refactor: optimize the logic for name conversion and the processing of the LoRA model (#955)

conditioner.hpp

@@ -111,7 +111,7 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
     bool load_embedding(std::string embd_name, std::string embd_path, std::vector<int32_t>& bpe_tokens) {
         // the order matters
         ModelLoader model_loader;
-        if (!model_loader.init_from_file(embd_path)) {
+        if (!model_loader.init_from_file_and_convert_name(embd_path)) {
             LOG_ERROR("embedding '%s' failed", embd_name.c_str());
             return false;
         }

control.hpp

@@ -442,7 +442,7 @@ struct ControlNet : public GGMLRunner {
         std::set<std::string> ignore_tensors;
 
         ModelLoader model_loader;
-        if (!model_loader.init_from_file(file_path)) {
+        if (!model_loader.init_from_file_and_convert_name(file_path)) {
             LOG_ERROR("init control net model loader from file failed: '%s'", file_path.c_str());
             return false;
         }

esrgan.hpp

@@ -169,7 +169,7 @@ struct ESRGAN : public GGMLRunner {
         LOG_INFO("loading esrgan from '%s'", file_path.c_str());
 
         ModelLoader model_loader;
-        if (!model_loader.init_from_file(file_path)) {
+        if (!model_loader.init_from_file_and_convert_name(file_path)) {
             LOG_ERROR("init esrgan model loader from file failed: '%s'", file_path.c_str());
             return false;
         }

flux.hpp

@@ -1398,7 +1398,7 @@ namespace Flux {
             ggml_type model_data_type = GGML_TYPE_Q8_0;
 
             ModelLoader model_loader;
-            if (!model_loader.init_from_file(file_path, "model.diffusion_model.")) {
+            if (!model_loader.init_from_file_and_convert_name(file_path, "model.diffusion_model.")) {
                 LOG_ERROR("init model loader from file failed: '%s'", file_path.c_str());
                 return;
             }

ggml_extend.hpp

@@ -1568,8 +1568,10 @@ protected:
     struct ggml_cgraph* get_compute_graph(get_graph_cb_t get_graph) {
         prepare_build_in_tensor_before();
         struct ggml_cgraph* gf = get_graph();
+        if (ggml_graph_n_nodes(gf) > 0) {
             auto result = ggml_graph_node(gf, -1);
             ggml_set_name(result, final_result_name.c_str());
+        }
         prepare_build_in_tensor_after(gf);
         return gf;
     }

mmdit.hpp

@@ -961,7 +961,7 @@ struct MMDiTRunner : public GGMLRunner {
             mmdit->get_param_tensors(tensors, "model.diffusion_model");
 
             ModelLoader model_loader;
-            if (!model_loader.init_from_file(file_path)) {
+            if (!model_loader.init_from_file_and_convert_name(file_path)) {
                 LOG_ERROR("init model loader from file failed: '%s'", file_path.c_str());
                 return;
             }

model.cpp

@@ -25,6 +25,7 @@
 #include "ggml-cpu.h"
 #include "ggml.h"
 
+#include "name_conversion.h"
 #include "stable-diffusion.h"
 
 #ifdef SD_USE_METAL
@@ -75,15 +76,6 @@ uint16_t read_short(uint8_t* buffer) {
 
 /*================================================= Preprocess ==================================================*/
 
-std::string self_attn_names[] = {
-    "self_attn.q_proj.weight",
-    "self_attn.k_proj.weight",
-    "self_attn.v_proj.weight",
-    "self_attn.q_proj.bias",
-    "self_attn.k_proj.bias",
-    "self_attn.v_proj.bias",
-};
-
 const char* unused_tensors[] = {
     "betas",
     "alphas_cumprod_prev",
@@ -97,9 +89,9 @@ const char* unused_tensors[] = {
     "posterior_mean_coef1",
     "posterior_mean_coef2",
     "cond_stage_model.transformer.text_model.embeddings.position_ids",
+    "cond_stage_model.1.model.text_model.embeddings.position_ids",
     "cond_stage_model.transformer.vision_model.embeddings.position_ids",
     "cond_stage_model.model.logit_scale",
-    "cond_stage_model.model.text_projection",
     "conditioner.embedders.0.transformer.text_model.embeddings.position_ids",
     "conditioner.embedders.0.model.logit_scale",
     "conditioner.embedders.1.model.logit_scale",
@@ -110,6 +102,7 @@ const char* unused_tensors[] = {
     "model_ema.diffusion_model",
     "embedding_manager",
     "denoiser.sigmas",
+    "edm_vpred.sigma_max",
     "text_encoders.t5xxl.transformer.encoder.embed_tokens.weight",  // only used during training
     "text_encoders.qwen2vl.output.weight",
     "text_encoders.qwen2vl.lm_head.",
@@ -124,622 +117,6 @@ bool is_unused_tensor(std::string name) {
     return false;
 }
 
-std::unordered_map<std::string, std::string> open_clip_to_hf_clip_model = {
-    {"model.ln_final.bias", "transformer.text_model.final_layer_norm.bias"},
-    {"model.ln_final.weight", "transformer.text_model.final_layer_norm.weight"},
-    {"model.positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"},
-    {"model.token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"},
-    {"model.text_projection", "transformer.text_model.text_projection"},
-    {"model.visual.class_embedding", "transformer.vision_model.embeddings.class_embedding"},
-    {"model.visual.conv1.weight", "transformer.vision_model.embeddings.patch_embedding.weight"},
-    {"model.visual.ln_post.bias", "transformer.vision_model.post_layernorm.bias"},
-    {"model.visual.ln_post.weight", "transformer.vision_model.post_layernorm.weight"},
-    {"model.visual.ln_pre.bias", "transformer.vision_model.pre_layernorm.bias"},
-    {"model.visual.ln_pre.weight", "transformer.vision_model.pre_layernorm.weight"},
-    {"model.visual.positional_embedding", "transformer.vision_model.embeddings.position_embedding.weight"},
-    {"model.visual.proj", "transformer.visual_projection.weight"},
-};
-
-std::unordered_map<std::string, std::string> open_clip_to_hf_clip_resblock = {
-    {"attn.in_proj_bias", "self_attn.in_proj.bias"},
-    {"attn.in_proj_weight", "self_attn.in_proj.weight"},
-    {"attn.out_proj.bias", "self_attn.out_proj.bias"},
-    {"attn.out_proj.weight", "self_attn.out_proj.weight"},
-    {"ln_1.bias", "layer_norm1.bias"},
-    {"ln_1.weight", "layer_norm1.weight"},
-    {"ln_2.bias", "layer_norm2.bias"},
-    {"ln_2.weight", "layer_norm2.weight"},
-    {"mlp.c_fc.bias", "mlp.fc1.bias"},
-    {"mlp.c_fc.weight", "mlp.fc1.weight"},
-    {"mlp.c_proj.bias", "mlp.fc2.bias"},
-    {"mlp.c_proj.weight", "mlp.fc2.weight"},
-};
-
-std::unordered_map<std::string, std::string> cond_model_name_map = {
-    {"transformer.vision_model.pre_layrnorm.weight", "transformer.vision_model.pre_layernorm.weight"},
-    {"transformer.vision_model.pre_layrnorm.bias", "transformer.vision_model.pre_layernorm.bias"},
-};
-
-std::unordered_map<std::string, std::string> vae_decoder_name_map = {
-    {"first_stage_model.decoder.mid.attn_1.to_k.bias", "first_stage_model.decoder.mid.attn_1.k.bias"},
-    {"first_stage_model.decoder.mid.attn_1.to_k.weight", "first_stage_model.decoder.mid.attn_1.k.weight"},
-    {"first_stage_model.decoder.mid.attn_1.to_out.0.bias", "first_stage_model.decoder.mid.attn_1.proj_out.bias"},
-    {"first_stage_model.decoder.mid.attn_1.to_out.0.weight", "first_stage_model.decoder.mid.attn_1.proj_out.weight"},
-    {"first_stage_model.decoder.mid.attn_1.to_q.bias", "first_stage_model.decoder.mid.attn_1.q.bias"},
-    {"first_stage_model.decoder.mid.attn_1.to_q.weight", "first_stage_model.decoder.mid.attn_1.q.weight"},
-    {"first_stage_model.decoder.mid.attn_1.to_v.bias", "first_stage_model.decoder.mid.attn_1.v.bias"},
-    {"first_stage_model.decoder.mid.attn_1.to_v.weight", "first_stage_model.decoder.mid.attn_1.v.weight"},
-};
-
-std::unordered_map<std::string, std::string> pmid_v2_name_map = {
-    {"pmid.qformer_perceiver.perceiver_resampler.layers.0.1.1.weight",
-     "pmid.qformer_perceiver.perceiver_resampler.layers.0.1.1.fc1.weight"},
-    {"pmid.qformer_perceiver.perceiver_resampler.layers.0.1.3.weight",
-     "pmid.qformer_perceiver.perceiver_resampler.layers.0.1.1.fc2.weight"},
-    {"pmid.qformer_perceiver.perceiver_resampler.layers.1.1.1.weight",
-     "pmid.qformer_perceiver.perceiver_resampler.layers.1.1.1.fc1.weight"},
-    {"pmid.qformer_perceiver.perceiver_resampler.layers.1.1.3.weight",
-     "pmid.qformer_perceiver.perceiver_resampler.layers.1.1.1.fc2.weight"},
-    {"pmid.qformer_perceiver.perceiver_resampler.layers.2.1.1.weight",
-     "pmid.qformer_perceiver.perceiver_resampler.layers.2.1.1.fc1.weight"},
-    {"pmid.qformer_perceiver.perceiver_resampler.layers.2.1.3.weight",
-     "pmid.qformer_perceiver.perceiver_resampler.layers.2.1.1.fc2.weight"},
-    {"pmid.qformer_perceiver.perceiver_resampler.layers.3.1.1.weight",
-     "pmid.qformer_perceiver.perceiver_resampler.layers.3.1.1.fc1.weight"},
-    {"pmid.qformer_perceiver.perceiver_resampler.layers.3.1.3.weight",
-     "pmid.qformer_perceiver.perceiver_resampler.layers.3.1.1.fc2.weight"},
-    {"pmid.qformer_perceiver.token_proj.0.bias",
-     "pmid.qformer_perceiver.token_proj.fc1.bias"},
-    {"pmid.qformer_perceiver.token_proj.2.bias",
-     "pmid.qformer_perceiver.token_proj.fc2.bias"},
-    {"pmid.qformer_perceiver.token_proj.0.weight",
-     "pmid.qformer_perceiver.token_proj.fc1.weight"},
-    {"pmid.qformer_perceiver.token_proj.2.weight",
-     "pmid.qformer_perceiver.token_proj.fc2.weight"},
-};
-
-std::unordered_map<std::string, std::string> qwenvl_name_map{
-    {"token_embd.", "model.embed_tokens."},
-    {"blk.", "model.layers."},
-    {"attn_q.", "self_attn.q_proj."},
-    {"attn_k.", "self_attn.k_proj."},
-    {"attn_v.", "self_attn.v_proj."},
-    {"attn_output.", "self_attn.o_proj."},
-    {"attn_norm.", "input_layernorm."},
-    {"ffn_down.", "mlp.down_proj."},
-    {"ffn_gate.", "mlp.gate_proj."},
-    {"ffn_up.", "mlp.up_proj."},
-    {"ffn_norm.", "post_attention_layernorm."},
-    {"output_norm.", "model.norm."},
-};
-
-std::unordered_map<std::string, std::string> qwenvl_vision_name_map{
-    {"mm.", "merger.mlp."},
-    {"v.post_ln.", "merger.ln_q."},
-    {"v.patch_embd.weight", "patch_embed.proj.0.weight"},
-    {"patch_embed.proj.0.weight.1", "patch_embed.proj.1.weight"},
-    {"v.patch_embd.weight.1", "patch_embed.proj.1.weight"},
-    {"v.blk.", "blocks."},
-    {"attn_q.", "attn.q_proj."},
-    {"attn_k.", "attn.k_proj."},
-    {"attn_v.", "attn.v_proj."},
-    {"attn_out.", "attn.proj."},
-    {"ffn_down.", "mlp.down_proj."},
-    {"ffn_gate.", "mlp.gate_proj."},
-    {"ffn_up.", "mlp.up_proj."},
-    {"ln1.", "norm1."},
-    {"ln2.", "norm2."},
-};
-
-std::string convert_cond_model_name(const std::string& name) {
-    std::string new_name = name;
-    std::string prefix;
-    if (contains(new_name, ".enc.")) {
-        // llama.cpp naming convention for T5
-        size_t pos = new_name.find(".enc.");
-        if (pos != std::string::npos) {
-            new_name.replace(pos, 5, ".encoder.");
-        }
-        pos = new_name.find("blk.");
-        if (pos != std::string::npos) {
-            new_name.replace(pos, 4, "block.");
-        }
-        pos = new_name.find("output_norm.");
-        if (pos != std::string::npos) {
-            new_name.replace(pos, 12, "final_layer_norm.");
-        }
-        pos = new_name.find("attn_k.");
-        if (pos != std::string::npos) {
-            new_name.replace(pos, 7, "layer.0.SelfAttention.k.");
-        }
-        pos = new_name.find("attn_v.");
-        if (pos != std::string::npos) {
-            new_name.replace(pos, 7, "layer.0.SelfAttention.v.");
-        }
-        pos = new_name.find("attn_o.");
-        if (pos != std::string::npos) {
-            new_name.replace(pos, 7, "layer.0.SelfAttention.o.");
-        }
-        pos = new_name.find("attn_q.");
-        if (pos != std::string::npos) {
-            new_name.replace(pos, 7, "layer.0.SelfAttention.q.");
-        }
-        pos = new_name.find("attn_norm.");
-        if (pos != std::string::npos) {
-            new_name.replace(pos, 10, "layer.0.layer_norm.");
-        }
-        pos = new_name.find("ffn_norm.");
-        if (pos != std::string::npos) {
-            new_name.replace(pos, 9, "layer.1.layer_norm.");
-        }
-        pos = new_name.find("ffn_up.");
-        if (pos != std::string::npos) {
-            new_name.replace(pos, 7, "layer.1.DenseReluDense.wi_1.");
-        }
-        pos = new_name.find("ffn_down.");
-        if (pos != std::string::npos) {
-            new_name.replace(pos, 9, "layer.1.DenseReluDense.wo.");
-        }
-        pos = new_name.find("ffn_gate.");
-        if (pos != std::string::npos) {
-            new_name.replace(pos, 9, "layer.1.DenseReluDense.wi_0.");
-        }
-        pos = new_name.find("attn_rel_b.");
-        if (pos != std::string::npos) {
-            new_name.replace(pos, 11, "layer.0.SelfAttention.relative_attention_bias.");
-        }
-    } else if (contains(name, "qwen2vl")) {
-        if (contains(name, "qwen2vl.visual")) {
-            for (auto kv : qwenvl_vision_name_map) {
-                size_t pos = new_name.find(kv.first);
-                if (pos != std::string::npos) {
-                    new_name.replace(pos, kv.first.size(), kv.second);
-                }
-            }
-        } else {
-            for (auto kv : qwenvl_name_map) {
-                size_t pos = new_name.find(kv.first);
-                if (pos != std::string::npos) {
-                    new_name.replace(pos, kv.first.size(), kv.second);
-                }
-            }
-        }
-    } else if (name == "text_encoders.t5xxl.transformer.token_embd.weight") {
-        new_name = "text_encoders.t5xxl.transformer.shared.weight";
-    }
-
-    if (starts_with(new_name, "conditioner.embedders.0.open_clip.")) {
-        prefix   = "cond_stage_model.";
-        new_name = new_name.substr(strlen("conditioner.embedders.0.open_clip."));
-    } else if (starts_with(new_name, "conditioner.embedders.0.")) {
-        prefix   = "cond_stage_model.";
-        new_name = new_name.substr(strlen("conditioner.embedders.0."));
-    } else if (starts_with(new_name, "conditioner.embedders.1.")) {
-        prefix   = "cond_stage_model.1.";
-        new_name = new_name.substr(strlen("conditioner.embedders.0."));
-    } else if (starts_with(new_name, "cond_stage_model.")) {
-        prefix   = "cond_stage_model.";
-        new_name = new_name.substr(strlen("cond_stage_model."));
-    } else if (ends_with(new_name, "vision_model.visual_projection.weight")) {
-        prefix   = new_name.substr(0, new_name.size() - strlen("vision_model.visual_projection.weight"));
-        new_name = prefix + "visual_projection.weight";
-        return new_name;
-    } else if (ends_with(new_name, "transformer.text_projection.weight")) {
-        prefix   = new_name.substr(0, new_name.size() - strlen("transformer.text_projection.weight"));
-        new_name = prefix + "transformer.text_model.text_projection";
-        return new_name;
-    } else {
-        return new_name;
-    }
-
-    if (new_name == "model.text_projection.weight") {
-        new_name = "transformer.text_model.text_projection";
-    }
-
-    if (open_clip_to_hf_clip_model.find(new_name) != open_clip_to_hf_clip_model.end()) {
-        new_name = open_clip_to_hf_clip_model[new_name];
-    }
-
-    if (cond_model_name_map.find(new_name) != cond_model_name_map.end()) {
-        new_name = cond_model_name_map[new_name];
-    }
-
-    std::string open_clip_resblock_prefix = "model.transformer.resblocks.";
-    std::string hf_clip_resblock_prefix   = "transformer.text_model.encoder.layers.";
-
-    auto replace_suffix = [&]() {
-        if (new_name.find(open_clip_resblock_prefix) == 0) {
-            std::string remain = new_name.substr(open_clip_resblock_prefix.length());
-            std::string idx    = remain.substr(0, remain.find("."));
-            std::string suffix = remain.substr(idx.length() + 1);
-
-            if (open_clip_to_hf_clip_resblock.find(suffix) != open_clip_to_hf_clip_resblock.end()) {
-                std::string new_suffix = open_clip_to_hf_clip_resblock[suffix];
-                new_name               = hf_clip_resblock_prefix + idx + "." + new_suffix;
-            }
-        }
-    };
-
-    replace_suffix();
-
-    open_clip_resblock_prefix = "model.visual.transformer.resblocks.";
-    hf_clip_resblock_prefix   = "transformer.vision_model.encoder.layers.";
-
-    replace_suffix();
-
-    return prefix + new_name;
-}
-
-std::string convert_vae_decoder_name(const std::string& name) {
-    if (vae_decoder_name_map.find(name) != vae_decoder_name_map.end()) {
-        return vae_decoder_name_map[name];
-    }
-    return name;
-}
-
-std::string convert_pmid_v2_name(const std::string& name) {
-    if (pmid_v2_name_map.find(name) != pmid_v2_name_map.end()) {
-        return pmid_v2_name_map[name];
-    }
-    return name;
-}
-
-/* If not a SDXL LoRA the unet" prefix will have already been replaced by this
- * point and "te2" and "te1" don't seem to appear in non-SDXL only "te_" */
-std::string convert_sdxl_lora_name(std::string tensor_name) {
-    const std::pair<std::string, std::string> sdxl_lora_name_lookup[] = {
-        {"unet", "model_diffusion_model"},
-        {"te2", "cond_stage_model_1_transformer"},
-        {"te1", "cond_stage_model_transformer"},
-        {"text_encoder_2", "cond_stage_model_1_transformer"},
-        {"text_encoder", "cond_stage_model_transformer"},
-    };
-    for (auto& pair_i : sdxl_lora_name_lookup) {
-        if (tensor_name.compare(0, pair_i.first.length(), pair_i.first) == 0) {
-            tensor_name = std::regex_replace(tensor_name, std::regex(pair_i.first), pair_i.second);
-            break;
-        }
-    }
-    return tensor_name;
-}
-
-std::unordered_map<std::string, std::unordered_map<std::string, std::string>> suffix_conversion_underline = {
-    {
-        "attentions",
-        {
-            {"to_k", "k"},
-            {"to_q", "q"},
-            {"to_v", "v"},
-            {"to_out_0", "proj_out"},
-            {"group_norm", "norm"},
-            {"key", "k"},
-            {"query", "q"},
-            {"value", "v"},
-            {"proj_attn", "proj_out"},
-        },
-    },
-    {
-        "resnets",
-        {
-            {"conv1", "in_layers_2"},
-            {"conv2", "out_layers_3"},
-            {"norm1", "in_layers_0"},
-            {"norm2", "out_layers_0"},
-            {"time_emb_proj", "emb_layers_1"},
-            {"conv_shortcut", "skip_connection"},
-        },
-    },
-};
-
-std::unordered_map<std::string, std::unordered_map<std::string, std::string>> suffix_conversion_dot = {
-    {
-        "attentions",
-        {
-            {"to_k", "k"},
-            {"to_q", "q"},
-            {"to_v", "v"},
-            {"to_out.0", "proj_out"},
-            {"group_norm", "norm"},
-            {"key", "k"},
-            {"query", "q"},
-            {"value", "v"},
-            {"proj_attn", "proj_out"},
-        },
-    },
-    {
-        "resnets",
-        {
-            {"conv1", "in_layers.2"},
-            {"conv2", "out_layers.3"},
-            {"norm1", "in_layers.0"},
-            {"norm2", "out_layers.0"},
-            {"time_emb_proj", "emb_layers.1"},
-            {"conv_shortcut", "skip_connection"},
-        },
-    },
-};
-
-std::string convert_diffusers_name_to_compvis(std::string key, char seq) {
-    std::vector<std::string> m;
-
-    auto match = [](std::vector<std::string>& match_list, const std::regex& regex, const std::string& key) {
-        auto r = std::smatch{};
-        if (!std::regex_match(key, r, regex)) {
-            return false;
-        }
-
-        match_list.clear();
-        for (size_t i = 1; i < r.size(); ++i) {
-            match_list.push_back(r.str(i));
-        }
-        return true;
-    };
-
-    std::unordered_map<std::string, std::unordered_map<std::string, std::string>> suffix_conversion;
-    if (seq == '_') {
-        suffix_conversion = suffix_conversion_underline;
-    } else {
-        suffix_conversion = suffix_conversion_dot;
-    }
-
-    auto get_converted_suffix = [&suffix_conversion](const std::string& outer_key, const std::string& inner_key) {
-        auto outer_iter = suffix_conversion.find(outer_key);
-        if (outer_iter != suffix_conversion.end()) {
-            auto inner_iter = outer_iter->second.find(inner_key);
-            if (inner_iter != outer_iter->second.end()) {
-                return inner_iter->second;
-            }
-        }
-        return inner_key;
-    };
-
-    // convert attn to out
-    if (ends_with(key, "to_out")) {
-        key += format("%c0", seq);
-    }
-
-    // unet
-    if (match(m, std::regex(format("unet%cconv_in(.*)", seq)), key)) {
-        return format("model%cdiffusion_model%cinput_blocks%c0%c0", seq, seq, seq, seq) + m[0];
-    }
-
-    if (match(m, std::regex(format("unet%cconv%cout(.*)", seq, seq)), key)) {
-        return format("model%cdiffusion_model%cout%c2", seq, seq, seq) + m[0];
-    }
-
-    if (match(m, std::regex(format("unet%cconv_norm_out(.*)", seq)), key)) {
-        return format("model%cdiffusion_model%cout%c0", seq, seq, seq) + m[0];
-    }
-
-    if (match(m, std::regex(format("unet%ctime_embedding%clinear_(\\d+)(.*)", seq, seq)), key)) {
-        return format("model%cdiffusion_model%ctime_embed%c", seq, seq, seq) + std::to_string(std::stoi(m[0]) * 2 - 2) + m[1];
-    }
-
-    if (match(m, std::regex(format("unet%cadd_embedding%clinear_(\\d+)(.*)", seq, seq)), key)) {
-        return format("model%cdiffusion_model%clabel_emb%c0%c", seq, seq, seq, seq) + std::to_string(std::stoi(m[0]) * 2 - 2) + m[1];
-    }
-
-    if (match(m, std::regex(format("unet%cdown_blocks%c(\\d+)%c(attentions|resnets)%c(\\d+)%c(.+)", seq, seq, seq, seq, seq)), key)) {
-        std::string suffix = get_converted_suffix(m[1], m[3]);
-        // LOG_DEBUG("%s %s %s %s", m[0].c_str(), m[1].c_str(), m[2].c_str(), m[3].c_str());
-        return format("model%cdiffusion_model%cinput_blocks%c", seq, seq, seq) + std::to_string(1 + std::stoi(m[0]) * 3 + std::stoi(m[2])) + seq +
-               (m[1] == "attentions" ? "1" : "0") + seq + suffix;
-    }
-
-    if (match(m, std::regex(format("unet%cmid_block%c(attentions|resnets)%c(\\d+)%c(.+)", seq, seq, seq, seq)), key)) {
-        std::string suffix = get_converted_suffix(m[0], m[2]);
-        return format("model%cdiffusion_model%cmiddle_block%c", seq, seq, seq) + (m[0] == "attentions" ? "1" : std::to_string(std::stoi(m[1]) * 2)) +
-               seq + suffix;
-    }
-
-    if (match(m, std::regex(format("unet%cup_blocks%c(\\d+)%c(attentions|resnets)%c(\\d+)%c(.+)", seq, seq, seq, seq, seq)), key)) {
-        std::string suffix = get_converted_suffix(m[1], m[3]);
-        return format("model%cdiffusion_model%coutput_blocks%c", seq, seq, seq) + std::to_string(std::stoi(m[0]) * 3 + std::stoi(m[2])) + seq +
-               (m[1] == "attentions" ? "1" : "0") + seq + suffix;
-    }
-
-    if (match(m, std::regex(format("unet%cdown_blocks%c(\\d+)%cdownsamplers%c0%cconv", seq, seq, seq, seq, seq)), key)) {
-        return format("model%cdiffusion_model%cinput_blocks%c", seq, seq, seq) + std::to_string(3 + std::stoi(m[0]) * 3) + seq + "0" + seq + "op";
-    }
-
-    if (match(m, std::regex(format("unet%cup_blocks%c(\\d+)%cupsamplers%c0%cconv", seq, seq, seq, seq, seq)), key)) {
-        return format("model%cdiffusion_model%coutput_blocks%c", seq, seq, seq) + std::to_string(2 + std::stoi(m[0]) * 3) + seq +
-               (std::stoi(m[0]) > 0 ? "2" : "1") + seq + "conv";
-    }
-
-    // clip
-    if (match(m, std::regex(format("te%ctext_model%cencoder%clayers%c(\\d+)%c(.+)", seq, seq, seq, seq, seq)), key)) {
-        return format("cond_stage_model%ctransformer%ctext_model%cencoder%clayers%c", seq, seq, seq, seq, seq) + m[0] + seq + m[1];
-    }
-
-    if (match(m, std::regex(format("te%ctext_model(.*)", seq)), key)) {
-        return format("cond_stage_model%ctransformer%ctext_model", seq, seq) + m[0];
-    }
-
-    // clip-g
-    if (match(m, std::regex(format("te%c1%ctext_model%cencoder%clayers%c(\\d+)%c(.+)", seq, seq, seq, seq, seq, seq)), key)) {
-        return format("cond_stage_model%c1%ctransformer%ctext_model%cencoder%clayers%c", seq, seq, seq, seq, seq, seq) + m[0] + seq + m[1];
-    }
-
-    if (match(m, std::regex(format("te%c1%ctext_model(.*)", seq, seq)), key)) {
-        return format("cond_stage_model%c1%ctransformer%ctext_model", seq, seq, seq) + m[0];
-    }
-
-    if (match(m, std::regex(format("te%c1%ctext_projection", seq, seq)), key)) {
-        return format("cond_stage_model%c1%ctransformer%ctext_model%ctext_projection", seq, seq, seq, seq);
-    }
-
-    // vae
-    if (match(m, std::regex(format("vae%c(.*)%cconv_norm_out(.*)", seq, seq)), key)) {
-        return format("first_stage_model%c%s%cnorm_out%s", seq, m[0].c_str(), seq, m[1].c_str());
-    }
-
-    if (match(m, std::regex(format("vae%c(.*)%cmid_block%c(attentions|resnets)%c(\\d+)%c(.+)", seq, seq, seq, seq, seq)), key)) {
-        std::string suffix;
-        std::string block_name;
-        if (m[1] == "attentions") {
-            block_name = "attn";
-            suffix     = get_converted_suffix(m[1], m[3]);
-        } else {
-            block_name = "block";
-            suffix     = m[3];
-        }
-        return format("first_stage_model%c%s%cmid%c%s_%d%c%s",
-                      seq, m[0].c_str(), seq, seq, block_name.c_str(), std::stoi(m[2]) + 1, seq, suffix.c_str());
-    }
-
-    if (match(m, std::regex(format("vae%c(.*)%cup_blocks%c(\\d+)%cresnets%c(\\d+)%c(.+)", seq, seq, seq, seq, seq, seq)), key)) {
-        std::string suffix = m[3];
-        if (suffix == "conv_shortcut") {
-            suffix = "nin_shortcut";
-        }
-        return format("first_stage_model%c%s%cup%c%d%cblock%c%s%c%s",
-                      seq, m[0].c_str(), seq, seq, 3 - std::stoi(m[1]), seq, seq, m[2].c_str(), seq, suffix.c_str());
-    }
-
-    if (match(m, std::regex(format("vae%c(.*)%cdown_blocks%c(\\d+)%cdownsamplers%c0%cconv", seq, seq, seq, seq, seq, seq)), key)) {
-        return format("first_stage_model%c%s%cdown%c%d%cdownsample%cconv",
-                      seq, m[0].c_str(), seq, seq, std::stoi(m[1]), seq, seq);
-    }
-
-    if (match(m, std::regex(format("vae%c(.*)%cdown_blocks%c(\\d+)%cresnets%c(\\d+)%c(.+)", seq, seq, seq, seq, seq, seq)), key)) {
-        std::string suffix = m[3];
-        if (suffix == "conv_shortcut") {
-            suffix = "nin_shortcut";
-        }
-        return format("first_stage_model%c%s%cdown%c%d%cblock%c%s%c%s",
-                      seq, m[0].c_str(), seq, seq, std::stoi(m[1]), seq, seq, m[2].c_str(), seq, suffix.c_str());
-    }
-
-    if (match(m, std::regex(format("vae%c(.*)%cup_blocks%c(\\d+)%cupsamplers%c0%cconv", seq, seq, seq, seq, seq, seq)), key)) {
-        return format("first_stage_model%c%s%cup%c%d%cupsample%cconv",
-                      seq, m[0].c_str(), seq, seq, 3 - std::stoi(m[1]), seq, seq);
-    }
-
-    if (match(m, std::regex(format("vae%c(.*)", seq)), key)) {
-        return format("first_stage_model%c", seq) + m[0];
-    }
-
-    return key;
-}
-
-std::string convert_tensor_name(std::string name) {
-    if (starts_with(name, "diffusion_model")) {
-        name = "model." + name;
-    }
-    if (starts_with(name, "model.diffusion_model.up_blocks.0.attentions.0.")) {
-        name.replace(0, sizeof("model.diffusion_model.up_blocks.0.attentions.0.") - 1,
-                     "model.diffusion_model.output_blocks.0.1.");
-    }
-    if (starts_with(name, "model.diffusion_model.up_blocks.0.attentions.1.")) {
-        name.replace(0, sizeof("model.diffusion_model.up_blocks.0.attentions.1.") - 1,
-                     "model.diffusion_model.output_blocks.1.1.");
-    }
-    // size_t pos = name.find("lora_A");
-    // if (pos != std::string::npos) {
-    //     name.replace(pos, strlen("lora_A"), "lora_up");
-    // }
-    // pos = name.find("lora_B");
-    // if (pos != std::string::npos) {
-    //     name.replace(pos, strlen("lora_B"), "lora_down");
-    // }
-    std::string new_name = name;
-    if (starts_with(name, "cond_stage_model.") ||
-        starts_with(name, "conditioner.embedders.") ||
-        starts_with(name, "text_encoders.") ||
-        ends_with(name, ".vision_model.visual_projection.weight") ||
-        starts_with(name, "qwen2vl")) {
-        new_name = convert_cond_model_name(name);
-    } else if (starts_with(name, "first_stage_model.decoder")) {
-        new_name = convert_vae_decoder_name(name);
-    } else if (starts_with(name, "pmid.qformer_perceiver")) {
-        new_name = convert_pmid_v2_name(name);
-    } else if (starts_with(name, "control_model.")) {  // for controlnet pth models
-        size_t pos = name.find('.');
-        if (pos != std::string::npos) {
-            new_name = name.substr(pos + 1);
-        }
-    } else if (starts_with(name, "lora_")) {  // for lora
-        size_t pos = name.find('.');
-        if (pos != std::string::npos) {
-            std::string name_without_network_parts = name.substr(5, pos - 5);
-            std::string network_part               = name.substr(pos + 1);
-
-            // LOG_DEBUG("%s %s", name_without_network_parts.c_str(), network_part.c_str());
-            std::string new_key = convert_diffusers_name_to_compvis(name_without_network_parts, '_');
-            /* For dealing with the new SDXL LoRA tensor naming convention */
-            new_key = convert_sdxl_lora_name(new_key);
-
-            if (new_key.empty()) {
-                new_name = name;
-            } else {
-                new_name = "lora." + new_key + "." + network_part;
-            }
-        } else {
-            new_name = name;
-        }
-    } else if (ends_with(name, ".diff") || ends_with(name, ".diff_b")) {
-        new_name = "lora." + name;
-    } else if (contains(name, "lora_up") || contains(name, "lora_down") ||
-               contains(name, "lora.up") || contains(name, "lora.down") ||
-               contains(name, "lora_linear") || ends_with(name, ".alpha")) {
-        size_t pos = new_name.find(".processor");
-        if (pos != std::string::npos) {
-            new_name.replace(pos, strlen(".processor"), "");
-        }
-        // if (starts_with(new_name, "transformer.transformer_blocks") || starts_with(new_name, "transformer.single_transformer_blocks")) {
-        //     new_name = "model.diffusion_model." + new_name;
-        // }
-        if (ends_with(name, ".alpha")) {
-            pos = new_name.rfind("alpha");
-        } else {
-            pos = new_name.rfind("lora");
-        }
-        if (pos != std::string::npos) {
-            std::string name_without_network_parts = new_name.substr(0, pos - 1);
-            std::string network_part               = new_name.substr(pos);
-            // LOG_DEBUG("%s %s", name_without_network_parts.c_str(), network_part.c_str());
-            std::string new_key = convert_diffusers_name_to_compvis(name_without_network_parts, '.');
-            new_key             = convert_sdxl_lora_name(new_key);
-            replace_all_chars(new_key, '.', '_');
-            size_t npos = network_part.rfind("_linear_layer");
-            if (npos != std::string::npos) {
-                network_part.replace(npos, strlen("_linear_layer"), "");
-            }
-            if (starts_with(network_part, "lora.")) {
-                network_part = "lora_" + network_part.substr(5);
-            }
-            if (new_key.size() > 0) {
-                new_name = "lora." + new_key + "." + network_part;
-            }
-            // LOG_DEBUG("new name: %s", new_name.c_str());
-        }
-    } else if (starts_with(name, "unet") || starts_with(name, "vae") || starts_with(name, "te")) {  // for diffuser
-        size_t pos = name.find_last_of('.');
-        if (pos != std::string::npos) {
-            std::string name_without_network_parts = name.substr(0, pos);
-            std::string network_part               = name.substr(pos + 1);
-            // LOG_DEBUG("%s %s", name_without_network_parts.c_str(), network_part.c_str());
-            std::string new_key = convert_diffusers_name_to_compvis(name_without_network_parts, '.');
-            if (new_key.empty()) {
-                new_name = name;
-            } else if (new_key == "cond_stage_model.1.transformer.text_model.text_projection") {
-                new_name = new_key;
-            } else {
-                new_name = new_key + "." + network_part;
-            }
-        } else {
-            new_name = name;
-        }
-    } else {
-        new_name = name;
-    }
-    // if (new_name != name) {
-    //     LOG_DEBUG("%s => %s", name.c_str(), new_name.c_str());
-    // }
-    return new_name;
-}
-
 float bf16_to_f32(uint16_t bfloat16) {
     uint32_t val_bits = (static_cast<uint32_t>(bfloat16) << 16);
     return *reinterpret_cast<float*>(&val_bits);
@@ -916,9 +293,7 @@ void convert_tensor(void* src,
 /*================================================= ModelLoader ==================================================*/
 
 void ModelLoader::add_tensor_storage(const TensorStorage& tensor_storage) {
-    TensorStorage copy            = tensor_storage;
+    tensor_storage_map[tensor_storage.name] = tensor_storage;
-    copy.name                     = convert_tensor_name(copy.name);
-    tensor_storage_map[copy.name] = std::move(copy);
 }
 
 bool is_zip_file(const std::string& file_path) {
@@ -1012,6 +387,31 @@ bool ModelLoader::init_from_file(const std::string& file_path, const std::string
     }
 }
 
+void ModelLoader::convert_tensors_name() {
+    SDVersion version = (version_ == VERSION_COUNT) ? get_sd_version() : version_;
+    String2TensorStorage new_map;
+
+    for (auto& [_, tensor_storage] : tensor_storage_map) {
+        auto new_name = convert_tensor_name(tensor_storage.name, version);
+        // LOG_DEBUG("%s -> %s", tensor_storage.name.c_str(), new_name.c_str());
+        tensor_storage.name = new_name;
+        new_map[new_name]   = std::move(tensor_storage);
+    }
+
+    tensor_storage_map.swap(new_map);
+}
+
+bool ModelLoader::init_from_file_and_convert_name(const std::string& file_path, const std::string& prefix, SDVersion version) {
+    if (version_ == VERSION_COUNT && version != VERSION_COUNT) {
+        version_ = version;
+    }
+    if (!init_from_file(file_path, prefix)) {
+        return false;
+    }
+    convert_tensors_name();
+    return true;
+}
+
 /*================================================= GGUFModelLoader ==================================================*/
 
 bool ModelLoader::init_from_gguf_file(const std::string& file_path, const std::string& prefix) {
@@ -1259,32 +659,6 @@ bool ModelLoader::init_from_diffusers_file(const std::string& file_path, const s
     if (!init_from_safetensors_file(unet_path, "unet.")) {
         return false;
     }
-    for (auto& [name, tensor_storage] : tensor_storage_map) {
-        if (name.find("add_embedding") != std::string::npos || name.find("label_emb") != std::string::npos) {
-            // probably SDXL
-            LOG_DEBUG("Fixing name for SDXL output blocks.2.2");
-            String2TensorStorage new_tensor_storage_map;
-
-            for (auto& [name, tensor_storage] : tensor_storage_map) {
-                int len  = 34;
-                auto pos = tensor_storage.name.find("unet.up_blocks.0.upsamplers.0.conv");
-                if (pos == std::string::npos) {
-                    len = 44;
-                    pos = tensor_storage.name.find("model.diffusion_model.output_blocks.2.1.conv");
-                }
-                if (pos != std::string::npos) {
-                    std::string new_name = "model.diffusion_model.output_blocks.2.2.conv" + name.substr(len);
-                    LOG_DEBUG("NEW NAME: %s", new_name.c_str());
-                    tensor_storage.name              = new_name;
-                    new_tensor_storage_map[new_name] = tensor_storage;
-                } else {
-                    new_tensor_storage_map[name] = tensor_storage;
-                }
-            }
-            tensor_storage_map = new_tensor_storage_map;
-            break;
-        }
-    }
 
     if (!init_from_safetensors_file(vae_path, "vae.")) {
         LOG_WARN("Couldn't find working VAE in %s", file_path.c_str());
@@ -1925,7 +1299,7 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
     int64_t start_time = ggml_time_ms();
 
     std::vector<TensorStorage> processed_tensor_storages;
-    for (auto& [name, tensor_storage] : tensor_storage_map) {
+    for (const auto& [name, tensor_storage] : tensor_storage_map) {
         if (is_unused_tensor(tensor_storage.name)) {
             continue;
         }
@@ -2394,6 +1768,7 @@ bool convert(const char* input_path, const char* vae_path, const char* output_pa
             return false;
         }
     }
+    model_loader.convert_tensors_name();
     bool success = model_loader.save_to_gguf_file(output_path, (ggml_type)output_type, tensor_type_rules);
     return success;
 }

model.h

@@ -15,6 +15,7 @@
 #include "ggml.h"
 #include "gguf.h"
 #include "json.hpp"
+#include "ordered_map.hpp"
 #include "zip.h"
 
 #define SD_MAX_DIMS 5
@@ -108,7 +109,11 @@ static inline bool sd_version_is_qwen_image(SDVersion version) {
 }
 
 static inline bool sd_version_is_inpaint(SDVersion version) {
-    if (version == VERSION_SD1_INPAINT || version == VERSION_SD2_INPAINT || version == VERSION_SDXL_INPAINT || version == VERSION_FLUX_FILL || version == VERSION_FLEX_2) {
+    if (version == VERSION_SD1_INPAINT ||
+        version == VERSION_SD2_INPAINT ||
+        version == VERSION_SDXL_INPAINT ||
+        version == VERSION_FLUX_FILL ||
+        version == VERSION_FLEX_2) {
         return true;
     }
     return false;
@@ -253,10 +258,11 @@ struct TensorStorage {
 
 typedef std::function<bool(const TensorStorage&, ggml_tensor**)> on_new_tensor_cb_t;
 
-typedef std::map<std::string, TensorStorage> String2TensorStorage;
+typedef OrderedMap<std::string, TensorStorage> String2TensorStorage;
 
 class ModelLoader {
 protected:
+    SDVersion version_ = VERSION_COUNT;
     std::vector<std::string> file_paths_;
     String2TensorStorage tensor_storage_map;
 
@@ -276,6 +282,10 @@ protected:
 
 public:
     bool init_from_file(const std::string& file_path, const std::string& prefix = "");
+    void convert_tensors_name();
+    bool init_from_file_and_convert_name(const std::string& file_path,
+                                         const std::string& prefix = "",
+                                         SDVersion version         = VERSION_COUNT);
     SDVersion get_sd_version();
     std::map<ggml_type, uint32_t> get_wtype_stat();
     std::map<ggml_type, uint32_t> get_conditioner_wtype_stat();

name_conversion.h

@@ -0,0 +1,10 @@
+#ifndef __NAME_CONVERSTION_H__
+#define __NAME_CONVERSTION_H__
+
+#include <string>
+
+#include "model.h"
+
+std::string convert_tensor_name(std::string name, SDVersion version);
+
+#endif  // __NAME_CONVERSTION_H__

ordered_map.hpp

@@ -0,0 +1,177 @@
+#ifndef __ORDERED_MAP_HPP__
+#define __ORDERED_MAP_HPP__
+
+#include <iostream>
+#include <list>
+#include <string>
+#include <unordered_map>
+
+#include <initializer_list>
+#include <iterator>
+#include <list>
+#include <stdexcept>
+#include <unordered_map>
+#include <utility>
+
+template <typename Key, typename T>
+class OrderedMap {
+public:
+    using key_type        = Key;
+    using mapped_type     = T;
+    using value_type      = std::pair<const Key, T>;
+    using list_type       = std::list<value_type>;
+    using size_type       = typename list_type::size_type;
+    using difference_type = typename list_type::difference_type;
+    using iterator        = typename list_type::iterator;
+    using const_iterator  = typename list_type::const_iterator;
+
+private:
+    list_type data_;
+    std::unordered_map<Key, iterator> index_;
+
+public:
+    // --- constructors ---
+    OrderedMap() = default;
+
+    OrderedMap(std::initializer_list<value_type> init) {
+        for (const auto& kv : init)
+            insert(kv);
+    }
+
+    OrderedMap(const OrderedMap&)                = default;
+    OrderedMap(OrderedMap&&) noexcept            = default;
+    OrderedMap& operator=(const OrderedMap&)     = default;
+    OrderedMap& operator=(OrderedMap&&) noexcept = default;
+
+    // --- element access ---
+    T& at(const Key& key) {
+        auto it = index_.find(key);
+        if (it == index_.end())
+            throw std::out_of_range("OrderedMap::at: key not found");
+        return it->second->second;
+    }
+
+    const T& at(const Key& key) const {
+        auto it = index_.find(key);
+        if (it == index_.end())
+            throw std::out_of_range("OrderedMap::at: key not found");
+        return it->second->second;
+    }
+
+    T& operator[](const Key& key) {
+        auto it = index_.find(key);
+        if (it == index_.end()) {
+            data_.emplace_back(key, T{});
+            auto iter   = std::prev(data_.end());
+            index_[key] = iter;
+            return iter->second;
+        }
+        return it->second->second;
+    }
+
+    // --- iterators ---
+    iterator begin() noexcept { return data_.begin(); }
+    const_iterator begin() const noexcept { return data_.begin(); }
+    const_iterator cbegin() const noexcept { return data_.cbegin(); }
+
+    iterator end() noexcept { return data_.end(); }
+    const_iterator end() const noexcept { return data_.end(); }
+    const_iterator cend() const noexcept { return data_.cend(); }
+
+    // --- capacity ---
+    bool empty() const noexcept { return data_.empty(); }
+    size_type size() const noexcept { return data_.size(); }
+
+    // --- modifiers ---
+    void clear() noexcept {
+        data_.clear();
+        index_.clear();
+    }
+
+    std::pair<iterator, bool> insert(const value_type& value) {
+        auto it = index_.find(value.first);
+        if (it != index_.end()) {
+            return {it->second, false};
+        }
+        data_.push_back(value);
+        auto iter           = std::prev(data_.end());
+        index_[value.first] = iter;
+        return {iter, true};
+    }
+
+    std::pair<iterator, bool> insert(value_type&& value) {
+        auto it = index_.find(value.first);
+        if (it != index_.end()) {
+            return {it->second, false};
+        }
+        data_.push_back(std::move(value));
+        auto iter           = std::prev(data_.end());
+        index_[iter->first] = iter;
+        return {iter, true};
+    }
+
+    void erase(const Key& key) {
+        auto it = index_.find(key);
+        if (it != index_.end()) {
+            data_.erase(it->second);
+            index_.erase(it);
+        }
+    }
+
+    iterator erase(iterator pos) {
+        index_.erase(pos->first);
+        return data_.erase(pos);
+    }
+
+    // --- lookup ---
+    size_type count(const Key& key) const {
+        return index_.count(key);
+    }
+
+    iterator find(const Key& key) {
+        auto it = index_.find(key);
+        if (it == index_.end())
+            return data_.end();
+        return it->second;
+    }
+
+    const_iterator find(const Key& key) const {
+        auto it = index_.find(key);
+        if (it == index_.end())
+            return data_.end();
+        return it->second;
+    }
+
+    bool contains(const Key& key) const {
+        return index_.find(key) != index_.end();
+    }
+
+    // --- comparison ---
+    bool operator==(const OrderedMap& other) const {
+        return data_ == other.data_;
+    }
+
+    bool operator!=(const OrderedMap& other) const {
+        return !(*this == other);
+    }
+
+    template <typename... Args>
+    std::pair<iterator, bool> emplace(Args&&... args) {
+        value_type value(std::forward<Args>(args)...);
+        auto it = index_.find(value.first);
+        if (it != index_.end()) {
+            return {it->second, false};
+        }
+        data_.push_back(std::move(value));
+        auto iter           = std::prev(data_.end());
+        index_[iter->first] = iter;
+        return {iter, true};
+    }
+
+    void swap(OrderedMap& other) noexcept {
+        data_.swap(other.data_);
+        index_.swap(other.index_);
+    }
+};
+
+#endif  // __ORDERED_MAP_HPP__

pmid.hpp

@@ -578,7 +578,7 @@ struct PhotoMakerIDEmbed : public GGMLRunner {
                       const std::string& file_path = "",
                       const std::string& prefix    = "")
         : file_path(file_path), GGMLRunner(backend, offload_params_to_cpu), model_loader(ml) {
-        if (!model_loader->init_from_file(file_path, prefix)) {
+        if (!model_loader->init_from_file_and_convert_name(file_path, prefix)) {
             load_failed = true;
         }
     }

qwen_image.hpp

@@ -644,7 +644,7 @@ namespace Qwen {
             ggml_type model_data_type = GGML_TYPE_Q8_0;
 
             ModelLoader model_loader;
-            if (!model_loader.init_from_file(file_path, "model.diffusion_model.")) {
+            if (!model_loader.init_from_file_and_convert_name(file_path, "model.diffusion_model.")) {
                 LOG_ERROR("init model loader from file failed: '%s'", file_path.c_str());
                 return;
             }

qwenvl.hpp

@@ -1342,7 +1342,7 @@ namespace Qwen {
             ggml_type model_data_type = GGML_TYPE_F16;
 
             ModelLoader model_loader;
-            if (!model_loader.init_from_file(file_path, "qwen2vl.")) {
+            if (!model_loader.init_from_file_and_convert_name(file_path, "qwen2vl.")) {
                 LOG_ERROR("init model loader from file failed: '%s'", file_path.c_str());
                 return;
             }

stable-diffusion.cpp

@@ -278,6 +278,8 @@ public:
             }
         }
 
+        model_loader.convert_tensors_name();
+
         version = model_loader.get_sd_version();
         if (version == VERSION_COUNT) {
             LOG_ERROR("get sd version from file failed: '%s'", SAFE_STR(sd_ctx_params->model_path));
@@ -569,13 +571,13 @@ public:
                                                                    version);
             }
             if (strlen(SAFE_STR(sd_ctx_params->photo_maker_path)) > 0) {
-                pmid_lora = std::make_shared<LoraModel>(backend, sd_ctx_params->photo_maker_path, "");
+                pmid_lora = std::make_shared<LoraModel>(backend, sd_ctx_params->photo_maker_path, "", version);
                 if (!pmid_lora->load_from_file(true, n_threads)) {
                     LOG_WARN("load photomaker lora tensors from %s failed", sd_ctx_params->photo_maker_path);
                     return false;
                 }
                 LOG_INFO("loading stacked ID embedding (PHOTOMAKER) model file from '%s'", sd_ctx_params->photo_maker_path);
-                if (!model_loader.init_from_file(sd_ctx_params->photo_maker_path, "pmid.")) {
+                if (!model_loader.init_from_file_and_convert_name(sd_ctx_params->photo_maker_path, "pmid.")) {
                     LOG_WARN("loading stacked ID embedding from '%s' failed", sd_ctx_params->photo_maker_path);
                 } else {
                     stacked_id = true;
@@ -609,7 +611,7 @@ public:
             ignore_tensors.insert("first_stage_model.");
         }
         if (stacked_id) {
-            ignore_tensors.insert("lora.");
+            ignore_tensors.insert("pmid.unet.");
         }
 
         if (vae_decode_only) {
@@ -925,7 +927,7 @@ public:
             LOG_WARN("can not find %s or %s for lora %s", st_file_path.c_str(), ckpt_file_path.c_str(), lora_name.c_str());
             return;
         }
-        LoraModel lora(backend, file_path, is_high_noise ? "model.high_noise_" : "");
+        LoraModel lora(backend, file_path, is_high_noise ? "model.high_noise_" : "", version);
         if (!lora.load_from_file(false, n_threads)) {
             LOG_WARN("load lora tensors from %s failed", file_path.c_str());
             return;

t5.hpp

@@ -1004,7 +1004,7 @@ struct T5Embedder {
         ggml_type model_data_type = GGML_TYPE_F16;
 
         ModelLoader model_loader;
-        if (!model_loader.init_from_file(file_path)) {
+        if (!model_loader.init_from_file_and_convert_name(file_path)) {
             LOG_ERROR("init model loader from file failed: '%s'", file_path.c_str());
             return;
         }

tae.hpp

@@ -222,7 +222,7 @@ struct TinyAutoEncoder : public GGMLRunner {
         }
 
         ModelLoader model_loader;
-        if (!model_loader.init_from_file(file_path)) {
+        if (!model_loader.init_from_file_and_convert_name(file_path)) {
             LOG_ERROR("init taesd model loader from file failed: '%s'", file_path.c_str());
             return false;
         }

upscaler.cpp

@@ -42,7 +42,7 @@ struct UpscalerGGML {
         backend = ggml_backend_sycl_init(0);
 #endif
         ModelLoader model_loader;
-        if (!model_loader.init_from_file(esrgan_path)) {
+        if (!model_loader.init_from_file_and_convert_name(esrgan_path)) {
             LOG_ERROR("init model loader from file failed: '%s'", esrgan_path.c_str());
         }
         model_loader.set_wtype_override(model_data_type);

wan.hpp

@@ -1271,7 +1271,7 @@ namespace WAN {
                 vae->get_param_tensors(tensors, "first_stage_model");
 
                 ModelLoader model_loader;
-                if (!model_loader.init_from_file(file_path, "vae.")) {
+                if (!model_loader.init_from_file_and_convert_name(file_path, "vae.")) {
                     LOG_ERROR("init model loader from file failed: '%s'", file_path.c_str());
                     return;
                 }
@@ -2255,7 +2255,7 @@ namespace WAN {
             LOG_INFO("loading from '%s'", file_path.c_str());
 
             ModelLoader model_loader;
-            if (!model_loader.init_from_file(file_path, "model.diffusion_model.")) {
+            if (!model_loader.init_from_file_and_convert_name(file_path, "model.diffusion_model.")) {
                 LOG_ERROR("init model loader from file failed: '%s'", file_path.c_str());
                 return;
             }