Add noise augmentation setting to unCLIPConditioning.

comfy/samplers.py

@@ -348,17 +348,27 @@ def encode_adm(noise_augmentor, conds, batch_size, device):
         if 'adm' in x[1]:
             adm_inputs = []
             weights = []
+            noise_aug = []
             adm_in = x[1]["adm"]
             for adm_c in adm_in:
                 adm_cond = adm_c[0].image_embeds
                 weight = adm_c[1]
-                c_adm, noise_level_emb = noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([0], device=device))
+                noise_augment = adm_c[2]
+                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)
-            #TODO: Apply Noise to Embedding Mix
+                #TODO: add a way to control this
+                noise_augment = 0.05
+                noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
+                print(noise_level)
+                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)
         else:
             adm_out = torch.zeros((1, noise_augmentor.time_embed.dim * 2), device=device)
         x[1] = x[1].copy()

nodes.py

@@ -445,17 +445,18 @@ class unCLIPConditioning:
         return {"required": {"conditioning": ("CONDITIONING", ),
                              "clip_vision_output": ("CLIP_VISION_OUTPUT", ),
                              "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+                             "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                              }}
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "apply_adm"
 
     CATEGORY = "_for_testing/unclip"
 
-    def apply_adm(self, conditioning, clip_vision_output, strength):
+    def apply_adm(self, conditioning, clip_vision_output, strength, noise_augmentation):
         c = []
         for t in conditioning:
             o = t[1].copy()
-            x = (clip_vision_output, strength)
+            x = (clip_vision_output, strength, noise_augmentation)
             if "adm" in o:
                 o["adm"] = o["adm"][:] + [x]
             else: