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temp fix for preprocessor_revision.py (#1860)

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fix for #1420. Skip noise augmentation if model doesn't provide the method; allows Revision controlnet preprocessors to function.
This commit is contained in:
Melyns
2024-09-19 01:09:51 +01:00
committed by GitHub
parent 93bcfd30fa
commit b20cb4bf0e
1 changed files with 19 additions and 11 deletions
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30
extensions-builtin/forge_preprocessor_revision/scripts/preprocessor_revision.py
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@@ -7,7 +7,9 @@ from modules_forge.shared import add_supported_preprocessor
def revision_conditioning_modifier(model, x, timestep, uncond, cond, cond_scale, model_options, seed):
revision_conditions = model_options['revision_conditions']
noise_augmentor = model.noise_augmentor
noise_augmentor = getattr(model, 'noise_augmentor', None)
noise_augment_merge = 0.0
ignore_prompt = False
@@ -18,10 +20,15 @@ def revision_conditioning_modifier(model, x, timestep, uncond, cond, cond_scale,
adm_cond = revision_condition['cond'].image_embeds
weight = revision_condition["weight"]
noise_augment = revision_condition["noise_aug"]
noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
c_adm, noise_level_emb = noise_augmentor(adm_cond.to(x.device),
noise_level=torch.tensor([noise_level], device=x.device), seed=seed)
adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight
if noise_augmentor is not None:
noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
c_adm, noise_level_emb = noise_augmentor(adm_cond.to(x.device),
noise_level=torch.tensor([noise_level], device=x.device), seed=seed)
adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight
else:
adm_out = adm_cond * weight # skip noise augmentation
weights.append(weight)
noise_aug.append(noise_augment)
adm_inputs.append(adm_out)
@@ -30,11 +37,12 @@ def revision_conditioning_modifier(model, x, timestep, uncond, cond, cond_scale,
if len(noise_aug) > 1:
adm_out = torch.stack(adm_inputs).sum(0)
noise_augment = noise_augment_merge
noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
c_adm, noise_level_emb = noise_augmentor(adm_out[:, :noise_augmentor.time_embed.dim],
noise_level=torch.tensor([noise_level], device=x.device))
adm_out = torch.cat((c_adm, noise_level_emb), 1)
if noise_augmentor is not None:
noise_augment = noise_augment_merge
noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
c_adm, noise_level_emb = noise_augmentor(adm_out[:, :noise_augmentor.time_embed.dim],
noise_level=torch.tensor([noise_level], device=x.device))
adm_out = torch.cat((c_adm, noise_level_emb), 1)
new_y = adm_out[:, :1280]
cond = copy.deepcopy(cond)
@@ -61,7 +69,7 @@ class PreprocessorClipVisionForRevision(PreprocessorClipVision):
self.do_not_need_model = True
self.ignore_prompt = ignore_prompt
self.slider_1 = PreprocessorParameter(
label="Noise Augmentation", minimum=0.0, maximum=1.0, value=0.0, visible=True)
label="Noise Augmentation", minimum=0.0, maximum=1.0, value=0.0, visible=False) # hiding the noise slider since it has no effect
def process_before_every_sampling(self, process, cond, mask, *args, **kwargs):
unit = kwargs['unit']