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import gradio as gr
from diffusers import StableDiffusionPipeline, ControlNetModel, StableDiffusionControlNetPipeline
from diffusers.utils import load_image
import torch
import cv2
import numpy as np
from PIL import Image
is_show_controlnet = True
prompts = ""
neg_prompt = "chinese letter"
repo_id = "calihyper/trad-kor-landscape-black"
#pipe = StableDiffusionPipeline.from_pretrained(repo_id).to("mps")
pipe = StableDiffusionPipeline.from_pretrained(repo_id)
def change_radio(input):
return input
def output_radio(output):
print(output)
def predict(prompt, style_prompt, neg_prompt, ins, gs, seed):
generator = torch.manual_seed(seed)
global pipe
output = pipe(
prompt + style_prompt,
negative_prompt=neg_prompt,
generator=generator,
num_inference_steps=ins,
guidance_scale=gs
)
return output.images[0]
with gr.Blocks() as demo:
gr.Markdown("# Aiffelthon Choosa Project")
with gr.Row():
with gr.Column():
out_image = gr.Image(shape=(512,512))
with gr.Column() as diff:
prompt = gr.Textbox(placeholder="prompts", label="prompt")
style_prompt = gr.Textbox(placeholder="style prompts", label="style prompt")
examples = gr.Examples(examples=["<trad-kor-landscape-black>", "<trad-kor-landscape-ink-wash-painting>", "<trad-kor-landscape-thick-brush-strokes>", "<trad-kor-plants-black>", "<trad-kor-plants-color>"],
inputs=style_prompt, label="style examples")
neg_prompt = gr.Textbox(placeholder="negative prompts", value=neg_prompt, label="negative prompt")
ins = gr.Slider(1, 60, 30, label="inference steps")
gs = gr.Slider(1, 10, 2.5, step=1, label="guidance scale")
seed = gr.Slider(0, 10, 2, step=1, label="seed")
btn1 = gr.Button("실행")
btn1.click(predict, [ prompt, style_prompt, neg_prompt, ins, gs, seed], out_image)
if __name__ == "__main__":
demo.launch() |