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| import cv2 | |
| import torch | |
| import os, glob | |
| import numpy as np | |
| import gradio as gr | |
| from PIL import Image | |
| from omegaconf import OmegaConf | |
| from contextlib import nullcontext | |
| from pytorch_lightning import seed_everything | |
| from os.path import join as ospj | |
| from util import * | |
| def predict(cfgs, model, sampler, batch): | |
| context = nullcontext if cfgs.aae_enabled else torch.no_grad | |
| with context(): | |
| batch, batch_uc_1, batch_uc_2 = prepare_batch(cfgs, batch) | |
| if cfgs.dual_conditioner: | |
| c, uc_1, uc_2 = model.conditioner.get_unconditional_conditioning( | |
| batch, | |
| batch_uc_1=batch_uc_1, | |
| batch_uc_2=batch_uc_2, | |
| force_uc_zero_embeddings=cfgs.force_uc_zero_embeddings, | |
| ) | |
| else: | |
| c, uc_1 = model.conditioner.get_unconditional_conditioning( | |
| batch, | |
| batch_uc=batch_uc_1, | |
| force_uc_zero_embeddings=cfgs.force_uc_zero_embeddings, | |
| ) | |
| if cfgs.dual_conditioner: | |
| x = sampler.get_init_noise(cfgs, model, cond=c, batch=batch, uc_1=uc_1, uc_2=uc_2) | |
| samples_z = sampler(model, x, cond=c, batch=batch, uc_1=uc_1, uc_2=uc_2, init_step=0, | |
| aae_enabled = cfgs.aae_enabled, detailed = cfgs.detailed) | |
| else: | |
| x = sampler.get_init_noise(cfgs, model, cond=c, batch=batch, uc=uc_1) | |
| samples_z = sampler(model, x, cond=c, batch=batch, uc=uc_1, init_step=0, | |
| aae_enabled = cfgs.aae_enabled, detailed = cfgs.detailed) | |
| samples_x = model.decode_first_stage(samples_z) | |
| samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0) | |
| return samples, samples_z | |
| def demo_predict(input_blk, text, num_samples, steps, scale, seed, show_detail): | |
| global cfgs, global_index | |
| global_index += 1 | |
| if num_samples > 1: cfgs.noise_iters = 0 | |
| cfgs.batch_size = num_samples | |
| cfgs.steps = steps | |
| cfgs.scale[0] = scale | |
| cfgs.detailed = show_detail | |
| seed_everything(seed) | |
| sampler = init_sampling(cfgs) | |
| image = input_blk["image"] | |
| mask = input_blk["mask"] | |
| image = cv2.resize(image, (cfgs.W, cfgs.H)) | |
| mask = cv2.resize(mask, (cfgs.W, cfgs.H)) | |
| mask = (mask == 0).astype(np.int32) | |
| image = torch.from_numpy(image.transpose(2,0,1)).to(dtype=torch.float32) / 127.5 - 1.0 | |
| mask = torch.from_numpy(mask.transpose(2,0,1)).to(dtype=torch.float32).mean(dim=0, keepdim=True) | |
| masked = image * mask | |
| mask = 1 - mask | |
| seg_mask = torch.cat((torch.ones(len(text)), torch.zeros(cfgs.seq_len-len(text)))) | |
| # additional cond | |
| txt = f"\"{text}\"" | |
| original_size_as_tuple = torch.tensor((cfgs.H, cfgs.W)) | |
| crop_coords_top_left = torch.tensor((0, 0)) | |
| target_size_as_tuple = torch.tensor((cfgs.H, cfgs.W)) | |
| image = torch.tile(image[None], (num_samples, 1, 1, 1)) | |
| mask = torch.tile(mask[None], (num_samples, 1, 1, 1)) | |
| masked = torch.tile(masked[None], (num_samples, 1, 1, 1)) | |
| seg_mask = torch.tile(seg_mask[None], (num_samples, 1)) | |
| original_size_as_tuple = torch.tile(original_size_as_tuple[None], (num_samples, 1)) | |
| crop_coords_top_left = torch.tile(crop_coords_top_left[None], (num_samples, 1)) | |
| target_size_as_tuple = torch.tile(target_size_as_tuple[None], (num_samples, 1)) | |
| text = [text for i in range(num_samples)] | |
| txt = [txt for i in range(num_samples)] | |
| name = [str(global_index) for i in range(num_samples)] | |
| batch = { | |
| "image": image, | |
| "mask": mask, | |
| "masked": masked, | |
| "seg_mask": seg_mask, | |
| "label": text, | |
| "txt": txt, | |
| "original_size_as_tuple": original_size_as_tuple, | |
| "crop_coords_top_left": crop_coords_top_left, | |
| "target_size_as_tuple": target_size_as_tuple, | |
| "name": name | |
| } | |
| samples, samples_z = predict(cfgs, model, sampler, batch) | |
| samples = samples.cpu().numpy().transpose(0, 2, 3, 1) * 255 | |
| results = [Image.fromarray(sample.astype(np.uint8)) for sample in samples] | |
| if cfgs.detailed: | |
| sections = [] | |
| attn_map = Image.open(f"./temp/attn_map/attn_map_{global_index}.png") | |
| seg_maps = np.load(f"./temp/seg_map/seg_{global_index}.npy") | |
| for i, seg_map in enumerate(seg_maps): | |
| seg_map = cv2.resize(seg_map, (cfgs.W, cfgs.H)) | |
| sections.append((seg_map, text[0][i])) | |
| seg = (results[0], sections) | |
| else: | |
| attn_map = None | |
| seg = None | |
| return results, attn_map, seg | |
| if __name__ == "__main__": | |
| os.makedirs("./temp/attn_map", exist_ok=True) | |
| os.makedirs("./temp/seg_map", exist_ok=True) | |
| cfgs = OmegaConf.load("./configs/demo.yaml") | |
| model = init_model(cfgs) | |
| global_index = 0 | |
| block = gr.Blocks().queue() | |
| with block: | |
| with gr.Row(): | |
| gr.HTML( | |
| """ | |
| <div style="text-align: center; max-width: 1200px; margin: 20px auto;"> | |
| <h1 style="font-weight: 600; font-size: 2rem; margin: 0.5rem;"> | |
| UDiffText: A Unified Framework for High-quality Text Synthesis in Arbitrary Images via Character-aware Diffusion Models | |
| </h1> | |
| <ul style="text-align: center; margin: 0.5rem;"> | |
| <li style="display: inline-block; margin:auto;"><a href='https://arxiv.org/pdf/******'><img src='https://img.shields.io/badge/Arxiv-******-DF826C'></a></li> | |
| <li style="display: inline-block; margin:auto;"><a href='https://github.com/ZYM-PKU/UDiffText'><img src='https://img.shields.io/badge/Code-UDiffText-D0F288'></a></li> | |
| <li style="display: inline-block; margin:auto;"><a href='https://udifftext.github.io'><img src='https://img.shields.io/badge/Project-UDiffText-8ADAB2'></a></li> | |
| </ul> | |
| <h2 style="text-align: left; font-weight: 450; font-size: 1rem; margin: 0.5rem;"> | |
| Our proposed UDiffText is capable of synthesizing accurate and harmonious text in either synthetic or real-word images, thus can be applied to tasks like scene text editing (a), arbitrary text generation (b) and accurate T2I generation (c) | |
| </h2> | |
| <div align=center><img src="file/demo/teaser.png" alt="UDiffText" width="80%"></div> | |
| </div> | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_blk = gr.Image(source='upload', tool='sketch', type="numpy", label="Input", height=512) | |
| text = gr.Textbox(label="Text to render:", info="the text you want to render at the masked region") | |
| run_button = gr.Button(variant="primary") | |
| with gr.Accordion("Advanced options", open=False): | |
| num_samples = gr.Slider(label="Images", info="number of generated images, locked as 1", minimum=1, maximum=1, value=1, step=1) | |
| steps = gr.Slider(label="Steps", info ="denoising sampling steps", minimum=1, maximum=200, value=50, step=1) | |
| scale = gr.Slider(label="Guidance Scale", info="the scale of classifier-free guidance (CFG)", minimum=0.0, maximum=10.0, value=4.0, step=0.1) | |
| seed = gr.Slider(label="Seed", info="random seed for noise initialization", minimum=0, maximum=2147483647, step=1, randomize=True) | |
| show_detail = gr.Checkbox(label="Show Detail", info="show the additional visualization results", value=True) | |
| with gr.Column(): | |
| gallery = gr.Gallery(label="Output", height=512, preview=True) | |
| with gr.Accordion("Visualization results", open=True): | |
| with gr.Tab(label="Attention Maps"): | |
| gr.Markdown("### Attention maps for each character (extracted from middle blocks at intermediate sampling step):") | |
| attn_map = gr.Image(show_label=False, show_download_button=False) | |
| with gr.Tab(label="Segmentation Maps"): | |
| gr.Markdown("### Character-level segmentation maps (using upscaled attention maps):") | |
| seg_map = gr.AnnotatedImage(height=384, show_label=False, show_download_button=False) | |
| # examples | |
| examples = [] | |
| example_paths = sorted(glob.glob(ospj("./demo/examples", "*"))) | |
| for example_path in example_paths: | |
| label = example_path.split(os.sep)[-1].split(".")[0].split("_")[0] | |
| examples.append([example_path, label]) | |
| gr.Markdown("## Examples:") | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[input_blk, text] | |
| ) | |
| run_button.click(fn=demo_predict, inputs=[input_blk, text, num_samples, steps, scale, seed, show_detail], outputs=[gallery, attn_map, seg_map]) | |
| block.launch() |