| import torch
|
| from sgm.models.diffusion import DiffusionEngine
|
| from sgm.util import instantiate_from_config
|
| import copy
|
| from sgm.modules.distributions.distributions import DiagonalGaussianDistribution
|
| import random
|
| from SUPIR.utils.colorfix import wavelet_reconstruction, adaptive_instance_normalization
|
| from pytorch_lightning import seed_everything
|
| from torch.nn.functional import interpolate
|
| from SUPIR.utils.tilevae import VAEHook
|
|
|
| class SUPIRModel(DiffusionEngine):
|
| def __init__(self, control_stage_config, ae_dtype='fp32', diffusion_dtype='fp32', p_p='', n_p='', *args, **kwargs):
|
| super().__init__(*args, **kwargs)
|
| control_model = instantiate_from_config(control_stage_config)
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| self.model.load_control_model(control_model)
|
| self.first_stage_model.denoise_encoder = copy.deepcopy(self.first_stage_model.encoder)
|
| self.sampler_config = kwargs['sampler_config']
|
|
|
| assert (ae_dtype in ['fp32', 'fp16', 'bf16']) and (diffusion_dtype in ['fp32', 'fp16', 'bf16'])
|
| if ae_dtype == 'fp32':
|
| ae_dtype = torch.float32
|
| elif ae_dtype == 'fp16':
|
| raise RuntimeError('fp16 cause NaN in AE')
|
| elif ae_dtype == 'bf16':
|
| ae_dtype = torch.bfloat16
|
|
|
| if diffusion_dtype == 'fp32':
|
| diffusion_dtype = torch.float32
|
| elif diffusion_dtype == 'fp16':
|
| diffusion_dtype = torch.float16
|
| elif diffusion_dtype == 'bf16':
|
| diffusion_dtype = torch.bfloat16
|
|
|
| self.ae_dtype = ae_dtype
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| self.model.dtype = diffusion_dtype
|
|
|
| self.p_p = p_p
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| self.n_p = n_p
|
|
|
| @torch.no_grad()
|
| def encode_first_stage(self, x):
|
| with torch.autocast("cuda", dtype=self.ae_dtype):
|
| z = self.first_stage_model.encode(x)
|
| z = self.scale_factor * z
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| return z
|
|
|
| @torch.no_grad()
|
| def encode_first_stage_with_denoise(self, x, use_sample=True, is_stage1=False):
|
| with torch.autocast("cuda", dtype=self.ae_dtype):
|
| if is_stage1:
|
| h = self.first_stage_model.denoise_encoder_s1(x)
|
| else:
|
| h = self.first_stage_model.denoise_encoder(x)
|
| moments = self.first_stage_model.quant_conv(h)
|
| posterior = DiagonalGaussianDistribution(moments)
|
| if use_sample:
|
| z = posterior.sample()
|
| else:
|
| z = posterior.mode()
|
| z = self.scale_factor * z
|
| return z
|
|
|
| @torch.no_grad()
|
| def decode_first_stage(self, z):
|
| z = 1.0 / self.scale_factor * z
|
| with torch.autocast("cuda", dtype=self.ae_dtype):
|
| out = self.first_stage_model.decode(z)
|
| return out.float()
|
|
|
| @torch.no_grad()
|
| def batchify_denoise(self, x, is_stage1=False):
|
| '''
|
| [N, C, H, W], [-1, 1], RGB
|
| '''
|
| x = self.encode_first_stage_with_denoise(x, use_sample=False, is_stage1=is_stage1)
|
| return self.decode_first_stage(x)
|
|
|
| @torch.no_grad()
|
| def batchify_sample(self, x, p, p_p='default', n_p='default', num_steps=100, restoration_scale=4.0, s_churn=0, s_noise=1.003, cfg_scale=4.0, seed=-1,
|
| num_samples=1, control_scale=1, color_fix_type='None', use_linear_CFG=False, use_linear_control_scale=False,
|
| cfg_scale_start=1.0, control_scale_start=0.0, **kwargs):
|
| '''
|
| [N, C], [-1, 1], RGB
|
| '''
|
| assert len(x) == len(p)
|
| assert color_fix_type in ['Wavelet', 'AdaIn', 'None']
|
|
|
| N = len(x)
|
| if num_samples > 1:
|
| assert N == 1
|
| N = num_samples
|
| x = x.repeat(N, 1, 1, 1)
|
| p = p * N
|
|
|
| if p_p == 'default':
|
| p_p = self.p_p
|
| if n_p == 'default':
|
| n_p = self.n_p
|
|
|
| self.sampler_config.params.num_steps = num_steps
|
| if use_linear_CFG:
|
| self.sampler_config.params.guider_config.params.scale_min = cfg_scale
|
| self.sampler_config.params.guider_config.params.scale = cfg_scale_start
|
| else:
|
| self.sampler_config.params.guider_config.params.scale_min = cfg_scale
|
| self.sampler_config.params.guider_config.params.scale = cfg_scale
|
| self.sampler_config.params.restore_cfg = restoration_scale
|
| self.sampler_config.params.s_churn = s_churn
|
| self.sampler_config.params.s_noise = s_noise
|
| self.sampler = instantiate_from_config(self.sampler_config)
|
|
|
| if seed == -1:
|
| seed = random.randint(0, 65535)
|
| seed_everything(seed)
|
|
|
| _z = self.encode_first_stage_with_denoise(x, use_sample=False)
|
| x_stage1 = self.decode_first_stage(_z)
|
| z_stage1 = self.encode_first_stage(x_stage1)
|
|
|
| c, uc = self.prepare_condition(_z, p, p_p, n_p, N)
|
|
|
| denoiser = lambda input, sigma, c, control_scale: self.denoiser(
|
| self.model, input, sigma, c, control_scale, **kwargs
|
| )
|
|
|
| noised_z = torch.randn_like(_z).to(_z.device)
|
|
|
| _samples = self.sampler(denoiser, noised_z, cond=c, uc=uc, x_center=z_stage1, control_scale=control_scale,
|
| use_linear_control_scale=use_linear_control_scale, control_scale_start=control_scale_start)
|
| samples = self.decode_first_stage(_samples)
|
| if color_fix_type == 'Wavelet':
|
| samples = wavelet_reconstruction(samples, x_stage1)
|
| elif color_fix_type == 'AdaIn':
|
| samples = adaptive_instance_normalization(samples, x_stage1)
|
| return samples
|
|
|
| def init_tile_vae(self, encoder_tile_size=512, decoder_tile_size=64):
|
| self.first_stage_model.denoise_encoder.original_forward = self.first_stage_model.denoise_encoder.forward
|
| self.first_stage_model.encoder.original_forward = self.first_stage_model.encoder.forward
|
| self.first_stage_model.decoder.original_forward = self.first_stage_model.decoder.forward
|
| self.first_stage_model.denoise_encoder.forward = VAEHook(
|
| self.first_stage_model.denoise_encoder, encoder_tile_size, is_decoder=False, fast_decoder=False,
|
| fast_encoder=False, color_fix=False, to_gpu=True)
|
| self.first_stage_model.encoder.forward = VAEHook(
|
| self.first_stage_model.encoder, encoder_tile_size, is_decoder=False, fast_decoder=False,
|
| fast_encoder=False, color_fix=False, to_gpu=True)
|
| self.first_stage_model.decoder.forward = VAEHook(
|
| self.first_stage_model.decoder, decoder_tile_size, is_decoder=True, fast_decoder=False,
|
| fast_encoder=False, color_fix=False, to_gpu=True)
|
|
|
| def prepare_condition(self, _z, p, p_p, n_p, N):
|
| batch = {}
|
| batch['original_size_as_tuple'] = torch.tensor([1024, 1024]).repeat(N, 1).to(_z.device)
|
| batch['crop_coords_top_left'] = torch.tensor([0, 0]).repeat(N, 1).to(_z.device)
|
| batch['target_size_as_tuple'] = torch.tensor([1024, 1024]).repeat(N, 1).to(_z.device)
|
| batch['aesthetic_score'] = torch.tensor([9.0]).repeat(N, 1).to(_z.device)
|
| batch['control'] = _z
|
|
|
| batch_uc = copy.deepcopy(batch)
|
| batch_uc['txt'] = [n_p for _ in p]
|
|
|
| if not isinstance(p[0], list):
|
| batch['txt'] = [''.join([_p, p_p]) for _p in p]
|
| with torch.cuda.amp.autocast(dtype=self.ae_dtype):
|
| c, uc = self.conditioner.get_unconditional_conditioning(batch, batch_uc)
|
| else:
|
| assert len(p) == 1, 'Support bs=1 only for local prompt conditioning.'
|
| p_tiles = p[0]
|
| c = []
|
| for i, p_tile in enumerate(p_tiles):
|
| batch['txt'] = [''.join([p_tile, p_p])]
|
| with torch.cuda.amp.autocast(dtype=self.ae_dtype):
|
| if i == 0:
|
| _c, uc = self.conditioner.get_unconditional_conditioning(batch, batch_uc)
|
| else:
|
| _c, _ = self.conditioner.get_unconditional_conditioning(batch, None)
|
| c.append(_c)
|
| return c, uc
|
|
|
|
|
| if __name__ == '__main__':
|
| from SUPIR.util import create_model, load_state_dict
|
|
|
| model = create_model('../../options/dev/SUPIR_paper_version.yaml')
|
|
|
| SDXL_CKPT = '/opt/data/private/AIGC_pretrain/SDXL_cache/sd_xl_base_1.0_0.9vae.safetensors'
|
| SUPIR_CKPT = '/opt/data/private/AIGC_pretrain/SUPIR_cache/SUPIR-paper.ckpt'
|
| model.load_state_dict(load_state_dict(SDXL_CKPT), strict=False)
|
| model.load_state_dict(load_state_dict(SUPIR_CKPT), strict=False)
|
| model = model.cuda()
|
|
|
| x = torch.randn(1, 3, 512, 512).cuda()
|
| p = ['a professional, detailed, high-quality photo']
|
| samples = model.batchify_sample(x, p, num_steps=50, restoration_scale=4.0, s_churn=0, cfg_scale=4.0, seed=-1, num_samples=1)
|
|
|
