an option to do exactly the amount of specified steps in img2img
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be0f82df12
commit
e49b1c5d73
2 changed files with 20 additions and 7 deletions
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@ -38,6 +38,17 @@ samplers = [
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samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
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def setup_img2img_steps(p):
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if opts.img2img_fix_steps:
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steps = int(p.steps / min(p.denoising_strength, 0.999))
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t_enc = p.steps - 1
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else:
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steps = p.steps
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t_enc = int(min(p.denoising_strength, 0.999) * steps)
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return steps, t_enc
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def sample_to_image(samples):
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x_sample = shared.sd_model.decode_first_stage(samples[0:1].type(shared.sd_model.dtype))[0]
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x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
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@ -105,13 +116,13 @@ class VanillaStableDiffusionSampler:
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return res
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def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
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t_enc = int(min(p.denoising_strength, 0.999) * p.steps)
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steps, t_enc = setup_img2img_steps(p)
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# existing code fails with cetain step counts, like 9
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try:
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self.sampler.make_schedule(ddim_num_steps=p.steps, verbose=False)
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self.sampler.make_schedule(ddim_num_steps=steps, verbose=False)
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except Exception:
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self.sampler.make_schedule(ddim_num_steps=p.steps+1, verbose=False)
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self.sampler.make_schedule(ddim_num_steps=steps+1, verbose=False)
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x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
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@ -230,14 +241,15 @@ class KDiffusionSampler:
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return res
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def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
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t_enc = int(min(p.denoising_strength, 0.999) * p.steps)
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sigmas = self.model_wrap.get_sigmas(p.steps)
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steps, t_enc = setup_img2img_steps(p)
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noise = noise * sigmas[p.steps - t_enc - 1]
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sigmas = self.model_wrap.get_sigmas(steps)
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noise = noise * sigmas[steps - t_enc - 1]
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xi = x + noise
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sigma_sched = sigmas[p.steps - t_enc - 1:]
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sigma_sched = sigmas[steps - t_enc - 1:]
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self.model_wrap_cfg.mask = p.mask
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self.model_wrap_cfg.nmask = p.nmask
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@ -125,6 +125,7 @@ class Options:
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"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
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"add_model_hash_to_info": OptionInfo(False, "Add model hash to generation information"),
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"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
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"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normaly you'd do less with less denoising)."),
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"enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
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"font": OptionInfo("", "Font for image grids that have text"),
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"enable_emphasis": OptionInfo(True, "Use (text) to make model pay more attention to text and [text] to make it pay less attention"),
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