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This commit is contained in:
AUTOMATIC1111 2023-08-08 22:09:40 +03:00
parent 54c3e5c913
commit f8ff8c0638
4 changed files with 55 additions and 18 deletions
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23
modules/sd_samplers_cfg_denoiser.py
View File

@ -38,16 +38,24 @@ class CFGDenoiser(torch.nn.Module):
negative prompt.
"""
def __init__(self, model, sampler):
def __init__(self, sampler):
super().__init__()
self.inner_model = model
self.model_wrap = None
self.mask = None
self.nmask = None
self.init_latent = None
self.steps = None
self.step = 0
self.image_cfg_scale = None
self.padded_cond_uncond = False
self.sampler = sampler
self.model_wrap = None
self.p = None
@property
def inner_model(self):
raise NotImplementedError()
def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
denoised_uncond = x_out[-uncond.shape[0]:]
@ -68,10 +76,21 @@ class CFGDenoiser(torch.nn.Module):
def get_pred_x0(self, x_in, x_out, sigma):
return x_out
def update_inner_model(self):
self.model_wrap = None
c, uc = self.p.get_conds()
self.sampler.sampler_extra_args['cond'] = c
self.sampler.sampler_extra_args['uncond'] = uc
def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
if state.interrupted or state.skipped:
raise sd_samplers_common.InterruptedException
if sd_samplers_common.apply_refiner(self):
cond = self.sampler.sampler_extra_args['cond']
uncond = self.sampler.sampler_extra_args['uncond']
# at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
# so is_edit_model is set to False to support AND composition.
is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0

6
modules/sd_samplers_common.py
View File

@ -202,8 +202,9 @@ class Sampler:
self.conditioning_key = shared.sd_model.model.conditioning_key
self.model_wrap = None
self.p = None
self.model_wrap_cfg = None
self.sampler_extra_args = None
def callback_state(self, d):
step = d['i']
@ -215,6 +216,7 @@ class Sampler:
shared.total_tqdm.update()
def launch_sampling(self, steps, func):
self.model_wrap_cfg.steps = steps
state.sampling_steps = steps
state.sampling_step = 0
@ -234,6 +236,8 @@ class Sampler:
return p.steps
def initialize(self, p) -> dict:
self.p = p
self.model_wrap_cfg.p = p
self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
self.model_wrap_cfg.step = 0

17
modules/sd_samplers_kdiffusion.py
View File

@ -52,17 +52,24 @@ k_diffusion_scheduler = {
}
class CFGDenoiserKDiffusion(sd_samplers_cfg_denoiser.CFGDenoiser):
@property
def inner_model(self):
if self.model_wrap is None:
denoiser = k_diffusion.external.CompVisVDenoiser if shared.sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
self.model_wrap = denoiser(shared.sd_model, quantize=shared.opts.enable_quantization)
return self.model_wrap
class KDiffusionSampler(sd_samplers_common.Sampler):
def __init__(self, funcname, sd_model):
super().__init__(funcname)
self.extra_params = sampler_extra_params.get(funcname, [])
self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
self.model_wrap_cfg = sd_samplers_cfg_denoiser.CFGDenoiser(self.model_wrap, self)
self.model_wrap_cfg = CFGDenoiserKDiffusion(self)
self.model_wrap = self.model_wrap_cfg.inner_model
def get_sigmas(self, p, steps):
discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)

27
modules/sd_samplers_timesteps.py
View File

@ -44,10 +44,10 @@ class CompVisTimestepsVDenoiser(torch.nn.Module):
class CFGDenoiserTimesteps(CFGDenoiser):
def __init__(self, model, sampler):
super().__init__(model, sampler)
def __init__(self, sampler):
super().__init__(sampler)
self.alphas = model.inner_model.alphas_cumprod
self.alphas = shared.sd_model.alphas_cumprod
def get_pred_x0(self, x_in, x_out, sigma):
ts = int(sigma.item())
@ -60,6 +60,14 @@ class CFGDenoiserTimesteps(CFGDenoiser):
return pred_x0
@property
def inner_model(self):
if self.model_wrap is None:
denoiser = CompVisTimestepsVDenoiser if shared.sd_model.parameterization == "v" else CompVisTimestepsDenoiser
self.model_wrap = denoiser(shared.sd_model)
return self.model_wrap
class CompVisSampler(sd_samplers_common.Sampler):
def __init__(self, funcname, sd_model):
@ -68,9 +76,7 @@ class CompVisSampler(sd_samplers_common.Sampler):
self.eta_option_field = 'eta_ddim'
self.eta_infotext_field = 'Eta DDIM'
denoiser = CompVisTimestepsVDenoiser if sd_model.parameterization == "v" else CompVisTimestepsDenoiser
self.model_wrap = denoiser(sd_model)
self.model_wrap_cfg = CFGDenoiserTimesteps(self.model_wrap, self)
self.model_wrap_cfg = CFGDenoiserTimesteps(self)
def get_timesteps(self, p, steps):
discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
@ -106,7 +112,7 @@ class CompVisSampler(sd_samplers_common.Sampler):
self.model_wrap_cfg.init_latent = x
self.last_latent = x
extra_args = {
self.sampler_extra_args = {
'cond': conditioning,
'image_cond': image_conditioning,
'uncond': unconditional_conditioning,
@ -114,7 +120,7 @@ class CompVisSampler(sd_samplers_common.Sampler):
's_min_uncond': self.s_min_uncond
}
samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
if self.model_wrap_cfg.padded_cond_uncond:
p.extra_generation_params["Pad conds"] = True
@ -132,13 +138,14 @@ class CompVisSampler(sd_samplers_common.Sampler):
extra_params_kwargs['timesteps'] = timesteps
self.last_latent = x
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
self.sampler_extra_args = {
'cond': conditioning,
'image_cond': image_conditioning,
'uncond': unconditional_conditioning,
'cond_scale': p.cfg_scale,
's_min_uncond': self.s_min_uncond
}, disable=False, callback=self.callback_state, **extra_params_kwargs))
}
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
if self.model_wrap_cfg.padded_cond_uncond:
p.extra_generation_params["Pad conds"] = True