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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("μ€ν") | |
| btn1.click(predict, [ prompt, style_prompt, neg_prompt, ins, gs, seed], out_image) | |
| if __name__ == "__main__": | |
| demo.launch() |