leejet · leejet · Dec 16, 2025 · Dec 11, 2025 · Dec 11, 2025 · Dec 11, 2025
diff --git a/examples/cli/README.md b/examples/cli/README.md
@@ -31,6 +31,7 @@ Context Options:
   --high-noise-diffusion-model <string>    path to the standalone high noise diffusion model
   --vae <string>                           path to standalone vae model
   --taesd <string>                         path to taesd. Using Tiny AutoEncoder for fast decoding (low quality)
+  --tae <string>                           alias of --taesd
   --control-net <string>                   path to control net model
   --embd-dir <string>                      embeddings directory
   --lora-model-dir <string>                lora model directory

diff --git a/examples/common/common.hpp b/examples/common/common.hpp
@@ -406,6 +406,10 @@ struct SDContextParams {
              "--taesd",
              "path to taesd. Using Tiny AutoEncoder for fast decoding (low quality)",
              &taesd_path},
+            {"",
+             "--tae",
+             "alias of --taesd",
+             &taesd_path},
             {"",
              "--control-net",
              "path to control net model",

diff --git a/examples/server/README.md b/examples/server/README.md
@@ -24,6 +24,7 @@ Context Options:
   --high-noise-diffusion-model <string>    path to the standalone high noise diffusion model
   --vae <string>                           path to standalone vae model
   --taesd <string>                         path to taesd. Using Tiny AutoEncoder for fast decoding (low quality)
+  --tae <string>                           alias of --taesd
   --control-net <string>                   path to control net model
   --embd-dir <string>                      embeddings directory
   --lora-model-dir <string>                lora model directory

diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
@@ -562,14 +562,27 @@ class StableDiffusionGGML {
             }
 
             if (sd_version_is_wan(version) || sd_version_is_qwen_image(version)) {
-                first_stage_model = std::make_shared<WAN::WanVAERunner>(vae_backend,
-                                                                        offload_params_to_cpu,
-                                                                        tensor_storage_map,
-                                                                        "first_stage_model",
-                                                                        vae_decode_only,
-                                                                        version);
-                first_stage_model->alloc_params_buffer();
-                first_stage_model->get_param_tensors(tensors, "first_stage_model");
+                if (!use_tiny_autoencoder) {
+                    first_stage_model = std::make_shared<WAN::WanVAERunner>(vae_backend,
+                                                                            offload_params_to_cpu,
+                                                                            tensor_storage_map,
+                                                                            "first_stage_model",
+                                                                            vae_decode_only,
+                                                                            version);
+                    first_stage_model->alloc_params_buffer();
+                    first_stage_model->get_param_tensors(tensors, "first_stage_model");
+                } else {
+                    tae_first_stage = std::make_shared<TinyVideoAutoEncoder>(vae_backend,
+                                                                             offload_params_to_cpu,
+                                                                             tensor_storage_map,
+                                                                             "decoder",
+                                                                             vae_decode_only,
+                                                                             version);
+                    if (sd_ctx_params->vae_conv_direct) {
+                        LOG_INFO("Using Conv2d direct in the tae model");
+                        tae_first_stage->set_conv2d_direct_enabled(true);
+                    }
+                }
             } else if (version == VERSION_CHROMA_RADIANCE) {
                 first_stage_model = std::make_shared<FakeVAE>(vae_backend,
                                                               offload_params_to_cpu);
@@ -596,14 +609,13 @@ class StableDiffusionGGML {
                 }
                 first_stage_model->alloc_params_buffer();
                 first_stage_model->get_param_tensors(tensors, "first_stage_model");
-            }
-            if (use_tiny_autoencoder) {
-                tae_first_stage = std::make_shared<TinyAutoEncoder>(vae_backend,
-                                                                    offload_params_to_cpu,
-                                                                    tensor_storage_map,
-                                                                    "decoder.layers",
-                                                                    vae_decode_only,
-                                                                    version);
+            } else if (use_tiny_autoencoder) {
+                tae_first_stage = std::make_shared<TinyImageAutoEncoder>(vae_backend,
+                                                                         offload_params_to_cpu,
+                                                                         tensor_storage_map,
+                                                                         "decoder.layers",
+                                                                         vae_decode_only,
+                                                                         version);
                 if (sd_ctx_params->vae_conv_direct) {
                     LOG_INFO("Using Conv2d direct in the tae model");
                     tae_first_stage->set_conv2d_direct_enabled(true);
@@ -3614,7 +3626,8 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
         denoise_mask = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, init_latent->ne[0], init_latent->ne[1], init_latent->ne[2], 1);
         ggml_set_f32(denoise_mask, 1.f);
 
-        sd_ctx->sd->process_latent_out(init_latent);
+        if (!sd_ctx->sd->use_tiny_autoencoder)
+            sd_ctx->sd->process_latent_out(init_latent);
 
         ggml_ext_tensor_iter(init_image_latent, [&](ggml_tensor* t, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
             float value = ggml_ext_tensor_get_f32(t, i0, i1, i2, i3);
@@ -3624,7 +3637,8 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
             }
         });
 
-        sd_ctx->sd->process_latent_in(init_latent);
+        if (!sd_ctx->sd->use_tiny_autoencoder)
+            sd_ctx->sd->process_latent_in(init_latent);
 
         int64_t t2 = ggml_time_ms();
         LOG_INFO("encode_first_stage completed, taking %" PRId64 " ms", t2 - t1);
@@ -3847,7 +3861,7 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
     struct ggml_tensor* vid = sd_ctx->sd->decode_first_stage(work_ctx, final_latent, true);
     int64_t t5              = ggml_time_ms();
     LOG_INFO("decode_first_stage completed, taking %.2fs", (t5 - t4) * 1.0f / 1000);
-    if (sd_ctx->sd->free_params_immediately) {
+    if (sd_ctx->sd->free_params_immediately && !sd_ctx->sd->use_tiny_autoencoder) {
         sd_ctx->sd->first_stage_model->free_params_buffer();
     }