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| from share import * | |
| import config | |
| import os | |
| import cv2 | |
| import einops | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| import random | |
| from pytorch_lightning import seed_everything | |
| from annotator.util import resize_image, HWC3 | |
| from annotator.uniformer import UniformerDetector | |
| from cldm.model import create_model, load_state_dict | |
| from cldm.ddim_hacked import DDIMSampler | |
| from PIL import Image | |
| # os.environ["no_proxy"] = "localhost,127.0.0.1,::1" | |
| device = "cpu" | |
| model = create_model('./models/cldm_v15_cpu.yaml').cpu() | |
| sd_model_path = "./models/sks_crack_ppl.ckpt" | |
| controlnet_path = "./models/sks_crack_controlnet.pth" | |
| model.load_state_dict(load_state_dict(sd_model_path, location='cpu'), strict = False) | |
| model.load_state_dict(load_state_dict(controlnet_path, location='cpu'), strict = False) | |
| # model = model.cuda() | |
| ddim_sampler = DDIMSampler(model) | |
| init_mask = Image.open("379.png").convert("L") | |
| def model_sample(mask, | |
| prompt = "sks crack, pavement cracks, HDR, Asphalt road, mudded", | |
| a_prompt="", | |
| n_prompt="", | |
| num_samples=1, ddim_steps=50, guess_mode=False, strength=1.0, scale=7.0, seed=-1, eta=0.0): | |
| # mask --- numpy | |
| ddim_sampler = DDIMSampler(model) | |
| with torch.no_grad(): | |
| mask = HWC3(mask) | |
| mask = resize_image(mask, 512) | |
| H, W, C= mask.shape | |
| control = torch.from_numpy(mask.copy()).float().to(device) / 255.0 | |
| control = torch.stack([control for _ in range(num_samples)], dim=0) | |
| control = einops.rearrange(control, 'b h w c -> b c h w').clone() | |
| if seed == -1: | |
| seed = random.randint(0, 65535) | |
| seed_everything(seed) | |
| cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]} | |
| un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]} | |
| shape = (4, H // 8, W // 8) | |
| model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13) # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01 | |
| samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples, | |
| shape, cond, verbose=False, eta=eta, | |
| unconditional_guidance_scale=scale, | |
| unconditional_conditioning=un_cond) | |
| x_samples = model.decode_first_stage(samples) | |
| x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8) | |
| results = [x_samples[i] for i in range(num_samples)] | |
| return results | |
| block = gr.Blocks().queue() | |
| with block: | |
| with gr.Row(): | |
| gr.Markdown("## Crack Diffusion") | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Tabs(elem_id="mode_img2img"): | |
| with gr.TabItem('txt2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img: | |
| init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="numpy", tool="editor", image_mode="L", value=init_mask).style(height=480) | |
| init_run_button = gr.Button(label="Run Init") | |
| with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch: | |
| sketch_img = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="canvas", interactive=True, type="numpy", tool="color-sketch", image_mode="L").style(height=480) | |
| sketch_run_button = gr.Button(label="Run Sketch") | |
| prompt = gr.Textbox(label="Prompt", value="sks crack") | |
| with gr.Row(): | |
| with gr.Accordion("Advanced options", open=False): | |
| num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1) | |
| image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=64) | |
| strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01) | |
| guess_mode = gr.Checkbox(label='Guess Mode', value=False) | |
| detect_resolution = gr.Slider(label="Segmentation Resolution", minimum=128, maximum=1024, value=512, step=1) | |
| ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1) | |
| scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=7.0, step=0.1) | |
| seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True) | |
| eta = gr.Number(label="eta (DDIM)", value=0.0) | |
| a_prompt = gr.Textbox(label="Added Prompt", value='') | |
| n_prompt = gr.Textbox(label="Negative Prompt", | |
| value='') | |
| with gr.Column(): | |
| result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto') | |
| init_ips = [init_img, prompt, a_prompt, n_prompt, num_samples, ddim_steps, guess_mode, strength, scale, seed, eta] | |
| sketch_ips = [sketch_img, prompt, a_prompt, n_prompt, num_samples, ddim_steps, guess_mode, strength, scale, seed, eta] | |
| init_run_button.click(fn=model_sample, inputs=init_ips, outputs=[result_gallery]) | |
| sketch_run_button.click(fn=model_sample, inputs=sketch_ips, outputs=[result_gallery]) | |
| block.launch() | |