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| import comfy.utils | |
| import logging | |
| LORA_CLIP_MAP = { | |
| "mlp.fc1": "mlp_fc1", | |
| "mlp.fc2": "mlp_fc2", | |
| "self_attn.k_proj": "self_attn_k_proj", | |
| "self_attn.q_proj": "self_attn_q_proj", | |
| "self_attn.v_proj": "self_attn_v_proj", | |
| "self_attn.out_proj": "self_attn_out_proj", | |
| } | |
| def load_lora(lora, 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) | |
| dora_scale_name = "{}.dora_scale".format(x) | |
| dora_scale = None | |
| if dora_scale_name in lora.keys(): | |
| dora_scale = lora[dora_scale_name] | |
| loaded_keys.add(dora_scale_name) | |
| regular_lora = "{}.lora_up.weight".format(x) | |
| diffusers_lora = "{}_lora.up.weight".format(x) | |
| diffusers2_lora = "{}.lora_B.weight".format(x) | |
| diffusers3_lora = "{}.lora.up.weight".format(x) | |
| transformers_lora = "{}.lora_linear_layer.up.weight".format(x) | |
| A_name = None | |
| if regular_lora in lora.keys(): | |
| A_name = regular_lora | |
| B_name = "{}.lora_down.weight".format(x) | |
| mid_name = "{}.lora_mid.weight".format(x) | |
| elif diffusers_lora in lora.keys(): | |
| A_name = diffusers_lora | |
| B_name = "{}_lora.down.weight".format(x) | |
| mid_name = None | |
| elif diffusers2_lora in lora.keys(): | |
| A_name = diffusers2_lora | |
| B_name = "{}.lora_A.weight".format(x) | |
| mid_name = None | |
| elif diffusers3_lora in lora.keys(): | |
| A_name = diffusers3_lora | |
| B_name = "{}.lora.down.weight".format(x) | |
| mid_name = None | |
| elif transformers_lora in lora.keys(): | |
| A_name = transformers_lora | |
| B_name ="{}.lora_linear_layer.down.weight".format(x) | |
| mid_name = None | |
| if A_name is not None: | |
| mid = None | |
| if mid_name is not None and mid_name in lora.keys(): | |
| mid = lora[mid_name] | |
| loaded_keys.add(mid_name) | |
| patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid, dora_scale)) | |
| loaded_keys.add(A_name) | |
| loaded_keys.add(B_name) | |
| ######## loha | |
| 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) | |
| hada_t1_name = "{}.hada_t1".format(x) | |
| hada_t2_name = "{}.hada_t2".format(x) | |
| if hada_w1_a_name in lora.keys(): | |
| hada_t1 = None | |
| hada_t2 = None | |
| if hada_t1_name in lora.keys(): | |
| hada_t1 = lora[hada_t1_name] | |
| hada_t2 = lora[hada_t2_name] | |
| loaded_keys.add(hada_t1_name) | |
| loaded_keys.add(hada_t2_name) | |
| patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2, dora_scale)) | |
| 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) | |
| ######## lokr | |
| lokr_w1_name = "{}.lokr_w1".format(x) | |
| lokr_w2_name = "{}.lokr_w2".format(x) | |
| lokr_w1_a_name = "{}.lokr_w1_a".format(x) | |
| lokr_w1_b_name = "{}.lokr_w1_b".format(x) | |
| lokr_t2_name = "{}.lokr_t2".format(x) | |
| lokr_w2_a_name = "{}.lokr_w2_a".format(x) | |
| lokr_w2_b_name = "{}.lokr_w2_b".format(x) | |
| lokr_w1 = None | |
| if lokr_w1_name in lora.keys(): | |
| lokr_w1 = lora[lokr_w1_name] | |
| loaded_keys.add(lokr_w1_name) | |
| lokr_w2 = None | |
| if lokr_w2_name in lora.keys(): | |
| lokr_w2 = lora[lokr_w2_name] | |
| loaded_keys.add(lokr_w2_name) | |
| lokr_w1_a = None | |
| if lokr_w1_a_name in lora.keys(): | |
| lokr_w1_a = lora[lokr_w1_a_name] | |
| loaded_keys.add(lokr_w1_a_name) | |
| lokr_w1_b = None | |
| if lokr_w1_b_name in lora.keys(): | |
| lokr_w1_b = lora[lokr_w1_b_name] | |
| loaded_keys.add(lokr_w1_b_name) | |
| lokr_w2_a = None | |
| if lokr_w2_a_name in lora.keys(): | |
| lokr_w2_a = lora[lokr_w2_a_name] | |
| loaded_keys.add(lokr_w2_a_name) | |
| lokr_w2_b = None | |
| if lokr_w2_b_name in lora.keys(): | |
| lokr_w2_b = lora[lokr_w2_b_name] | |
| loaded_keys.add(lokr_w2_b_name) | |
| lokr_t2 = None | |
| if lokr_t2_name in lora.keys(): | |
| lokr_t2 = lora[lokr_t2_name] | |
| loaded_keys.add(lokr_t2_name) | |
| if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): | |
| patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2, dora_scale)) | |
| #glora | |
| a1_name = "{}.a1.weight".format(x) | |
| a2_name = "{}.a2.weight".format(x) | |
| b1_name = "{}.b1.weight".format(x) | |
| b2_name = "{}.b2.weight".format(x) | |
| if a1_name in lora: | |
| patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha, dora_scale)) | |
| loaded_keys.add(a1_name) | |
| loaded_keys.add(a2_name) | |
| loaded_keys.add(b1_name) | |
| loaded_keys.add(b2_name) | |
| w_norm_name = "{}.w_norm".format(x) | |
| b_norm_name = "{}.b_norm".format(x) | |
| w_norm = lora.get(w_norm_name, None) | |
| b_norm = lora.get(b_norm_name, None) | |
| if w_norm is not None: | |
| loaded_keys.add(w_norm_name) | |
| patch_dict[to_load[x]] = ("diff", (w_norm,)) | |
| if b_norm is not None: | |
| loaded_keys.add(b_norm_name) | |
| patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (b_norm,)) | |
| diff_name = "{}.diff".format(x) | |
| diff_weight = lora.get(diff_name, None) | |
| if diff_weight is not None: | |
| patch_dict[to_load[x]] = ("diff", (diff_weight,)) | |
| loaded_keys.add(diff_name) | |
| diff_bias_name = "{}.diff_b".format(x) | |
| diff_bias = lora.get(diff_bias_name, None) | |
| if diff_bias is not None: | |
| patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (diff_bias,)) | |
| loaded_keys.add(diff_bias_name) | |
| for x in lora.keys(): | |
| if x not in loaded_keys: | |
| logging.warning("lora key not loaded: {}".format(x)) | |
| return patch_dict | |
| def model_lora_keys_clip(model, key_map={}): | |
| sdk = model.state_dict().keys() | |
| text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}" | |
| clip_l_present = False | |
| for b in range(32): #TODO: clean up | |
| for c in LORA_CLIP_MAP: | |
| k = "clip_h.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) | |
| if k in sdk: | |
| lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) | |
| key_map[lora_key] = k | |
| lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) | |
| key_map[lora_key] = k | |
| lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora | |
| key_map[lora_key] = k | |
| k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) | |
| if k in sdk: | |
| lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) | |
| key_map[lora_key] = k | |
| lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base | |
| key_map[lora_key] = k | |
| clip_l_present = True | |
| lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora | |
| key_map[lora_key] = k | |
| k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) | |
| if k in sdk: | |
| if clip_l_present: | |
| lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base | |
| key_map[lora_key] = k | |
| lora_key = "text_encoder_2.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora | |
| key_map[lora_key] = k | |
| else: | |
| lora_key = "lora_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #TODO: test if this is correct for SDXL-Refiner | |
| key_map[lora_key] = k | |
| lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora | |
| key_map[lora_key] = k | |
| lora_key = "lora_prior_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #cascade lora: TODO put lora key prefix in the model config | |
| key_map[lora_key] = k | |
| for k in sdk: #OneTrainer SD3 lora | |
| if k.startswith("t5xxl.transformer.") and k.endswith(".weight"): | |
| l_key = k[len("t5xxl.transformer."):-len(".weight")] | |
| lora_key = "lora_te3_{}".format(l_key.replace(".", "_")) | |
| key_map[lora_key] = k | |
| k = "clip_g.transformer.text_projection.weight" | |
| if k in sdk: | |
| key_map["lora_prior_te_text_projection"] = k #cascade lora? | |
| # key_map["text_encoder.text_projection"] = k #TODO: check if other lora have the text_projection too | |
| key_map["lora_te2_text_projection"] = k #OneTrainer SD3 lora | |
| k = "clip_l.transformer.text_projection.weight" | |
| if k in sdk: | |
| key_map["lora_te1_text_projection"] = k #OneTrainer SD3 lora, not necessary but omits warning | |
| return key_map | |
| def model_lora_keys_unet(model, key_map={}): | |
| sd = model.state_dict() | |
| sdk = sd.keys() | |
| for k in sdk: | |
| if k.startswith("diffusion_model.") and k.endswith(".weight"): | |
| key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") | |
| key_map["lora_unet_{}".format(key_lora)] = k | |
| key_map["lora_prior_unet_{}".format(key_lora)] = k #cascade lora: TODO put lora key prefix in the model config | |
| diffusers_keys = comfy.utils.unet_to_diffusers(model.model_config.unet_config) | |
| for k in diffusers_keys: | |
| if k.endswith(".weight"): | |
| unet_key = "diffusion_model.{}".format(diffusers_keys[k]) | |
| key_lora = k[:-len(".weight")].replace(".", "_") | |
| key_map["lora_unet_{}".format(key_lora)] = unet_key | |
| diffusers_lora_prefix = ["", "unet."] | |
| for p in diffusers_lora_prefix: | |
| diffusers_lora_key = "{}{}".format(p, k[:-len(".weight")].replace(".to_", ".processor.to_")) | |
| if diffusers_lora_key.endswith(".to_out.0"): | |
| diffusers_lora_key = diffusers_lora_key[:-2] | |
| key_map[diffusers_lora_key] = unet_key | |
| if isinstance(model, comfy.model_base.SD3): #Diffusers lora SD3 | |
| diffusers_keys = comfy.utils.mmdit_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") | |
| for k in diffusers_keys: | |
| if k.endswith(".weight"): | |
| to = diffusers_keys[k] | |
| key_lora = "transformer.{}".format(k[:-len(".weight")]) #regular diffusers sd3 lora format | |
| key_map[key_lora] = to | |
| key_lora = "base_model.model.{}".format(k[:-len(".weight")]) #format for flash-sd3 lora and others? | |
| key_map[key_lora] = to | |
| key_lora = "lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_")) #OneTrainer lora | |
| key_map[key_lora] = to | |
| if isinstance(model, comfy.model_base.AuraFlow): #Diffusers lora AuraFlow | |
| diffusers_keys = comfy.utils.auraflow_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") | |
| for k in diffusers_keys: | |
| if k.endswith(".weight"): | |
| to = diffusers_keys[k] | |
| key_lora = "transformer.{}".format(k[:-len(".weight")]) #simpletrainer and probably regular diffusers lora format | |
| key_map[key_lora] = to | |
| if isinstance(model, comfy.model_base.HunyuanDiT): | |
| for k in sdk: | |
| if k.startswith("diffusion_model.") and k.endswith(".weight"): | |
| key_lora = k[len("diffusion_model."):-len(".weight")] | |
| key_map["base_model.model.{}".format(key_lora)] = k #official hunyuan lora format | |
| return key_map | |