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Merge pull request #1199 from C43H66N12O12S2/k-eta

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Add Eta parameter to K Ancestral samplers
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AUTOMATIC1111 2022-09-28 08:31:40 +03:00 committed by GitHub
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3 changed files with 7 additions and 5 deletions
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4
modules/processing.py
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@ -79,7 +79,7 @@ class StableDiffusionProcessing:
self.color_corrections = None self.color_corrections = None
self.denoising_strength: float = 0 self.denoising_strength: float = 0
self.ddim_eta = opts.ddim_eta self.eta = opts.eta
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
@ -124,7 +124,7 @@ class Processed:
self.extra_generation_params = p.extra_generation_params self.extra_generation_params = p.extra_generation_params
self.index_of_first_image = index_of_first_image self.index_of_first_image = index_of_first_image
self.ddim_eta = p.ddim_eta self.eta = p.eta
self.ddim_discretize = p.ddim_discretize self.ddim_discretize = p.ddim_discretize
self.s_churn = p.s_churn self.s_churn = p.s_churn
self.s_tmin = p.s_tmin self.s_tmin = p.s_tmin

6
modules/sd_samplers.py
View file

@ -39,8 +39,10 @@ samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
sampler_extra_params = { sampler_extra_params = {
'sample_euler':['s_churn','s_tmin','s_tmax','s_noise'], 'sample_euler':['s_churn','s_tmin','s_tmax','s_noise'],
'sample_euler_ancestral':['eta'],
'sample_heun' :['s_churn','s_tmin','s_tmax','s_noise'], 'sample_heun' :['s_churn','s_tmin','s_tmax','s_noise'],
'sample_dpm_2':['s_churn','s_tmin','s_tmax','s_noise'], 'sample_dpm_2':['s_churn','s_tmin','s_tmax','s_noise'],
'sample_dpm_2_ancestral':['eta'],
} }
def setup_img2img_steps(p, steps=None): def setup_img2img_steps(p, steps=None):
@ -154,9 +156,9 @@ class VanillaStableDiffusionSampler:
# existing code fails with cetin step counts, like 9 # existing code fails with cetin step counts, like 9
try: try:
samples_ddim, _ = 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=p.ddim_eta) samples_ddim, _ = 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=p.eta)
except Exception: except Exception:
samples_ddim, _ = self.sampler.sample(S=steps+1, 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=p.ddim_eta) samples_ddim, _ = self.sampler.sample(S=steps+1, 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=p.eta)
return samples_ddim return samples_ddim

2
modules/shared.py
View file

@ -221,7 +221,7 @@ options_templates.update(options_section(('ui', "User interface"), {
})) }))
options_templates.update(options_section(('sampler-params', "Sampler parameters"), { options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
"ddim_eta": OptionInfo(0.0, "DDIM eta", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), "eta": OptionInfo(0.0, "DDIM and K Ancestral eta", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform','quad']}), "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform','quad']}),
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), 's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), 's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),