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Set the seed in the SDE samplers to make them more reproducible.

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comfyanonymous
2023-06-25 02:41:31 -04:00
parent cef6aa62b2
commit 4eab00e14b
4 changed files with 16 additions and 14 deletions
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14
comfy/samplers.py
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@@ -13,7 +13,7 @@ def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9)
#The main sampling function shared by all the samplers
#Returns predicted noise
def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, cond_concat=None, model_options={}):
def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, cond_concat=None, model_options={}, seed=None):
def get_area_and_mult(cond, x_in, cond_concat_in, timestep_in):
area = (x_in.shape[2], x_in.shape[3], 0, 0)
strength = 1.0
@@ -292,8 +292,8 @@ class CFGNoisePredictor(torch.nn.Module):
super().__init__()
self.inner_model = model
self.alphas_cumprod = model.alphas_cumprod
def apply_model(self, x, timestep, cond, uncond, cond_scale, cond_concat=None, model_options={}):
out = sampling_function(self.inner_model.apply_model, x, timestep, uncond, cond, cond_scale, cond_concat, model_options=model_options)
def apply_model(self, x, timestep, cond, uncond, cond_scale, cond_concat=None, model_options={}, seed=None):
out = sampling_function(self.inner_model.apply_model, x, timestep, uncond, cond, cond_scale, cond_concat, model_options=model_options, seed=seed)
return out
@@ -301,11 +301,11 @@ class KSamplerX0Inpaint(torch.nn.Module):
def __init__(self, model):
super().__init__()
self.inner_model = model
def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, cond_concat=None, model_options={}):
def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, cond_concat=None, model_options={}, seed=None):
if denoise_mask is not None:
latent_mask = 1. - denoise_mask
x = x * denoise_mask + (self.latent_image + self.noise * sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1))) * latent_mask
out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, cond_concat=cond_concat, model_options=model_options)
out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, cond_concat=cond_concat, model_options=model_options, seed=seed)
if denoise_mask is not None:
out *= denoise_mask
@@ -542,7 +542,7 @@ class KSampler:
sigmas = self.calculate_sigmas(new_steps).to(self.device)
self.sigmas = sigmas[-(steps + 1):]
def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False):
def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
if sigmas is None:
sigmas = self.sigmas
sigma_min = self.sigma_min
@@ -589,7 +589,7 @@ class KSampler:
if latent_image is not None:
latent_image = self.model.process_latent_in(latent_image)
extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": self.model_options}
extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": self.model_options, "seed":seed}
cond_concat = None
if hasattr(self.model, 'concat_keys'): #inpaint