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Make zero denoise a NOP.

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comfyanonymous 2024-04-04 11:38:25 -04:00
parent fcfd2bdf8a
commit c6bd456c45
2 changed files with 14 additions and 3 deletions
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6
comfy/samplers.py
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@ -624,6 +624,9 @@ class CFGGuider:
return self.inner_model.process_latent_out(samples.to(torch.float32)) return self.inner_model.process_latent_out(samples.to(torch.float32))
def sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None): def sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
if sigmas.shape[-1] == 0:
return latent_image
self.conds = {} self.conds = {}
for k in self.original_conds: for k in self.original_conds:
self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k])) self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k]))
@ -721,6 +724,9 @@ class KSampler:
self.steps = steps self.steps = steps
if denoise is None or denoise > 0.9999: if denoise is None or denoise > 0.9999:
self.sigmas = self.calculate_sigmas(steps).to(self.device) self.sigmas = self.calculate_sigmas(steps).to(self.device)
else:
if denoise <= 0.0:
self.sigmas = torch.FloatTensor([])
else: else:
new_steps = int(steps/denoise) new_steps = int(steps/denoise)
sigmas = self.calculate_sigmas(new_steps).to(self.device) sigmas = self.calculate_sigmas(new_steps).to(self.device)

5
comfy_extras/nodes_custom_sampler.py
View File

@ -24,6 +24,8 @@ class BasicScheduler:
def get_sigmas(self, model, scheduler, steps, denoise): def get_sigmas(self, model, scheduler, steps, denoise):
total_steps = steps total_steps = steps
if denoise < 1.0: if denoise < 1.0:
if denoise <= 0.0:
return (torch.FloatTensor([]),)
total_steps = int(steps/denoise) total_steps = int(steps/denoise)
comfy.model_management.load_models_gpu([model]) comfy.model_management.load_models_gpu([model])
@ -160,6 +162,9 @@ class FlipSigmas:
FUNCTION = "get_sigmas" FUNCTION = "get_sigmas"
def get_sigmas(self, sigmas): def get_sigmas(self, sigmas):
if len(sigmas) == 0:
return (sigmas,)
sigmas = sigmas.flip(0) sigmas = sigmas.flip(0)
if sigmas[0] == 0: if sigmas[0] == 0:
sigmas[0] = 0.0001 sigmas[0] = 0.0001