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| import os | |
| import gradio as gr | |
| import argparse | |
| import numpy as np | |
| import torch | |
| import einops | |
| import copy | |
| import math | |
| import time | |
| import random | |
| import spaces | |
| import re | |
| import uuid | |
| from gradio_imageslider import ImageSlider | |
| from PIL import Image | |
| from BOOXEL.util import HWC3, upscale_image, fix_resize, convert_dtype, create_BOOXEL_model, load_QF_ckpt | |
| from huggingface_hub import hf_hub_download | |
| from pillow_heif import register_heif_opener | |
| register_heif_opener() | |
| max_64_bit_int = np.iinfo(np.int32).max | |
| hf_hub_download(repo_id="laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", filename="open_clip_pytorch_model.bin", local_dir="laion_CLIP-ViT-bigG-14-laion2B-39B-b160k") | |
| hf_hub_download(repo_id="ckpt/sd_xl_base_1.0", filename="sd_xl_base_1.0_0.9vae.safetensors", local_dir="ckpt_sd_xl_base_1.0") | |
| hf_hub_download(repo_id="yanranxiaoxi/booxel", filename="BOOXEL-v0.F.ckpt", local_dir="yanranxiaoxi_booxel", token=os.environ.get('MODEL_ACCESS_TOKEN')) | |
| hf_hub_download(repo_id="yanranxiaoxi/booxel", filename="BOOXEL-v0.Q.ckpt", local_dir="yanranxiaoxi_booxel", token=os.environ.get('MODEL_ACCESS_TOKEN')) | |
| hf_hub_download(repo_id="RunDiffusion/Juggernaut-XL-Lightning", filename="Juggernaut_RunDiffusionPhoto2_Lightning_4Steps.safetensors", local_dir="RunDiffusion_Juggernaut-XL-Lightning") | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--opt", type=str, default='options/BOOXEL_v0.yaml') | |
| parser.add_argument("--ip", type=str, default='127.0.0.1') | |
| parser.add_argument("--port", type=int, default='6688') | |
| parser.add_argument("--no_llava", action='store_true', default=True)#False | |
| parser.add_argument("--use_image_slider", action='store_true', default=False)#False | |
| parser.add_argument("--log_history", action='store_true', default=False) | |
| parser.add_argument("--loading_half_params", action='store_true', default=False)#False | |
| parser.add_argument("--use_tile_vae", action='store_true', default=True)#False | |
| parser.add_argument("--encoder_tile_size", type=int, default=512) | |
| parser.add_argument("--decoder_tile_size", type=int, default=64) | |
| parser.add_argument("--load_8bit_llava", action='store_true', default=False) | |
| args = parser.parse_args() | |
| if torch.cuda.device_count() > 0: | |
| BOOXEL_device = 'cuda:0' | |
| # 加载 BOOXEL | |
| model, default_setting = create_BOOXEL_model(args.opt, BOOXEL_sign='Q', load_default_setting=True) | |
| if args.loading_half_params: | |
| model = model.half() | |
| if args.use_tile_vae: | |
| model.init_tile_vae(encoder_tile_size=args.encoder_tile_size, decoder_tile_size=args.decoder_tile_size) | |
| model = model.to(BOOXEL_device) | |
| model.first_stage_model.denoise_encoder_s1 = copy.deepcopy(model.first_stage_model.denoise_encoder) | |
| model.current_model = 'v0-Q' | |
| ckpt_Q, ckpt_F = load_QF_ckpt(args.opt) | |
| def check_upload(input_image): | |
| if input_image is None: | |
| raise gr.Error("请提供要处理的图像。") | |
| return gr.update(visible = True) | |
| def update_seed(is_randomize_seed, seed): | |
| if is_randomize_seed: | |
| return random.randint(0, max_64_bit_int) | |
| return seed | |
| def reset(): | |
| return [ | |
| None, | |
| 0, | |
| None, | |
| None, | |
| "电影级，高对比度，高度精细，使用哈苏相机拍摄，超精细照片，逼真的最大细节，32K，调色，超高清，极致的细节，皮肤毛孔细节，超清晰度，完美无变形。", | |
| "绘画，油画，插图，绘图，艺术，素描，动漫，卡通，CG 风格，3D 渲染，虚幻引擎，模糊，混色，不清晰，怪异纹理，丑陋，肮脏，凌乱，质量最差，质量低，框架，水印，签名，JPEG 伪影，变形，低分辨率，过度平滑", | |
| 1, | |
| 1024, | |
| 1, | |
| 2, | |
| 50, | |
| -1.0, | |
| 1., | |
| default_setting.s_cfg_Quality if torch.cuda.device_count() > 0 else 1.0, | |
| True, | |
| random.randint(0, max_64_bit_int), | |
| 5, | |
| 1.003, | |
| "Wavelet", | |
| "fp32", | |
| "fp32", | |
| 1.0, | |
| True, | |
| False, | |
| default_setting.spt_linear_CFG_Quality if torch.cuda.device_count() > 0 else 1.0, | |
| 0., | |
| "v0-Q", | |
| "input", | |
| 6 | |
| ] | |
| def check(input_image): | |
| if input_image is None: | |
| raise gr.Error("请提供要处理的图像。") | |
| def stage1_process( | |
| input_image, | |
| gamma_correction, | |
| diff_dtype, | |
| ae_dtype | |
| ): | |
| print('stage1_process ==>>') | |
| if torch.cuda.device_count() == 0: | |
| gr.Warning('将此 Spaces 设置为 GPU 配置以使其正常工作。') | |
| return None, None | |
| torch.cuda.set_device(BOOXEL_device) | |
| LQ = HWC3(np.array(Image.open(input_image))) | |
| LQ = fix_resize(LQ, 512) | |
| # stage1 | |
| LQ = np.array(LQ) / 255 * 2 - 1 | |
| LQ = torch.tensor(LQ, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0).to(BOOXEL_device)[:, :3, :, :] | |
| model.ae_dtype = convert_dtype(ae_dtype) | |
| model.model.dtype = convert_dtype(diff_dtype) | |
| LQ = model.batchify_denoise(LQ, is_stage1=True) | |
| LQ = (LQ[0].permute(1, 2, 0) * 127.5 + 127.5).cpu().numpy().round().clip(0, 255).astype(np.uint8) | |
| # 伽玛校正 | |
| LQ = LQ / 255.0 | |
| LQ = np.power(LQ, gamma_correction) | |
| LQ *= 255.0 | |
| LQ = LQ.round().clip(0, 255).astype(np.uint8) | |
| print('<<== stage1_process') | |
| return LQ, gr.update(visible = True) | |
| def stage2_process(*args, **kwargs): | |
| try: | |
| return restore_in_Xmin(*args, **kwargs) | |
| except Exception as e: | |
| print('异常的类型 ' + str(type(e))) | |
| if type(e).__name__ == "<class 'gradio.exceptions.Error'>": | |
| print('异常的名称 ' + type(e).__name__) | |
| raise e | |
| def restore_in_Xmin( | |
| noisy_image, | |
| rotation, | |
| denoise_image, | |
| prompt, | |
| a_prompt, | |
| n_prompt, | |
| num_samples, | |
| min_size, | |
| downscale, | |
| upscale, | |
| edm_steps, | |
| s_stage1, | |
| s_stage2, | |
| s_cfg, | |
| randomize_seed, | |
| seed, | |
| s_churn, | |
| s_noise, | |
| color_fix_type, | |
| diff_dtype, | |
| ae_dtype, | |
| gamma_correction, | |
| linear_CFG, | |
| linear_s_stage2, | |
| spt_linear_CFG, | |
| spt_linear_s_stage2, | |
| model_select, | |
| output_format, | |
| allocation | |
| ): | |
| print("noisy_image:\n" + str(noisy_image)) | |
| print("denoise_image:\n" + str(denoise_image)) | |
| print("rotation: " + str(rotation)) | |
| print("prompt: " + str(prompt)) | |
| print("a_prompt: " + str(a_prompt)) | |
| print("n_prompt: " + str(n_prompt)) | |
| print("num_samples: " + str(num_samples)) | |
| print("min_size: " + str(min_size)) | |
| print("downscale: " + str(downscale)) | |
| print("upscale: " + str(upscale)) | |
| print("edm_steps: " + str(edm_steps)) | |
| print("s_stage1: " + str(s_stage1)) | |
| print("s_stage2: " + str(s_stage2)) | |
| print("s_cfg: " + str(s_cfg)) | |
| print("randomize_seed: " + str(randomize_seed)) | |
| print("seed: " + str(seed)) | |
| print("s_churn: " + str(s_churn)) | |
| print("s_noise: " + str(s_noise)) | |
| print("color_fix_type: " + str(color_fix_type)) | |
| print("diff_dtype: " + str(diff_dtype)) | |
| print("ae_dtype: " + str(ae_dtype)) | |
| print("gamma_correction: " + str(gamma_correction)) | |
| print("linear_CFG: " + str(linear_CFG)) | |
| print("linear_s_stage2: " + str(linear_s_stage2)) | |
| print("spt_linear_CFG: " + str(spt_linear_CFG)) | |
| print("spt_linear_s_stage2: " + str(spt_linear_s_stage2)) | |
| print("model_select: " + str(model_select)) | |
| print("GPU time allocation: " + str(allocation) + " min") | |
| print("output_format: " + str(output_format)) | |
| input_format = re.sub(r"^.*\.([^\.]+)$", r"\1", noisy_image) | |
| if input_format not in ['png', 'webp', 'jpg', 'jpeg', 'gif', 'bmp', 'heic']: | |
| gr.Warning('错误的图像格式。当前仅支持 *.png, *.webp, *.jpg, *.jpeg, *.gif, *.bmp 或 *.heic。') | |
| return None, None, None, None | |
| if output_format == "input": | |
| if noisy_image is None: | |
| output_format = "png" | |
| else: | |
| output_format = input_format | |
| print("最终的 output_format：" + str(output_format)) | |
| if prompt is None: | |
| prompt = "" | |
| if a_prompt is None: | |
| a_prompt = "" | |
| if n_prompt is None: | |
| n_prompt = "" | |
| if prompt != "" and a_prompt != "": | |
| a_prompt = prompt + ", " + a_prompt | |
| else: | |
| a_prompt = prompt + a_prompt | |
| print("最终提示词：" + str(a_prompt)) | |
| denoise_image = np.array(Image.open(noisy_image if denoise_image is None else denoise_image)) | |
| if rotation == 90: | |
| denoise_image = np.array(list(zip(*denoise_image[::-1]))) | |
| elif rotation == 180: | |
| denoise_image = np.array(list(zip(*denoise_image[::-1]))) | |
| denoise_image = np.array(list(zip(*denoise_image[::-1]))) | |
| elif rotation == -90: | |
| denoise_image = np.array(list(zip(*denoise_image))[::-1]) | |
| if 1 < downscale: | |
| input_height, input_width, input_channel = denoise_image.shape | |
| denoise_image = np.array(Image.fromarray(denoise_image).resize((input_width // downscale, input_height // downscale), Image.LANCZOS)) | |
| denoise_image = HWC3(denoise_image) | |
| if torch.cuda.device_count() == 0: | |
| gr.Warning('将此 Spaces 设置为 GPU 配置以使其正常工作。') | |
| return [noisy_image, denoise_image], gr.update(label="可下载的结果为 *." + output_format + " 格式", format = output_format, value = [denoise_image]), None, gr.update(visible=True) | |
| if model_select != model.current_model: | |
| print('载入 ' + model_select) | |
| if model_select == 'v0-Q': | |
| model.load_state_dict(ckpt_Q, strict=False) | |
| elif model_select == 'v0-F': | |
| model.load_state_dict(ckpt_F, strict=False) | |
| model.current_model = model_select | |
| model.ae_dtype = convert_dtype(ae_dtype) | |
| model.model.dtype = convert_dtype(diff_dtype) | |
| # 分配 | |
| if allocation == 1: | |
| return restore_in_1min( | |
| noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format, allocation | |
| ) | |
| if allocation == 2: | |
| return restore_in_2min( | |
| noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format, allocation | |
| ) | |
| if allocation == 3: | |
| return restore_in_3min( | |
| noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format, allocation | |
| ) | |
| if allocation == 4: | |
| return restore_in_4min( | |
| noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format, allocation | |
| ) | |
| if allocation == 5: | |
| return restore_in_5min( | |
| noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format, allocation | |
| ) | |
| if allocation == 7: | |
| return restore_in_7min( | |
| noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format, allocation | |
| ) | |
| if allocation == 8: | |
| return restore_in_8min( | |
| noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format, allocation | |
| ) | |
| if allocation == 9: | |
| return restore_in_9min( | |
| noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format, allocation | |
| ) | |
| if allocation == 10: | |
| return restore_in_10min( | |
| noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format, allocation | |
| ) | |
| else: | |
| return restore_in_6min( | |
| noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format, allocation | |
| ) | |
| def restore_in_1min(*args, **kwargs): | |
| return restore_on_gpu(*args, **kwargs) | |
| def restore_in_2min(*args, **kwargs): | |
| return restore_on_gpu(*args, **kwargs) | |
| def restore_in_3min(*args, **kwargs): | |
| return restore_on_gpu(*args, **kwargs) | |
| def restore_in_4min(*args, **kwargs): | |
| return restore_on_gpu(*args, **kwargs) | |
| def restore_in_5min(*args, **kwargs): | |
| return restore_on_gpu(*args, **kwargs) | |
| def restore_in_6min(*args, **kwargs): | |
| return restore_on_gpu(*args, **kwargs) | |
| def restore_in_7min(*args, **kwargs): | |
| return restore_on_gpu(*args, **kwargs) | |
| def restore_in_8min(*args, **kwargs): | |
| return restore_on_gpu(*args, **kwargs) | |
| def restore_in_9min(*args, **kwargs): | |
| return restore_on_gpu(*args, **kwargs) | |
| def restore_in_10min(*args, **kwargs): | |
| return restore_on_gpu(*args, **kwargs) | |
| def restore_on_gpu( | |
| noisy_image, | |
| input_image, | |
| prompt, | |
| a_prompt, | |
| n_prompt, | |
| num_samples, | |
| min_size, | |
| downscale, | |
| upscale, | |
| edm_steps, | |
| s_stage1, | |
| s_stage2, | |
| s_cfg, | |
| randomize_seed, | |
| seed, | |
| s_churn, | |
| s_noise, | |
| color_fix_type, | |
| diff_dtype, | |
| ae_dtype, | |
| gamma_correction, | |
| linear_CFG, | |
| linear_s_stage2, | |
| spt_linear_CFG, | |
| spt_linear_s_stage2, | |
| model_select, | |
| output_format, | |
| allocation | |
| ): | |
| start = time.time() | |
| print('restore ==>>') | |
| torch.cuda.set_device(BOOXEL_device) | |
| with torch.no_grad(): | |
| input_image = upscale_image(input_image, upscale, unit_resolution=32, min_size=min_size) | |
| LQ = np.array(input_image) / 255.0 | |
| LQ = np.power(LQ, gamma_correction) | |
| LQ *= 255.0 | |
| LQ = LQ.round().clip(0, 255).astype(np.uint8) | |
| LQ = LQ / 255 * 2 - 1 | |
| LQ = torch.tensor(LQ, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0).to(BOOXEL_device)[:, :3, :, :] | |
| captions = [''] | |
| samples = model.batchify_sample(LQ, captions, num_steps=edm_steps, restoration_scale=s_stage1, s_churn=s_churn, | |
| s_noise=s_noise, cfg_scale=s_cfg, control_scale=s_stage2, seed=seed, | |
| num_samples=num_samples, p_p=a_prompt, n_p=n_prompt, color_fix_type=color_fix_type, | |
| use_linear_CFG=linear_CFG, use_linear_control_scale=linear_s_stage2, | |
| cfg_scale_start=spt_linear_CFG, control_scale_start=spt_linear_s_stage2) | |
| x_samples = (einops.rearrange(samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().round().clip( | |
| 0, 255).astype(np.uint8) | |
| results = [x_samples[i] for i in range(num_samples)] | |
| torch.cuda.empty_cache() | |
| # 所有结果的大小相同 | |
| input_height, input_width, input_channel = np.array(input_image).shape | |
| result_height, result_width, result_channel = np.array(results[0]).shape | |
| print('<<== restore') | |
| end = time.time() | |
| secondes = int(end - start) | |
| minutes = math.floor(secondes / 60) | |
| secondes = secondes - (minutes * 60) | |
| hours = math.floor(minutes / 60) | |
| minutes = minutes - (hours * 60) | |
| information = ("如果想获得不同的结果，请重新开始。" if randomize_seed else "") + \ | |
| "如果您没有得到想要的图片，请在 « 图片描述 » 中添加更多细节。" + \ | |
| "等待 " + str(allocation) + " 分钟以避免 GPU 配额处罚，或也可以使用另一台计算机。" + \ | |
| "该图片已在 " + \ | |
| ((str(hours) + " 小时 ") if hours != 0 else "") + \ | |
| ((str(minutes) + " 分钟 ") if hours != 0 or minutes != 0 else "") + \ | |
| str(secondes) + " 秒 内生成。" + \ | |
| "新图像的分辨率为 " + str(result_width) + \ | |
| " 像素宽， " + str(result_height) + \ | |
| " 像素高，最终总分辨率为 " + f'{result_width * result_height:,}' + " 像素。" | |
| print(information) | |
| try: | |
| print("初始分辨率：" + f'{input_width * input_height:,}') | |
| print("最终分辨率：" + f'{result_width * result_height:,}') | |
| print("edm_steps: " + str(edm_steps)) | |
| print("num_samples: " + str(num_samples)) | |
| print("缩小规模：" + str(downscale)) | |
| print("预计分钟数：" + f'{(((result_width * result_height**(1/1.75)) * input_width * input_height * (edm_steps**(1/2)) * (num_samples**(1/2.5)))**(1/2.5)) / 25000:,}') | |
| except Exception as e: | |
| print('估算错误') | |
| # 滑动块中只能显示一张图像 | |
| return [noisy_image] + [results[0]], gr.update(label="可下载的结果为 *." + output_format + " 格式", format = output_format, value = results), gr.update(value = information, visible = True), gr.update(visible=True) | |
| def load_and_reset(param_setting): | |
| print('load_and_reset ==>>') | |
| if torch.cuda.device_count() == 0: | |
| gr.Warning('将此 Spaces 设置为 GPU 配置以使其正常工作。') | |
| return None, None, None, None, None, None, None, None, None, None, None, None, None, None | |
| edm_steps = default_setting.edm_steps | |
| s_stage2 = 1.0 | |
| s_stage1 = -1.0 | |
| s_churn = 5 | |
| s_noise = 1.003 | |
| # 积极提示词 | |
| a_prompt = '电影级，高对比度，高度精细，使用哈苏相机拍摄，超精细照片，逼真的最大细节，32K，调色，超高清，极致的细节，皮肤毛孔细节，超清晰度，完美无变形。' | |
| # 消极提示词 | |
| n_prompt = '绘画，油画，插图，绘图，艺术，素描，动漫，卡通，CG 风格，3D 渲染，虚幻引擎，模糊，混色，不清晰，怪异纹理，丑陋，肮脏，凌乱，质量最差，质量低，框架，水印，签名，JPEG 伪影，变形，低分辨率，过度平滑' | |
| color_fix_type = 'Wavelet' | |
| spt_linear_s_stage2 = 0.0 | |
| linear_s_stage2 = False | |
| linear_CFG = True | |
| if param_setting == "Quality": | |
| s_cfg = default_setting.s_cfg_Quality | |
| spt_linear_CFG = default_setting.spt_linear_CFG_Quality | |
| model_select = "v0-Q" | |
| elif param_setting == "Fidelity": | |
| s_cfg = default_setting.s_cfg_Fidelity | |
| spt_linear_CFG = default_setting.spt_linear_CFG_Fidelity | |
| model_select = "v0-F" | |
| else: | |
| raise NotImplementedError | |
| gr.Info('参数已重置。') | |
| print('<<== load_and_reset') | |
| return edm_steps, s_cfg, s_stage2, s_stage1, s_churn, s_noise, a_prompt, n_prompt, color_fix_type, linear_CFG, \ | |
| linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select | |
| def log_information(result_gallery): | |
| print('log_information') | |
| if result_gallery is not None: | |
| for i, result in enumerate(result_gallery): | |
| print(result[0]) | |
| def on_select_result(result_slider, result_gallery, evt: gr.SelectData): | |
| print('on_select_result') | |
| if result_gallery is not None: | |
| for i, result in enumerate(result_gallery): | |
| print(result[0]) | |
| return [result_slider[0], result_gallery[evt.index][0]] | |
| # Gradio 接口 | |
| with gr.Blocks() as interface: | |
| gr.Markdown(""" | |
| # BOOXEL —— Boost Pixel! | |
| 提供你的提示词，借助先进的生成实验和模型放大的力量，获取非凡的逼真画面。 | |
| 我们收集了一个包含 600 万张高分辨率、高质量图像的真实世界采集的数据集用于模型训练，每张图像都关联了清晰且详尽的描述性文本注释。 | |
| 我们提供了使用文本提示操纵恢复图像的能力，此外，还引入了消极质量提示和恢复指导的采样方法，以进一步提高生成图像的质量和保真度。 | |
| """) | |
| input_image = gr.Image(label="输入图像（*.png, *.webp, *.jpeg, *.jpg, *.gif, *.bmp, *.heic）", show_label=True, type="filepath", height=600, elem_id="image-input") | |
| rotation = gr.Radio([["不旋转", 0], ["⤵ 旋转 +90°", 90], ["↩ 旋转 180°", 180], ["⤴ 旋转 -90°", -90]], label="方向校正", info="在还原图像之前，将应用以下旋转功能；人工智能需要良好的定位才能理解内容", value=0, interactive=True, visible=False) | |
| with gr.Group(): | |
| prompt = gr.Textbox(label="图像描述", info="帮助人工智能理解图像所代表的内容；尽可能多地描述，尤其是我们在原始图像上看不到的细节；可以用任何语言书写", value="", placeholder="长春，上午，秋天，英短蓝白猫，走在，花丛小径上，真实图像", lines=3) | |
| upscale = gr.Radio([["x1", 1], ["x2", 2], ["x3", 3], ["x4", 4], ["x5", 5], ["x6", 6], ["x7", 7], ["x8", 8], ["x9", 9], ["x10", 10]], label="像素放大倍率", info="1 到 10 倍放大倍率", value=2, interactive=True) | |
| allocation = gr.Radio([["1 min", 1], ["2 min", 2], ["3 min", 3], ["4 min", 4], ["5 min", 5], ["6 min", 6], ["7 min", 7], ["8 min（不建议）", 8], ["9 min（不建议）", 9], ["10 min（不建议）", 10]], label="GPU 分配时间", info="设置为较低值可中止运行；设置为较高值后，下次运行会受到配额处罚", value=5, interactive=True) | |
| with gr.Accordion("预降噪（可选）", open=False): | |
| gamma_correction = gr.Slider(label="伽玛校正", info="较低的值图像将会更亮，反之亦然", minimum=0.1, maximum=2.0, value=1.0, step=0.1) | |
| denoise_button = gr.Button(value="预降噪") | |
| denoise_image = gr.Image(label="降噪图像", show_label=True, type="filepath", sources=[], interactive = False, height=600, elem_id="image-s1") | |
| denoise_information = gr.HTML(value="如果存在，去噪图像将被用于修复，而不是输入图像。", visible=False) | |
| with gr.Accordion("高级选项", open=False): | |
| output_format = gr.Radio([["与输入一致", "input"], ["*.png", "png"], ["*.webp", "webp"], ["*.jpeg", "jpeg"], ["*.gif", "gif"], ["*.bmp", "bmp"]], label="生成的图像格式", info="文件扩展名", value="input", interactive=True) | |
| a_prompt = gr.Textbox(label="补充图片说明", | |
| info="完整的主图像描述", | |
| value='电影级，高对比度，高度精细，使用哈苏相机拍摄，超精细照片，逼真的最大细节，32K，调色，超高清，极致的细节，皮肤毛孔细节，超清晰度，完美无变形。', | |
| lines=3) | |
| n_prompt = gr.Textbox(label="负面图像描述", | |
| info="通过列出图像不代表的内容来消除歧义", | |
| value='绘画，油画，插图，绘图，艺术，素描，动漫，卡通，CG 风格，3D 渲染，虚幻引擎，模糊，混色，不清晰，怪异纹理，丑陋，肮脏，凌乱，质量最差，质量低，框架，水印，签名，JPEG 伪影，变形，低分辨率，过度平滑', | |
| lines=3) | |
| edm_steps = gr.Slider(label="步骤数", info="较低的值生成将会更快；较高的值将会获得更多的细节", minimum=1, maximum=200, value=default_setting.edm_steps if torch.cuda.device_count() > 0 else 1, step=1) | |
| num_samples = gr.Slider(label="生成数", info="生成的结果图像的数量", minimum=1, maximum=4 if not args.use_image_slider else 1 | |
| , value=1, step=1) | |
| min_size = gr.Slider(label="最小尺寸", info="结果的最小高度和最小宽度", minimum=32, maximum=4096, value=1024, step=32) | |
| downscale = gr.Radio([["/1", 1], ["/2", 2], ["/3", 3], ["/4", 4], ["/5", 5], ["/6", 6], ["/7", 7], ["/8", 8], ["/9", 9], ["/10", 10]], label="缩减前因数", info="减少图像模糊，缩短处理时间", value=1, interactive=True) | |
| with gr.Row(): | |
| with gr.Column(): | |
| model_select = gr.Radio([["质量 (v0-Q)", "v0-Q"], ["保真度 (v0-F)", "v0-F"]], label="模型选择", info="预训练模型", value="v0-Q", | |
| interactive=True) | |
| with gr.Column(): | |
| color_fix_type = gr.Radio([["None", "None"], ["AdaIn (改进风格)", "AdaIn"], ["Wavelet (针对 JPEG 伪图象)", "Wavelet"]], label="色彩修复类型", info="AdaIn 改进画面风格；Wavelet 用于 JPEG 伪图像", value="Wavelet", | |
| interactive=True) | |
| s_cfg = gr.Slider(label="文本指导等级", info="较低的值将更加跟随源图像；较高的值将更加跟随提示", minimum=1.0, maximum=15.0, | |
| value=default_setting.s_cfg_Quality if torch.cuda.device_count() > 0 else 1.0, step=0.1) | |
| s_stage2 = gr.Slider(label="修复指导强度", minimum=0., maximum=1., value=1., step=0.05) | |
| s_stage1 = gr.Slider(label="预降噪指导强度", minimum=-1.0, maximum=6.0, value=-1.0, step=1.0) | |
| s_churn = gr.Slider(label="S-Churn", minimum=0, maximum=40, value=5, step=1) | |
| s_noise = gr.Slider(label="S-Noise", minimum=1.0, maximum=1.1, value=1.003, step=0.001) | |
| with gr.Row(): | |
| with gr.Column(): | |
| linear_CFG = gr.Checkbox(label="线性 CFG", value=True) | |
| spt_linear_CFG = gr.Slider(label="CFG 起始", minimum=1.0, | |
| maximum=9.0, value=default_setting.spt_linear_CFG_Quality if torch.cuda.device_count() > 0 else 1.0, step=0.5) | |
| with gr.Column(): | |
| linear_s_stage2 = gr.Checkbox(label="线性修复指导", value=False) | |
| spt_linear_s_stage2 = gr.Slider(label="指导起始", minimum=0., | |
| maximum=1., value=0., step=0.05) | |
| with gr.Column(): | |
| diff_dtype = gr.Radio([["fp32 (精确)", "fp32"], ["fp16 (中等)", "fp16"], ["bf16 (快速)", "bf16"]], label="扩散数据类型", value="fp32", | |
| interactive=True) | |
| with gr.Column(): | |
| ae_dtype = gr.Radio([["fp32 (精确)", "fp32"], ["bf16 (快速)", "bf16"]], label="自动编码器数据类型", value="fp32", | |
| interactive=True) | |
| randomize_seed = gr.Checkbox(label = "\U0001F3B2 随机种子", value=True, info="如果选中，结果将总是不同") | |
| seed = gr.Slider(label="种子", minimum=0, maximum=max_64_bit_int, step=1, randomize=True) | |
| with gr.Group(): | |
| param_setting = gr.Radio(["Quality", "Fidelity"], interactive=True, label="预配", value="Quality") | |
| restart_button = gr.Button(value="应用预配") | |
| with gr.Column(): | |
| diffusion_button = gr.Button(value="开始处理", variant="primary", elem_id="process_button") | |
| reset_btn = gr.Button(value="重新初始化页面", variant="stop", elem_id="reset_button", visible=False) | |
| restore_information = gr.HTML(value="重启进程，获得另一个结果。", visible=False) | |
| result_slider = ImageSlider(label='对比结果', show_label=True, interactive=False, elem_id="slider1", show_download_button=False) | |
| result_gallery = gr.Gallery(label='可下载的结果', show_label=True, interactive=False, elem_id="gallery1") | |
| gr.Examples( | |
| examples = [ | |
| [ | |
| "./Examples/Example1.png", | |
| 0, | |
| None, | |
| "一群人，快乐地在街上行走，逼真，8K，极其精细", | |
| "电影级，高对比度，高度精细，使用哈苏相机拍摄，超精细照片，逼真的最大细节，32K，调色，超高清，极致的细节，皮肤毛孔细节，超清晰度，完美无变形。", | |
| "绘画，油画，插图，绘图，艺术，素描，动漫，卡通，CG 风格，3D 渲染，虚幻引擎，模糊，混色，不清晰，怪异纹理，丑陋，肮脏，凌乱，质量最差，质量低，框架，水印，签名，JPEG 伪影，变形，低分辨率，过度平滑", | |
| 2, | |
| 1024, | |
| 1, | |
| 8, | |
| 200, | |
| -1, | |
| 1, | |
| 7.5, | |
| False, | |
| 42, | |
| 5, | |
| 1.003, | |
| "AdaIn", | |
| "fp16", | |
| "bf16", | |
| 1.0, | |
| True, | |
| 4, | |
| False, | |
| 0., | |
| "v0-Q", | |
| "input", | |
| 5 | |
| ], | |
| [ | |
| "./Examples/Example2.jpeg", | |
| 0, | |
| None, | |
| "一只虎斑猫的头部，在一间房子里，逼真，8K，极其细腻。", | |
| "电影级，高对比度，高度精细，使用哈苏相机拍摄，超精细照片，逼真的最大细节，32K，调色，超高清，极致的细节，皮肤毛孔细节，超清晰度，完美无变形。", | |
| "绘画，油画，插图，绘图，艺术，素描，动漫，卡通，CG 风格，3D 渲染，虚幻引擎，模糊，混色，不清晰，怪异纹理，丑陋，肮脏，凌乱，质量最差，质量低，框架，水印，签名，JPEG 伪影，变形，低分辨率，过度平滑", | |
| 1, | |
| 1024, | |
| 1, | |
| 1, | |
| 200, | |
| -1, | |
| 1, | |
| 7.5, | |
| False, | |
| 42, | |
| 5, | |
| 1.003, | |
| "Wavelet", | |
| "fp16", | |
| "bf16", | |
| 1.0, | |
| True, | |
| 4, | |
| False, | |
| 0., | |
| "v0-Q", | |
| "input", | |
| 4 | |
| ], | |
| [ | |
| "./Examples/Example3.webp", | |
| 0, | |
| None, | |
| "一个红色的苹果", | |
| "电影级，高对比度，高度精细，使用哈苏相机拍摄，超精细照片，逼真的最大细节，32K，调色，超高清，极致的细节，皮肤毛孔细节，超清晰度，完美无变形。", | |
| "绘画，油画，插图，绘图，艺术，素描，动漫，卡通，CG 风格，3D 渲染，虚幻引擎，模糊，混色，不清晰，怪异纹理，丑陋，肮脏，凌乱，质量最差，质量低，框架，水印，签名，JPEG 伪影，变形，低分辨率，过度平滑", | |
| 1, | |
| 1024, | |
| 1, | |
| 1, | |
| 200, | |
| -1, | |
| 1, | |
| 7.5, | |
| False, | |
| 42, | |
| 5, | |
| 1.003, | |
| "Wavelet", | |
| "fp16", | |
| "bf16", | |
| 1.0, | |
| True, | |
| 4, | |
| False, | |
| 0., | |
| "v0-Q", | |
| "input", | |
| 4 | |
| ], | |
| [ | |
| "./Examples/Example3.webp", | |
| 0, | |
| None, | |
| "一块红色大理石", | |
| "电影级，高对比度，高度精细，使用哈苏相机拍摄，超精细照片，逼真的最大细节，32K，调色，超高清，极致的细节，皮肤毛孔细节，超清晰度，完美无变形。", | |
| "绘画，油画，插图，绘图，艺术，素描，动漫，卡通，CG 风格，3D 渲染，虚幻引擎，模糊，混色，不清晰，怪异纹理，丑陋，肮脏，凌乱，质量最差，质量低，框架，水印，签名，JPEG 伪影，变形，低分辨率，过度平滑", | |
| 1, | |
| 1024, | |
| 1, | |
| 1, | |
| 200, | |
| -1, | |
| 1, | |
| 7.5, | |
| False, | |
| 42, | |
| 5, | |
| 1.003, | |
| "Wavelet", | |
| "fp16", | |
| "bf16", | |
| 1.0, | |
| True, | |
| 4, | |
| False, | |
| 0., | |
| "v0-Q", | |
| "input", | |
| 4 | |
| ], | |
| ], | |
| run_on_click = True, | |
| fn = stage2_process, | |
| inputs = [ | |
| input_image, | |
| rotation, | |
| denoise_image, | |
| prompt, | |
| a_prompt, | |
| n_prompt, | |
| num_samples, | |
| min_size, | |
| downscale, | |
| upscale, | |
| edm_steps, | |
| s_stage1, | |
| s_stage2, | |
| s_cfg, | |
| randomize_seed, | |
| seed, | |
| s_churn, | |
| s_noise, | |
| color_fix_type, | |
| diff_dtype, | |
| ae_dtype, | |
| gamma_correction, | |
| linear_CFG, | |
| linear_s_stage2, | |
| spt_linear_CFG, | |
| spt_linear_s_stage2, | |
| model_select, | |
| output_format, | |
| allocation | |
| ], | |
| outputs = [ | |
| result_slider, | |
| result_gallery, | |
| restore_information, | |
| reset_btn | |
| ], | |
| cache_examples = False, | |
| ) | |
| input_image.upload(fn = check_upload, inputs = [ | |
| input_image | |
| ], outputs = [ | |
| rotation | |
| ], queue = False, show_progress = False) | |
| denoise_button.click(fn = check, inputs = [ | |
| input_image | |
| ], outputs = [], queue = False, show_progress = False).success(fn = stage1_process, inputs = [ | |
| input_image, | |
| gamma_correction, | |
| diff_dtype, | |
| ae_dtype | |
| ], outputs=[ | |
| denoise_image, | |
| denoise_information | |
| ]) | |
| diffusion_button.click(fn = update_seed, inputs = [ | |
| randomize_seed, | |
| seed | |
| ], outputs = [ | |
| seed | |
| ], queue = False, show_progress = False).then(fn = check, inputs = [ | |
| input_image | |
| ], outputs = [], queue = False, show_progress = False).success(fn=stage2_process, inputs = [ | |
| input_image, | |
| rotation, | |
| denoise_image, | |
| prompt, | |
| a_prompt, | |
| n_prompt, | |
| num_samples, | |
| min_size, | |
| downscale, | |
| upscale, | |
| edm_steps, | |
| s_stage1, | |
| s_stage2, | |
| s_cfg, | |
| randomize_seed, | |
| seed, | |
| s_churn, | |
| s_noise, | |
| color_fix_type, | |
| diff_dtype, | |
| ae_dtype, | |
| gamma_correction, | |
| linear_CFG, | |
| linear_s_stage2, | |
| spt_linear_CFG, | |
| spt_linear_s_stage2, | |
| model_select, | |
| output_format, | |
| allocation | |
| ], outputs = [ | |
| result_slider, | |
| result_gallery, | |
| restore_information, | |
| reset_btn | |
| ]).success(fn = log_information, inputs = [ | |
| result_gallery | |
| ], outputs = [], queue = False, show_progress = False) | |
| result_gallery.change(on_select_result, [result_slider, result_gallery], result_slider) | |
| result_gallery.select(on_select_result, [result_slider, result_gallery], result_slider) | |
| restart_button.click(fn = load_and_reset, inputs = [ | |
| param_setting | |
| ], outputs = [ | |
| edm_steps, | |
| s_cfg, | |
| s_stage2, | |
| s_stage1, | |
| s_churn, | |
| s_noise, | |
| a_prompt, | |
| n_prompt, | |
| color_fix_type, | |
| linear_CFG, | |
| linear_s_stage2, | |
| spt_linear_CFG, | |
| spt_linear_s_stage2, | |
| model_select | |
| ]) | |
| reset_btn.click(fn = reset, inputs = [], outputs = [ | |
| input_image, | |
| rotation, | |
| denoise_image, | |
| prompt, | |
| a_prompt, | |
| n_prompt, | |
| num_samples, | |
| min_size, | |
| downscale, | |
| upscale, | |
| edm_steps, | |
| s_stage1, | |
| s_stage2, | |
| s_cfg, | |
| randomize_seed, | |
| seed, | |
| s_churn, | |
| s_noise, | |
| color_fix_type, | |
| diff_dtype, | |
| ae_dtype, | |
| gamma_correction, | |
| linear_CFG, | |
| linear_s_stage2, | |
| spt_linear_CFG, | |
| spt_linear_s_stage2, | |
| model_select, | |
| output_format, | |
| allocation | |
| ], queue = False, show_progress = False) | |
| interface.queue(10).launch() |