fix: mark encoder.embed_tokens.weight as unused tensor (#721)

examples/cli/main.cpp

@@ -660,7 +660,7 @@ void parse_args(int argc, const char** argv, SDParams& params) {
             params.ref_image_paths.push_back(argv[i]);
         } else if (arg == "--chroma-disable-dit-mask") {
             params.chroma_use_dit_mask = false;
-        } else if (arg == "--chroma-use-t5-mask") {
+        } else if (arg == "--chroma-enable-t5-mask") {
             params.chroma_use_t5_mask = true;
         } else if (arg == "--chroma-t5-mask-pad") {
             if (++i >= argc) {

lora.hpp

@@ -3,7 +3,7 @@
 
 #include "ggml_extend.hpp"
 
-#define LORA_GRAPH_SIZE 15360
+#define LORA_GRAPH_BASE_SIZE 10240
 
 struct LoraModel : public GGMLRunner {
     enum lora_t {
@@ -238,7 +238,8 @@ struct LoraModel : public GGMLRunner {
     }
 
     struct ggml_cgraph* build_lora_graph(std::map<std::string, struct ggml_tensor*> model_tensors, SDVersion version) {
-        struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, LORA_GRAPH_SIZE, false);
+        size_t lora_graph_size = LORA_GRAPH_BASE_SIZE + lora_tensors.size() * 10;
+        struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, lora_graph_size, false);
 
         zero_index = ggml_new_tensor_1d(compute_ctx, GGML_TYPE_I32, 1);
         set_backend_tensor_data(zero_index, zero_index_vec.data());

model.cpp

@@ -100,6 +100,7 @@ const char* unused_tensors[] = {
     "model_ema.diffusion_model",
     "embedding_manager",
     "denoiser.sigmas",
+    "text_encoders.t5xxl.transformer.encoder.embed_tokens.weight", // only used during training
 };
 
 bool is_unused_tensor(std::string name) {