Add loha support.

README.md

@@ -16,7 +16,7 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 - Works even if you don't have a GPU with: ```--cpu``` (slow)
 - Can load both ckpt and safetensors models/checkpoints. Standalone VAEs and CLIP models.
 - Embeddings/Textual inversion
-- [Loras (regular and locon)](https://comfyanonymous.github.io/ComfyUI_examples/lora/)
+- [Loras (regular, locon and loha)](https://comfyanonymous.github.io/ComfyUI_examples/lora/)
 - Loading full workflows (with seeds) from generated PNG files.
 - Saving/Loading workflows as Json files.
 - Nodes interface can be used to create complex workflows like one for [Hires fix](https://comfyanonymous.github.io/ComfyUI_examples/2_pass_txt2img/) or much more advanced ones.

comfy/sd.py

@@ -126,15 +126,17 @@ def load_lora(path, to_load):
     patch_dict = {}
     loaded_keys = set()
     for x in to_load:
+        alpha_name = "{}.alpha".format(x)
+        alpha = None
+        if alpha_name in lora.keys():
+            alpha = lora[alpha_name].item()
+            loaded_keys.add(alpha_name)
+
         A_name = "{}.lora_up.weight".format(x)
         B_name = "{}.lora_down.weight".format(x)
-        alpha_name = "{}.alpha".format(x)
         mid_name = "{}.lora_mid.weight".format(x)
+
         if A_name in lora.keys():
-            alpha = None
-            if alpha_name in lora.keys():
-                alpha = lora[alpha_name].item()
-                loaded_keys.add(alpha_name)
             mid = None
             if mid_name in lora.keys():
                 mid = lora[mid_name]
@@ -142,6 +144,18 @@ def load_lora(path, to_load):
             patch_dict[to_load[x]] = (lora[A_name], lora[B_name], alpha, mid)
             loaded_keys.add(A_name)
             loaded_keys.add(B_name)
+
+        hada_w1_a_name = "{}.hada_w1_a".format(x)
+        hada_w1_b_name = "{}.hada_w1_b".format(x)
+        hada_w2_a_name = "{}.hada_w2_a".format(x)
+        hada_w2_b_name = "{}.hada_w2_b".format(x)
+        if hada_w1_a_name in lora.keys():
+            patch_dict[to_load[x]] = (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name])
+            loaded_keys.add(hada_w1_a_name)
+            loaded_keys.add(hada_w1_b_name)
+            loaded_keys.add(hada_w2_a_name)
+            loaded_keys.add(hada_w2_b_name)
+
     for x in lora.keys():
         if x not in loaded_keys:
             print("lora key not loaded", x)
@@ -280,15 +294,25 @@ class ModelPatcher:
                     self.backup[key] = weight.clone()
 
                 alpha = p[0]
-                mat1 = v[0]
+
-                mat2 = v[1]
+                if len(v) == 4: #lora/locon
-                if v[2] is not None:
+                    mat1 = v[0]
-                    alpha *= v[2] / mat2.shape[0]
+                    mat2 = v[1]
-                if v[3] is not None:
+                    if v[2] is not None:
-                    #locon mid weights, hopefully the math is fine because I didn't properly test it
+                        alpha *= v[2] / mat2.shape[0]
-                    final_shape = [mat2.shape[1], mat2.shape[0], v[3].shape[2], v[3].shape[3]]
+                    if v[3] is not None:
-                    mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1).float(), v[3].transpose(0, 1).flatten(start_dim=1).float()).reshape(final_shape).transpose(0, 1)
+                        #locon mid weights, hopefully the math is fine because I didn't properly test it
-                weight += (alpha * torch.mm(mat1.flatten(start_dim=1).float(), mat2.flatten(start_dim=1).float())).reshape(weight.shape).type(weight.dtype).to(weight.device)
+                        final_shape = [mat2.shape[1], mat2.shape[0], v[3].shape[2], v[3].shape[3]]
+                        mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1).float(), v[3].transpose(0, 1).flatten(start_dim=1).float()).reshape(final_shape).transpose(0, 1)
+                    weight += (alpha * torch.mm(mat1.flatten(start_dim=1).float(), mat2.flatten(start_dim=1).float())).reshape(weight.shape).type(weight.dtype).to(weight.device)
+                else: #loha
+                    w1a = v[0]
+                    w1b = v[1]
+                    if v[2] is not None:
+                        alpha *= v[2] / w1b.shape[0]
+                    w2a = v[3]
+                    w2b = v[4]
+                    weight += (alpha * torch.mm(w1a.float(), w1b.float()) * torch.mm(w2a.float(), w2b.float())).reshape(weight.shape).type(weight.dtype).to(weight.device)
         return self.model
     def unpatch_model(self):
         model_sd = self.model.state_dict()