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Fixes for second order scheduler timesteps

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This commit is contained in:
Sergey Borisov 2023-08-13 19:31:47 +03:00
parent 94636ddb03
commit 6e0beb1ed4
1 changed files with 4 additions and 2 deletions
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6
invokeai/app/invocations/latent.py
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@ -317,6 +317,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
return control_data
# original idea by https://github.com/AmericanPresidentJimmyCarter
# TODO: research more for second order schedulers timesteps
def init_scheduler(self, scheduler, device, steps, denoising_start, denoising_end):
if scheduler.config.get("cpu_only", False):
device = torch.device("cpu")
@ -329,8 +330,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
t_start_val = int(round(scheduler.config.num_train_timesteps * (1 - denoising_start)))
t_start_idx = len(list(filter(lambda ts: ts >= t_start_val, timesteps)))
timesteps = timesteps[t_start_idx:]
if scheduler.order == 2:
# TODO: research for second order schedulers timesteps
if scheduler.order == 2 and t_start_idx > 0:
timesteps = timesteps[1:]
# save start timestep to apply noise
@ -339,6 +339,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
# apply denoising_end
t_end_val = int(round(scheduler.config.num_train_timesteps * (1 - denoising_end)))
t_end_idx = len(list(filter(lambda ts: ts >= t_end_val, timesteps)))
if scheduler.order == 2 and t_end_idx > 0:
t_end_idx += 1
timesteps = timesteps[:t_end_idx]
# calculate step count based on scheduler order