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import os
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import gradio as gr
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from gradio_imageslider import ImageSlider
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import argparse
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from SUPIR.util import HWC3, upscale_image, fix_resize, convert_dtype
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import numpy as np
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import torch
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from SUPIR.util import create_SUPIR_model, load_QF_ckpt
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from PIL import Image
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from llava.llava_agent import LLavaAgent
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from CKPT_PTH import LLAVA_MODEL_PATH
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import einops
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import copy
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import time
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from omegaconf import OmegaConf
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from SUPIR.utils.face_restoration_helper import FaceRestoreHelper
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parser = argparse.ArgumentParser()
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parser.add_argument("--ip", type=str, default='127.0.0.1')
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parser.add_argument("--port", type=int, default='6688')
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parser.add_argument("--no_llava", action='store_true', default=False)
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parser.add_argument("--use_image_slider", action='store_true', default=False)
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parser.add_argument("--log_history", action='store_true', default=False)
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parser.add_argument("--loading_half_params", action='store_true', default=False)
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parser.add_argument("--use_tile_vae", action='store_true', default=False)
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parser.add_argument("--load_8bit_llava", action='store_true', default=False)
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parser.add_argument("--local_prompt", action='store_true', default=False)
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args = parser.parse_args()
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server_ip = args.ip
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server_port = args.port
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use_llava = not args.no_llava
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if torch.cuda.device_count() >= 2:
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SUPIR_device = 'cuda:0'
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LLaVA_device = 'cuda:1'
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elif torch.cuda.device_count() == 1:
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SUPIR_device = 'cuda:0'
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LLaVA_device = 'cuda:0'
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else:
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raise ValueError('Currently support CUDA only.')
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config_path = 'options/SUPIR_v0.yaml'
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config = OmegaConf.load(config_path)
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model = create_SUPIR_model(config_path, SUPIR_sign='Q')
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if args.loading_half_params:
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model = model.half()
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if args.use_tile_vae:
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model.init_tile_vae(encoder_tile_size=512, decoder_tile_size=64)
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model = model.to(SUPIR_device)
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model.first_stage_model.denoise_encoder_s1 = copy.deepcopy(model.first_stage_model.denoise_encoder)
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model.current_model = 'v0-Q'
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ckpt_Q, ckpt_F = load_QF_ckpt('options/SUPIR_v0.yaml')
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if use_llava:
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llava_agent = LLavaAgent(LLAVA_MODEL_PATH, device=LLaVA_device, load_8bit=args.load_8bit_llava, load_4bit=False)
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else:
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llava_agent = None
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face_helper = FaceRestoreHelper(
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device=SUPIR_device,
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upscale_factor=1,
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face_size=1024,
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use_parse=True,
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det_model='retinaface_resnet50'
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)
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def stage1_process(input_image, gamma_correction):
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torch.cuda.set_device(SUPIR_device)
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LQ = HWC3(input_image)
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LQ = fix_resize(LQ, 512)
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LQ = np.array(LQ) / 255 * 2 - 1
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LQ = torch.tensor(LQ, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0).to(SUPIR_device)[:, :3, :, :]
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LQ = model.batchify_denoise(LQ, is_stage1=True)
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LQ = (LQ[0].permute(1, 2, 0) * 127.5 + 127.5).cpu().numpy().round().clip(0, 255).astype(np.uint8)
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LQ = LQ / 255.0
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LQ = np.power(LQ, gamma_correction)
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LQ *= 255.0
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LQ = LQ.round().clip(0, 255).astype(np.uint8)
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return LQ
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def llave_process(input_image, upscale, temperature, top_p, qs=None):
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torch.cuda.set_device(SUPIR_device)
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input_image = HWC3(input_image)
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input_image = upscale_image(input_image, upscale, unit_resolution=32,
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min_size=1024)
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LQ = np.array(input_image) / 255 * 2 - 1
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LQ = torch.tensor(LQ, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0).to(SUPIR_device)[:, :3, :, :]
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LQ = model.batchify_denoise(LQ, is_stage1=True)
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LQ = (LQ[0].permute(1, 2, 0) * 127.5 + 127.5).cpu().numpy().round().clip(0, 255).astype(np.uint8)
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LQs = [Image.fromarray(LQ)]
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face_helper.clean_all()
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face_helper.read_image(LQ)
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face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
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face_helper.align_warp_face()
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for face in face_helper.cropped_faces:
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LQs.append(Image.fromarray(face))
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captions = []
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torch.cuda.set_device(LLaVA_device)
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if use_llava:
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for LQ in LQs:
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captions += llava_agent.gen_image_caption([LQ], temperature=temperature, top_p=top_p, qs=qs)
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else:
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captions = ['LLaVA is not available. Please add text manually.']
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del LQs[0]
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return str(captions), [np.array(face) for face in LQs]
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def stage2_process(input_image, prompt, a_prompt, n_prompt, num_samples, upscale, edm_steps, s_stage1, s_stage2,
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s_cfg, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction,
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linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select,
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face_resolution, apply_bg, apply_face):
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torch.cuda.set_device(SUPIR_device)
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event_id = str(time.time_ns())
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event_dict = {'event_id': event_id, 'localtime': time.ctime(), 'prompt': prompt, 'a_prompt': a_prompt,
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'n_prompt': n_prompt, 'num_samples': num_samples, 'upscale': upscale, 'edm_steps': edm_steps,
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's_stage1': s_stage1, 's_stage2': s_stage2, 's_cfg': s_cfg, 'seed': seed, 's_churn': s_churn,
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's_noise': s_noise, 'color_fix_type': color_fix_type, 'diff_dtype': diff_dtype, 'ae_dtype': ae_dtype,
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'gamma_correction': gamma_correction, 'linear_CFG': linear_CFG, 'linear_s_stage2': linear_s_stage2,
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'spt_linear_CFG': spt_linear_CFG, 'spt_linear_s_stage2': spt_linear_s_stage2,
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'model_select': model_select}
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if model_select != model.current_model:
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if model_select == 'v0-Q':
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print('load v0-Q')
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model.load_state_dict(ckpt_Q, strict=False)
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model.current_model = 'v0-Q'
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elif model_select == 'v0-F':
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print('load v0-F')
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model.load_state_dict(ckpt_F, strict=False)
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model.current_model = 'v0-F'
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input_image = HWC3(input_image)
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input_image = upscale_image(input_image, upscale, unit_resolution=32,
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min_size=1024)
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LQ = np.array(input_image)
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face_helper.clean_all()
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face_helper.read_image(LQ)
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face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
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face_helper.align_warp_face()
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LQ = LQ / 255 * 2 - 1
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LQ = torch.tensor(LQ, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0).to(SUPIR_device)[:, :3, :, :]
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if use_llava and prompt != '':
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captions = eval(prompt)
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else:
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captions = [''] * (1 + len(face_helper.cropped_faces))
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bg_caption, face_captions = captions[0], captions[1:]
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model.ae_dtype = convert_dtype(ae_dtype)
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model.model.dtype = convert_dtype(diff_dtype)
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_faces = []
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if apply_face:
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faces = []
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for face in face_helper.cropped_faces:
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_faces.append(face)
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face = np.array(face) / 255 * 2 - 1
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face = torch.tensor(face, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0).to(SUPIR_device)[:, :3, :, :]
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faces.append(face)
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for face, caption in zip(faces, face_captions):
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caption = [caption]
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from torch.nn.functional import interpolate
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face = interpolate(face, size=face_resolution, mode='bilinear', align_corners=False)
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if face_resolution < 1024:
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face = torch.nn.functional.pad(face, (512-face_resolution//2, 512-face_resolution//2,
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512-face_resolution//2, 512-face_resolution//2), 'constant', 0)
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samples = model.batchify_sample(face, caption, num_steps=edm_steps, restoration_scale=s_stage1, s_churn=s_churn,
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s_noise=s_noise, cfg_scale=s_cfg, control_scale=s_stage2, seed=seed,
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num_samples=num_samples, p_p=a_prompt, n_p=n_prompt, color_fix_type=color_fix_type,
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use_linear_CFG=linear_CFG, use_linear_control_scale=linear_s_stage2,
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cfg_scale_start=spt_linear_CFG, control_scale_start=spt_linear_s_stage2)
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if face_resolution < 1024:
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samples = samples[:, :, 512-face_resolution//2:512+face_resolution//2,
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512-face_resolution//2:512+face_resolution//2]
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samples = interpolate(samples, size=face_helper.face_size, mode='bilinear', align_corners=False)
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x_samples = (einops.rearrange(samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().round().clip(
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0, 255).astype(np.uint8)
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face_helper.add_restored_face(x_samples[0])
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_faces.append(x_samples[0])
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if apply_bg:
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caption = [bg_caption]
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samples = model.batchify_sample(LQ, caption, num_steps=edm_steps, restoration_scale=s_stage1,
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s_churn=s_churn,
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s_noise=s_noise, cfg_scale=s_cfg, control_scale=s_stage2, seed=seed,
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num_samples=num_samples, p_p=a_prompt, n_p=n_prompt,
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color_fix_type=color_fix_type,
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use_linear_CFG=linear_CFG, use_linear_control_scale=linear_s_stage2,
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cfg_scale_start=spt_linear_CFG, control_scale_start=spt_linear_s_stage2)
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else:
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samples = LQ
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_bg = (einops.rearrange(samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().round().clip(
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0, 255).astype(np.uint8)
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face_helper.get_inverse_affine(None)
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results = [face_helper.paste_faces_to_input_image(upsample_img=_bg[0])]
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else:
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caption = [bg_caption]
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samples = model.batchify_sample(LQ, caption, num_steps=edm_steps, restoration_scale=s_stage1,
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s_churn=s_churn,
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s_noise=s_noise, cfg_scale=s_cfg, control_scale=s_stage2, seed=seed,
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num_samples=num_samples, p_p=a_prompt, n_p=n_prompt,
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color_fix_type=color_fix_type,
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use_linear_CFG=linear_CFG, use_linear_control_scale=linear_s_stage2,
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cfg_scale_start=spt_linear_CFG, control_scale_start=spt_linear_s_stage2)
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x_samples = (einops.rearrange(samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().round().clip(
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0, 255).astype(np.uint8)
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results = [x_samples[0]]
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if args.log_history:
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os.makedirs(f'./history/{event_id[:5]}/{event_id[5:]}', exist_ok=True)
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with open(f'./history/{event_id[:5]}/{event_id[5:]}/logs.txt', 'w') as f:
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f.write(str(event_dict))
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f.close()
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Image.fromarray(input_image).save(f'./history/{event_id[:5]}/{event_id[5:]}/LQ.png')
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for i, result in enumerate(results):
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Image.fromarray(result).save(f'./history/{event_id[:5]}/{event_id[5:]}/HQ_{i}.png')
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return [input_image] + results, event_id, 3, '', _faces
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def load_and_reset(param_setting):
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edm_steps = 50
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s_stage2 = 1.0
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s_stage1 = -1.0
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s_churn = 5
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s_noise = 1.003
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a_prompt = 'Cinematic, High Contrast, highly detailed, taken using a Canon EOS R camera, hyper detailed photo - ' \
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'realistic maximum detail, 32k, Color Grading, ultra HD, extreme meticulous detailing, skin pore ' \
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'detailing, hyper sharpness, perfect without deformations.'
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n_prompt = 'painting, oil painting, illustration, drawing, art, sketch, oil painting, cartoon, CG Style, ' \
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'3D render, unreal engine, blurring, dirty, messy, worst quality, low quality, frames, watermark, ' \
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'signature, jpeg artifacts, deformed, lowres, over-smooth'
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color_fix_type = 'Wavelet'
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spt_linear_s_stage2 = 0.0
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linear_s_stage2 = False
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linear_CFG = True
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if param_setting == "Quality":
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s_cfg = 7.5
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spt_linear_CFG = 4.0
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elif param_setting == "Fidelity":
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s_cfg = 4.0
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spt_linear_CFG = 1.0
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else:
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raise NotImplementedError
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return edm_steps, s_cfg, s_stage2, s_stage1, s_churn, s_noise, a_prompt, n_prompt, color_fix_type, linear_CFG, \
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linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2
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def submit_feedback(event_id, fb_score, fb_text):
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if args.log_history:
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with open(f'./history/{event_id[:5]}/{event_id[5:]}/logs.txt', 'r') as f:
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event_dict = eval(f.read())
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f.close()
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event_dict['feedback'] = {'score': fb_score, 'text': fb_text}
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with open(f'./history/{event_id[:5]}/{event_id[5:]}/logs.txt', 'w') as f:
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f.write(str(event_dict))
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f.close()
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return 'Submit successfully, thank you for your comments!'
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else:
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return 'Submit failed, the server is not set to log history.'
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title_md = """
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# **SUPIR: Practicing Model Scaling for Photo-Realistic Image Restoration**
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⚠️SUPIR is still a research project under tested and is not yet a stable commercial product.
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[[Paper](https://arxiv.org/abs/2401.13627)]   [[Project Page](http://supir.xpixel.group/)]   [[How to play](https://github.com/Fanghua-Yu/SUPIR/blob/master/assets/DemoGuide.png)]
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"""
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claim_md = """
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## **Terms of use**
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By using this service, users are required to agree to the following terms: The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research. Please submit a feedback to us if you get any inappropriate answer! We will collect those to keep improving our models. For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality.
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## **License**
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The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/Fanghua-Yu/SUPIR) of SUPIR.
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"""
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block = gr.Blocks(title='SUPIR').queue()
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with block:
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with gr.Row():
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gr.Markdown(title_md)
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with gr.Row():
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with gr.Column():
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with gr.Row(equal_height=True):
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with gr.Column():
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gr.Markdown("<center>Input</center>")
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input_image = gr.Image(type="numpy", elem_id="image-input", height=400, width=400)
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with gr.Column():
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gr.Markdown("<center>Stage1 Output</center>")
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denoise_image = gr.Image(type="numpy", elem_id="image-s1", height=400, width=400)
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prompt = gr.Textbox(label="Prompt", value="")
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with gr.Accordion("Stage1 options", open=False):
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gamma_correction = gr.Slider(label="Gamma Correction", minimum=0.1, maximum=2.0, value=1.0, step=0.1)
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with gr.Accordion("LLaVA options", open=False):
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temperature = gr.Slider(label="Temperature", minimum=0., maximum=1.0, value=0.2, step=0.1)
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top_p = gr.Slider(label="Top P", minimum=0., maximum=1.0, value=0.7, step=0.1)
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qs = gr.Textbox(label="Question", value="Describe this image and its style in a very detailed manner. "
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"The image is a realistic photography, not an art painting.")
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with gr.Accordion("Stage2 options", open=False):
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num_samples = gr.Slider(label="Num Samples", minimum=1, maximum=4 if not args.use_image_slider else 1
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, value=1, step=1)
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upscale = gr.Slider(label="Upscale", minimum=1, maximum=8, value=1, step=1)
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edm_steps = gr.Slider(label="Steps", minimum=20, maximum=200, value=50, step=1)
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s_cfg = gr.Slider(label="Text Guidance Scale", minimum=1.0, maximum=15.0, value=7.5, step=0.1)
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s_stage2 = gr.Slider(label="Stage2 Guidance Strength", minimum=0., maximum=1., value=1., step=0.05)
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s_stage1 = gr.Slider(label="Stage1 Guidance Strength", minimum=-1.0, maximum=6.0, value=-1.0, step=1.0)
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seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
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s_churn = gr.Slider(label="S-Churn", minimum=0, maximum=40, value=5, step=1)
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s_noise = gr.Slider(label="S-Noise", minimum=1.0, maximum=1.1, value=1.003, step=0.001)
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a_prompt = gr.Textbox(label="Default Positive Prompt",
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value='Cinematic, High Contrast, highly detailed, taken using a Canon EOS R '
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'camera, hyper detailed photo - realistic maximum detail, 32k, Color '
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'Grading, ultra HD, extreme meticulous detailing, skin pore detailing, '
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'hyper sharpness, perfect without deformations.')
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n_prompt = gr.Textbox(label="Default Negative Prompt",
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value='painting, oil painting, illustration, drawing, art, sketch, oil painting, '
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'cartoon, CG Style, 3D render, unreal engine, blurring, dirty, messy, '
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'worst quality, low quality, frames, watermark, signature, jpeg artifacts, '
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'deformed, lowres, over-smooth')
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with gr.Row():
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with gr.Column():
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linear_CFG = gr.Checkbox(label="Linear CFG", value=True)
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spt_linear_CFG = gr.Slider(label="CFG Start", minimum=1.0,
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maximum=9.0, value=4.0, step=0.5)
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with gr.Column():
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linear_s_stage2 = gr.Checkbox(label="Linear Stage2 Guidance", value=False)
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spt_linear_s_stage2 = gr.Slider(label="Guidance Start", minimum=0.,
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maximum=1., value=0., step=0.05)
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with gr.Row():
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with gr.Column():
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diff_dtype = gr.Radio(['fp32', 'fp16', 'bf16'], label="Diffusion Data Type", value="fp16",
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interactive=True)
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with gr.Column():
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ae_dtype = gr.Radio(['fp32', 'bf16'], label="Auto-Encoder Data Type", value="bf16",
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interactive=True)
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with gr.Column():
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color_fix_type = gr.Radio(["None", "AdaIn", "Wavelet"], label="Color-Fix Type", value="Wavelet",
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interactive=True)
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with gr.Column():
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model_select = gr.Radio(["v0-Q", "v0-F"], label="Model Selection", value="v0-Q",
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interactive=True)
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with gr.Column():
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gr.Markdown("<center>Stage2 Output</center>")
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|
if not args.use_image_slider:
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result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery1")
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|
else:
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result_gallery = ImageSlider(label='Output', show_label=False, elem_id="gallery1")
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|
with gr.Row():
|
|
with gr.Column():
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|
denoise_button = gr.Button(value="Stage1 Run")
|
|
with gr.Column():
|
|
llave_button = gr.Button(value="LlaVa Run")
|
|
with gr.Column():
|
|
diffusion_button = gr.Button(value="Stage2 Run")
|
|
with gr.Row():
|
|
with gr.Column():
|
|
param_setting = gr.Dropdown(["Quality", "Fidelity"], interactive=True, label="Param Setting",
|
|
value="Quality")
|
|
with gr.Column():
|
|
restart_button = gr.Button(value="Reset Param", scale=2)
|
|
with gr.Accordion("Face Options", open=True):
|
|
face_resolution = gr.Slider(label="Text Guidance Scale", minimum=256, maximum=2048, value=1024, step=32)
|
|
with gr.Row():
|
|
with gr.Column():
|
|
apply_bg = gr.Checkbox(label="BG restoration", value=True)
|
|
with gr.Column():
|
|
apply_face = gr.Checkbox(label="Face restoration", value=True)
|
|
with gr.Accordion("Feedback", open=False):
|
|
fb_score = gr.Slider(label="Feedback Score", minimum=1, maximum=5, value=3, step=1,
|
|
interactive=True)
|
|
fb_text = gr.Textbox(label="Feedback Text", value="", placeholder='Please enter your feedback here.')
|
|
submit_button = gr.Button(value="Submit Feedback")
|
|
face_gallery = gr.Gallery(label='Faces', show_label=False, elem_id="gallery2")
|
|
with gr.Row():
|
|
gr.Markdown(claim_md)
|
|
event_id = gr.Textbox(label="Event ID", value="", visible=False)
|
|
|
|
llave_button.click(fn=llave_process, inputs=[input_image, upscale, temperature, top_p, qs], outputs=[prompt, face_gallery])
|
|
denoise_button.click(fn=stage1_process, inputs=[input_image, gamma_correction],
|
|
outputs=[denoise_image])
|
|
stage2_ips = [input_image, prompt, a_prompt, n_prompt, num_samples, upscale, edm_steps, s_stage1, s_stage2,
|
|
s_cfg, 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, face_resolution, apply_bg, apply_face]
|
|
diffusion_button.click(fn=stage2_process, inputs=stage2_ips, outputs=[result_gallery, event_id, fb_score, fb_text, face_gallery])
|
|
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])
|
|
submit_button.click(fn=submit_feedback, inputs=[event_id, fb_score, fb_text], outputs=[fb_text])
|
|
block.launch(server_name=server_ip, server_port=server_port)
|
|
|
