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Different approach to skip/interrupt with highres fix

This commit is contained in:
Vladimir Repin 2022-10-20 23:49:14 +03:00
parent d1cb08bfb2
commit d23a46ceaa
2 changed files with 7 additions and 1 deletions
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4
modules/processing.py
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@ -587,7 +587,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
x = None x = None
devices.torch_gc() devices.torch_gc()
return self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) or samples samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)
return samples
class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):

4
modules/sd_samplers.py
View File

@ -196,6 +196,7 @@ class VanillaStableDiffusionSampler:
x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise) x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
self.init_latent = x self.init_latent = x
self.last_latent = x
self.step = 0 self.step = 0
samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)) samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
@ -206,6 +207,7 @@ class VanillaStableDiffusionSampler:
self.initialize(p) self.initialize(p)
self.init_latent = None self.init_latent = None
self.last_latent = x
self.step = 0 self.step = 0
steps = steps or p.steps steps = steps or p.steps
@ -388,6 +390,7 @@ class KDiffusionSampler:
extra_params_kwargs['sigmas'] = sigma_sched extra_params_kwargs['sigmas'] = sigma_sched
self.model_wrap_cfg.init_latent = x self.model_wrap_cfg.init_latent = x
self.last_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, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
@ -414,6 +417,7 @@ class KDiffusionSampler:
else: else:
extra_params_kwargs['sigmas'] = sigmas extra_params_kwargs['sigmas'] = sigmas
self.last_latent = x
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, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
return samples return samples