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update settings and fix references to state

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Vladimir Mandic
2023-10-06 17:18:42 -04:00
parent 3c794fa124
commit a47c4cf04d
8 changed files with 56 additions and 58 deletions
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modules/sd_samplers_kdiffusion.py
+17 -18
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@@ -7,7 +7,6 @@ from modules import prompt_parser
from modules import devices
from modules import sd_samplers_common
from modules.shared import opts, state
import modules.shared as shared
from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
@@ -78,12 +77,12 @@ class CFGDenoiser(torch.nn.Module):
return denoised
def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
if state.interrupted or state.skipped:
if shared.state.interrupted or shared.state.skipped:
raise sd_samplers_common.InterruptedException
if state.paused:
if shared.state.paused:
shared.log.debug('Sampling paused')
while state.paused:
if state.interrupted or state.skipped:
while shared.state.paused:
if shared.state.interrupted or shared.state.skipped:
raise sd_samplers_common.InterruptedException
import time
time.sleep(0.1)
@@ -116,7 +115,7 @@ class CFGDenoiser(torch.nn.Module):
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, shared.state.sampling_step, shared.state.sampling_steps, tensor, uncond)
cfg_denoiser_callback(denoiser_params)
x_in = denoiser_params.x
image_cond_in = denoiser_params.image_cond
@@ -179,14 +178,14 @@ class CFGDenoiser(torch.nn.Module):
fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
denoised_params = CFGDenoisedParams(x_out, shared.state.sampling_step, shared.state.sampling_steps, self.inner_model)
cfg_denoised_callback(denoised_params)
devices.test_for_nans(x_out, "unet")
if opts.live_preview_content == "Prompt":
if shared.opts.live_preview_content == "Prompt":
sd_samplers_common.store_latent(torch.cat([x_out[i:i+1] for i in denoised_image_indexes]))
elif opts.live_preview_content == "Negative prompt":
elif shared.opts.live_preview_content == "Negative prompt":
sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
if is_edit_model:
@@ -199,7 +198,7 @@ class CFGDenoiser(torch.nn.Module):
if self.mask is not None:
denoised = self.init_latent * self.mask + self.nmask * denoised
after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
after_cfg_callback_params = AfterCFGCallbackParams(denoised, shared.state.sampling_step, shared.state.sampling_steps)
cfg_after_cfg_callback(after_cfg_callback_params)
denoised = after_cfg_callback_params.x
@@ -253,16 +252,16 @@ class KDiffusionSampler:
def callback_state(self, d):
step = d['i']
latent = d["denoised"]
if opts.live_preview_content == "Combined":
if shared.opts.live_preview_content == "Combined":
sd_samplers_common.store_latent(latent)
self.last_latent = latent
if self.stop_at is not None and step > self.stop_at:
raise sd_samplers_common.InterruptedException
state.sampling_step = step
shared.state.sampling_step = step
def launch_sampling(self, steps, func):
state.sampling_steps = steps
state.sampling_step = 0
shared.state.sampling_steps = steps
shared.state.sampling_step = 0
try:
return func()
except sd_samplers_common.InterruptedException:
@@ -273,9 +272,9 @@ class KDiffusionSampler:
def initialize(self, p):
if self.config.options.get('brownian_noise', None) is not None:
self.config.options['brownian_noise'] = opts.data.get('schedulers_brownian_noise', False)
self.config.options['brownian_noise'] = shared.opts.data.get('schedulers_brownian_noise', False)
if self.config.options.get('scheduler', None) is not None:
self.config.options['scheduler'] = opts.data.get('schedulers_sigma', None)
self.config.options['scheduler'] = shared.opts.data.get('schedulers_sigma', None)
if p is None:
return
@@ -283,7 +282,7 @@ class KDiffusionSampler:
self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
self.model_wrap_cfg.step = 0
self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
self.eta = p.eta if p.eta is not None else opts.scheduler_eta
self.eta = p.eta if p.eta is not None else shared.opts.scheduler_eta
self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
@@ -300,7 +299,7 @@ class KDiffusionSampler:
return extra_params_kwargs
def get_sigmas(self, p, steps): # pylint: disable=unused-argument
discard_next_to_last_sigma = opts.data.get('schedulers_discard_penultimate', True) if self.config.options.get('discard_next_to_last_sigma', None) is not None else False
discard_next_to_last_sigma = shared.opts.data.get('schedulers_discard_penultimate', True) if self.config.options.get('discard_next_to_last_sigma', None) is not None else False
steps += 1 if discard_next_to_last_sigma else 0
if self.config.options.get('scheduler', None) == 'default' or self.config.options.get('scheduler', None) is None: