Merge branch 'AUTOMATIC1111:master' into trunk

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RnDMonkey 2022-10-01 15:52:21 -07:00 committed by GitHub
commit 37c9073f58
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2 changed files with 10 additions and 4 deletions

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@ -79,7 +79,7 @@ class StableDiffusionProcessing:
self.paste_to = None self.paste_to = None
self.color_corrections = None self.color_corrections = None
self.denoising_strength: float = 0 self.denoising_strength: float = 0
self.sampler_noise_scheduler_override = None
self.ddim_discretize = opts.ddim_discretize self.ddim_discretize = opts.ddim_discretize
self.s_churn = opts.s_churn self.s_churn = opts.s_churn
self.s_tmin = opts.s_tmin self.s_tmin = opts.s_tmin
@ -130,7 +130,7 @@ class Processed:
self.s_tmin = p.s_tmin self.s_tmin = p.s_tmin
self.s_tmax = p.s_tmax self.s_tmax = p.s_tmax
self.s_noise = p.s_noise self.s_noise = p.s_noise
self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override
self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0] self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0] self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) self.seed = int(self.seed if type(self.seed) != list else self.seed[0])

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@ -290,7 +290,10 @@ class KDiffusionSampler:
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None): def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
steps, t_enc = setup_img2img_steps(p, steps) steps, t_enc = setup_img2img_steps(p, steps)
sigmas = self.model_wrap.get_sigmas(steps) if p.sampler_noise_scheduler_override:
sigmas = p.sampler_noise_scheduler_override(steps)
else:
sigmas = self.model_wrap.get_sigmas(steps)
noise = noise * sigmas[steps - t_enc - 1] noise = noise * sigmas[steps - t_enc - 1]
xi = x + noise xi = x + noise
@ -306,7 +309,10 @@ class KDiffusionSampler:
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None): def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
steps = steps or p.steps steps = steps or p.steps
sigmas = self.model_wrap.get_sigmas(steps) if p.sampler_noise_scheduler_override:
sigmas = p.sampler_noise_scheduler_override(steps)
else:
sigmas = self.model_wrap.get_sigmas(steps)
x = x * sigmas[0] x = x * sigmas[0]
extra_params_kwargs = self.initialize(p) extra_params_kwargs = self.initialize(p)