Refactor unclip code.

comfy/model_base.py

@@ -105,6 +105,29 @@ class BaseModel(torch.nn.Module):
 
         return {**unet_state_dict, **vae_state_dict, **clip_state_dict}
 
+def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0):
+    adm_inputs = []
+    weights = []
+    noise_aug = []
+    for unclip_cond in unclip_conditioning:
+        for adm_cond in unclip_cond["clip_vision_output"].image_embeds:
+            weight = unclip_cond["strength"]
+            noise_augment = unclip_cond["noise_augmentation"]
+            noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
+            c_adm, noise_level_emb = noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device))
+            adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight
+            weights.append(weight)
+            noise_aug.append(noise_augment)
+            adm_inputs.append(adm_out)
+
+    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=device))
+        adm_out = torch.cat((c_adm, noise_level_emb), 1)
+
+    return adm_out
 
 class SD21UNCLIP(BaseModel):
     def __init__(self, model_config, noise_aug_config, model_type=ModelType.V_PREDICTION, device=None):
@@ -114,33 +137,11 @@ class SD21UNCLIP(BaseModel):
     def encode_adm(self, **kwargs):
         unclip_conditioning = kwargs.get("unclip_conditioning", None)
         device = kwargs["device"]
-
-        if unclip_conditioning is not None:
-            adm_inputs = []
-            weights = []
-            noise_aug = []
-            for unclip_cond in unclip_conditioning:
-                for adm_cond in unclip_cond["clip_vision_output"].image_embeds:
-                    weight = unclip_cond["strength"]
-                    noise_augment = unclip_cond["noise_augmentation"]
-                    noise_level = round((self.noise_augmentor.max_noise_level - 1) * noise_augment)
-                    c_adm, noise_level_emb = self.noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device))
-                    adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight
-                    weights.append(weight)
-                    noise_aug.append(noise_augment)
-                    adm_inputs.append(adm_out)
-
-            if len(noise_aug) > 1:
-                adm_out = torch.stack(adm_inputs).sum(0)
-                #TODO: add a way to control this
-                noise_augment = 0.05
-                noise_level = round((self.noise_augmentor.max_noise_level - 1) * noise_augment)
-                c_adm, noise_level_emb = self.noise_augmentor(adm_out[:, :self.noise_augmentor.time_embed.dim], noise_level=torch.tensor([noise_level], device=device))
-                adm_out = torch.cat((c_adm, noise_level_emb), 1)
+        if unclip_conditioning is None:
+            return torch.zeros((1, self.adm_channels))
         else:
-            adm_out = torch.zeros((1, self.adm_channels))
+            return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05))
 
-        return adm_out
 
 class SDInpaint(BaseModel):
     def __init__(self, model_config, model_type=ModelType.EPS, device=None):