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Running
on
L4
| from share import * | |
| import config | |
| 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 cldm.model import create_model, load_state_dict | |
| from cldm.ddim_hacked import DDIMSampler | |
| model_name = 'control_v11p_sd15_inpaint' | |
| model = create_model(f'./models/{model_name}.yaml').cpu() | |
| model.load_state_dict(load_state_dict('./models/v1-5-pruned.ckpt', location='cuda'), strict=False) | |
| model.load_state_dict(load_state_dict(f'./models/{model_name}.pth', location='cuda'), strict=False) | |
| model = model.cuda() | |
| ddim_sampler = DDIMSampler(model) | |
| def process(input_image_and_mask, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, seed, eta, mask_blur): | |
| with torch.no_grad(): | |
| input_image = HWC3(input_image_and_mask['image']) | |
| input_mask = input_image_and_mask['mask'] | |
| img_raw = resize_image(input_image, image_resolution).astype(np.float32) | |
| H, W, C = img_raw.shape | |
| mask_pixel = cv2.resize(input_mask[:, :, 0], (W, H), interpolation=cv2.INTER_LINEAR).astype(np.float32) / 255.0 | |
| mask_pixel = cv2.GaussianBlur(mask_pixel, (0, 0), mask_blur) | |
| mask_latent = cv2.resize(mask_pixel, (W // 8, H // 8), interpolation=cv2.INTER_AREA) | |
| detected_map = img_raw.copy() | |
| detected_map[mask_pixel > 0.5] = - 255.0 | |
| control = torch.from_numpy(detected_map.copy()).float().cuda() / 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() | |
| mask = 1.0 - torch.from_numpy(mask_latent.copy()).float().cuda() | |
| mask = torch.stack([mask for _ in range(num_samples)], dim=0) | |
| mask = einops.rearrange(mask, 'b h w -> b 1 h w').clone() | |
| x0 = torch.from_numpy(img_raw.copy()).float().cuda() / 127.0 - 1.0 | |
| x0 = torch.stack([x0 for _ in range(num_samples)], dim=0) | |
| x0 = einops.rearrange(x0, 'b h w c -> b c h w').clone() | |
| mask_pixel_batched = mask_pixel[None, :, :, None] | |
| img_pixel_batched = img_raw.copy()[None] | |
| if seed == -1: | |
| seed = random.randint(0, 65535) | |
| seed_everything(seed) | |
| if config.save_memory: | |
| model.low_vram_shift(is_diffusing=False) | |
| 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) | |
| if config.save_memory: | |
| model.low_vram_shift(is_diffusing=False) | |
| ddim_sampler.make_schedule(ddim_steps, ddim_eta=eta, verbose=True) | |
| x0 = model.get_first_stage_encoding(model.encode_first_stage(x0)) | |
| if config.save_memory: | |
| model.low_vram_shift(is_diffusing=True) | |
| 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, x0=x0, mask=mask) | |
| if config.save_memory: | |
| model.low_vram_shift(is_diffusing=False) | |
| 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().astype(np.float32) | |
| x_samples = x_samples * mask_pixel_batched + img_pixel_batched * (1.0 - mask_pixel_batched) | |
| results = [x_samples[i].clip(0, 255).astype(np.uint8) for i in range(num_samples)] | |
| return [detected_map.clip(0, 255).astype(np.uint8)] + results | |
| block = gr.Blocks().queue() | |
| with block: | |
| with gr.Row(): | |
| gr.Markdown("## Control Stable Diffusion with Inpaint Mask") | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image(source='upload', type="numpy", tool="sketch") | |
| prompt = gr.Textbox(label="Prompt") | |
| run_button = gr.Button(label="Run") | |
| num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1) | |
| seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, value=12345) | |
| mask_blur = gr.Slider(label="Mask Blur", minimum=0.1, maximum=7.0, value=5.0, step=0.01) | |
| with gr.Accordion("Advanced options", open=False): | |
| 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) | |
| 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=9.0, step=0.1) | |
| eta = gr.Slider(label="DDIM ETA", minimum=0.0, maximum=1.0, value=1.0, step=0.01) | |
| a_prompt = gr.Textbox(label="Added Prompt", value='best quality') | |
| n_prompt = gr.Textbox(label="Negative Prompt", value='lowres, bad anatomy, bad hands, cropped, worst quality') | |
| with gr.Column(): | |
| result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto') | |
| ips = [input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, seed, eta, mask_blur] | |
| run_button.click(fn=process, inputs=ips, outputs=[result_gallery]) | |
| block.launch(server_name='0.0.0.0') | |