Added support for RunwayML inpainting model
This commit is contained in:
random_thoughtss
parent
604620a7f0
commit
8e7097d06a
|
|
@ -546,7 +546,16 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
|||
|
||||
if not self.enable_hr:
|
||||
x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
|
||||
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
|
||||
|
||||
# The "masked-image" in this case will just be all zeros since the entire image is masked.
|
||||
image_conditioning = torch.zeros(x.shape[0], 3, self.height, self.width, device=x.device)
|
||||
image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning))
|
||||
|
||||
# Add the fake full 1s mask to the first dimension.
|
||||
image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
|
||||
image_conditioning = image_conditioning.to(x.dtype)
|
||||
|
||||
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=image_conditioning)
|
||||
return samples
|
||||
|
||||
x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
|
||||
|
|
@ -714,10 +723,31 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
|||
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([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
|
||||
|
||||
samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning)
|
||||
samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
|
||||
|
||||
if self.mask is not None:
|
||||
samples = samples * self.nmask + self.init_latent * self.mask
|
||||
|
|
|
|||
|
|
@ -0,0 +1,208 @@
|
|||
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
|
||||
|
|
@ -9,6 +9,7 @@ from ldm.util import instantiate_from_config
|
|||
|
||||
from modules import shared, modelloader, devices
|
||||
from modules.paths import models_path
|
||||
from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
|
||||
|
||||
model_dir = "Stable-diffusion"
|
||||
model_path = os.path.abspath(os.path.join(models_path, model_dir))
|
||||
|
|
@ -211,6 +212,19 @@ def load_model():
|
|||
print(f"Loading config from: {checkpoint_info.config}")
|
||||
|
||||
sd_config = OmegaConf.load(checkpoint_info.config)
|
||||
|
||||
if should_hijack_inpainting(checkpoint_info):
|
||||
do_inpainting_hijack()
|
||||
|
||||
# Hardcoded config for now...
|
||||
sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
|
||||
sd_config.model.params.use_ema = False
|
||||
sd_config.model.params.conditioning_key = "hybrid"
|
||||
sd_config.model.params.unet_config.params.in_channels = 9
|
||||
|
||||
# Create a "fake" config with a different name so that we know to unload it when switching models.
|
||||
checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
|
||||
|
||||
sd_model = instantiate_from_config(sd_config.model)
|
||||
load_model_weights(sd_model, checkpoint_info)
|
||||
|
||||
|
|
@ -234,7 +248,7 @@ def reload_model_weights(sd_model, info=None):
|
|||
if sd_model.sd_model_checkpoint == checkpoint_info.filename:
|
||||
return
|
||||
|
||||
if sd_model.sd_checkpoint_info.config != checkpoint_info.config:
|
||||
if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
|
||||
checkpoints_loaded.clear()
|
||||
shared.sd_model = load_model()
|
||||
return shared.sd_model
|
||||
|
|
|
|||
|
|
@ -136,6 +136,12 @@ class VanillaStableDiffusionSampler:
|
|||
if self.stop_at is not None and self.step > self.stop_at:
|
||||
raise InterruptedException
|
||||
|
||||
# Have to unwrap the inpainting conditioning here to perform pre-preocessing
|
||||
image_conditioning = None
|
||||
if isinstance(cond, dict):
|
||||
image_conditioning = cond["c_concat"][0]
|
||||
cond = cond["c_crossattn"][0]
|
||||
unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
|
||||
|
||||
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
|
||||
unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
|
||||
|
|
@ -157,6 +163,10 @@ class VanillaStableDiffusionSampler:
|
|||
img_orig = self.sampler.model.q_sample(self.init_latent, ts)
|
||||
x_dec = img_orig * self.mask + self.nmask * x_dec
|
||||
|
||||
if image_conditioning is not None:
|
||||
cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
|
||||
unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
|
||||
|
||||
res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
|
||||
|
||||
if self.mask is not None:
|
||||
|
|
@ -182,7 +192,7 @@ class VanillaStableDiffusionSampler:
|
|||
self.mask = p.mask if hasattr(p, 'mask') else None
|
||||
self.nmask = p.nmask if hasattr(p, 'nmask') else None
|
||||
|
||||
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
|
||||
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
|
||||
steps, t_enc = setup_img2img_steps(p, steps)
|
||||
|
||||
self.initialize(p)
|
||||
|
|
@ -202,7 +212,7 @@ class VanillaStableDiffusionSampler:
|
|||
|
||||
return samples
|
||||
|
||||
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
|
||||
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
|
||||
self.initialize(p)
|
||||
|
||||
self.init_latent = None
|
||||
|
|
@ -210,6 +220,11 @@ class VanillaStableDiffusionSampler:
|
|||
|
||||
steps = steps or p.steps
|
||||
|
||||
# Wrap the conditioning models with additional image conditioning for inpainting model
|
||||
if image_conditioning is not None:
|
||||
conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
|
||||
unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
|
||||
|
||||
# existing code fails with certain step counts, like 9
|
||||
try:
|
||||
samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
|
||||
|
|
@ -228,7 +243,7 @@ class CFGDenoiser(torch.nn.Module):
|
|||
self.init_latent = None
|
||||
self.step = 0
|
||||
|
||||
def forward(self, x, sigma, uncond, cond, cond_scale):
|
||||
def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
|
||||
if state.interrupted or state.skipped:
|
||||
raise InterruptedException
|
||||
|
||||
|
|
@ -239,28 +254,29 @@ class CFGDenoiser(torch.nn.Module):
|
|||
repeats = [len(conds_list[i]) for i in range(batch_size)]
|
||||
|
||||
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
|
||||
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
|
||||
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
|
||||
|
||||
if tensor.shape[1] == uncond.shape[1]:
|
||||
cond_in = torch.cat([tensor, uncond])
|
||||
|
||||
if shared.batch_cond_uncond:
|
||||
x_out = self.inner_model(x_in, sigma_in, cond=cond_in)
|
||||
x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
|
||||
else:
|
||||
x_out = torch.zeros_like(x_in)
|
||||
for batch_offset in range(0, x_out.shape[0], batch_size):
|
||||
a = batch_offset
|
||||
b = a + batch_size
|
||||
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b])
|
||||
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})
|
||||
else:
|
||||
x_out = torch.zeros_like(x_in)
|
||||
batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
|
||||
for batch_offset in range(0, tensor.shape[0], batch_size):
|
||||
a = batch_offset
|
||||
b = min(a + batch_size, tensor.shape[0])
|
||||
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=tensor[a:b])
|
||||
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [tensor[a:b]], "c_concat": [image_cond_in[a:b]]})
|
||||
|
||||
x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=uncond)
|
||||
x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
|
||||
|
||||
denoised_uncond = x_out[-uncond.shape[0]:]
|
||||
denoised = torch.clone(denoised_uncond)
|
||||
|
|
@ -361,7 +377,7 @@ class KDiffusionSampler:
|
|||
|
||||
return extra_params_kwargs
|
||||
|
||||
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
|
||||
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
|
||||
steps, t_enc = setup_img2img_steps(p, steps)
|
||||
|
||||
if p.sampler_noise_scheduler_override:
|
||||
|
|
@ -389,11 +405,16 @@ class KDiffusionSampler:
|
|||
|
||||
self.model_wrap_cfg.init_latent = x
|
||||
|
||||
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
|
||||
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={
|
||||
'cond': conditioning,
|
||||
'image_cond': image_conditioning,
|
||||
'uncond': unconditional_conditioning,
|
||||
'cond_scale': p.cfg_scale
|
||||
}, disable=False, callback=self.callback_state, **extra_params_kwargs))
|
||||
|
||||
return samples
|
||||
|
||||
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
|
||||
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None):
|
||||
steps = steps or p.steps
|
||||
|
||||
if p.sampler_noise_scheduler_override:
|
||||
|
|
@ -414,7 +435,12 @@ class KDiffusionSampler:
|
|||
else:
|
||||
extra_params_kwargs['sigmas'] = sigmas
|
||||
|
||||
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
|
||||
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
|
||||
'cond': conditioning,
|
||||
'image_cond': image_conditioning,
|
||||
'uncond': unconditional_conditioning,
|
||||
'cond_scale': p.cfg_scale
|
||||
}, disable=False, callback=self.callback_state, **extra_params_kwargs))
|
||||
|
||||
return samples
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue