Merge pull request #1199 from C43H66N12O12S2/k-eta
Add Eta parameter to K Ancestral samplers
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commit
d4e36db6de
3 changed files with 7 additions and 5 deletions
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@ -79,7 +79,7 @@ class StableDiffusionProcessing:
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self.color_corrections = None
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self.denoising_strength: float = 0
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self.ddim_eta = opts.ddim_eta
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self.eta = opts.eta
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self.ddim_discretize = opts.ddim_discretize
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self.s_churn = opts.s_churn
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self.s_tmin = opts.s_tmin
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@ -124,7 +124,7 @@ class Processed:
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self.extra_generation_params = p.extra_generation_params
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self.index_of_first_image = index_of_first_image
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self.ddim_eta = p.ddim_eta
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self.eta = p.eta
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self.ddim_discretize = p.ddim_discretize
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self.s_churn = p.s_churn
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self.s_tmin = p.s_tmin
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@ -39,8 +39,10 @@ samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
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sampler_extra_params = {
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'sample_euler':['s_churn','s_tmin','s_tmax','s_noise'],
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'sample_euler_ancestral':['eta'],
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'sample_heun' :['s_churn','s_tmin','s_tmax','s_noise'],
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'sample_dpm_2':['s_churn','s_tmin','s_tmax','s_noise'],
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'sample_dpm_2_ancestral':['eta'],
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}
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def setup_img2img_steps(p, steps=None):
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@ -154,9 +156,9 @@ class VanillaStableDiffusionSampler:
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# existing code fails with cetin step counts, like 9
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try:
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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)
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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)
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except Exception:
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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)
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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)
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return samples_ddim
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@ -221,7 +221,7 @@ options_templates.update(options_section(('ui', "User interface"), {
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}))
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options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
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"ddim_eta": OptionInfo(0.0, "DDIM eta", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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"eta": OptionInfo(0.0, "DDIM and K Ancestral eta", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform','quad']}),
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's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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