mirror of
https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-14 22:53:25 +03:00
208 lines
9.4 KiB
Python
208 lines
9.4 KiB
Python
import torch
|
|
import numpy as np
|
|
|
|
from tqdm import tqdm
|
|
from einops import rearrange, repeat
|
|
from omegaconf import ListConfig
|
|
|
|
from types import MethodType
|
|
|
|
import ldm.models.diffusion.ddpm
|
|
import ldm.models.diffusion.ddim
|
|
|
|
from ldm.models.diffusion.ddpm import LatentDiffusion
|
|
from ldm.models.diffusion.ddim import DDIMSampler, noise_like
|
|
|
|
# =================================================================================================
|
|
# Monkey patch DDIMSampler methods from RunwayML repo directly.
|
|
# Adapted from:
|
|
# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py
|
|
# =================================================================================================
|
|
@torch.no_grad()
|
|
def sample(
|
|
self,
|
|
S,
|
|
batch_size,
|
|
shape,
|
|
conditioning=None,
|
|
callback=None,
|
|
normals_sequence=None,
|
|
img_callback=None,
|
|
quantize_x0=False,
|
|
eta=0.,
|
|
mask=None,
|
|
x0=None,
|
|
temperature=1.,
|
|
noise_dropout=0.,
|
|
score_corrector=None,
|
|
corrector_kwargs=None,
|
|
verbose=True,
|
|
x_T=None,
|
|
log_every_t=100,
|
|
unconditional_guidance_scale=1.,
|
|
unconditional_conditioning=None,
|
|
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
|
|
**kwargs
|
|
):
|
|
if conditioning is not None:
|
|
if isinstance(conditioning, dict):
|
|
ctmp = conditioning[list(conditioning.keys())[0]]
|
|
while isinstance(ctmp, list):
|
|
ctmp = elf.inpainting_fill == 2:
|
|
self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
|
|
elif self.inpainting_fill == 3:
|
|
self.init_latent = self.init_latent * self.mask
|
|
|
|
if self.image_mask is not None:
|
|
conditioning_mask = np.array(self.image_mask.convert("L"))
|
|
conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
|
|
conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
|
|
|
|
# Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
|
|
conditioning_mask = torch.round(conditioning_mask)
|
|
else:
|
|
conditioning_mask = torch.ones(1, 1, *image.shape[-2:])
|
|
|
|
# Create another latent image, this time with a masked version of the original input.
|
|
conditioning_mask = conditioning_mask.to(image.device)
|
|
conditioning_image = image * (1.0 - conditioning_mask)
|
|
conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
|
|
|
|
# Create the concatenated conditioning tensor to be fed to `c_concat`
|
|
conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:])
|
|
conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
|
|
self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
|
|
self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
|
|
|
|
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
|
|
x = create_random_tensors([opctmp[0]
|
|
cbs = ctmp.shape[0]
|
|
if cbs != batch_size:
|
|
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
|
|
else:
|
|
if conditioning.shape[0] != batch_size:
|
|
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
|
|
|
|
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
|
|
# sampling
|
|
C, H, W = shape
|
|
size = (batch_size, C, H, W)
|
|
print(f'Data shape for DDIM sampling is {size}, eta {eta}')
|
|
|
|
samples, intermediates = self.ddim_sampling(conditioning, size,
|
|
callback=callback,
|
|
img_callback=img_callback,
|
|
quantize_denoised=quantize_x0,
|
|
mask=mask, x0=x0,
|
|
ddim_use_original_steps=False,
|
|
noise_dropout=noise_dropout,
|
|
temperature=temperature,
|
|
score_corrector=score_corrector,
|
|
corrector_kwargs=corrector_kwargs,
|
|
x_T=x_T,
|
|
log_every_t=log_every_t,
|
|
unconditional_guidance_scale=unconditional_guidance_scale,
|
|
unconditional_conditioning=unconditional_conditioning,
|
|
)
|
|
return samples, intermediates
|
|
|
|
|
|
@torch.no_grad()
|
|
def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
|
|
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
|
|
unconditional_guidance_scale=1., unconditional_conditioning=None):
|
|
b, *_, device = *x.shape, x.device
|
|
|
|
if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
|
|
e_t = self.model.apply_model(x, t, c)
|
|
else:
|
|
x_in = torch.cat([x] * 2)
|
|
t_in = torch.cat([t] * 2)
|
|
if isinstance(c, dict):
|
|
assert isinstance(unconditional_conditioning, dict)
|
|
c_in = dict()
|
|
for k in c:
|
|
if isinstance(c[k], list):
|
|
c_in[k] = [
|
|
torch.cat([unconditional_conditioning[k][i], c[k][i]])
|
|
for i in range(len(c[k]))
|
|
]
|
|
else:
|
|
c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
|
|
else:
|
|
c_in = torch.cat([unconditional_conditioning, c])
|
|
e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
|
|
e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
|
|
|
|
if score_corrector is not None:
|
|
assert self.model.parameterization == "eps"
|
|
e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
|
|
|
|
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
|
|
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
|
|
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
|
|
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
|
|
# select parameters corresponding to the currently considered timestep
|
|
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
|
|
a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
|
|
sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
|
|
sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
|
|
|
|
# current prediction for x_0
|
|
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
|
|
if quantize_denoised:
|
|
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
|
|
# direction pointing to x_t
|
|
dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
|
|
noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
|
|
if noise_dropout > 0.:
|
|
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
|
|
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
|
|
return x_prev, pred_x0
|
|
|
|
|
|
# =================================================================================================
|
|
# Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config.
|
|
# Adapted from:
|
|
# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddpm.py
|
|
# =================================================================================================
|
|
|
|
@torch.no_grad()
|
|
def get_unconditional_conditioning(self, batch_size, null_label=None):
|
|
if null_label is not None:
|
|
xc = null_label
|
|
if isinstance(xc, ListConfig):
|
|
xc = list(xc)
|
|
if isinstance(xc, dict) or isinstance(xc, list):
|
|
c = self.get_learned_conditioning(xc)
|
|
else:
|
|
if hasattr(xc, "to"):
|
|
xc = xc.to(self.device)
|
|
c = self.get_learned_conditioning(xc)
|
|
else:
|
|
# todo: get null label from cond_stage_model
|
|
raise NotImplementedError()
|
|
c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device)
|
|
return c
|
|
|
|
class LatentInpaintDiffusion(LatentDiffusion):
|
|
def __init__(
|
|
self,
|
|
concat_keys=("mask", "masked_image"),
|
|
masked_image_key="masked_image",
|
|
*args,
|
|
**kwargs,
|
|
):
|
|
super().__init__(*args, **kwargs)
|
|
self.masked_image_key = masked_image_key
|
|
assert self.masked_image_key in concat_keys
|
|
self.concat_keys = concat_keys
|
|
|
|
def should_hijack_inpainting(checkpoint_info):
|
|
return str(checkpoint_info.filename).endswith("inpainting.ckpt") and not checkpoint_info.config.endswith("inpainting.yaml")
|
|
|
|
def do_inpainting_hijack():
|
|
ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
|
|
ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
|
|
ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
|
|
ldm.models.diffusion.ddim.DDIMSampler.sample = sample |