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| import cv2 | |
| import einops | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| from pytorch_lightning import seed_everything | |
| from util import resize_image, HWC3, apply_canny | |
| from ldm.models.diffusion.ddim import DDIMSampler | |
| from cldm.model import create_model, load_state_dict | |
| from huggingface_hub import hf_hub_url, cached_download | |
| REPO_ID = "lllyasviel/ControlNet" | |
| FILENAME = "models/control_sd15_canny.pth" | |
| model = create_model('./models/cldm_v15.yaml') | |
| model.load_state_dict(load_state_dict(cached_download( | |
| hf_hub_url(REPO_ID, FILENAME) | |
| ), location='cpu')) | |
| ddim_sampler = DDIMSampler(model) | |
| def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta, low_threshold, high_threshold): | |
| with torch.no_grad(): | |
| img = resize_image(HWC3(input_image), image_resolution) | |
| H, W, C = img.shape | |
| detected_map = apply_canny(img, low_threshold, high_threshold) | |
| detected_map = HWC3(detected_map) | |
| control = torch.from_numpy(detected_map.copy()).float() / 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() | |
| seed_everything(seed) | |
| cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]} | |
| un_cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]} | |
| shape = (4, H // 8, W // 8) | |
| 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 [255 - detected_map] + results | |
| block = gr.Blocks().queue() | |
| with block: | |
| with gr.Row(): | |
| gr.Markdown("## Control Stable Diffusion with Canny Edge Maps") | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image(source='upload', type="numpy") | |
| prompt = gr.Textbox(label="Prompt") | |
| run_button = gr.Button(label="Run") | |
| 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=256) | |
| low_threshold = gr.Slider(label="Canny low threshold", minimum=1, maximum=255, value=100, step=1) | |
| high_threshold = gr.Slider(label="Canny high threshold", minimum=1, maximum=255, value=200, 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=9.0, step=0.1) | |
| seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True) | |
| eta = gr.Number(label="eta (DDIM)", value=0.0) | |
| a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed') | |
| n_prompt = gr.Textbox(label="Negative Prompt", | |
| value='longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair,extra digit, fewer digits, cropped, worst quality, low 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, scale, seed, eta, low_threshold, high_threshold] | |
| run_button.click(fn=process, inputs=ips, outputs=[result_gallery]) | |
| examples = gr.Examples(examples=[["bird.png.", "bird"]]) | |
| block.launch(debug = True) |