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) 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 self.model.dtype = diffusion_dtype self.p_p = p_p 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 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)