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Add KSamplerAdvanced node.

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This node exposes more sampling options and makes it possible for example
to sample the first few steps on the latent image, do some operations on it
 and then do the rest of the sampling steps. This can be achieved using the
start_at_step and end_at_step options.
This commit is contained in:
comfyanonymous
2023-01-31 03:09:38 -05:00
parent f8f165e2c3
commit 69df7eba94
2 changed files with 83 additions and 31 deletions
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15
comfy/samplers.py
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@@ -168,15 +168,24 @@ class KSampler:
self.sigmas = sigmas[-(steps + 1):]
def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None):
def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False):
sigmas = self.sigmas
sigma_min = self.sigma_min
if last_step is not None:
if last_step is not None and last_step < (len(sigmas) - 1):
sigma_min = sigmas[last_step]
sigmas = sigmas[:last_step + 1]
if force_full_denoise:
sigmas[-1] = 0
if start_step is not None:
sigmas = sigmas[start_step:]
if start_step < (len(sigmas) - 1):
sigmas = sigmas[start_step:]
else:
if latent_image is not None:
return latent_image
else:
return torch.zeros_like(noise)
noise *= sigmas[0]
if latent_image is not None: