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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"
controlnet_repo_id = "calihyper/trad-kor-controlnet"
repo_id = "calihyper/trad-kor-landscape-black"
controlnet = ControlNetModel.from_pretrained(controlnet_repo_id)
pipe = StableDiffusionControlNetPipeline.from_pretrained(repo_id, controlnet=controlnet)
def change_radio(input):
return input
def output_radio(output):
print(output)
def predict(canny, lt, ht, prompt, style_prompt, neg_prompt, ins, gs, seed):
np_image = np.array(canny)
low_threshold = lt
high_threshold = ht
np_image = cv2.Canny(np_image, low_threshold, high_threshold)
np_image = np_image[:, :, None]
np_image = np.concatenate([np_image, np_image, np_image], axis=2)
canny_image = Image.fromarray(np_image)
generator = torch.manual_seed(seed)
global pipe
output = pipe(
prompt + style_prompt,
canny_image,
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() as controlnet:
canny_image = gr.Image(label="input_image", visible=is_show_controlnet , shape=(512,512), interactive=True)
controlnet_radio = gr.Radio([True, False], label="Use ControlNet")
lt = gr.Slider(50, 300, 120, step=1, label="Low threshold")
ht = gr.Slider(50, 300, 120, step=1, label="High threshold")
with gr.Column():
out_image = gr.Image()
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, [canny_image, lt, ht, prompt,style_prompt, neg_prompt, ins, gs, seed ], out_image)
if __name__ == "__main__":
demo.launch() |