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