PixArt LoRA changes

#60

PixArt/diffusers_convert.py

@@ -104,24 +104,42 @@ def convert_state_dict(state_dict):
 	return new_state_dict
 
 # Same as above but for LoRA weights:
-def convert_lora_state_dict(state_dict):
-	# peft
-	rep_ap = lambda x: x.replace(".weight", ".lora_A.weight")
-	rep_bp = lambda x: x.replace(".weight", ".lora_B.weight")
+def convert_lora_state_dict(state_dict, peft=True):
 	# koyha
 	rep_ak = lambda x: x.replace(".weight", ".lora_down.weight")
 	rep_bk = lambda x: x.replace(".weight", ".lora_up.weight")
-
-	prefix = find_prefix(state_dict, "adaln_single.linear.lora_A.weight")
-	state_dict = {k[len(prefix):]:v for k,v in state_dict.items()}
+	rep_pk = lambda x: x.replace(".weight", ".alpha")
+	if peft: # peft
+		rep_ap = lambda x: x.replace(".weight", ".lora_A.weight")
+		rep_bp = lambda x: x.replace(".weight", ".lora_B.weight")
+		rep_pp = lambda x: x.replace(".weight", ".alpha")
+
+		prefix = find_prefix(state_dict, "adaln_single.linear.lora_A.weight")
+		state_dict = {k[len(prefix):]:v for k,v in state_dict.items()}
+	else: # OneTrainer
+		rep_ap = lambda x: x.replace(".", "_")[:-7] + ".lora_down.weight"
+		rep_bp = lambda x: x.replace(".", "_")[:-7] + ".lora_up.weight"
+		rep_pp = lambda x: x.replace(".", "_")[:-7] + ".alpha"
+
+		prefix = "lora_transformer_"
+		t5_marker = "lora_te_encoder"
+		t5_keys = []
+		for key in list(state_dict.keys()):
+			if key.startswith(prefix):
+				state_dict[key[len(prefix):]] = state_dict.pop(key)
+			elif t5_marker in key:
+				t5_keys.append(state_dict.pop(key))
+		if len(t5_keys) > 0:
+			print(f"Text Encoder not supported for PixArt LoRA, ignoring {len(t5_keys)} keys")
 
 	cmap = []
 	cmap_unet = conversion_map + conversion_map_ms # todo: 512 model
 	for k, v in cmap_unet:
-		if not v.endswith(".weight"):
-			continue
-		cmap.append((rep_ak(k), rep_ap(v)))
-		cmap.append((rep_bk(k), rep_bp(v)))
+		if v.endswith(".weight"):
+			cmap.append((rep_ak(k), rep_ap(v)))
+			cmap.append((rep_bk(k), rep_bp(v)))
+			if not peft:
+				cmap.append((rep_pk(k), rep_pp(v)))
 
 	missing = [k for k,v in cmap if v not in state_dict]
 	new_state_dict = {k: state_dict[v] for k,v in cmap if k not in missing}
@@ -134,7 +152,12 @@ def convert_lora_state_dict(state_dict):
 			new_state_dict[fk(f"blocks.{depth}.attn.qkv.weight")] = torch.cat((
 				state_dict[key('q')], state_dict[key('k')], state_dict[key('v')]
 			), dim=0)
+
 			matched += [key('q'), key('k'), key('v')]
+			if not peft:
+				akey = lambda a: rep_pp(f"transformer_blocks.{depth}.attn1.to_{a}.weight")
+				new_state_dict[rep_pk((f"blocks.{depth}.attn.qkv.weight"))] = state_dict[akey("q")]
+				matched += [akey('q'), akey('k'), akey('v')]
 
 			# Cross-attention (linear)
 			key = lambda a: fp(f"transformer_blocks.{depth}.attn2.to_{a}.weight")
@@ -143,13 +166,19 @@ def convert_lora_state_dict(state_dict):
 				state_dict[key('k')], state_dict[key('v')]
 			), dim=0)
 			matched += [key('q'), key('k'), key('v')]
+			if not peft:
+				akey = lambda a: rep_pp(f"transformer_blocks.{depth}.attn2.to_{a}.weight")
+				new_state_dict[rep_pk((f"blocks.{depth}.cross_attn.q_linear.weight"))]  = state_dict[akey("q")]
+				new_state_dict[rep_pk((f"blocks.{depth}.cross_attn.kv_linear.weight"))] = state_dict[akey("k")]
+				matched += [akey('q'), akey('k'), akey('v')]
+
 
 	if len(matched) < len(state_dict):
 		print(f"PixArt: LoRA conversion has leftover keys! ({len(matched)} vs {len(state_dict)})")
 		print(list( set(state_dict.keys()) - set(matched) ))
 
 	if len(missing) > 0:
-		print(f"PixArt: LoRA conversion has missing keys!")
+		print(f"PixArt: LoRA conversion has missing keys! (probably)")
 		print(missing)
 
 	return new_state_dict

PixArt/lora.py

@@ -87,7 +87,6 @@ def replace_model_patcher(model):
 
 	n.object_patches = model.object_patches.copy()
 	n.model_options = copy.deepcopy(model.model_options)
-	n.model_keys = model.model_keys
 	return n
 
 def find_peft_alpha(path):
@@ -120,9 +119,11 @@ def load_pixart_lora(model, lora, lora_path, strength):
 	k_back = lambda x: x.replace(".lora_up.weight", "")
 	# need to convert the actual weights for this to work.
 	if any(True for x in lora.keys() if x.endswith("adaln_single.linear.lora_A.weight")):
-		lora = convert_lora_state_dict(lora)
+		lora = convert_lora_state_dict(lora, peft=True)
 		alpha = find_peft_alpha(lora_path)
 		lora.update({f"{k_back(x)}.alpha":torch.tensor(alpha) for x in lora.keys() if "lora_up" in x})
+	else: # OneTrainer
+		lora = convert_lora_state_dict(lora, peft=False)
 
 	key_map = {k_back(x):f"diffusion_model.{k_back(x)}.weight" for x in lora.keys() if "lora_up" in x} # fake