| 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" | |
| def change_radio(input): | |
| return input | |
| def output_radio(output): | |
| print(output) | |
| def predict(canny, lt, ht, prompt, neg_prompt, ins, gs, seed): | |
| print(canny, lt, ht, prompt, neg_prompt, ins, gs) | |
| ''' | |
| 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) | |
| controlnet_repo_id = "calihyper/kor-portrait-controlnet" | |
| controlnet = ControlNetModel.from_pretrained(controlnet_repo_id, torch_dtype=torch.float16) | |
| ''' | |
| repo_id = "calihyper/trad-kor-landscape-black" | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| repo_id, torch_dtype=torch.float16 | |
| ) | |
| generator = torch.manual_seed(seed) | |
| output = pipe( | |
| 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() as controlnet: | |
| canny_image = gr.Image(label="cannyimage", 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") | |
| 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("์คํ") | |
| btn1.click(predict, [canny_image, lt, ht, prompt, neg_prompt, ins, gs, seed], out_image) | |
| if __name__ == "__main__": | |
| demo.launch() |