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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 BOOXEL.utils.colorfix import wavelet_reconstruction, adaptive_instance_normalization
from pytorch_lightning import seed_everything
from torch.nn.functional import interpolate
from BOOXEL.utils.tilevae import VAEHook
class BOOXELModel(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, '仅在本地提示调节时支持 bs=1。'
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 BOOXEL.util import create_model, load_state_dict
model = create_model('../../options/dev/BOOXEL_paper_version.yaml')
SDXL_CKPT = '/opt/data/private/AIGC_pretrain/SDXL_cache/sd_xl_base_1.0_0.9vae.safetensors'
BOOXEL_CKPT = '/opt/data/private/AIGC_pretrain/BOOXEL_cache/BOOXEL-paper.ckpt'
model.load_state_dict(load_state_dict(SDXL_CKPT), strict=False)
model.load_state_dict(load_state_dict(BOOXEL_CKPT), strict=False)
model = model.cuda()
x = torch.randn(1, 3, 512, 512).cuda()
p = ['一张专业、详细、高质量的照片']
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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