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Remove legacy/unused code

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This commit is contained in:
Sergey Borisov
2023-08-08 20:49:01 +03:00
parent da0184a786
commit a7e44678fb
10 changed files with 5 additions and 1893 deletions
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70
invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
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@ -295,7 +295,6 @@ class InvokeAIDiffuserComponent:
) -> torch.Tensor:
if postprocessing_settings is not None:
percent_through = step_index / total_step_count
latents = self.apply_threshold(postprocessing_settings, latents, percent_through)
latents = self.apply_symmetry(postprocessing_settings, latents, percent_through)
return latents
@ -516,63 +515,6 @@ class InvokeAIDiffuserComponent:
combined_next_x = unconditioned_next_x + scaled_delta
return combined_next_x
def apply_threshold(
self,
postprocessing_settings: PostprocessingSettings,
latents: torch.Tensor,
percent_through: float,
) -> torch.Tensor:
if postprocessing_settings.threshold is None or postprocessing_settings.threshold == 0.0:
return latents
threshold = postprocessing_settings.threshold
warmup = postprocessing_settings.warmup
if percent_through < warmup:
current_threshold = threshold + threshold * 5 * (1 - (percent_through / warmup))
else:
current_threshold = threshold
if current_threshold <= 0:
return latents
maxval = latents.max().item()
minval = latents.min().item()
scale = 0.7 # default value from #395
if self.debug_thresholding:
std, mean = [i.item() for i in torch.std_mean(latents)]
outside = torch.count_nonzero((latents < -current_threshold) | (latents > current_threshold))
logger.info(f"Threshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})")
logger.debug(f"min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}")
logger.debug(f"{outside / latents.numel() * 100:.2f}% values outside threshold")
if maxval < current_threshold and minval > -current_threshold:
return latents
num_altered = 0
# MPS torch.rand_like is fine because torch.rand_like is wrapped in generate.py!
if maxval > current_threshold:
latents = torch.clone(latents)
maxval = np.clip(maxval * scale, 1, current_threshold)
num_altered += torch.count_nonzero(latents > maxval)
latents[latents > maxval] = torch.rand_like(latents[latents > maxval]) * maxval
if minval < -current_threshold:
latents = torch.clone(latents)
minval = np.clip(minval * scale, -current_threshold, -1)
num_altered += torch.count_nonzero(latents < minval)
latents[latents < minval] = torch.rand_like(latents[latents < minval]) * minval
if self.debug_thresholding:
logger.debug(f"min, , max = {minval:.3f}, , {maxval:.3f}\t(scaled by {scale})")
logger.debug(f"{num_altered / latents.numel() * 100:.2f}% values altered")
return latents
def apply_symmetry(
self,
postprocessing_settings: PostprocessingSettings,
@ -634,18 +576,6 @@ class InvokeAIDiffuserComponent:
self.last_percent_through = percent_through
return latents.to(device=dev)
def estimate_percent_through(self, step_index, sigma):
if step_index is not None and self.cross_attention_control_context is not None:
# percent_through will never reach 1.0 (but this is intended)
return float(step_index) / float(self.cross_attention_control_context.step_count)
# find the best possible index of the current sigma in the sigma sequence
smaller_sigmas = torch.nonzero(self.model.sigmas <= sigma)
sigma_index = smaller_sigmas[-1].item() if smaller_sigmas.shape[0] > 0 else 0
# flip because sigmas[0] is for the fully denoised image
# percent_through must be <1
return 1.0 - float(sigma_index + 1) / float(self.model.sigmas.shape[0])
# print('estimated percent_through', percent_through, 'from sigma', sigma.item())
# todo: make this work
@classmethod
def apply_conjunction(cls, x, t, forward_func, uc, c_or_weighted_c_list, global_guidance_scale):