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Add a way to pass options to the transformers blocks.

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comfyanonymous
2023-03-31 13:04:39 -04:00
parent 04b42bad87
commit 61ec3c9d5d
5 changed files with 33 additions and 29 deletions
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7
comfy/samplers.py
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@@ -26,7 +26,7 @@ class CFGDenoiser(torch.nn.Module):
#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):
def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, cond_concat=None, model_options={}):
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
@@ -169,6 +169,9 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
if control is not None:
c['control'] = control.get_control(input_x, timestep_, c['c_crossattn'], len(cond_or_uncond))
if 'transformer_options' in model_options:
c['transformer_options'] = model_options['transformer_options']
output = model_function(input_x, timestep_, cond=c).chunk(batch_chunks)
del input_x
@@ -467,7 +470,7 @@ class KSampler:
x_T=z_enc,
x0=latent_image,
denoise_function=sampling_function,
cond_concat=cond_concat,
extra_args=extra_args,
mask=noise_mask,
to_zero=sigmas[-1]==0,
end_step=sigmas.shape[0] - 1)