|
|
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 ...SUPIR.utils.tilevae import VAEHook
|
|
|
from ...SUPIR.util import convert_dtype
|
|
|
from contextlib import nullcontext
|
|
|
import comfy.model_management
|
|
|
|
|
|
device = comfy.model_management.get_torch_device()
|
|
|
|
|
|
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']
|
|
|
|
|
|
self.ae_dtype = convert_dtype(ae_dtype)
|
|
|
self.model.dtype = convert_dtype(diffusion_dtype)
|
|
|
|
|
|
self.p_p = p_p
|
|
|
self.n_p = n_p
|
|
|
|
|
|
@torch.no_grad()
|
|
|
def encode_first_stage(self, x):
|
|
|
|
|
|
autocast_condition = (self.ae_dtype == torch.float16 or self.ae_dtype == torch.bfloat16) and not comfy.model_management.is_device_mps(device)
|
|
|
with torch.autocast(comfy.model_management.get_autocast_device(device), dtype=self.ae_dtype) if autocast_condition else nullcontext():
|
|
|
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):
|
|
|
|
|
|
self.first_stage_model.to(self.ae_dtype)
|
|
|
autocast_condition = (self.model.dtype == torch.float16 or self.model.dtype == torch.bfloat16) and not comfy.model_management.is_device_mps(device)
|
|
|
with torch.autocast(comfy.model_management.get_autocast_device(device), dtype=self.ae_dtype) if autocast_condition else nullcontext():
|
|
|
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
|
|
|
autocast_condition = (self.ae_dtype == torch.float16 or self.ae_dtype == torch.bfloat16) and not comfy.model_management.is_device_mps(device)
|
|
|
with torch.autocast(comfy.model_management.get_autocast_device(device), dtype=self.ae_dtype) if autocast_condition else nullcontext():
|
|
|
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)
|
|
|
|
|
|
print("Sampler: ", self.sampler_config.target)
|
|
|
print("sampler_config: ", self.sampler_config.params)
|
|
|
|
|
|
if seed == -1:
|
|
|
seed = random.randint(0, 65535)
|
|
|
seed_everything(seed)
|
|
|
|
|
|
|
|
|
self.model.to('cpu')
|
|
|
self.conditioner.to('cpu')
|
|
|
|
|
|
|
|
|
self.first_stage_model.to(device)
|
|
|
_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)
|
|
|
self.first_stage_model.to('cpu')
|
|
|
|
|
|
|
|
|
self.conditioner.to(device)
|
|
|
c, uc = self.prepare_condition(_z, p, p_p, n_p, N)
|
|
|
self.conditioner.to('cpu')
|
|
|
|
|
|
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)
|
|
|
|
|
|
comfy.model_management.soft_empty_cache()
|
|
|
|
|
|
|
|
|
self.model.diffusion_model.to(device)
|
|
|
self.model.control_model.to(device)
|
|
|
self.denoiser.to(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)
|
|
|
self.model.diffusion_model.to('cpu')
|
|
|
self.model.control_model.to('cpu')
|
|
|
|
|
|
|
|
|
self.first_stage_model.to(device)
|
|
|
samples = self.decode_first_stage(_samples)
|
|
|
self.first_stage_model.to('cpu')
|
|
|
|
|
|
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]
|
|
|
autocast_condition = (self.model.dtype == torch.float16 or self.model.dtype == torch.bfloat16) and not comfy.model_management.is_device_mps(device)
|
|
|
if not isinstance(p[0], list):
|
|
|
print("Using local prompt: ")
|
|
|
batch['txt'] = [''.join([_p, p_p]) for _p in p]
|
|
|
print(batch['txt'])
|
|
|
with torch.autocast(comfy.model_management.get_autocast_device(device), dtype=self.model.dtype) if autocast_condition else nullcontext():
|
|
|
c, uc = self.conditioner.get_unconditional_conditioning(batch, batch_uc)
|
|
|
else:
|
|
|
print("Using tile prompts")
|
|
|
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.autocast(comfy.model_management.get_autocast_device(device), dtype=self.model.dtype) if autocast_condition else nullcontext():
|
|
|
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 |
