(
pipeline.scheduler.config)
device = torch.device(
'cuda:0' if torch.cuda.is_available() else 'cpu')
if device.type == 'cuda':
pipeline.enable_xformers_memory_efficient_attention()
pipeline.to(device)
torch.cuda.empty_cache()
gc.collect()
content_image = pipeline(text,
num_inference_steps=steps,
negative_prompt=negative_prompts,
guidance_scale=guidance_scale).images[0]
result = style_transfer_method(content_image,style_image)
return content_image,result
if __name__ == '__main__':
demo = gr.Interface(title="CCLAP",
description = (
"This is the demo of CCLAP to generate Chinese landscape painting."
),
css="",
fn=generate,
inputs=[gr.Image(label="Style Image",shape=(512,512)),
gr.Textbox(lines=3, placeholder="Input the prompt", label="Prompt"),
gr.Textbox(lines=3, placeholder="low quality", label="Negative prompt"),
gr.Slider(minimum=0, maximum=100, value=20,label='Steps'),
gr.Slider(minimum=0, maximum=30, value=7.5,label='Guidance_scale'),
],
outputs=[gr.Image(label="Content Output",shape=(256,256)),
gr.Image(label="Final Output",shape=(256,256))],
examples = [
[
'style_image/style1.jpg',
'A Chinese landscape painting of a mountain landscape with trees',
'low quality',
20,
7.5
],
[
'style_image/style2.jpg',
'A Chinese landscape painting of a building with trees in front of it',
'low quality',
20,
7.5
],
[
'style_image/style3.jpg',
'A Chinese landscape painting of a landscape with mountains in the background',
'low quality',
20,
7.5
],
[
'style_image/style4.jpg',
'A Chinese landscape painting of a landscape with mountains and a river',
'low quality',
20,
7.5
],
],
)
demo.launch() |