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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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from pytorch_lightning import seed_everything |
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from util import resize_image, HWC3, apply_canny |
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from ldm.models.diffusion.ddim import DDIMSampler |
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from annotator.openpose import apply_openpose |
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from cldm.model import create_model, load_state_dict |
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from huggingface_hub import hf_hub_url, cached_download |
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REPO_ID = "Thaweewat/ControlNet-Architecture" |
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canny_checkpoint = "models/control_sd15_canny.pth" |
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scribble_checkpoint = "models/control_sd15_scribble.pth" |
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pose_checkpoint = "models/control_sd15_openpose.pth" |
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canny_model = create_model('./models/cldm_v15.yaml').cpu() |
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canny_model.load_state_dict(load_state_dict(cached_download( |
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hf_hub_url(REPO_ID, canny_checkpoint) |
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), location='cpu')) |
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canny_model = canny_model.cuda() |
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ddim_sampler = DDIMSampler(canny_model) |
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pose_model = create_model('./models/cldm_v15.yaml').cpu() |
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pose_model.load_state_dict(load_state_dict(cached_download( |
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hf_hub_url(REPO_ID, pose_checkpoint) |
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), location='cpu')) |
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pose_model = pose_model.cuda() |
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ddim_sampler_pose = DDIMSampler(pose_model) |
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scribble_model = create_model('./models/cldm_v15.yaml').cpu() |
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scribble_model.load_state_dict(load_state_dict(cached_download( |
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hf_hub_url(REPO_ID, scribble_checkpoint) |
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), location='cpu')) |
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scribble_model = scribble_model.cuda() |
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ddim_sampler_scribble = DDIMSampler(scribble_model) |
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save_memory = False |
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def process(input_image, prompt, input_control, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta, low_threshold, high_threshold): |
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if input_control == "Scribble": |
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return process_scribble(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta) |
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elif input_control == "Pose": |
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return process_pose(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, image_resolution, ddim_steps, scale, seed, eta) |
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return process_canny(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta, low_threshold, high_threshold) |
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def process_canny(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta, low_threshold, high_threshold): |
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with torch.no_grad(): |
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img = resize_image(HWC3(input_image), image_resolution) |
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H, W, C = img.shape |
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detected_map = apply_canny(img, low_threshold, high_threshold) |
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detected_map = HWC3(detected_map) |
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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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seed_everything(seed) |
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if save_memory: |
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canny_model.low_vram_shift(is_diffusing=False) |
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cond = {"c_concat": [control], "c_crossattn": [canny_model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]} |
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un_cond = {"c_concat": [control], "c_crossattn": [canny_model.get_learned_conditioning([n_prompt] * num_samples)]} |
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shape = (4, H // 8, W // 8) |
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if save_memory: |
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canny_model.low_vram_shift(is_diffusing=False) |
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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 save_memory: |
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canny_model.low_vram_shift(is_diffusing=False) |
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x_samples = canny_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 - detected_map] + results |
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def process_scribble(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta): |
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with torch.no_grad(): |
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img = resize_image(HWC3(input_image), image_resolution) |
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H, W, C = img.shape |
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detected_map = np.zeros_like(img, dtype=np.uint8) |
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detected_map[np.min(img, axis=2) < 127] = 255 |
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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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seed_everything(seed) |
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if save_memory: |
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scribble_model.low_vram_shift(is_diffusing=False) |
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cond = {"c_concat": [control], "c_crossattn": [scribble_model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]} |
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un_cond = {"c_concat": [control], "c_crossattn": [scribble_model.get_learned_conditioning([n_prompt] * num_samples)]} |
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shape = (4, H // 8, W // 8) |
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if save_memory: |
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scribble_model.low_vram_shift(is_diffusing=False) |
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samples, intermediates = ddim_sampler_scribble.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 save_memory: |
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scribble_model.low_vram_shift(is_diffusing=False) |
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x_samples = scribble_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 - detected_map] + results |
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def process_pose(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, scale, seed, eta): |
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with torch.no_grad(): |
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input_image = HWC3(input_image) |
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detected_map, _ = apply_openpose(resize_image(input_image, detect_resolution)) |
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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 save_memory: |
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pose_model.low_vram_shift(is_diffusing=False) |
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cond = {"c_concat": [control], "c_crossattn": [pose_model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]} |
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un_cond = {"c_concat": [control], "c_crossattn": [pose_model.get_learned_conditioning([n_prompt] * num_samples)]} |
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shape = (4, H // 8, W // 8) |
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if save_memory: |
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pose_model.low_vram_shift(is_diffusing=False) |
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samples, intermediates = ddim_sampler_pose.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 save_memory: |
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pose_model.low_vram_shift(is_diffusing=False) |
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x_samples = pose_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 [detected_map] + results |
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def create_canvas(w, h): |
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new_control_options = ["Interactive Scribble"] |
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return np.zeros(shape=(h, w, 3), dtype=np.uint8) + 255 |
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block = gr.Blocks().queue() |
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control_task_list = [ |
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"Canny Edge Map", |
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"Scribble", |
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"Pose" |
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] |
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with block: |
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gr.Markdown("## Adding Conditional Control to Text-to-Image Diffusion Models") |
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gr.HTML(''' |
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<p style="margin-bottom: 10px; font-size: 94%"> |
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This is an unofficial demo for ControlNet, which is a neural network structure to control diffusion models by adding extra conditions such as canny edge detection. The demo is based on the <a href="https://github.com/lllyasviel/ControlNet" style="text-decoration: underline;" target="_blank"> Github </a> implementation. |
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</p> |
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''') |
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gr.HTML("<p>You can duplicate this Space to run it privately without a queue and load additional checkpoints. : <a style='display:inline-block' href='https://huggingface.co/spaces/RamAnanth1/ControlNet?duplicate=true'><img src='https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14' alt='Duplicate Space'></a> <a style='display:inline-block' href='https://colab.research.google.com/github/camenduru/controlnet-colab/blob/main/controlnet-colab.ipynb'><img src = 'https://colab.research.google.com/assets/colab-badge.svg' alt='Open in Colab'></a></p>") |
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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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input_control = gr.Dropdown(control_task_list, value="Scribble", label="Control Task") |
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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=256) |
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low_threshold = gr.Slider(label="Canny low threshold", minimum=1, maximum=255, value=100, step=1) |
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high_threshold = gr.Slider(label="Canny high threshold", minimum=1, maximum=255, value=200, step=1) |
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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=0, maximum=2147483647, step=1, randomize=True) |
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eta = gr.Slider(label="eta (DDIM)", minimum=0.0,maximum =1.0, value=0.0, step=0.1) |
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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, pubic hair,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, input_control, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta, low_threshold, high_threshold] |
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run_button.click(fn=process, inputs=ips, outputs=[result_gallery]) |
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block.launch(debug = True) |
