temp fix for preprocessor_revision.py (#1860)

fix for #1420. Skip noise augmentation if model doesn't provide the method; allows Revision controlnet preprocessors to function.

extensions-builtin/forge_preprocessor_revision/scripts/preprocessor_revision.py

@@ -7,7 +7,9 @@
 
 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 @@ def __init__(self, name, url, filename, ignore_prompt=False):
         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']