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from share import * |
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import config |
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import cv2 |
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import einops |
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import gradio as gr |
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import numpy as np |
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import torch |
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import random |
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from pytorch_lightning import seed_everything |
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from annotator.util import resize_image, HWC3 |
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from annotator.mlsd import MLSDdetector |
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from cldm.model import create_model, load_state_dict |
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from cldm.ddim_hacked import DDIMSampler |
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apply_mlsd = MLSDdetector() |
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model = create_model('./models/cldm_v15.yaml').cpu() |
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model.load_state_dict(load_state_dict('./models/control_sd15_mlsd.pth', location='cuda')) |
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model = model.cuda() |
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ddim_sampler = DDIMSampler(model) |
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def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta, value_threshold, distance_threshold): |
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with torch.no_grad(): |
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input_image = HWC3(input_image) |
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detected_map = apply_mlsd(resize_image(input_image, detect_resolution), value_threshold, distance_threshold) |
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detected_map = HWC3(detected_map) |
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img = resize_image(input_image, image_resolution) |
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H, W, C = img.shape |
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detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_NEAREST) |
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control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0 |
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control = torch.stack([control for _ in range(num_samples)], dim=0) |
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control = einops.rearrange(control, 'b h w c -> b c h w').clone() |
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if seed == -1: |
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seed = random.randint(0, 65535) |
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seed_everything(seed) |
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if config.save_memory: |
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model.low_vram_shift(is_diffusing=False) |
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cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]} |
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un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]} |
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shape = (4, H // 8, W // 8) |
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if config.save_memory: |
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model.low_vram_shift(is_diffusing=True) |
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model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13) |
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samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples, |
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shape, cond, verbose=False, eta=eta, |
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unconditional_guidance_scale=scale, |
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unconditional_conditioning=un_cond) |
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if config.save_memory: |
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model.low_vram_shift(is_diffusing=False) |
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x_samples = model.decode_first_stage(samples) |
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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) |
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results = [x_samples[i] for i in range(num_samples)] |
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return [255 - cv2.dilate(detected_map, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)] + results |
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block = gr.Blocks().queue() |
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with block: |
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with gr.Row(): |
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gr.Markdown("## Control Stable Diffusion with Hough Line Maps") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(source='upload', type="numpy") |
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prompt = gr.Textbox(label="Prompt") |
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run_button = gr.Button(label="Run") |
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with gr.Accordion("Advanced options", open=False): |
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num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1) |
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image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=64) |
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strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01) |
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guess_mode = gr.Checkbox(label='Guess Mode', value=False) |
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detect_resolution = gr.Slider(label="Hough Resolution", minimum=128, maximum=1024, value=512, step=1) |
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value_threshold = gr.Slider(label="Hough value threshold (MLSD)", minimum=0.01, maximum=2.0, value=0.1, step=0.01) |
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distance_threshold = gr.Slider(label="Hough distance threshold (MLSD)", minimum=0.01, maximum=20.0, value=0.1, step=0.01) |
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ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1) |
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scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1) |
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seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True) |
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eta = gr.Number(label="eta (DDIM)", value=0.0) |
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a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed') |
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n_prompt = gr.Textbox(label="Negative Prompt", |
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value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality') |
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with gr.Column(): |
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result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto') |
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ips = [input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta, value_threshold, distance_threshold] |
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run_button.click(fn=process, inputs=ips, outputs=[result_gallery]) |
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block.launch(server_name='0.0.0.0') |
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