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| import os | |
| import os.path as osp | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| from basicsr.utils import img2tensor, tensor2img | |
| from pytorch_lightning import seed_everything | |
| from ldm.models.diffusion.plms import PLMSSampler | |
| from ldm.modules.encoders.adapter import Adapter | |
| from ldm.util import instantiate_from_config | |
| from model_edge import pidinet | |
| import gradio as gr | |
| from omegaconf import OmegaConf | |
| def load_model_from_config(config, ckpt, verbose=False): | |
| print(f"Loading model from {ckpt}") | |
| pl_sd = torch.load(ckpt, map_location="cpu") | |
| if "global_step" in pl_sd: | |
| print(f"Global Step: {pl_sd['global_step']}") | |
| if "state_dict" in pl_sd: | |
| sd = pl_sd["state_dict"] | |
| else: | |
| sd = pl_sd | |
| model = instantiate_from_config(config.model) | |
| m, u = model.load_state_dict(sd, strict=False) | |
| # if len(m) > 0 and verbose: | |
| # print("missing keys:") | |
| # print(m) | |
| # if len(u) > 0 and verbose: | |
| # print("unexpected keys:") | |
| # print(u) | |
| model.cuda() | |
| model.eval() | |
| return model | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| config = OmegaConf.load("configs/stable-diffusion/test_sketch.yaml") | |
| config.model.params.cond_stage_config.params.device = device | |
| model = load_model_from_config(config, "models/sd-v1-4.ckpt").to(device) | |
| current_base = 'sd-v1-4.ckpt' | |
| model_ad = Adapter(channels=[320, 640, 1280, 1280][:4], nums_rb=2, ksize=1, sk=True, use_conv=False).to(device) | |
| model_ad.load_state_dict(torch.load("models/t2iadapter_sketch_sd14v1.pth")) | |
| net_G = pidinet() | |
| ckp = torch.load('models/table5_pidinet.pth', map_location='cpu')['state_dict'] | |
| net_G.load_state_dict({k.replace('module.',''):v for k, v in ckp.items()}) | |
| net_G.to(device) | |
| sampler = PLMSSampler(model) | |
| save_memory=True | |
| W, H = 512, 512 | |
| def process(input_img, type_in, color_back, prompt, neg_prompt, fix_sample, scale, con_strength, base_model): | |
| global current_base | |
| if current_base != base_model: | |
| ckpt = os.path.join("models", base_model) | |
| pl_sd = torch.load(ckpt, map_location="cpu") | |
| if "state_dict" in pl_sd: | |
| sd = pl_sd["state_dict"] | |
| else: | |
| sd = pl_sd | |
| model.load_state_dict(sd, strict=False) #load_model_from_config(config, os.path.join("models", base_model)).to(device) | |
| current_base = base_model | |
| con_strength = int((1-con_strength)*50) | |
| if fix_sample == 'True': | |
| seed_everything(42) | |
| im = cv2.resize(input_img,(W,H)) | |
| if type_in == 'Sketch': | |
| if color_back == 'White': | |
| im = 255-im | |
| im_edge = im.copy() | |
| im = img2tensor(im)[0].unsqueeze(0).unsqueeze(0)/255. | |
| im = im>0.5 | |
| im = im.float() | |
| elif type_in == 'Image': | |
| im = img2tensor(im).unsqueeze(0)/255. | |
| im = net_G(im.to(device))[-1] | |
| im = im>0.5 | |
| im = im.float() | |
| im_edge = tensor2img(im) | |
| with torch.no_grad(): | |
| c = model.get_learned_conditioning([prompt]) | |
| nc = model.get_learned_conditioning([neg_prompt]) | |
| # extract condition features | |
| features_adapter = model_ad(im.to(device)) | |
| shape = [4, W//8, H//8] | |
| # sampling | |
| samples_ddim, _ = sampler.sample(S=50, | |
| conditioning=c, | |
| batch_size=1, | |
| shape=shape, | |
| verbose=False, | |
| unconditional_guidance_scale=scale, | |
| unconditional_conditioning=nc, | |
| eta=0.0, | |
| x_T=None, | |
| features_adapter1=features_adapter, | |
| mode = 'sketch', | |
| con_strength = con_strength) | |
| x_samples_ddim = model.decode_first_stage(samples_ddim) | |
| x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) | |
| x_samples_ddim = x_samples_ddim.to('cpu') | |
| x_samples_ddim = x_samples_ddim.permute(0, 2, 3, 1).numpy()[0] | |
| x_samples_ddim = 255.*x_samples_ddim | |
| x_samples_ddim = x_samples_ddim.astype(np.uint8) | |
| return [im_edge, x_samples_ddim] | |
| DESCRIPTION = '''# T2I-Adapter (Sketch) | |
| [Paper](https://arxiv.org/abs/2302.08453) [GitHub](https://github.com/TencentARC/T2I-Adapter) | |
| This gradio demo is for sketch-guided generation. The current functions include: | |
| - Sketch to Image Generation | |
| - Image to Image Generation | |
| - Generation with **Anything** setting | |
| ''' | |
| block = gr.Blocks().queue() | |
| with block: | |
| with gr.Row(): | |
| gr.Markdown(DESCRIPTION) | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_img = gr.Image(source='upload', type="numpy") | |
| prompt = gr.Textbox(label="Prompt") | |
| neg_prompt = gr.Textbox(label="Negative Prompt", | |
| value='ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, extra limbs, disfigured, deformed, body out of frame, bad anatomy, watermark, signature, cut off, low contrast, underexposed, overexposed, bad art, beginner, amateur, distorted face') | |
| with gr.Row(): | |
| type_in = gr.inputs.Radio(['Sketch', 'Image'], type="value", default='Image', label='Input Types\n (You can input an image or a sketch)') | |
| color_back = gr.inputs.Radio(['White', 'Black'], type="value", default='Black', label='Color of the sketch background\n (Only work for sketch input)') | |
| run_button = gr.Button(label="Run") | |
| con_strength = gr.Slider(label="Controling Strength (The guidance strength of the sketch to the result)", minimum=0, maximum=1, value=0.4, step=0.1) | |
| scale = gr.Slider(label="Guidance Scale (Classifier free guidance)", minimum=0.1, maximum=30.0, value=9, step=0.1) | |
| fix_sample = gr.inputs.Radio(['True', 'False'], type="value", default='False', label='Fix Sampling\n (Fix the random seed)') | |
| base_model = gr.inputs.Radio(['sd-v1-4.ckpt', 'anything-v4.0-pruned.ckpt'], type="value", default='sd-v1-4.ckpt', label='The base model you want to use') | |
| with gr.Column(): | |
| result = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto') | |
| ips = [input_img, type_in, color_back, prompt, neg_prompt, fix_sample, scale, con_strength, base_model] | |
| run_button.click(fn=process, inputs=ips, outputs=[result]) | |
| block.launch(server_name='0.0.0.0') | |