lllyasviel/stable-diffusion-webui-forge
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predictor is a better name

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layerdiffusion
2024-08-01 21:29:34 -07:00
parent bc9977a305
commit f6981339b0
10 changed files with 17 additions and 17 deletions
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8
backend/modules/k_model.py
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@@ -12,11 +12,11 @@ class KModel(torch.nn.Module):
self.computation_dtype = computation_dtype
self.diffusion_model = huggingface_components['unet']
self.prediction = k_prediction_from_diffusers_scheduler(huggingface_components['scheduler'])
self.predictor = k_prediction_from_diffusers_scheduler(huggingface_components['scheduler'])
def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
sigma = t
xc = self.prediction.calculate_input(sigma, x)
xc = self.predictor.calculate_input(sigma, x)
if c_concat is not None:
xc = torch.cat([xc] + [c_concat], dim=1)
@@ -24,7 +24,7 @@ class KModel(torch.nn.Module):
dtype = self.computation_dtype
xc = xc.to(dtype)
t = self.prediction.timestep(t).float()
t = self.predictor.timestep(t).float()
context = context.to(dtype)
extra_conds = {}
for o in kwargs:
@@ -35,7 +35,7 @@ class KModel(torch.nn.Module):
extra_conds[o] = extra
model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
return self.prediction.calculate_denoised(sigma, model_output, x)
return self.predictor.calculate_denoised(sigma, model_output, x)
def memory_required(self, input_shape):
area = input_shape[0] * input_shape[2] * input_shape[3]