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Uni_PC: make max denoise behave more like other samplers.

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On the KSamplers denoise of 1.0 is the same as txt2img but there was a
small difference on UniPC.
This commit is contained in:
comfyanonymous 2023-02-22 02:04:21 -05:00
parent c9daec4c89
commit 2976c1ad28
2 changed files with 15 additions and 4 deletions
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9
comfy/extra_samplers/uni_pc.py
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@ -833,7 +833,7 @@ def expand_dims(v, dims):
def sample_unipc(model, noise, image, sigmas, sampling_function, extra_args=None, callback=None, disable=None, noise_mask=None, variant='bh1'): def sample_unipc(model, noise, image, sigmas, sampling_function, max_denoise, extra_args=None, callback=None, disable=None, noise_mask=None, variant='bh1'):
to_zero = False to_zero = False
if sigmas[-1] == 0: if sigmas[-1] == 0:
timesteps = torch.nn.functional.interpolate(sigmas[None,None,:-1], size=(len(sigmas),), mode='linear')[0][0] timesteps = torch.nn.functional.interpolate(sigmas[None,None,:-1], size=(len(sigmas),), mode='linear')[0][0]
@ -847,7 +847,12 @@ def sample_unipc(model, noise, image, sigmas, sampling_function, extra_args=None
ns = NoiseScheduleVP('discrete', alphas_cumprod=model.inner_model.alphas_cumprod) ns = NoiseScheduleVP('discrete', alphas_cumprod=model.inner_model.alphas_cumprod)
if image is not None: if image is not None:
img = image * ns.marginal_alpha(timesteps[0]) + noise * ns.marginal_std(timesteps[0]) img = image * ns.marginal_alpha(timesteps[0])
if max_denoise:
noise_mult = 1.0
else:
noise_mult = ns.marginal_std(timesteps[0])
img += noise * noise_mult
else: else:
img = noise img = noise

10
comfy/samplers.py
View File

@ -334,6 +334,7 @@ class KSampler:
self.sigma_min=float(self.model_wrap.sigma_min) self.sigma_min=float(self.model_wrap.sigma_min)
self.sigma_max=float(self.model_wrap.sigma_max) self.sigma_max=float(self.model_wrap.sigma_max)
self.set_steps(steps, denoise) self.set_steps(steps, denoise)
self.denoise = denoise
def _calculate_sigmas(self, steps): def _calculate_sigmas(self, steps):
sigmas = None sigmas = None
@ -417,11 +418,16 @@ class KSampler:
cond_concat.append(blank_inpaint_image_like(noise)) cond_concat.append(blank_inpaint_image_like(noise))
extra_args["cond_concat"] = cond_concat extra_args["cond_concat"] = cond_concat
if sigmas[0] != self.sigmas[0] or (self.denoise is not None and self.denoise < 1.0):
max_denoise = False
else:
max_denoise = True
with precision_scope(self.device): with precision_scope(self.device):
if self.sampler == "uni_pc": if self.sampler == "uni_pc":
samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, extra_args=extra_args, noise_mask=denoise_mask) samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, max_denoise=max_denoise, extra_args=extra_args, noise_mask=denoise_mask)
elif self.sampler == "uni_pc_bh2": elif self.sampler == "uni_pc_bh2":
samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, extra_args=extra_args, noise_mask=denoise_mask, variant='bh2') samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, max_denoise=max_denoise, extra_args=extra_args, noise_mask=denoise_mask, variant='bh2')
else: else:
extra_args["denoise_mask"] = denoise_mask extra_args["denoise_mask"] = denoise_mask
self.model_k.latent_image = latent_image self.model_k.latent_image = latent_image