fix: correct img2img time (#616)

Introduced in 2b6ec97fe2

.github/workflows/build.yml

@@ -155,15 +155,15 @@ jobs:
       matrix:
         include:
           - build: "noavx"
-            defines: "-DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF -DSD_BUILD_SHARED_LIBS=ON"
+            defines: "-DGGML_NATIVE=OFF -DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF -DSD_BUILD_SHARED_LIBS=ON"
           - build: "avx2"
-            defines: "-DGGML_AVX2=ON -DSD_BUILD_SHARED_LIBS=ON"
+            defines: "-DGGML_NATIVE=OFF -DGGML_AVX2=ON -DSD_BUILD_SHARED_LIBS=ON"
           - build: "avx"
-            defines: "-DGGML_AVX2=OFF -DSD_BUILD_SHARED_LIBS=ON"
+            defines: "-DGGML_NATIVE=OFF -DGGML_AVX=ON -DGGML_AVX2=OFF -DSD_BUILD_SHARED_LIBS=ON"
           - build: "avx512"
-            defines: "-DGGML_AVX512=ON -DSD_BUILD_SHARED_LIBS=ON"
+            defines: "-DGGML_NATIVE=OFF -DGGML_AVX512=ON -DGGML_AVX=ON -DGGML_AVX2=ON -DSD_BUILD_SHARED_LIBS=ON"
           - build: "cuda12"
-            defines: "-DSD_CUBLAS=ON -DSD_BUILD_SHARED_LIBS=ON"
+            defines: "-DSD_CUDA=ON -DSD_BUILD_SHARED_LIBS=ON -DCMAKE_CUDA_ARCHITECTURES=90;89;80;75"
           # - build: "rocm5.5"
           #   defines: '-G Ninja -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DAMDGPU_TARGETS="gfx1100;gfx1102;gfx1030" -DSD_BUILD_SHARED_LIBS=ON'
           - build: 'vulkan'
@@ -178,9 +178,9 @@ jobs:
       - name: Install cuda-toolkit
         id: cuda-toolkit
         if: ${{ matrix.build == 'cuda12' }}
-        uses: Jimver/cuda-toolkit@v0.2.11
+        uses: Jimver/cuda-toolkit@v0.2.19
         with:
-          cuda: "12.2.0"
+          cuda: "12.6.2"
           method: "network"
           sub-packages: '["nvcc", "cudart", "cublas", "cublas_dev", "thrust", "visual_studio_integration"]'
 

CMakeLists.txt

@@ -24,19 +24,20 @@ endif()
 # general
 #option(SD_BUILD_TESTS                "sd: build tests"    ${SD_STANDALONE})
 option(SD_BUILD_EXAMPLES             "sd: build examples" ${SD_STANDALONE})
-option(SD_CUBLAS                     "sd: cuda backend" OFF)
+option(SD_CUDA                       "sd: cuda backend" OFF)
 option(SD_HIPBLAS                    "sd: rocm backend" OFF)
 option(SD_METAL                      "sd: metal backend" OFF)
 option(SD_VULKAN                     "sd: vulkan backend" OFF)
 option(SD_SYCL                       "sd: sycl backend" OFF)
+option(SD_MUSA                       "sd: musa backend" OFF)
 option(SD_FAST_SOFTMAX               "sd: x1.5 faster softmax, indeterministic (sometimes, same seed don't generate same image), cuda only" OFF)
 option(SD_BUILD_SHARED_LIBS          "sd: build shared libs" OFF)
 #option(SD_BUILD_SERVER               "sd: build server example"                           ON)
 
-if(SD_CUBLAS)
-    message("-- Use CUBLAS as backend stable-diffusion")
+if(SD_CUDA)
+    message("-- Use CUDA as backend stable-diffusion")
     set(GGML_CUDA ON)
-    add_definitions(-DSD_USE_CUBLAS)
+    add_definitions(-DSD_USE_CUDA)
 endif()
 
 if(SD_METAL)
@@ -53,16 +54,25 @@ endif ()
 
 if (SD_HIPBLAS)
     message("-- Use HIPBLAS as backend stable-diffusion")
-    set(GGML_HIPBLAS ON)
-    add_definitions(-DSD_USE_CUBLAS)
+    set(GGML_HIP ON)
+    add_definitions(-DSD_USE_CUDA)
     if(SD_FAST_SOFTMAX)
         set(GGML_CUDA_FAST_SOFTMAX ON)
     endif()
 endif ()
 
+if(SD_MUSA)
+    message("-- Use MUSA as backend stable-diffusion")
+    set(GGML_MUSA ON)
+    add_definitions(-DSD_USE_CUDA)
+    if(SD_FAST_SOFTMAX)
+        set(GGML_CUDA_FAST_SOFTMAX ON)
+    endif()
+endif()
+
 set(SD_LIB stable-diffusion)
 
-file(GLOB SD_LIB_SOURCES 
+file(GLOB SD_LIB_SOURCES
     "*.h"
     "*.cpp"
     "*.hpp"
@@ -86,6 +96,7 @@ endif()
 if(SD_SYCL)
     message("-- Use SYCL as backend stable-diffusion")
     set(GGML_SYCL ON)
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing -fsycl")
     add_definitions(-DSD_USE_SYCL)
     # disable fast-math on host, see:
     # https://www.intel.com/content/www/us/en/docs/cpp-compiler/developer-guide-reference/2021-10/fp-model-fp.html

Dockerfile.musa

@@ -0,0 +1,19 @@
+ARG MUSA_VERSION=rc3.1.1
+
+FROM mthreads/musa:${MUSA_VERSION}-devel-ubuntu22.04 as build
+
+RUN apt-get update && apt-get install -y cmake
+
+WORKDIR /sd.cpp
+
+COPY . .
+
+RUN mkdir build && cd build && \
+    cmake .. -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_MUSA=ON -DCMAKE_BUILD_TYPE=Release && \
+    cmake --build . --config Release
+
+FROM mthreads/musa:${MUSA_VERSION}-runtime-ubuntu22.04 as runtime
+
+COPY --from=build /sd.cpp/build/bin/sd /sd
+
+ENTRYPOINT [ "/sd" ]

README.md

@@ -113,12 +113,12 @@ cmake .. -DGGML_OPENBLAS=ON
 cmake --build . --config Release
 ```
 
-##### Using CUBLAS
+##### Using CUDA
 
 This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads). Recommended to have at least 4 GB of VRAM.
 
 ```
-cmake .. -DSD_CUBLAS=ON
+cmake .. -DSD_CUDA=ON
 cmake --build . --config Release
 ```
 
@@ -132,6 +132,14 @@ cmake .. -G "Ninja" -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_H
 cmake --build . --config Release
 ```
 
+##### Using MUSA
+
+This provides BLAS acceleration using the MUSA cores of your Moore Threads GPU. Make sure to have the MUSA toolkit installed.
+
+```bash
+cmake .. -DCMAKE_C_COMPILER=/usr/local/musa/bin/clang -DCMAKE_CXX_COMPILER=/usr/local/musa/bin/clang++ -DSD_MUSA=ON -DCMAKE_BUILD_TYPE=Release
+cmake --build . --config Release
+```
 
 ##### Using Metal
 
@@ -232,6 +240,10 @@ arguments:
   -p, --prompt [PROMPT]              the prompt to render
   -n, --negative-prompt PROMPT       the negative prompt (default: "")
   --cfg-scale SCALE                  unconditional guidance scale: (default: 7.0)
+  --skip-layers LAYERS               Layers to skip for SLG steps: (default: [7,8,9])
+  --skip-layer-start START           SLG enabling point: (default: 0.01)
+  --skip-layer-end END               SLG disabling point: (default: 0.2)
+									 SLG will be enabled at step int([STEPS]*[START]) and disabled at int([STEPS]*[END])
   --strength STRENGTH                strength for noising/unnoising (default: 0.75)
   --style-ratio STYLE-RATIO          strength for keeping input identity (default: 20%)
   --control-strength STRENGTH        strength to apply Control Net (default: 0.9)
@@ -314,6 +326,7 @@ These projects use `stable-diffusion.cpp` as a backend for their image generatio
 
 - [Jellybox](https://jellybox.com)
 - [Stable Diffusion GUI](https://github.com/fszontagh/sd.cpp.gui.wx)
+- [Stable Diffusion CLI-GUI](https://github.com/piallai/stable-diffusion.cpp)
 
 ## Contributors
 

clip.hpp

@@ -546,7 +546,7 @@ protected:
     int64_t num_positions;
 
     void init_params(struct ggml_context* ctx, std::map<std::string, enum ggml_type>& tensor_types, const std::string prefix = "") {
-        enum ggml_type token_wtype    = (tensor_types.find(prefix + "token_embedding.weight") != tensor_types.end()) ? tensor_types[prefix + "token_embedding.weight"] : GGML_TYPE_F32;
+        enum ggml_type token_wtype    = GGML_TYPE_F32;  //(tensor_types.find(prefix + "token_embedding.weight") != tensor_types.end()) ? tensor_types[prefix + "token_embedding.weight"] : GGML_TYPE_F32;
         enum ggml_type position_wtype = GGML_TYPE_F32;  //(tensor_types.find(prefix + "position_embedding.weight") != tensor_types.end()) ? tensor_types[prefix + "position_embedding.weight"] : GGML_TYPE_F32;
 
         params["token_embedding.weight"]    = ggml_new_tensor_2d(ctx, token_wtype, embed_dim, vocab_size);

conditioner.hpp

@@ -51,7 +51,8 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
 
     std::string trigger_word = "img";  // should be user settable
     std::string embd_dir;
-    int32_t num_custom_embeddings = 0;
+    int32_t num_custom_embeddings   = 0;
+    int32_t num_custom_embeddings_2 = 0;
     std::vector<uint8_t> token_embed_custom;
     std::vector<std::string> readed_embeddings;
 
@@ -131,28 +132,55 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
         params.no_alloc               = false;
         struct ggml_context* embd_ctx = ggml_init(params);
         struct ggml_tensor* embd      = NULL;
-        int64_t hidden_size           = text_model->model.hidden_size;
+        struct ggml_tensor* embd2     = NULL;
         auto on_load                  = [&](const TensorStorage& tensor_storage, ggml_tensor** dst_tensor) {
-            if (tensor_storage.ne[0] != hidden_size) {
-                LOG_DEBUG("embedding wrong hidden size, got %i, expected %i", tensor_storage.ne[0], hidden_size);
-                return false;
+            if (tensor_storage.ne[0] != text_model->model.hidden_size) {
+                if (text_model2) {
+                    if (tensor_storage.ne[0] == text_model2->model.hidden_size) {
+                        embd2       = ggml_new_tensor_2d(embd_ctx, tensor_storage.type, text_model2->model.hidden_size, tensor_storage.n_dims > 1 ? tensor_storage.ne[1] : 1);
+                        *dst_tensor = embd2;
+                    } else {
+                        LOG_DEBUG("embedding wrong hidden size, got %i, expected %i or %i", tensor_storage.ne[0], text_model->model.hidden_size, text_model2->model.hidden_size);
+                        return false;
+                    }
+                } else {
+                    LOG_DEBUG("embedding wrong hidden size, got %i, expected %i", tensor_storage.ne[0], text_model->model.hidden_size);
+                    return false;
+                }
+            } else {
+                embd        = ggml_new_tensor_2d(embd_ctx, tensor_storage.type, text_model->model.hidden_size, tensor_storage.n_dims > 1 ? tensor_storage.ne[1] : 1);
+                *dst_tensor = embd;
             }
-            embd        = ggml_new_tensor_2d(embd_ctx, tensor_storage.type, hidden_size, tensor_storage.n_dims > 1 ? tensor_storage.ne[1] : 1);
-            *dst_tensor = embd;
             return true;
         };
         model_loader.load_tensors(on_load, NULL);
         readed_embeddings.push_back(embd_name);
-        token_embed_custom.resize(token_embed_custom.size() + ggml_nbytes(embd));
-        memcpy((void*)(token_embed_custom.data() + num_custom_embeddings * hidden_size * ggml_type_size(embd->type)),
-               embd->data,
-               ggml_nbytes(embd));
-        for (int i = 0; i < embd->ne[1]; i++) {
-            bpe_tokens.push_back(text_model->model.vocab_size + num_custom_embeddings);
-            // LOG_DEBUG("new custom token: %i", text_model.vocab_size + num_custom_embeddings);
-            num_custom_embeddings++;
+        if (embd) {
+            int64_t hidden_size = text_model->model.hidden_size;
+            token_embed_custom.resize(token_embed_custom.size() + ggml_nbytes(embd));
+            memcpy((void*)(token_embed_custom.data() + num_custom_embeddings * hidden_size * ggml_type_size(embd->type)),
+                   embd->data,
+                   ggml_nbytes(embd));
+            for (int i = 0; i < embd->ne[1]; i++) {
+                bpe_tokens.push_back(text_model->model.vocab_size + num_custom_embeddings);
+                // LOG_DEBUG("new custom token: %i", text_model.vocab_size + num_custom_embeddings);
+                num_custom_embeddings++;
+            }
+            LOG_DEBUG("embedding '%s' applied, custom embeddings: %i", embd_name.c_str(), num_custom_embeddings);
+        }
+        if (embd2) {
+            int64_t hidden_size = text_model2->model.hidden_size;
+            token_embed_custom.resize(token_embed_custom.size() + ggml_nbytes(embd2));
+            memcpy((void*)(token_embed_custom.data() + num_custom_embeddings_2 * hidden_size * ggml_type_size(embd2->type)),
+                   embd2->data,
+                   ggml_nbytes(embd2));
+            for (int i = 0; i < embd2->ne[1]; i++) {
+                bpe_tokens.push_back(text_model2->model.vocab_size + num_custom_embeddings_2);
+                // LOG_DEBUG("new custom token: %i", text_model.vocab_size + num_custom_embeddings);
+                num_custom_embeddings_2++;
+            }
+            LOG_DEBUG("embedding '%s' applied, custom embeddings: %i (text model 2)", embd_name.c_str(), num_custom_embeddings_2);
         }
-        LOG_DEBUG("embedding '%s' applied, custom embeddings: %i", embd_name.c_str(), num_custom_embeddings);
         return true;
     }
 

denoiser.hpp

@@ -474,7 +474,8 @@ static void sample_k_diffusion(sample_method_t method,
                                ggml_context* work_ctx,
                                ggml_tensor* x,
                                std::vector<float> sigmas,
-                               std::shared_ptr<RNG> rng) {
+                               std::shared_ptr<RNG> rng,
+                               float eta) {
     size_t steps = sigmas.size() - 1;
     // sample_euler_ancestral
     switch (method) {
@@ -1005,6 +1006,374 @@ static void sample_k_diffusion(sample_method_t method,
                 }
             }
         } break;
+        case DDIM_TRAILING:  // Denoising Diffusion Implicit Models
+                             // with the "trailing" timestep spacing
+        {
+            // See J. Song et al., "Denoising Diffusion Implicit
+            // Models", arXiv:2010.02502 [cs.LG]
+            //
+            // DDIM itself needs alphas_cumprod (DDPM, J. Ho et al.,
+            // arXiv:2006.11239 [cs.LG] with k-diffusion's start and
+            // end beta) (which unfortunately k-diffusion's data
+            // structure hides from the denoiser), and the sigmas are
+            // also needed to invert the behavior of CompVisDenoiser
+            // (k-diffusion's LMSDiscreteScheduler)
+            float beta_start = 0.00085f;
+            float beta_end = 0.0120f;
+            std::vector<double> alphas_cumprod;
+            std::vector<double> compvis_sigmas;
+
+            alphas_cumprod.reserve(TIMESTEPS);
+            compvis_sigmas.reserve(TIMESTEPS);
+            for (int i = 0; i < TIMESTEPS; i++) {
+                alphas_cumprod[i] =
+                    (i == 0 ? 1.0f : alphas_cumprod[i - 1]) *
+                    (1.0f -
+                     std::pow(sqrtf(beta_start) +
+                              (sqrtf(beta_end) - sqrtf(beta_start)) *
+                              ((float)i / (TIMESTEPS - 1)), 2));
+                compvis_sigmas[i] =
+                    std::sqrt((1 - alphas_cumprod[i]) /
+                              alphas_cumprod[i]);
+            }
+
+            struct ggml_tensor* pred_original_sample =
+                ggml_dup_tensor(work_ctx, x);
+            struct ggml_tensor* variance_noise =
+                ggml_dup_tensor(work_ctx, x);
+
+            for (int i = 0; i < steps; i++) {
+                // The "trailing" DDIM timestep, see S. Lin et al.,
+                // "Common Diffusion Noise Schedules and Sample Steps
+                // are Flawed", arXiv:2305.08891 [cs], p. 4, Table
+                // 2. Most variables below follow Diffusers naming
+                //
+                // Diffuser naming vs. Song et al. (2010), p. 5, (12)
+                // and p. 16, (16) (<variable name> -> <name in
+                // paper>):
+                //
+                // - pred_noise_t -> epsilon_theta^(t)(x_t)
+                // - pred_original_sample -> f_theta^(t)(x_t) or x_0
+                // - std_dev_t -> sigma_t (not the LMS sigma)
+                // - eta -> eta (set to 0 at the moment)
+                // - pred_sample_direction -> "direction pointing to
+                //   x_t"
+                // - pred_prev_sample -> "x_t-1"
+                int timestep =
+                    roundf(TIMESTEPS -
+                           i * ((float)TIMESTEPS / steps)) - 1;
+                // 1. get previous step value (=t-1)
+                int prev_timestep = timestep - TIMESTEPS / steps;
+                // The sigma here is chosen to cause the
+                // CompVisDenoiser to produce t = timestep
+                float sigma = compvis_sigmas[timestep];
+                if (i == 0) {
+                    // The function add_noise intializes x to
+                    // Diffusers' latents * sigma (as in Diffusers'
+                    // pipeline) or sample * sigma (Diffusers'
+                    // scheduler), where this sigma = init_noise_sigma
+                    // in Diffusers. For DDPM and DDIM however,
+                    // init_noise_sigma = 1. But the k-diffusion
+                    // model() also evaluates F_theta(c_in(sigma) x;
+                    // ...) instead of the bare U-net F_theta, with
+                    // c_in = 1 / sqrt(sigma^2 + 1), as defined in
+                    // T. Karras et al., "Elucidating the Design Space
+                    // of Diffusion-Based Generative Models",
+                    // arXiv:2206.00364 [cs.CV], p. 3, Table 1. Hence
+                    // the first call has to be prescaled as x <- x /
+                    // (c_in * sigma) with the k-diffusion pipeline
+                    // and CompVisDenoiser.
+                    float* vec_x = (float*)x->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_x[j] *= std::sqrt(sigma * sigma + 1) /
+                            sigma;
+                    }
+                }
+                else {
+                    // For the subsequent steps after the first one,
+                    // at this point x = latents or x = sample, and
+                    // needs to be prescaled with x <- sample / c_in
+                    // to compensate for model() applying the scale
+                    // c_in before the U-net F_theta
+                    float* vec_x = (float*)x->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_x[j] *= std::sqrt(sigma * sigma + 1);
+                    }
+                }
+                // Note (also noise_pred in Diffuser's pipeline)
+                // model_output = model() is the D(x, sigma) as
+                // defined in Karras et al. (2022), p. 3, Table 1 and
+                // p. 8 (7), compare also p. 38 (226) therein.
+                struct ggml_tensor* model_output =
+                    model(x, sigma, i + 1);
+                // Here model_output is still the k-diffusion denoiser
+                // output, not the U-net output F_theta(c_in(sigma) x;
+                // ...) in Karras et al. (2022), whereas Diffusers'
+                // model_output is F_theta(...). Recover the actual
+                // model_output, which is also referred to as the
+                // "Karras ODE derivative" d or d_cur in several
+                // samplers above.
+                {
+                    float* vec_x = (float*)x->data;
+                    float* vec_model_output =
+                        (float*)model_output->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_model_output[j] =
+                            (vec_x[j] - vec_model_output[j]) *
+                            (1 / sigma);
+                    }
+                }
+                // 2. compute alphas, betas
+                float alpha_prod_t = alphas_cumprod[timestep];
+                // Note final_alpha_cumprod = alphas_cumprod[0] due to
+                // trailing timestep spacing
+                float alpha_prod_t_prev = prev_timestep >= 0 ?
+                    alphas_cumprod[prev_timestep] : alphas_cumprod[0];
+                float beta_prod_t = 1 - alpha_prod_t;
+                // 3. compute predicted original sample from predicted
+                // noise also called "predicted x_0" of formula (12)
+                // from https://arxiv.org/pdf/2010.02502.pdf
+                {
+                    float* vec_x = (float*)x->data;
+                    float* vec_model_output =
+                        (float*)model_output->data;
+                    float* vec_pred_original_sample =
+                        (float*)pred_original_sample->data;
+                    // Note the substitution of latents or sample = x
+                    // * c_in = x / sqrt(sigma^2 + 1)
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_pred_original_sample[j] =
+                            (vec_x[j] / std::sqrt(sigma * sigma + 1) -
+                             std::sqrt(beta_prod_t) *
+                             vec_model_output[j]) *
+                            (1 / std::sqrt(alpha_prod_t));
+                    }
+                }
+                // Assuming the "epsilon" prediction type, where below
+                // pred_epsilon = model_output is inserted, and is not
+                // defined/copied explicitly.
+                //
+                // 5. compute variance: "sigma_t(eta)" -> see formula
+                // (16)
+                //
+                // sigma_t = sqrt((1 - alpha_t-1)/(1 - alpha_t)) *
+                // sqrt(1 - alpha_t/alpha_t-1)
+                float beta_prod_t_prev = 1 - alpha_prod_t_prev;
+                float variance = (beta_prod_t_prev / beta_prod_t) *
+                    (1 - alpha_prod_t / alpha_prod_t_prev);
+                float std_dev_t = eta * std::sqrt(variance);
+                // 6. compute "direction pointing to x_t" of formula
+                // (12) from https://arxiv.org/pdf/2010.02502.pdf
+                // 7. compute x_t without "random noise" of formula
+                // (12) from https://arxiv.org/pdf/2010.02502.pdf
+                {
+                    float* vec_model_output = (float*)model_output->data;
+                    float* vec_pred_original_sample =
+                        (float*)pred_original_sample->data;
+                    float* vec_x = (float*)x->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        // Two step inner loop without an explicit
+                        // tensor
+                        float pred_sample_direction =
+                            std::sqrt(1 - alpha_prod_t_prev -
+                                      std::pow(std_dev_t, 2)) *
+                            vec_model_output[j];
+                        vec_x[j] = std::sqrt(alpha_prod_t_prev) *
+                            vec_pred_original_sample[j] +
+                            pred_sample_direction;
+                    }
+                }
+                if (eta > 0) {
+                    ggml_tensor_set_f32_randn(variance_noise, rng);
+                    float* vec_variance_noise =
+                        (float*)variance_noise->data;
+                    float* vec_x = (float*)x->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_x[j] += std_dev_t * vec_variance_noise[j];
+                    }
+                }
+                // See the note above: x = latents or sample here, and
+                // is not scaled by the c_in. For the final output
+                // this is correct, but for subsequent iterations, x
+                // needs to be prescaled again, since k-diffusion's
+                // model() differes from the bare U-net F_theta by the
+                // factor c_in.
+            }
+        } break;
+        case TCD:  // Strategic Stochastic Sampling (Algorithm 4) in
+                   // Trajectory Consistency Distillation
+        {
+            // See J. Zheng et al., "Trajectory Consistency
+            // Distillation: Improved Latent Consistency Distillation
+            // by Semi-Linear Consistency Function with Trajectory
+            // Mapping", arXiv:2402.19159 [cs.CV]
+            float beta_start = 0.00085f;
+            float beta_end = 0.0120f;
+            std::vector<double> alphas_cumprod;
+            std::vector<double> compvis_sigmas;
+
+            alphas_cumprod.reserve(TIMESTEPS);
+            compvis_sigmas.reserve(TIMESTEPS);
+            for (int i = 0; i < TIMESTEPS; i++) {
+                alphas_cumprod[i] =
+                    (i == 0 ? 1.0f : alphas_cumprod[i - 1]) *
+                    (1.0f -
+                     std::pow(sqrtf(beta_start) +
+                              (sqrtf(beta_end) - sqrtf(beta_start)) *
+                              ((float)i / (TIMESTEPS - 1)), 2));
+                compvis_sigmas[i] =
+                    std::sqrt((1 - alphas_cumprod[i]) /
+                              alphas_cumprod[i]);
+            }
+            int original_steps = 50;
+
+            struct ggml_tensor* pred_original_sample =
+                ggml_dup_tensor(work_ctx, x);
+            struct ggml_tensor* noise =
+                ggml_dup_tensor(work_ctx, x);
+
+            for (int i = 0; i < steps; i++) {
+                // Analytic form for TCD timesteps
+                int timestep = TIMESTEPS - 1 -
+                    (TIMESTEPS / original_steps) *
+                    (int)floor(i * ((float)original_steps / steps));
+                // 1. get previous step value
+                int prev_timestep = i >= steps - 1 ? 0 :
+                    TIMESTEPS - 1 - (TIMESTEPS / original_steps) *
+                    (int)floor((i + 1) *
+                               ((float)original_steps / steps));
+                // Here timestep_s is tau_n' in Algorithm 4. The _s
+                // notation appears to be that from C. Lu,
+                // "DPM-Solver: A Fast ODE Solver for Diffusion
+                // Probabilistic Model Sampling in Around 10 Steps",
+                // arXiv:2206.00927 [cs.LG], but this notation is not
+                // continued in Algorithm 4, where _n' is used.
+                int timestep_s =
+                    (int)floor((1 - eta) * prev_timestep);
+                // Begin k-diffusion specific workaround for
+                // evaluating F_theta(x; ...) from D(x, sigma), same
+                // as in DDIM (and see there for detailed comments)
+                float sigma = compvis_sigmas[timestep];
+                if (i == 0) {
+                    float* vec_x = (float*)x->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_x[j] *= std::sqrt(sigma * sigma + 1) /
+                            sigma;
+                    }
+                }
+                else {
+                    float* vec_x = (float*)x->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_x[j] *= std::sqrt(sigma * sigma + 1);
+                    }
+                }
+                struct ggml_tensor* model_output =
+                    model(x, sigma, i + 1);
+                {
+                    float* vec_x = (float*)x->data;
+                    float* vec_model_output =
+                        (float*)model_output->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_model_output[j] =
+                            (vec_x[j] - vec_model_output[j]) *
+                            (1 / sigma);
+                    }
+                }
+                // 2. compute alphas, betas
+                //
+                // When comparing TCD with DDPM/DDIM note that Zheng
+                // et al. (2024) follows the DPM-Solver notation for
+                // alpha. One can find the following comment in the
+                // original DPM-Solver code
+                // (https://github.com/LuChengTHU/dpm-solver/):
+                // "**Important**: Please pay special attention for
+                // the args for `alphas_cumprod`: The `alphas_cumprod`
+                // is the \hat{alpha_n} arrays in the notations of
+                // DDPM. [...] Therefore, the notation \hat{alpha_n}
+                // is different from the notation alpha_t in
+                // DPM-Solver. In fact, we have alpha_{t_n} =
+                // \sqrt{\hat{alpha_n}}, [...]"
+                float alpha_prod_t = alphas_cumprod[timestep];
+                float beta_prod_t = 1 - alpha_prod_t;
+                // Note final_alpha_cumprod = alphas_cumprod[0] since
+                // TCD is always "trailing"
+                float alpha_prod_t_prev = prev_timestep >= 0 ?
+                    alphas_cumprod[prev_timestep] : alphas_cumprod[0];
+                // The subscript _s are the only portion in this
+                // section (2) unique to TCD
+                float alpha_prod_s = alphas_cumprod[timestep_s];
+                float beta_prod_s = 1 - alpha_prod_s;
+                // 3. Compute the predicted noised sample x_s based on
+                // the model parameterization
+                //
+                // This section is also exactly the same as DDIM
+                {
+                    float* vec_x = (float*)x->data;
+                    float* vec_model_output =
+                        (float*)model_output->data;
+                    float* vec_pred_original_sample =
+                        (float*)pred_original_sample->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_pred_original_sample[j] =
+                            (vec_x[j] / std::sqrt(sigma * sigma + 1) -
+                             std::sqrt(beta_prod_t) *
+                             vec_model_output[j]) *
+                            (1 / std::sqrt(alpha_prod_t));
+                    }
+                }
+                // This consistency function step can be difficult to
+                // decipher from Algorithm 4, as it is simply stated
+                // using a consistency function. This step is the
+                // modified DDIM, i.e. p. 8 (32) in Zheng et
+                // al. (2024), with eta set to 0 (see the paragraph
+                // immediately thereafter that states this somewhat
+                // obliquely).
+                {
+                    float* vec_pred_original_sample =
+                        (float*)pred_original_sample->data;
+                    float* vec_model_output =
+                        (float*)model_output->data;
+                    float* vec_x = (float*)x->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        // Substituting x = pred_noised_sample and
+                        // pred_epsilon = model_output
+                        vec_x[j] =
+                            std::sqrt(alpha_prod_s) *
+                            vec_pred_original_sample[j] +
+                            std::sqrt(beta_prod_s) *
+                            vec_model_output[j];
+                    }
+                }
+                // 4. Sample and inject noise z ~ N(0, I) for
+                // MultiStep Inference Noise is not used on the final
+                // timestep of the timestep schedule. This also means
+                // that noise is not used for one-step sampling. Eta
+                // (referred to as "gamma" in the paper) was
+                // introduced to control the stochasticity in every
+                // step. When eta = 0, it represents deterministic
+                // sampling, whereas eta = 1 indicates full stochastic
+                // sampling.
+                if (eta > 0 && i != steps - 1) {
+                    // In this case, x is still pred_noised_sample,
+                    // continue in-place
+                    ggml_tensor_set_f32_randn(noise, rng);
+                    float* vec_x = (float*)x->data;
+                    float* vec_noise = (float*)noise->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        // Corresponding to (35) in Zheng et
+                        // al. (2024), substituting x =
+                        // pred_noised_sample
+                        vec_x[j] =
+                            std::sqrt(alpha_prod_t_prev /
+                                      alpha_prod_s) *
+                            vec_x[j] +
+                            std::sqrt(1 - alpha_prod_t_prev /
+                                      alpha_prod_s) *
+                            vec_noise[j];
+                    }
+                }
+            }
+        } break;
 
         default:
             LOG_ERROR("Attempting to sample with nonexisting sample method %i", method);

examples/cli/main.cpp

@@ -39,6 +39,8 @@ const char* sample_method_str[] = {
     "ipndm",
     "ipndm_v",
     "lcm",
+    "ddim_trailing",
+    "tcd",
 };
 
 // Names of the sigma schedule overrides, same order as sample_schedule in stable-diffusion.h
@@ -93,6 +95,7 @@ struct SDParams {
     float min_cfg     = 1.0f;
     float cfg_scale   = 7.0f;
     float guidance    = 3.5f;
+    float eta         = 0.f;
     float style_ratio = 20.f;
     int clip_skip     = -1;  // <= 0 represents unspecified
     int width         = 512;
@@ -123,9 +126,9 @@ struct SDParams {
     int upscale_repeats           = 1;
 
     std::vector<int> skip_layers = {7, 8, 9};
-    float slg_scale              = 0.;
-    float skip_layer_start       = 0.01;
-    float skip_layer_end         = 0.2;
+    float slg_scale              = 0.f;
+    float skip_layer_start       = 0.01f;
+    float skip_layer_end         = 0.2f;
 };
 
 void print_params(SDParams params) {
@@ -162,6 +165,7 @@ void print_params(SDParams params) {
     printf("    cfg_scale:         %.2f\n", params.cfg_scale);
     printf("    slg_scale:         %.2f\n", params.slg_scale);
     printf("    guidance:          %.2f\n", params.guidance);
+    printf("    eta:               %.2f\n", params.eta);
     printf("    clip_skip:         %d\n", params.clip_skip);
     printf("    width:             %d\n", params.width);
     printf("    height:            %d\n", params.height);
@@ -202,16 +206,19 @@ void print_usage(int argc, const char* argv[]) {
     printf("                                     If not specified, the default is the type of the weight file\n");
     printf("  --lora-model-dir [DIR]             lora model directory\n");
     printf("  -i, --init-img [IMAGE]             path to the input image, required by img2img\n");
+    printf("  --mask [MASK]                      path to the mask image, required by img2img with mask\n");
     printf("  --control-image [IMAGE]            path to image condition, control net\n");
     printf("  -o, --output OUTPUT                path to write result image to (default: ./output.png)\n");
     printf("  -p, --prompt [PROMPT]              the prompt to render\n");
     printf("  -n, --negative-prompt PROMPT       the negative prompt (default: \"\")\n");
     printf("  --cfg-scale SCALE                  unconditional guidance scale: (default: 7.0)\n");
+    printf("  --guidance SCALE                   guidance scale for img2img (default: 3.5)\n");
     printf("  --slg-scale SCALE                  skip layer guidance (SLG) scale, only for DiT models: (default: 0)\n");
     printf("                                     0 means disabled, a value of 2.5 is nice for sd3.5 medium\n");
-    printf("  --skip_layers LAYERS               Layers to skip for SLG steps: (default: [7,8,9])\n");
-    printf("  --skip_layer_start START           SLG enabling point: (default: 0.01)\n");
-    printf("  --skip_layer_end END               SLG disabling point: (default: 0.2)\n");
+    printf("  --eta SCALE                        eta in DDIM, only for DDIM and TCD: (default: 0)\n");
+    printf("  --skip-layers LAYERS               Layers to skip for SLG steps: (default: [7,8,9])\n");
+    printf("  --skip-layer-start START           SLG enabling point: (default: 0.01)\n");
+    printf("  --skip-layer-end END               SLG disabling point: (default: 0.2)\n");
     printf("                                     SLG will be enabled at step int([STEPS]*[START]) and disabled at int([STEPS]*[END])\n");
     printf("  --strength STRENGTH                strength for noising/unnoising (default: 0.75)\n");
     printf("  --style-ratio STYLE-RATIO          strength for keeping input identity (default: 20%%)\n");
@@ -219,7 +226,7 @@ void print_usage(int argc, const char* argv[]) {
     printf("                                     1.0 corresponds to full destruction of information in init image\n");
     printf("  -H, --height H                     image height, in pixel space (default: 512)\n");
     printf("  -W, --width W                      image width, in pixel space (default: 512)\n");
-    printf("  --sampling-method {euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm}\n");
+    printf("  --sampling-method {euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd}\n");
     printf("                                     sampling method (default: \"euler_a\")\n");
     printf("  --steps  STEPS                     number of sample steps (default: 20)\n");
     printf("  --rng {std_default, cuda}          RNG (default: cuda)\n");
@@ -438,6 +445,12 @@ void parse_args(int argc, const char** argv, SDParams& params) {
                 break;
             }
             params.guidance = std::stof(argv[i]);
+        } else if (arg == "--eta") {
+            if (++i >= argc) {
+                invalid_arg = true;
+                break;
+            }
+            params.eta = std::stof(argv[i]);
         } else if (arg == "--strength") {
             if (++i >= argc) {
                 invalid_arg = true;
@@ -717,6 +730,7 @@ std::string get_image_params(SDParams params, int64_t seed) {
         parameter_string += "Skip layer end: " + std::to_string(params.skip_layer_end) + ", ";
     }
     parameter_string += "Guidance: " + std::to_string(params.guidance) + ", ";
+    parameter_string += "Eta: " + std::to_string(params.eta) + ", ";
     parameter_string += "Seed: " + std::to_string(seed) + ", ";
     parameter_string += "Size: " + std::to_string(params.width) + "x" + std::to_string(params.height) + ", ";
     parameter_string += "Model: " + sd_basename(params.model_path) + ", ";
@@ -917,12 +931,12 @@ int main(int argc, const char* argv[]) {
         }
     }
 
+    std::vector<uint8_t> default_mask_image_vec(params.width * params.height, 255);
     if (params.mask_path != "") {
         int c             = 0;
         mask_image_buffer = stbi_load(params.mask_path.c_str(), &params.width, &params.height, &c, 1);
     } else {
-        std::vector<uint8_t> arr(params.width * params.height, 255);
-        mask_image_buffer = arr.data();
+        mask_image_buffer = default_mask_image_vec.data();
     }
     sd_image_t mask_image = {(uint32_t)params.width,
                              (uint32_t)params.height,
@@ -937,6 +951,7 @@ int main(int argc, const char* argv[]) {
                           params.clip_skip,
                           params.cfg_scale,
                           params.guidance,
+                          params.eta,
                           params.width,
                           params.height,
                           params.sample_method,
@@ -1004,6 +1019,7 @@ int main(int argc, const char* argv[]) {
                               params.clip_skip,
                               params.cfg_scale,
                               params.guidance,
+                              params.eta,
                               params.width,
                               params.height,
                               params.sample_method,
@@ -1057,16 +1073,41 @@ int main(int argc, const char* argv[]) {
         }
     }
 
-    size_t last            = params.output_path.find_last_of(".");
-    std::string dummy_name = last != std::string::npos ? params.output_path.substr(0, last) : params.output_path;
+    std::string dummy_name, ext, lc_ext;
+    bool is_jpg;
+    size_t last = params.output_path.find_last_of(".");
+    size_t last_path = std::min(params.output_path.find_last_of("/"),
+                                params.output_path.find_last_of("\\"));
+    if (last != std::string::npos // filename has extension
+    && (last_path == std::string::npos || last > last_path)) {
+        dummy_name = params.output_path.substr(0, last);
+        ext = lc_ext = params.output_path.substr(last);
+        std::transform(ext.begin(), ext.end(), lc_ext.begin(), ::tolower);
+        is_jpg = lc_ext == ".jpg" || lc_ext == ".jpeg" || lc_ext == ".jpe";
+    } else {
+        dummy_name = params.output_path;
+        ext = lc_ext = "";
+        is_jpg = false;
+    }
+    // appending ".png" to absent or unknown extension
+    if (!is_jpg && lc_ext != ".png") {
+        dummy_name += ext;
+        ext = ".png";
+    }
     for (int i = 0; i < params.batch_count; i++) {
         if (results[i].data == NULL) {
             continue;
         }
-        std::string final_image_path = i > 0 ? dummy_name + "_" + std::to_string(i + 1) + ".png" : dummy_name + ".png";
-        stbi_write_png(final_image_path.c_str(), results[i].width, results[i].height, results[i].channel,
-                       results[i].data, 0, get_image_params(params, params.seed + i).c_str());
-        printf("save result image to '%s'\n", final_image_path.c_str());
+        std::string final_image_path = i > 0 ? dummy_name + "_" + std::to_string(i + 1) + ext : dummy_name + ext;
+        if(is_jpg) {
+            stbi_write_jpg(final_image_path.c_str(), results[i].width, results[i].height, results[i].channel,
+                           results[i].data, 90, get_image_params(params, params.seed + i).c_str());
+            printf("save result JPEG image to '%s'\n", final_image_path.c_str());
+        } else {
+            stbi_write_png(final_image_path.c_str(), results[i].width, results[i].height, results[i].channel,
+                           results[i].data, 0, get_image_params(params, params.seed + i).c_str());
+            printf("save result PNG image to '%s'\n", final_image_path.c_str());
+        }
         free(results[i].data);
         results[i].data = NULL;
     }

ggml_extend.hpp

@@ -27,7 +27,7 @@
 
 #include "model.h"
 
-#ifdef SD_USE_CUBLAS
+#ifdef SD_USE_CUDA
 #include "ggml-cuda.h"
 #endif
 
@@ -52,6 +52,71 @@
 #define __STATIC_INLINE__ static inline
 #endif
 
+// n-mode trensor-matrix product
+// example: 2-mode product
+// A: [ne03, k, ne01, ne00]
+// B: k rows, m columns => [k, m]
+// result is [ne03, m, ne01, ne00]
+__STATIC_INLINE__ struct ggml_tensor* ggml_mul_n_mode(struct ggml_context* ctx, struct ggml_tensor* a, struct ggml_tensor* b, int mode = 0) {
+    // reshape A
+    // swap 0th and nth axis
+    a       = ggml_cont(ctx, ggml_permute(ctx, a, mode, mode != 1 ? 1 : 0, mode != 2 ? 2 : 0, mode != 3 ? 3 : 0));
+    int ne1 = a->ne[1];
+    int ne2 = a->ne[2];
+    int ne3 = a->ne[3];
+    // make 2D
+    a = ggml_cont(ctx, ggml_reshape_2d(ctx, a, a->ne[0], (ne3 * ne2 * ne1)));
+
+    struct ggml_tensor* result = ggml_cont(ctx, ggml_transpose(ctx, ggml_mul_mat(ctx, a, b)));
+
+    // reshape output (same shape as a after permutation except first dim)
+    result = ggml_reshape_4d(ctx, result, result->ne[0], ne1, ne2, ne3);
+    // swap back 0th and nth axis
+    result = ggml_permute(ctx, result, mode, mode != 1 ? 1 : 0, mode != 2 ? 2 : 0, mode != 3 ? 3 : 0);
+    return result;
+}
+
+__STATIC_INLINE__ struct ggml_tensor* ggml_merge_lora(ggml_context* ctx, struct ggml_tensor* lora_down, struct ggml_tensor* lora_up, struct ggml_tensor* lora_mid = NULL) {
+    struct ggml_tensor* updown;
+    // flat lora tensors to multiply it
+    int64_t lora_up_rows  = lora_up->ne[ggml_n_dims(lora_up) - 1];
+    lora_up               = ggml_reshape_2d(ctx, lora_up, ggml_nelements(lora_up) / lora_up_rows, lora_up_rows);
+    auto lora_down_n_dims = ggml_n_dims(lora_down);
+    // assume n_dims should always be a multiple of 2 (otherwise rank 1 doesn't work)
+    lora_down_n_dims       = (lora_down_n_dims + lora_down_n_dims % 2);
+    int64_t lora_down_rows = lora_down->ne[lora_down_n_dims - 1];
+    lora_down              = ggml_reshape_2d(ctx, lora_down, ggml_nelements(lora_down) / lora_down_rows, lora_down_rows);
+
+    // ggml_mul_mat requires tensor b transposed
+    lora_down = ggml_cont(ctx, ggml_transpose(ctx, lora_down));
+    if (lora_mid == NULL) {
+        updown = ggml_mul_mat(ctx, lora_up, lora_down);
+        updown = ggml_cont(ctx, ggml_transpose(ctx, updown));
+    } else {
+        // undoing tucker decomposition for conv layers.
+        // lora_mid  has shape (3,    3,   Rank, Rank)
+        // lora_down has shape (Rank, In,  1,    1)
+        // lora_up   has shape (Rank, Out, 1,    1)
+        // conv layer shape is (3,    3,   Out,  In)
+        updown = ggml_mul_n_mode(ctx, ggml_mul_n_mode(ctx, lora_mid, lora_down, 3), lora_up, 2);
+        updown = ggml_cont(ctx, updown);
+    }
+    return updown;
+}
+
+// Kronecker product
+// [ne03,ne02,ne01,ne00] x [ne13,ne12,ne11,ne10] => [ne03*ne13,ne02*ne12,ne01*ne11,ne00*ne10]
+__STATIC_INLINE__ struct ggml_tensor* ggml_kronecker(ggml_context* ctx, struct ggml_tensor* a, struct ggml_tensor* b) {
+    return ggml_mul(ctx,
+                    ggml_upscale_ext(ctx,
+                                     a,
+                                     a->ne[0] * b->ne[0],
+                                     a->ne[1] * b->ne[1],
+                                     a->ne[2] * b->ne[2],
+                                     a->ne[3] * b->ne[3]),
+                    b);
+}
+
 __STATIC_INLINE__ void ggml_log_callback_default(ggml_log_level level, const char* text, void* user_data) {
     (void)level;
     (void)user_data;
@@ -318,8 +383,10 @@ __STATIC_INLINE__ void sd_apply_mask(struct ggml_tensor* image_data,
     for (int ix = 0; ix < width; ix++) {
         for (int iy = 0; iy < height; iy++) {
             float m = ggml_tensor_get_f32(mask, ix, iy);
+            m       = round(m);  // inpaint models need binary masks
+            ggml_tensor_set_f32(mask, m, ix, iy);
             for (int k = 0; k < channels; k++) {
-                float value = ((float)(m < 254.5/255)) * (ggml_tensor_get_f32(image_data, ix, iy, k) - .5) + .5;
+                float value = (1 - m) * (ggml_tensor_get_f32(image_data, ix, iy, k) - .5) + .5;
                 ggml_tensor_set_f32(output, value, ix, iy, k);
             }
         }
@@ -708,7 +775,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_nn_attention(struct ggml_context* ctx
                                                         struct ggml_tensor* k,
                                                         struct ggml_tensor* v,
                                                         bool mask = false) {
-#if defined(SD_USE_FLASH_ATTENTION) && !defined(SD_USE_CUBLAS) && !defined(SD_USE_METAL) && !defined(SD_USE_VULKAN) && !defined(SD_USE_SYCL)
+#if defined(SD_USE_FLASH_ATTENTION) && !defined(SD_USE_CUDA) && !defined(SD_USE_METAL) && !defined(SD_USE_VULKAN) && !defined(SD_USE_SYCL)
     struct ggml_tensor* kqv = ggml_flash_attn(ctx, q, k, v, false);  // [N * n_head, n_token, d_head]
 #else
     float d_head           = (float)q->ne[0];
@@ -864,7 +931,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_nn_group_norm(struct ggml_context* ct
 }
 
 __STATIC_INLINE__ void ggml_backend_tensor_get_and_sync(ggml_backend_t backend, const struct ggml_tensor* tensor, void* data, size_t offset, size_t size) {
-#if defined(SD_USE_CUBLAS) || defined(SD_USE_SYCL)
+#if defined(SD_USE_CUDA) || defined(SD_USE_SYCL)
     if (!ggml_backend_is_cpu(backend)) {
         ggml_backend_tensor_get_async(backend, tensor, data, offset, size);
         ggml_backend_synchronize(backend);
@@ -987,8 +1054,8 @@ __STATIC_INLINE__ size_t ggml_tensor_num(ggml_context* ctx) {
 }
 
 /* SDXL with LoRA requires more space */
-#define MAX_PARAMS_TENSOR_NUM 15360
-#define MAX_GRAPH_SIZE 15360
+#define MAX_PARAMS_TENSOR_NUM 32768
+#define MAX_GRAPH_SIZE 32768
 
 struct GGMLRunner {
 protected:

gits_noise.inl

@@ -329,21 +329,21 @@ const std::vector<std::vector<float>> GITS_NOISE_1_50 = {
 };
 
 const std::vector<const std::vector<std::vector<float>>*> GITS_NOISE = {
-    { &GITS_NOISE_0_80 },
-    { &GITS_NOISE_0_85 },
-    { &GITS_NOISE_0_90 },
-    { &GITS_NOISE_0_95 },
-    { &GITS_NOISE_1_00 },
-    { &GITS_NOISE_1_05 },
-    { &GITS_NOISE_1_10 },
-    { &GITS_NOISE_1_15 },
-    { &GITS_NOISE_1_20 },
-    { &GITS_NOISE_1_25 },
-    { &GITS_NOISE_1_30 },
-    { &GITS_NOISE_1_35 },
-    { &GITS_NOISE_1_40 },
-    { &GITS_NOISE_1_45 },
-    { &GITS_NOISE_1_50 }
+    &GITS_NOISE_0_80,
+    &GITS_NOISE_0_85,
+    &GITS_NOISE_0_90,
+    &GITS_NOISE_0_95,
+    &GITS_NOISE_1_00,
+    &GITS_NOISE_1_05,
+    &GITS_NOISE_1_10,
+    &GITS_NOISE_1_15,
+    &GITS_NOISE_1_20,
+    &GITS_NOISE_1_25,
+    &GITS_NOISE_1_30,
+    &GITS_NOISE_1_35,
+    &GITS_NOISE_1_40,
+    &GITS_NOISE_1_45,
+    &GITS_NOISE_1_50
 };
 
 #endif // GITS_NOISE_INL

lora.hpp

@@ -197,6 +197,10 @@ struct LoraModel : public GGMLRunner {
                 blk_name.replace(blk_name.find(".joint_blocks"), sizeof(".joint_blocks") - 1, ".transformer_blocks");
             }
 
+            if (blk_name.find("text_encoders.clip_l") != std::string::npos) {
+                blk_name.replace(blk_name.find("text_encoders.clip_l"), sizeof("text_encoders.clip_l") - 1, "cond_stage_model");
+            }
+
             for (const auto& item : alt_names) {
                 size_t match = blk_name.find(item.first);
                 if (match != std::string::npos) {
@@ -217,13 +221,17 @@ struct LoraModel : public GGMLRunner {
                     keys.push_back(split_blk);
                 }
             }
+            keys.push_back(blk_name);
         }
-        keys.push_back(blk_name);
 
         std::vector<std::string> ret;
         for (std::string& key : keys) {
             ret.push_back(key);
             replace_all_chars(key, '.', '_');
+            // fix for some sdxl lora, like lcm-lora-xl
+            if (key == "model_diffusion_model_output_blocks_2_2_conv") {
+                ret.push_back("model_diffusion_model_output_blocks_2_1_conv");
+            }
             ret.push_back(key);
         }
         return ret;
@@ -244,387 +252,545 @@ struct LoraModel : public GGMLRunner {
             std::vector<std::string> keys = to_lora_keys(k_tensor, version);
             if (keys.size() == 0)
                 continue;
-            ggml_tensor* lora_up   = NULL;
-            ggml_tensor* lora_down = NULL;
+
             for (auto& key : keys) {
-                std::string alpha_name         = "";
-                std::string scale_name         = "";
-                std::string split_q_scale_name = "";
-                std::string lora_down_name     = "";
-                std::string lora_up_name       = "";
-
-                if (starts_with(key, "SPLIT|")) {
+                bool is_qkv_split = starts_with(key, "SPLIT|");
+                if (is_qkv_split) {
                     key = key.substr(sizeof("SPLIT|") - 1);
-                    // TODO: Handle alphas
-                    std::string suffix  = "";
-                    auto split_q_d_name = lora_pre[type] + key + "q" + suffix + lora_downs[type] + ".weight";
-
-                    if (lora_tensors.find(split_q_d_name) == lora_tensors.end()) {
-                        suffix         = "_proj";
-                        split_q_d_name = lora_pre[type] + key + "q" + suffix + lora_downs[type] + ".weight";
-                    }
-                    if (lora_tensors.find(split_q_d_name) != lora_tensors.end()) {
-                        // print_ggml_tensor(it.second, true);  //[3072, 21504, 1, 1]
-                        // find qkv and mlp up parts in LoRA model
-                        auto split_k_d_name = lora_pre[type] + key + "k" + suffix + lora_downs[type] + ".weight";
-                        auto split_v_d_name = lora_pre[type] + key + "v" + suffix + lora_downs[type] + ".weight";
-
-                        auto split_q_u_name = lora_pre[type] + key + "q" + suffix + lora_ups[type] + ".weight";
-                        auto split_k_u_name = lora_pre[type] + key + "k" + suffix + lora_ups[type] + ".weight";
-                        auto split_v_u_name = lora_pre[type] + key + "v" + suffix + lora_ups[type] + ".weight";
-
-                        auto split_q_scale_name = lora_pre[type] + key + "q" + suffix + ".scale";
-                        auto split_k_scale_name = lora_pre[type] + key + "k" + suffix + ".scale";
-                        auto split_v_scale_name = lora_pre[type] + key + "v" + suffix + ".scale";
-
-                        auto split_q_alpha_name = lora_pre[type] + key + "q" + suffix + ".alpha";
-                        auto split_k_alpha_name = lora_pre[type] + key + "k" + suffix + ".alpha";
-                        auto split_v_alpha_name = lora_pre[type] + key + "v" + suffix + ".alpha";
-
-                        ggml_tensor* lora_q_down = NULL;
-                        ggml_tensor* lora_q_up   = NULL;
-                        ggml_tensor* lora_k_down = NULL;
-                        ggml_tensor* lora_k_up   = NULL;
-                        ggml_tensor* lora_v_down = NULL;
-                        ggml_tensor* lora_v_up   = NULL;
-
-                        lora_q_down = to_f32(compute_ctx, lora_tensors[split_q_d_name]);
-
-                        if (lora_tensors.find(split_q_u_name) != lora_tensors.end()) {
-                            lora_q_up = to_f32(compute_ctx, lora_tensors[split_q_u_name]);
-                        }
-
-                        if (lora_tensors.find(split_k_d_name) != lora_tensors.end()) {
-                            lora_k_down = to_f32(compute_ctx, lora_tensors[split_k_d_name]);
-                        }
-
-                        if (lora_tensors.find(split_k_u_name) != lora_tensors.end()) {
-                            lora_k_up = to_f32(compute_ctx, lora_tensors[split_k_u_name]);
-                        }
-
-                        if (lora_tensors.find(split_v_d_name) != lora_tensors.end()) {
-                            lora_v_down = to_f32(compute_ctx, lora_tensors[split_v_d_name]);
-                        }
-
-                        if (lora_tensors.find(split_v_u_name) != lora_tensors.end()) {
-                            lora_v_up = to_f32(compute_ctx, lora_tensors[split_v_u_name]);
-                        }
-
-                        float q_rank = lora_q_up->ne[0];
-                        float k_rank = lora_k_up->ne[0];
-                        float v_rank = lora_v_up->ne[0];
-
-                        float lora_q_scale = 1;
-                        float lora_k_scale = 1;
-                        float lora_v_scale = 1;
-
-                        if (lora_tensors.find(split_q_scale_name) != lora_tensors.end()) {
-                            lora_q_scale = ggml_backend_tensor_get_f32(lora_tensors[split_q_scale_name]);
-                            applied_lora_tensors.insert(split_q_scale_name);
-                        }
-                        if (lora_tensors.find(split_k_scale_name) != lora_tensors.end()) {
-                            lora_k_scale = ggml_backend_tensor_get_f32(lora_tensors[split_k_scale_name]);
-                            applied_lora_tensors.insert(split_k_scale_name);
-                        }
-                        if (lora_tensors.find(split_v_scale_name) != lora_tensors.end()) {
-                            lora_v_scale = ggml_backend_tensor_get_f32(lora_tensors[split_v_scale_name]);
-                            applied_lora_tensors.insert(split_v_scale_name);
-                        }
-
-                        if (lora_tensors.find(split_q_alpha_name) != lora_tensors.end()) {
-                            float lora_q_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_q_alpha_name]);
-                            applied_lora_tensors.insert(split_q_alpha_name);
-                            lora_q_scale = lora_q_alpha / q_rank;
-                        }
-                        if (lora_tensors.find(split_k_alpha_name) != lora_tensors.end()) {
-                            float lora_k_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_k_alpha_name]);
-                            applied_lora_tensors.insert(split_k_alpha_name);
-                            lora_k_scale = lora_k_alpha / k_rank;
-                        }
-                        if (lora_tensors.find(split_v_alpha_name) != lora_tensors.end()) {
-                            float lora_v_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_v_alpha_name]);
-                            applied_lora_tensors.insert(split_v_alpha_name);
-                            lora_v_scale = lora_v_alpha / v_rank;
-                        }
-
-                        ggml_scale_inplace(compute_ctx, lora_q_down, lora_q_scale);
-                        ggml_scale_inplace(compute_ctx, lora_k_down, lora_k_scale);
-                        ggml_scale_inplace(compute_ctx, lora_v_down, lora_v_scale);
-
-                        // print_ggml_tensor(lora_q_down, true);  //[3072, R, 1, 1]
-                        // print_ggml_tensor(lora_k_down, true);  //[3072, R, 1, 1]
-                        // print_ggml_tensor(lora_v_down, true);  //[3072, R, 1, 1]
-                        // print_ggml_tensor(lora_q_up, true);    //[R, 3072, 1, 1]
-                        // print_ggml_tensor(lora_k_up, true);    //[R, 3072, 1, 1]
-                        // print_ggml_tensor(lora_v_up, true);    //[R, 3072, 1, 1]
-
-                        // these need to be stitched together this way:
-                        //                          |q_up,0   ,0   |
-                        //                          |0   ,k_up,0   |
-                        //                          |0   ,0   ,v_up|
-                        // (q_down,k_down,v_down) . (q   ,k   ,v)
-
-                        // up_concat will be [9216, R*3, 1, 1]
-                        // down_concat will be [R*3, 3072, 1, 1]
-                        ggml_tensor* lora_down_concat = ggml_concat(compute_ctx, ggml_concat(compute_ctx, lora_q_down, lora_k_down, 1), lora_v_down, 1);
-
-                        ggml_tensor* z = ggml_dup_tensor(compute_ctx, lora_q_up);
-                        ggml_scale(compute_ctx, z, 0);
-                        ggml_tensor* zz = ggml_concat(compute_ctx, z, z, 1);
-
-                        ggml_tensor* q_up = ggml_concat(compute_ctx, lora_q_up, zz, 1);
-                        ggml_tensor* k_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, z, lora_k_up, 1), z, 1);
-                        ggml_tensor* v_up = ggml_concat(compute_ctx, zz, lora_v_up, 1);
-                        // print_ggml_tensor(q_up, true);  //[R, 9216, 1, 1]
-                        // print_ggml_tensor(k_up, true);  //[R, 9216, 1, 1]
-                        // print_ggml_tensor(v_up, true);  //[R, 9216, 1, 1]
-                        ggml_tensor* lora_up_concat = ggml_concat(compute_ctx, ggml_concat(compute_ctx, q_up, k_up, 0), v_up, 0);
-                        // print_ggml_tensor(lora_up_concat, true);  //[R*3, 9216, 1, 1]
-
-                        lora_down = ggml_cont(compute_ctx, lora_down_concat);
-                        lora_up   = ggml_cont(compute_ctx, lora_up_concat);
-
-                        applied_lora_tensors.insert(split_q_u_name);
-                        applied_lora_tensors.insert(split_k_u_name);
-                        applied_lora_tensors.insert(split_v_u_name);
-
-                        applied_lora_tensors.insert(split_q_d_name);
-                        applied_lora_tensors.insert(split_k_d_name);
-                        applied_lora_tensors.insert(split_v_d_name);
-                    }
                 }
-                if (starts_with(key, "SPLIT_L|")) {
+                bool is_qkvm_split = starts_with(key, "SPLIT_L|");
+                if (is_qkvm_split) {
                     key = key.substr(sizeof("SPLIT_L|") - 1);
-
-                    auto split_q_d_name = lora_pre[type] + key + "attn.to_q" + lora_downs[type] + ".weight";
-                    if (lora_tensors.find(split_q_d_name) != lora_tensors.end()) {
-                        // print_ggml_tensor(it.second, true);  //[3072, 21504, 1, 1]
-                        // find qkv and mlp up parts in LoRA model
-                        auto split_k_d_name = lora_pre[type] + key + "attn.to_k" + lora_downs[type] + ".weight";
-                        auto split_v_d_name = lora_pre[type] + key + "attn.to_v" + lora_downs[type] + ".weight";
-
-                        auto split_q_u_name = lora_pre[type] + key + "attn.to_q" + lora_ups[type] + ".weight";
-                        auto split_k_u_name = lora_pre[type] + key + "attn.to_k" + lora_ups[type] + ".weight";
-                        auto split_v_u_name = lora_pre[type] + key + "attn.to_v" + lora_ups[type] + ".weight";
-
-                        auto split_m_d_name = lora_pre[type] + key + "proj_mlp" + lora_downs[type] + ".weight";
-                        auto split_m_u_name = lora_pre[type] + key + "proj_mlp" + lora_ups[type] + ".weight";
-
-                        auto split_q_scale_name = lora_pre[type] + key + "attn.to_q" + ".scale";
-                        auto split_k_scale_name = lora_pre[type] + key + "attn.to_k" + ".scale";
-                        auto split_v_scale_name = lora_pre[type] + key + "attn.to_v" + ".scale";
-                        auto split_m_scale_name = lora_pre[type] + key + "proj_mlp" + ".scale";
-
-                        auto split_q_alpha_name = lora_pre[type] + key + "attn.to_q" + ".alpha";
-                        auto split_k_alpha_name = lora_pre[type] + key + "attn.to_k" + ".alpha";
-                        auto split_v_alpha_name = lora_pre[type] + key + "attn.to_v" + ".alpha";
-                        auto split_m_alpha_name = lora_pre[type] + key + "proj_mlp" + ".alpha";
-
-                        ggml_tensor* lora_q_down = NULL;
-                        ggml_tensor* lora_q_up   = NULL;
-                        ggml_tensor* lora_k_down = NULL;
-                        ggml_tensor* lora_k_up   = NULL;
-                        ggml_tensor* lora_v_down = NULL;
-                        ggml_tensor* lora_v_up   = NULL;
-
-                        ggml_tensor* lora_m_down = NULL;
-                        ggml_tensor* lora_m_up   = NULL;
-
-                        lora_q_up = to_f32(compute_ctx, lora_tensors[split_q_u_name]);
-
-                        if (lora_tensors.find(split_q_d_name) != lora_tensors.end()) {
-                            lora_q_down = to_f32(compute_ctx, lora_tensors[split_q_d_name]);
-                        }
-
-                        if (lora_tensors.find(split_q_u_name) != lora_tensors.end()) {
-                            lora_q_up = to_f32(compute_ctx, lora_tensors[split_q_u_name]);
-                        }
-
-                        if (lora_tensors.find(split_k_d_name) != lora_tensors.end()) {
-                            lora_k_down = to_f32(compute_ctx, lora_tensors[split_k_d_name]);
-                        }
-
-                        if (lora_tensors.find(split_k_u_name) != lora_tensors.end()) {
-                            lora_k_up = to_f32(compute_ctx, lora_tensors[split_k_u_name]);
-                        }
-
-                        if (lora_tensors.find(split_v_d_name) != lora_tensors.end()) {
-                            lora_v_down = to_f32(compute_ctx, lora_tensors[split_v_d_name]);
-                        }
-
-                        if (lora_tensors.find(split_v_u_name) != lora_tensors.end()) {
-                            lora_v_up = to_f32(compute_ctx, lora_tensors[split_v_u_name]);
-                        }
-
-                        if (lora_tensors.find(split_m_d_name) != lora_tensors.end()) {
-                            lora_m_down = to_f32(compute_ctx, lora_tensors[split_m_d_name]);
-                        }
-
-                        if (lora_tensors.find(split_m_u_name) != lora_tensors.end()) {
-                            lora_m_up = to_f32(compute_ctx, lora_tensors[split_m_u_name]);
-                        }
-
-                        float q_rank = lora_q_up->ne[0];
-                        float k_rank = lora_k_up->ne[0];
-                        float v_rank = lora_v_up->ne[0];
-                        float m_rank = lora_v_up->ne[0];
-
-                        float lora_q_scale = 1;
-                        float lora_k_scale = 1;
-                        float lora_v_scale = 1;
-                        float lora_m_scale = 1;
-
-                        if (lora_tensors.find(split_q_scale_name) != lora_tensors.end()) {
-                            lora_q_scale = ggml_backend_tensor_get_f32(lora_tensors[split_q_scale_name]);
-                            applied_lora_tensors.insert(split_q_scale_name);
-                        }
-                        if (lora_tensors.find(split_k_scale_name) != lora_tensors.end()) {
-                            lora_k_scale = ggml_backend_tensor_get_f32(lora_tensors[split_k_scale_name]);
-                            applied_lora_tensors.insert(split_k_scale_name);
-                        }
-                        if (lora_tensors.find(split_v_scale_name) != lora_tensors.end()) {
-                            lora_v_scale = ggml_backend_tensor_get_f32(lora_tensors[split_v_scale_name]);
-                            applied_lora_tensors.insert(split_v_scale_name);
-                        }
-                        if (lora_tensors.find(split_m_scale_name) != lora_tensors.end()) {
-                            lora_m_scale = ggml_backend_tensor_get_f32(lora_tensors[split_m_scale_name]);
-                            applied_lora_tensors.insert(split_m_scale_name);
-                        }
-
-                        if (lora_tensors.find(split_q_alpha_name) != lora_tensors.end()) {
-                            float lora_q_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_q_alpha_name]);
-                            applied_lora_tensors.insert(split_q_alpha_name);
-                            lora_q_scale = lora_q_alpha / q_rank;
-                        }
-                        if (lora_tensors.find(split_k_alpha_name) != lora_tensors.end()) {
-                            float lora_k_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_k_alpha_name]);
-                            applied_lora_tensors.insert(split_k_alpha_name);
-                            lora_k_scale = lora_k_alpha / k_rank;
-                        }
-                        if (lora_tensors.find(split_v_alpha_name) != lora_tensors.end()) {
-                            float lora_v_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_v_alpha_name]);
-                            applied_lora_tensors.insert(split_v_alpha_name);
-                            lora_v_scale = lora_v_alpha / v_rank;
-                        }
-                        if (lora_tensors.find(split_m_alpha_name) != lora_tensors.end()) {
-                            float lora_m_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_m_alpha_name]);
-                            applied_lora_tensors.insert(split_m_alpha_name);
-                            lora_m_scale = lora_m_alpha / m_rank;
-                        }
-
-                        ggml_scale_inplace(compute_ctx, lora_q_down, lora_q_scale);
-                        ggml_scale_inplace(compute_ctx, lora_k_down, lora_k_scale);
-                        ggml_scale_inplace(compute_ctx, lora_v_down, lora_v_scale);
-                        ggml_scale_inplace(compute_ctx, lora_m_down, lora_m_scale);
-
-                        // print_ggml_tensor(lora_q_down, true);  //[3072, R, 1, 1]
-                        // print_ggml_tensor(lora_k_down, true);  //[3072, R, 1, 1]
-                        // print_ggml_tensor(lora_v_down, true);  //[3072, R, 1, 1]
-                        // print_ggml_tensor(lora_m_down, true);  //[3072, R, 1, 1]
-                        // print_ggml_tensor(lora_q_up, true);  //[R, 3072, 1, 1]
-                        // print_ggml_tensor(lora_k_up, true);  //[R, 3072, 1, 1]
-                        // print_ggml_tensor(lora_v_up, true);  //[R, 3072, 1, 1]
-                        // print_ggml_tensor(lora_m_up, true);  //[R, 12288, 1, 1]
-
-                        // these need to be stitched together this way:
-                        //                                 |q_up,0   ,0   ,0   |
-                        //                                 |0   ,k_up,0   ,0   |
-                        //                                 |0   ,0   ,v_up,0   |
-                        //                                 |0   ,0   ,0   ,m_up|
-                        // (q_down,k_down,v_down,m_down) . (q   ,k   ,v   ,m)
-
-                        // up_concat will be [21504, R*4, 1, 1]
-                        // down_concat will be [R*4, 3072, 1, 1]
-
-                        ggml_tensor* lora_down_concat = ggml_concat(compute_ctx, ggml_concat(compute_ctx, lora_q_down, lora_k_down, 1), ggml_concat(compute_ctx, lora_v_down, lora_m_down, 1), 1);
-                        // print_ggml_tensor(lora_down_concat, true);  //[3072, R*4, 1, 1]
-
-                        // this also means that if rank is bigger than 672, it is less memory efficient to do it this way (should be fine)
-                        // print_ggml_tensor(lora_q_up, true);  //[3072, R, 1, 1]
-                        ggml_tensor* z     = ggml_dup_tensor(compute_ctx, lora_q_up);
-                        ggml_tensor* mlp_z = ggml_dup_tensor(compute_ctx, lora_m_up);
-                        ggml_scale(compute_ctx, z, 0);
-                        ggml_scale(compute_ctx, mlp_z, 0);
-                        ggml_tensor* zz = ggml_concat(compute_ctx, z, z, 1);
-
-                        ggml_tensor* q_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, lora_q_up, zz, 1), mlp_z, 1);
-                        ggml_tensor* k_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, z, lora_k_up, 1), ggml_concat(compute_ctx, z, mlp_z, 1), 1);
-                        ggml_tensor* v_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, zz, lora_v_up, 1), mlp_z, 1);
-                        ggml_tensor* m_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, zz, z, 1), lora_m_up, 1);
-                        // print_ggml_tensor(q_up, true);  //[R, 21504, 1, 1]
-                        // print_ggml_tensor(k_up, true);  //[R, 21504, 1, 1]
-                        // print_ggml_tensor(v_up, true);  //[R, 21504, 1, 1]
-                        // print_ggml_tensor(m_up, true);  //[R, 21504, 1, 1]
-
-                        ggml_tensor* lora_up_concat = ggml_concat(compute_ctx, ggml_concat(compute_ctx, q_up, k_up, 0), ggml_concat(compute_ctx, v_up, m_up, 0), 0);
-                        // print_ggml_tensor(lora_up_concat, true);  //[R*4, 21504, 1, 1]
-
-                        lora_down = ggml_cont(compute_ctx, lora_down_concat);
-                        lora_up   = ggml_cont(compute_ctx, lora_up_concat);
-
-                        applied_lora_tensors.insert(split_q_u_name);
-                        applied_lora_tensors.insert(split_k_u_name);
-                        applied_lora_tensors.insert(split_v_u_name);
-                        applied_lora_tensors.insert(split_m_u_name);
-
-                        applied_lora_tensors.insert(split_q_d_name);
-                        applied_lora_tensors.insert(split_k_d_name);
-                        applied_lora_tensors.insert(split_v_d_name);
-                        applied_lora_tensors.insert(split_m_d_name);
-                    }
                 }
-                if (lora_up == NULL || lora_down == NULL) {
-                    lora_up_name = lora_pre[type] + key + lora_ups[type] + ".weight";
-                    if (lora_tensors.find(lora_up_name) == lora_tensors.end()) {
-                        if (key == "model_diffusion_model_output_blocks_2_2_conv") {
-                            // fix for some sdxl lora, like lcm-lora-xl
-                            key          = "model_diffusion_model_output_blocks_2_1_conv";
-                            lora_up_name = lora_pre[type] + key + lora_ups[type] + ".weight";
-                        }
+                struct ggml_tensor* updown = NULL;
+                float scale_value          = 1.0f;
+                std::string fk             = lora_pre[type] + key;
+                if (lora_tensors.find(fk + ".hada_w1_a") != lora_tensors.end()) {
+                    // LoHa mode
+
+                    // TODO: split qkv convention for LoHas (is it ever used?)
+                    if (is_qkv_split || is_qkvm_split) {
+                        LOG_ERROR("Split qkv isn't supported for LoHa models.");
+                        break;
+                    }
+                    std::string alpha_name = "";
+
+                    ggml_tensor* hada_1_mid  = NULL;  // tau for tucker decomposition
+                    ggml_tensor* hada_1_up   = NULL;
+                    ggml_tensor* hada_1_down = NULL;
+
+                    ggml_tensor* hada_2_mid  = NULL;  // tau for tucker decomposition
+                    ggml_tensor* hada_2_up   = NULL;
+                    ggml_tensor* hada_2_down = NULL;
+
+                    std::string hada_1_mid_name  = "";
+                    std::string hada_1_down_name = "";
+                    std::string hada_1_up_name   = "";
+
+                    std::string hada_2_mid_name  = "";
+                    std::string hada_2_down_name = "";
+                    std::string hada_2_up_name   = "";
+
+
+                    hada_1_down_name = fk + ".hada_w1_b";
+                    hada_1_up_name   = fk + ".hada_w1_a";
+                    hada_1_mid_name  = fk + ".hada_t1";
+                    if (lora_tensors.find(hada_1_down_name) != lora_tensors.end()) {
+                        hada_1_down = to_f32(compute_ctx, lora_tensors[hada_1_down_name]);
+                    }
+                    if (lora_tensors.find(hada_1_up_name) != lora_tensors.end()) {
+                        hada_1_up = to_f32(compute_ctx, lora_tensors[hada_1_up_name]);
+                    }
+                    if (lora_tensors.find(hada_1_mid_name) != lora_tensors.end()) {
+                        hada_1_mid = to_f32(compute_ctx, lora_tensors[hada_1_mid_name]);
+                        applied_lora_tensors.insert(hada_1_mid_name);
+                        hada_1_up = ggml_cont(compute_ctx, ggml_transpose(compute_ctx, hada_1_up));
                     }
 
-                    lora_down_name = lora_pre[type] + key + lora_downs[type] + ".weight";
-                    alpha_name     = lora_pre[type] + key + ".alpha";
-                    scale_name     = lora_pre[type] + key + ".scale";
-
-                    if (lora_tensors.find(lora_up_name) != lora_tensors.end()) {
-                        lora_up = lora_tensors[lora_up_name];
+                    hada_2_down_name = fk + ".hada_w2_b";
+                    hada_2_up_name   = fk + ".hada_w2_a";
+                    hada_2_mid_name  = fk + ".hada_t2";
+                    if (lora_tensors.find(hada_2_down_name) != lora_tensors.end()) {
+                        hada_2_down = to_f32(compute_ctx, lora_tensors[hada_2_down_name]);
+                    }
+                    if (lora_tensors.find(hada_2_up_name) != lora_tensors.end()) {
+                        hada_2_up = to_f32(compute_ctx, lora_tensors[hada_2_up_name]);
+                    }
+                    if (lora_tensors.find(hada_2_mid_name) != lora_tensors.end()) {
+                        hada_2_mid = to_f32(compute_ctx, lora_tensors[hada_2_mid_name]);
+                        applied_lora_tensors.insert(hada_2_mid_name);
+                        hada_2_up = ggml_cont(compute_ctx, ggml_transpose(compute_ctx, hada_2_up));
                     }
 
-                    if (lora_tensors.find(lora_down_name) != lora_tensors.end()) {
-                        lora_down = lora_tensors[lora_down_name];
-                    }
-                    applied_lora_tensors.insert(lora_up_name);
-                    applied_lora_tensors.insert(lora_down_name);
+                    alpha_name = fk + ".alpha";
+
+                    applied_lora_tensors.insert(hada_1_down_name);
+                    applied_lora_tensors.insert(hada_1_up_name);
+                    applied_lora_tensors.insert(hada_2_down_name);
+                    applied_lora_tensors.insert(hada_2_up_name);
+
                     applied_lora_tensors.insert(alpha_name);
-                    applied_lora_tensors.insert(scale_name);
-                }
+                    if (hada_1_up == NULL || hada_1_down == NULL || hada_2_up == NULL || hada_2_down == NULL) {
+                        continue;
+                    }
 
-                if (lora_up == NULL || lora_down == NULL) {
-                    continue;
-                }
-                // calc_scale
-                int64_t dim       = lora_down->ne[ggml_n_dims(lora_down) - 1];
-                float scale_value = 1.0f;
-                if (lora_tensors.find(scale_name) != lora_tensors.end()) {
-                    scale_value = ggml_backend_tensor_get_f32(lora_tensors[scale_name]);
-                } else if (lora_tensors.find(alpha_name) != lora_tensors.end()) {
-                    float alpha = ggml_backend_tensor_get_f32(lora_tensors[alpha_name]);
-                    scale_value = alpha / dim;
+                    struct ggml_tensor* updown_1 = ggml_merge_lora(compute_ctx, hada_1_down, hada_1_up, hada_1_mid);
+                    struct ggml_tensor* updown_2 = ggml_merge_lora(compute_ctx, hada_2_down, hada_2_up, hada_2_mid);
+                    updown                       = ggml_mul_inplace(compute_ctx, updown_1, updown_2);
+
+                    // calc_scale
+                    // TODO: .dora_scale?
+                    int64_t rank = hada_1_down->ne[ggml_n_dims(hada_1_down) - 1];
+                    if (lora_tensors.find(alpha_name) != lora_tensors.end()) {
+                        float alpha = ggml_backend_tensor_get_f32(lora_tensors[alpha_name]);
+                        scale_value = alpha / rank;
+                    }
+                } else if (lora_tensors.find(fk + ".lokr_w1") != lora_tensors.end() || lora_tensors.find(fk + ".lokr_w1_a") != lora_tensors.end()) {
+                    // LoKr mode
+
+                    // TODO: split qkv convention for LoKrs (is it ever used?)
+                    if (is_qkv_split || is_qkvm_split) {
+                        LOG_ERROR("Split qkv isn't supported for LoKr models.");
+                        break;
+                    }
+
+                    std::string alpha_name = fk + ".alpha";
+
+                    ggml_tensor* lokr_w1 = NULL;
+                    ggml_tensor* lokr_w2 = NULL;
+
+                    std::string lokr_w1_name = "";
+                    std::string lokr_w2_name = "";
+
+                    lokr_w1_name = fk + ".lokr_w1";
+                    lokr_w2_name = fk + ".lokr_w2";
+
+                    if (lora_tensors.find(lokr_w1_name) != lora_tensors.end()) {
+                        lokr_w1 = to_f32(compute_ctx, lora_tensors[lokr_w1_name]);
+                        applied_lora_tensors.insert(lokr_w1_name);
+                    } else {
+                        ggml_tensor* down     = NULL;
+                        ggml_tensor* up       = NULL;
+                        std::string down_name = lokr_w1_name + "_b";
+                        std::string up_name   = lokr_w1_name + "_a";
+                        if (lora_tensors.find(down_name) != lora_tensors.end()) {
+                            // w1 should not be low rank normally, sometimes w1 and w2 are swapped
+                            down = to_f32(compute_ctx, lora_tensors[down_name]);
+                            applied_lora_tensors.insert(down_name);
+
+                            int64_t rank = down->ne[ggml_n_dims(down) - 1];
+                            if (lora_tensors.find(alpha_name) != lora_tensors.end()) {
+                                float alpha = ggml_backend_tensor_get_f32(lora_tensors[alpha_name]);
+                                scale_value = alpha / rank;
+                            }
+                        }
+                        if (lora_tensors.find(up_name) != lora_tensors.end()) {
+                            up = to_f32(compute_ctx, lora_tensors[up_name]);
+                            applied_lora_tensors.insert(up_name);
+                        }
+                        lokr_w1 = ggml_merge_lora(compute_ctx, down, up);
+                    }
+                    if (lora_tensors.find(lokr_w2_name) != lora_tensors.end()) {
+                        lokr_w2 = to_f32(compute_ctx, lora_tensors[lokr_w2_name]);
+                        applied_lora_tensors.insert(lokr_w2_name);
+                    } else {
+                        ggml_tensor* down     = NULL;
+                        ggml_tensor* up       = NULL;
+                        std::string down_name = lokr_w2_name + "_b";
+                        std::string up_name   = lokr_w2_name + "_a";
+                        if (lora_tensors.find(down_name) != lora_tensors.end()) {
+                            down = to_f32(compute_ctx, lora_tensors[down_name]);
+                            applied_lora_tensors.insert(down_name);
+
+                            int64_t rank = down->ne[ggml_n_dims(down) - 1];
+                            if (lora_tensors.find(alpha_name) != lora_tensors.end()) {
+                                float alpha = ggml_backend_tensor_get_f32(lora_tensors[alpha_name]);
+                                scale_value = alpha / rank;
+                            }
+                        }
+                        if (lora_tensors.find(up_name) != lora_tensors.end()) {
+                            up = to_f32(compute_ctx, lora_tensors[up_name]);
+                            applied_lora_tensors.insert(up_name);
+                        }
+                        lokr_w2 = ggml_merge_lora(compute_ctx, down, up);
+                    }
+                    
+                    // Technically it might be unused, but I believe it's the expected behavior
+                    applied_lora_tensors.insert(alpha_name);
+
+                    updown = ggml_kronecker(compute_ctx, lokr_w1, lokr_w2);
+
+                } else {
+                    // LoRA mode
+                    ggml_tensor* lora_mid  = NULL;  // tau for tucker decomposition
+                    ggml_tensor* lora_up   = NULL;
+                    ggml_tensor* lora_down = NULL;
+
+                    std::string alpha_name         = "";
+                    std::string scale_name         = "";
+                    std::string split_q_scale_name = "";
+                    std::string lora_mid_name      = "";
+                    std::string lora_down_name     = "";
+                    std::string lora_up_name       = "";
+
+                    if (is_qkv_split) {
+                        std::string suffix  = "";
+                        auto split_q_d_name = fk + "q" + suffix + lora_downs[type] + ".weight";
+
+                        if (lora_tensors.find(split_q_d_name) == lora_tensors.end()) {
+                            suffix         = "_proj";
+                            split_q_d_name = fk + "q" + suffix + lora_downs[type] + ".weight";
+                        }
+                        if (lora_tensors.find(split_q_d_name) != lora_tensors.end()) {
+                            // print_ggml_tensor(it.second, true);  //[3072, 21504, 1, 1]
+                            // find qkv and mlp up parts in LoRA model
+                            auto split_k_d_name = fk + "k" + suffix + lora_downs[type] + ".weight";
+                            auto split_v_d_name = fk + "v" + suffix + lora_downs[type] + ".weight";
+
+                            auto split_q_u_name = fk + "q" + suffix + lora_ups[type] + ".weight";
+                            auto split_k_u_name = fk + "k" + suffix + lora_ups[type] + ".weight";
+                            auto split_v_u_name = fk + "v" + suffix + lora_ups[type] + ".weight";
+
+                            auto split_q_scale_name = fk + "q" + suffix + ".scale";
+                            auto split_k_scale_name = fk + "k" + suffix + ".scale";
+                            auto split_v_scale_name = fk + "v" + suffix + ".scale";
+
+                            auto split_q_alpha_name = fk + "q" + suffix + ".alpha";
+                            auto split_k_alpha_name = fk + "k" + suffix + ".alpha";
+                            auto split_v_alpha_name = fk + "v" + suffix + ".alpha";
+
+                            ggml_tensor* lora_q_down = NULL;
+                            ggml_tensor* lora_q_up   = NULL;
+                            ggml_tensor* lora_k_down = NULL;
+                            ggml_tensor* lora_k_up   = NULL;
+                            ggml_tensor* lora_v_down = NULL;
+                            ggml_tensor* lora_v_up   = NULL;
+
+                            lora_q_down = to_f32(compute_ctx, lora_tensors[split_q_d_name]);
+
+                            if (lora_tensors.find(split_q_u_name) != lora_tensors.end()) {
+                                lora_q_up = to_f32(compute_ctx, lora_tensors[split_q_u_name]);
+                            }
+
+                            if (lora_tensors.find(split_k_d_name) != lora_tensors.end()) {
+                                lora_k_down = to_f32(compute_ctx, lora_tensors[split_k_d_name]);
+                            }
+
+                            if (lora_tensors.find(split_k_u_name) != lora_tensors.end()) {
+                                lora_k_up = to_f32(compute_ctx, lora_tensors[split_k_u_name]);
+                            }
+
+                            if (lora_tensors.find(split_v_d_name) != lora_tensors.end()) {
+                                lora_v_down = to_f32(compute_ctx, lora_tensors[split_v_d_name]);
+                            }
+
+                            if (lora_tensors.find(split_v_u_name) != lora_tensors.end()) {
+                                lora_v_up = to_f32(compute_ctx, lora_tensors[split_v_u_name]);
+                            }
+
+                            float q_rank = lora_q_up->ne[0];
+                            float k_rank = lora_k_up->ne[0];
+                            float v_rank = lora_v_up->ne[0];
+
+                            float lora_q_scale = 1;
+                            float lora_k_scale = 1;
+                            float lora_v_scale = 1;
+
+                            if (lora_tensors.find(split_q_scale_name) != lora_tensors.end()) {
+                                lora_q_scale = ggml_backend_tensor_get_f32(lora_tensors[split_q_scale_name]);
+                                applied_lora_tensors.insert(split_q_scale_name);
+                            }
+                            if (lora_tensors.find(split_k_scale_name) != lora_tensors.end()) {
+                                lora_k_scale = ggml_backend_tensor_get_f32(lora_tensors[split_k_scale_name]);
+                                applied_lora_tensors.insert(split_k_scale_name);
+                            }
+                            if (lora_tensors.find(split_v_scale_name) != lora_tensors.end()) {
+                                lora_v_scale = ggml_backend_tensor_get_f32(lora_tensors[split_v_scale_name]);
+                                applied_lora_tensors.insert(split_v_scale_name);
+                            }
+
+                            if (lora_tensors.find(split_q_alpha_name) != lora_tensors.end()) {
+                                float lora_q_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_q_alpha_name]);
+                                applied_lora_tensors.insert(split_q_alpha_name);
+                                lora_q_scale = lora_q_alpha / q_rank;
+                            }
+                            if (lora_tensors.find(split_k_alpha_name) != lora_tensors.end()) {
+                                float lora_k_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_k_alpha_name]);
+                                applied_lora_tensors.insert(split_k_alpha_name);
+                                lora_k_scale = lora_k_alpha / k_rank;
+                            }
+                            if (lora_tensors.find(split_v_alpha_name) != lora_tensors.end()) {
+                                float lora_v_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_v_alpha_name]);
+                                applied_lora_tensors.insert(split_v_alpha_name);
+                                lora_v_scale = lora_v_alpha / v_rank;
+                            }
+
+                            ggml_scale_inplace(compute_ctx, lora_q_down, lora_q_scale);
+                            ggml_scale_inplace(compute_ctx, lora_k_down, lora_k_scale);
+                            ggml_scale_inplace(compute_ctx, lora_v_down, lora_v_scale);
+
+                            // print_ggml_tensor(lora_q_down, true);  //[3072, R, 1, 1]
+                            // print_ggml_tensor(lora_k_down, true);  //[3072, R, 1, 1]
+                            // print_ggml_tensor(lora_v_down, true);  //[3072, R, 1, 1]
+                            // print_ggml_tensor(lora_q_up, true);    //[R, 3072, 1, 1]
+                            // print_ggml_tensor(lora_k_up, true);    //[R, 3072, 1, 1]
+                            // print_ggml_tensor(lora_v_up, true);    //[R, 3072, 1, 1]
+
+                            // these need to be stitched together this way:
+                            //                          |q_up,0   ,0   |
+                            //                          |0   ,k_up,0   |
+                            //                          |0   ,0   ,v_up|
+                            // (q_down,k_down,v_down) . (q   ,k   ,v)
+
+                            // up_concat will be [9216, R*3, 1, 1]
+                            // down_concat will be [R*3, 3072, 1, 1]
+                            ggml_tensor* lora_down_concat = ggml_concat(compute_ctx, ggml_concat(compute_ctx, lora_q_down, lora_k_down, 1), lora_v_down, 1);
+
+                            ggml_tensor* z = ggml_dup_tensor(compute_ctx, lora_q_up);
+                            ggml_scale(compute_ctx, z, 0);
+                            ggml_tensor* zz = ggml_concat(compute_ctx, z, z, 1);
+
+                            ggml_tensor* q_up = ggml_concat(compute_ctx, lora_q_up, zz, 1);
+                            ggml_tensor* k_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, z, lora_k_up, 1), z, 1);
+                            ggml_tensor* v_up = ggml_concat(compute_ctx, zz, lora_v_up, 1);
+                            // print_ggml_tensor(q_up, true);  //[R, 9216, 1, 1]
+                            // print_ggml_tensor(k_up, true);  //[R, 9216, 1, 1]
+                            // print_ggml_tensor(v_up, true);  //[R, 9216, 1, 1]
+                            ggml_tensor* lora_up_concat = ggml_concat(compute_ctx, ggml_concat(compute_ctx, q_up, k_up, 0), v_up, 0);
+                            // print_ggml_tensor(lora_up_concat, true);  //[R*3, 9216, 1, 1]
+
+                            lora_down = ggml_cont(compute_ctx, lora_down_concat);
+                            lora_up   = ggml_cont(compute_ctx, lora_up_concat);
+
+                            applied_lora_tensors.insert(split_q_u_name);
+                            applied_lora_tensors.insert(split_k_u_name);
+                            applied_lora_tensors.insert(split_v_u_name);
+
+                            applied_lora_tensors.insert(split_q_d_name);
+                            applied_lora_tensors.insert(split_k_d_name);
+                            applied_lora_tensors.insert(split_v_d_name);
+                        }
+                    } else if (is_qkvm_split) {
+                        auto split_q_d_name = fk + "attn.to_q" + lora_downs[type] + ".weight";
+                        if (lora_tensors.find(split_q_d_name) != lora_tensors.end()) {
+                            // print_ggml_tensor(it.second, true);  //[3072, 21504, 1, 1]
+                            // find qkv and mlp up parts in LoRA model
+                            auto split_k_d_name = fk + "attn.to_k" + lora_downs[type] + ".weight";
+                            auto split_v_d_name = fk + "attn.to_v" + lora_downs[type] + ".weight";
+
+                            auto split_q_u_name = fk + "attn.to_q" + lora_ups[type] + ".weight";
+                            auto split_k_u_name = fk + "attn.to_k" + lora_ups[type] + ".weight";
+                            auto split_v_u_name = fk + "attn.to_v" + lora_ups[type] + ".weight";
+
+                            auto split_m_d_name = fk + "proj_mlp" + lora_downs[type] + ".weight";
+                            auto split_m_u_name = fk + "proj_mlp" + lora_ups[type] + ".weight";
+
+                            auto split_q_scale_name = fk + "attn.to_q" + ".scale";
+                            auto split_k_scale_name = fk + "attn.to_k" + ".scale";
+                            auto split_v_scale_name = fk + "attn.to_v" + ".scale";
+                            auto split_m_scale_name = fk + "proj_mlp" + ".scale";
+
+                            auto split_q_alpha_name = fk + "attn.to_q" + ".alpha";
+                            auto split_k_alpha_name = fk + "attn.to_k" + ".alpha";
+                            auto split_v_alpha_name = fk + "attn.to_v" + ".alpha";
+                            auto split_m_alpha_name = fk + "proj_mlp" + ".alpha";
+
+                            ggml_tensor* lora_q_down = NULL;
+                            ggml_tensor* lora_q_up   = NULL;
+                            ggml_tensor* lora_k_down = NULL;
+                            ggml_tensor* lora_k_up   = NULL;
+                            ggml_tensor* lora_v_down = NULL;
+                            ggml_tensor* lora_v_up   = NULL;
+
+                            ggml_tensor* lora_m_down = NULL;
+                            ggml_tensor* lora_m_up   = NULL;
+
+                            lora_q_up = to_f32(compute_ctx, lora_tensors[split_q_u_name]);
+
+                            if (lora_tensors.find(split_q_d_name) != lora_tensors.end()) {
+                                lora_q_down = to_f32(compute_ctx, lora_tensors[split_q_d_name]);
+                            }
+
+                            if (lora_tensors.find(split_q_u_name) != lora_tensors.end()) {
+                                lora_q_up = to_f32(compute_ctx, lora_tensors[split_q_u_name]);
+                            }
+
+                            if (lora_tensors.find(split_k_d_name) != lora_tensors.end()) {
+                                lora_k_down = to_f32(compute_ctx, lora_tensors[split_k_d_name]);
+                            }
+
+                            if (lora_tensors.find(split_k_u_name) != lora_tensors.end()) {
+                                lora_k_up = to_f32(compute_ctx, lora_tensors[split_k_u_name]);
+                            }
+
+                            if (lora_tensors.find(split_v_d_name) != lora_tensors.end()) {
+                                lora_v_down = to_f32(compute_ctx, lora_tensors[split_v_d_name]);
+                            }
+
+                            if (lora_tensors.find(split_v_u_name) != lora_tensors.end()) {
+                                lora_v_up = to_f32(compute_ctx, lora_tensors[split_v_u_name]);
+                            }
+
+                            if (lora_tensors.find(split_m_d_name) != lora_tensors.end()) {
+                                lora_m_down = to_f32(compute_ctx, lora_tensors[split_m_d_name]);
+                            }
+
+                            if (lora_tensors.find(split_m_u_name) != lora_tensors.end()) {
+                                lora_m_up = to_f32(compute_ctx, lora_tensors[split_m_u_name]);
+                            }
+
+                            float q_rank = lora_q_up->ne[0];
+                            float k_rank = lora_k_up->ne[0];
+                            float v_rank = lora_v_up->ne[0];
+                            float m_rank = lora_v_up->ne[0];
+
+                            float lora_q_scale = 1;
+                            float lora_k_scale = 1;
+                            float lora_v_scale = 1;
+                            float lora_m_scale = 1;
+
+                            if (lora_tensors.find(split_q_scale_name) != lora_tensors.end()) {
+                                lora_q_scale = ggml_backend_tensor_get_f32(lora_tensors[split_q_scale_name]);
+                                applied_lora_tensors.insert(split_q_scale_name);
+                            }
+                            if (lora_tensors.find(split_k_scale_name) != lora_tensors.end()) {
+                                lora_k_scale = ggml_backend_tensor_get_f32(lora_tensors[split_k_scale_name]);
+                                applied_lora_tensors.insert(split_k_scale_name);
+                            }
+                            if (lora_tensors.find(split_v_scale_name) != lora_tensors.end()) {
+                                lora_v_scale = ggml_backend_tensor_get_f32(lora_tensors[split_v_scale_name]);
+                                applied_lora_tensors.insert(split_v_scale_name);
+                            }
+                            if (lora_tensors.find(split_m_scale_name) != lora_tensors.end()) {
+                                lora_m_scale = ggml_backend_tensor_get_f32(lora_tensors[split_m_scale_name]);
+                                applied_lora_tensors.insert(split_m_scale_name);
+                            }
+
+                            if (lora_tensors.find(split_q_alpha_name) != lora_tensors.end()) {
+                                float lora_q_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_q_alpha_name]);
+                                applied_lora_tensors.insert(split_q_alpha_name);
+                                lora_q_scale = lora_q_alpha / q_rank;
+                            }
+                            if (lora_tensors.find(split_k_alpha_name) != lora_tensors.end()) {
+                                float lora_k_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_k_alpha_name]);
+                                applied_lora_tensors.insert(split_k_alpha_name);
+                                lora_k_scale = lora_k_alpha / k_rank;
+                            }
+                            if (lora_tensors.find(split_v_alpha_name) != lora_tensors.end()) {
+                                float lora_v_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_v_alpha_name]);
+                                applied_lora_tensors.insert(split_v_alpha_name);
+                                lora_v_scale = lora_v_alpha / v_rank;
+                            }
+                            if (lora_tensors.find(split_m_alpha_name) != lora_tensors.end()) {
+                                float lora_m_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_m_alpha_name]);
+                                applied_lora_tensors.insert(split_m_alpha_name);
+                                lora_m_scale = lora_m_alpha / m_rank;
+                            }
+
+                            ggml_scale_inplace(compute_ctx, lora_q_down, lora_q_scale);
+                            ggml_scale_inplace(compute_ctx, lora_k_down, lora_k_scale);
+                            ggml_scale_inplace(compute_ctx, lora_v_down, lora_v_scale);
+                            ggml_scale_inplace(compute_ctx, lora_m_down, lora_m_scale);
+
+                            // print_ggml_tensor(lora_q_down, true);  //[3072, R, 1, 1]
+                            // print_ggml_tensor(lora_k_down, true);  //[3072, R, 1, 1]
+                            // print_ggml_tensor(lora_v_down, true);  //[3072, R, 1, 1]
+                            // print_ggml_tensor(lora_m_down, true);  //[3072, R, 1, 1]
+                            // print_ggml_tensor(lora_q_up, true);  //[R, 3072, 1, 1]
+                            // print_ggml_tensor(lora_k_up, true);  //[R, 3072, 1, 1]
+                            // print_ggml_tensor(lora_v_up, true);  //[R, 3072, 1, 1]
+                            // print_ggml_tensor(lora_m_up, true);  //[R, 12288, 1, 1]
+
+                            // these need to be stitched together this way:
+                            //                                 |q_up,0   ,0   ,0   |
+                            //                                 |0   ,k_up,0   ,0   |
+                            //                                 |0   ,0   ,v_up,0   |
+                            //                                 |0   ,0   ,0   ,m_up|
+                            // (q_down,k_down,v_down,m_down) . (q   ,k   ,v   ,m)
+
+                            // up_concat will be [21504, R*4, 1, 1]
+                            // down_concat will be [R*4, 3072, 1, 1]
+
+                            ggml_tensor* lora_down_concat = ggml_concat(compute_ctx, ggml_concat(compute_ctx, lora_q_down, lora_k_down, 1), ggml_concat(compute_ctx, lora_v_down, lora_m_down, 1), 1);
+                            // print_ggml_tensor(lora_down_concat, true);  //[3072, R*4, 1, 1]
+
+                            // this also means that if rank is bigger than 672, it is less memory efficient to do it this way (should be fine)
+                            // print_ggml_tensor(lora_q_up, true);  //[3072, R, 1, 1]
+                            ggml_tensor* z     = ggml_dup_tensor(compute_ctx, lora_q_up);
+                            ggml_tensor* mlp_z = ggml_dup_tensor(compute_ctx, lora_m_up);
+                            ggml_scale(compute_ctx, z, 0);
+                            ggml_scale(compute_ctx, mlp_z, 0);
+                            ggml_tensor* zz = ggml_concat(compute_ctx, z, z, 1);
+
+                            ggml_tensor* q_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, lora_q_up, zz, 1), mlp_z, 1);
+                            ggml_tensor* k_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, z, lora_k_up, 1), ggml_concat(compute_ctx, z, mlp_z, 1), 1);
+                            ggml_tensor* v_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, zz, lora_v_up, 1), mlp_z, 1);
+                            ggml_tensor* m_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, zz, z, 1), lora_m_up, 1);
+                            // print_ggml_tensor(q_up, true);  //[R, 21504, 1, 1]
+                            // print_ggml_tensor(k_up, true);  //[R, 21504, 1, 1]
+                            // print_ggml_tensor(v_up, true);  //[R, 21504, 1, 1]
+                            // print_ggml_tensor(m_up, true);  //[R, 21504, 1, 1]
+
+                            ggml_tensor* lora_up_concat = ggml_concat(compute_ctx, ggml_concat(compute_ctx, q_up, k_up, 0), ggml_concat(compute_ctx, v_up, m_up, 0), 0);
+                            // print_ggml_tensor(lora_up_concat, true);  //[R*4, 21504, 1, 1]
+
+                            lora_down = ggml_cont(compute_ctx, lora_down_concat);
+                            lora_up   = ggml_cont(compute_ctx, lora_up_concat);
+
+                            applied_lora_tensors.insert(split_q_u_name);
+                            applied_lora_tensors.insert(split_k_u_name);
+                            applied_lora_tensors.insert(split_v_u_name);
+                            applied_lora_tensors.insert(split_m_u_name);
+
+                            applied_lora_tensors.insert(split_q_d_name);
+                            applied_lora_tensors.insert(split_k_d_name);
+                            applied_lora_tensors.insert(split_v_d_name);
+                            applied_lora_tensors.insert(split_m_d_name);
+                        }
+                    } else {
+                        lora_up_name   = fk + lora_ups[type] + ".weight";
+                        lora_down_name = fk + lora_downs[type] + ".weight";
+                        lora_mid_name  = fk + ".lora_mid.weight";
+
+                        alpha_name = fk + ".alpha";
+                        scale_name = fk + ".scale";
+
+                        if (lora_tensors.find(lora_up_name) != lora_tensors.end()) {
+                            lora_up = to_f32(compute_ctx, lora_tensors[lora_up_name]);
+                        }
+
+                        if (lora_tensors.find(lora_down_name) != lora_tensors.end()) {
+                            lora_down = to_f32(compute_ctx, lora_tensors[lora_down_name]);
+                        }
+
+                        if (lora_tensors.find(lora_mid_name) != lora_tensors.end()) {
+                            lora_mid = to_f32(compute_ctx, lora_tensors[lora_mid_name]);
+                            applied_lora_tensors.insert(lora_mid_name);
+                        }
+
+                        applied_lora_tensors.insert(lora_up_name);
+                        applied_lora_tensors.insert(lora_down_name);
+                        applied_lora_tensors.insert(alpha_name);
+                        applied_lora_tensors.insert(scale_name);
+                    }
+
+                    if (lora_up == NULL || lora_down == NULL) {
+                        continue;
+                    }
+                    // calc_scale
+                    // TODO: .dora_scale?
+                    int64_t rank = lora_down->ne[ggml_n_dims(lora_down) - 1];
+                    if (lora_tensors.find(scale_name) != lora_tensors.end()) {
+                        scale_value = ggml_backend_tensor_get_f32(lora_tensors[scale_name]);
+                    } else if (lora_tensors.find(alpha_name) != lora_tensors.end()) {
+                        float alpha = ggml_backend_tensor_get_f32(lora_tensors[alpha_name]);
+                        scale_value = alpha / rank;
+                    }
+
+                    updown = ggml_merge_lora(compute_ctx, lora_down, lora_up, lora_mid);
                 }
                 scale_value *= multiplier;
-
-                // flat lora tensors to multiply it
-                int64_t lora_up_rows   = lora_up->ne[ggml_n_dims(lora_up) - 1];
-                lora_up                = ggml_reshape_2d(compute_ctx, lora_up, ggml_nelements(lora_up) / lora_up_rows, lora_up_rows);
-                int64_t lora_down_rows = lora_down->ne[ggml_n_dims(lora_down) - 1];
-                lora_down              = ggml_reshape_2d(compute_ctx, lora_down, ggml_nelements(lora_down) / lora_down_rows, lora_down_rows);
-
-                // ggml_mul_mat requires tensor b transposed
-                lora_down                  = ggml_cont(compute_ctx, ggml_transpose(compute_ctx, lora_down));
-                struct ggml_tensor* updown = ggml_mul_mat(compute_ctx, lora_up, lora_down);
-                updown                     = ggml_cont(compute_ctx, ggml_transpose(compute_ctx, updown));
-                updown                     = ggml_reshape(compute_ctx, updown, weight);
+                updown = ggml_reshape(compute_ctx, updown, weight);
                 GGML_ASSERT(ggml_nelements(updown) == ggml_nelements(weight));
                 updown = ggml_scale_inplace(compute_ctx, updown, scale_value);
                 ggml_tensor* final_weight;

model.cpp

@@ -558,6 +558,26 @@ std::string convert_tensor_name(std::string name) {
     return new_name;
 }
 
+void add_preprocess_tensor_storage_types(std::map<std::string, enum ggml_type>& tensor_storages_types, std::string name, enum ggml_type type) {
+    std::string new_name = convert_tensor_name(name);
+
+    if (new_name.find("cond_stage_model") != std::string::npos && ends_with(new_name, "attn.in_proj_weight")) {
+        size_t prefix_size                                        = new_name.find("attn.in_proj_weight");
+        std::string prefix                                        = new_name.substr(0, prefix_size);
+        tensor_storages_types[prefix + "self_attn.q_proj.weight"] = type;
+        tensor_storages_types[prefix + "self_attn.k_proj.weight"] = type;
+        tensor_storages_types[prefix + "self_attn.v_proj.weight"] = type;
+    } else if (new_name.find("cond_stage_model") != std::string::npos && ends_with(new_name, "attn.in_proj_bias")) {
+        size_t prefix_size                                      = new_name.find("attn.in_proj_bias");
+        std::string prefix                                      = new_name.substr(0, prefix_size);
+        tensor_storages_types[prefix + "self_attn.q_proj.bias"] = type;
+        tensor_storages_types[prefix + "self_attn.k_proj.bias"] = type;
+        tensor_storages_types[prefix + "self_attn.v_proj.bias"] = type;
+    } else {
+        tensor_storages_types[new_name] = type;
+    }
+}
+
 void preprocess_tensor(TensorStorage tensor_storage,
                        std::vector<TensorStorage>& processed_tensor_storages) {
     std::vector<TensorStorage> result;
@@ -927,7 +947,7 @@ bool ModelLoader::init_from_gguf_file(const std::string& file_path, const std::s
         GGML_ASSERT(ggml_nbytes(dummy) == tensor_storage.nbytes());
 
         tensor_storages.push_back(tensor_storage);
-        tensor_storages_types[tensor_storage.name] = tensor_storage.type;
+        add_preprocess_tensor_storage_types(tensor_storages_types, tensor_storage.name, tensor_storage.type);
     }
 
     gguf_free(ctx_gguf_);
@@ -1072,7 +1092,7 @@ bool ModelLoader::init_from_safetensors_file(const std::string& file_path, const
         }
 
         tensor_storages.push_back(tensor_storage);
-        tensor_storages_types[tensor_storage.name] = tensor_storage.type;
+        add_preprocess_tensor_storage_types(tensor_storages_types, tensor_storage.name, tensor_storage.type);
 
         // LOG_DEBUG("%s %s", tensor_storage.to_string().c_str(), dtype.c_str());
     }
@@ -1403,7 +1423,7 @@ bool ModelLoader::parse_data_pkl(uint8_t* buffer,
                         // printf(" ZIP got tensor %s \n ", reader.tensor_storage.name.c_str());
                         reader.tensor_storage.name = prefix + reader.tensor_storage.name;
                         tensor_storages.push_back(reader.tensor_storage);
-                        tensor_storages_types[reader.tensor_storage.name] = reader.tensor_storage.type;
+                        add_preprocess_tensor_storage_types(tensor_storages_types, reader.tensor_storage.name, reader.tensor_storage.type);
 
                         // LOG_DEBUG("%s", reader.tensor_storage.name.c_str());
                         // reset
@@ -1461,10 +1481,10 @@ SDVersion ModelLoader::get_sd_version() {
     TensorStorage token_embedding_weight, input_block_weight;
     bool input_block_checked = false;
 
-    bool has_multiple_encoders   = false;
-    bool is_unet = false;
+    bool has_multiple_encoders = false;
+    bool is_unet               = false;
 
-    bool is_xl = false;
+    bool is_xl   = false;
     bool is_flux = false;
 
 #define found_family (is_xl || is_flux)
@@ -1481,7 +1501,7 @@ SDVersion ModelLoader::get_sd_version() {
             }
             if (tensor_storage.name.find("model.diffusion_model.input_blocks.") != std::string::npos) {
                 is_unet = true;
-                if(has_multiple_encoders){
+                if (has_multiple_encoders) {
                     is_xl = true;
                     if (input_block_checked) {
                         break;
@@ -1490,7 +1510,7 @@ SDVersion ModelLoader::get_sd_version() {
             }
             if (tensor_storage.name.find("conditioner.embedders.1") != std::string::npos || tensor_storage.name.find("cond_stage_model.1") != std::string::npos) {
                 has_multiple_encoders = true;
-                if(is_unet){
+                if (is_unet) {
                     is_xl = true;
                     if (input_block_checked) {
                         break;
@@ -1635,11 +1655,20 @@ ggml_type ModelLoader::get_vae_wtype() {
 void ModelLoader::set_wtype_override(ggml_type wtype, std::string prefix) {
     for (auto& pair : tensor_storages_types) {
         if (prefix.size() < 1 || pair.first.substr(0, prefix.size()) == prefix) {
+            bool found = false;
             for (auto& tensor_storage : tensor_storages) {
-                if (tensor_storage.name == pair.first) {
-                    if (tensor_should_be_converted(tensor_storage, wtype)) {
-                        pair.second = wtype;
+                std::map<std::string, ggml_type> temp;
+                add_preprocess_tensor_storage_types(temp, tensor_storage.name, tensor_storage.type);
+                for (auto& preprocessed_name : temp) {
+                    if (preprocessed_name.first == pair.first) {
+                        if (tensor_should_be_converted(tensor_storage, wtype)) {
+                            pair.second = wtype;
+                        }
+                        found = true;
+                        break;
                     }
+                }
+                if (found) {
                     break;
                 }
             }
@@ -1748,9 +1777,11 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
             }
             return true;
         };
-
+        int tensor_count = 0;
+        int64_t t1       = ggml_time_ms();
         for (auto& tensor_storage : processed_tensor_storages) {
             if (tensor_storage.file_index != file_index) {
+                ++tensor_count;
                 continue;
             }
             ggml_tensor* dst_tensor = NULL;
@@ -1762,6 +1793,7 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
             }
 
             if (dst_tensor == NULL) {
+                ++tensor_count;
                 continue;
             }
 
@@ -1828,6 +1860,9 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
                     ggml_backend_tensor_set(dst_tensor, convert_buffer.data(), 0, ggml_nbytes(dst_tensor));
                 }
             }
+            int64_t t2 = ggml_time_ms();
+            pretty_progress(++tensor_count, processed_tensor_storages.size(), (t2 - t1) / 1000.0f);
+            t1 = t2;
         }
 
         if (zip != NULL) {
@@ -1894,9 +1929,6 @@ bool ModelLoader::load_tensors(std::map<std::string, struct ggml_tensor*>& tenso
         if (pair.first.find("cond_stage_model.transformer.text_model.encoder.layers.23") != std::string::npos) {
             continue;
         }
-        if (pair.first.find("alphas_cumprod") != std::string::npos) {
-            continue;
-        }
 
         if (pair.first.find("alphas_cumprod") != std::string::npos) {
             continue;

model.h

@@ -14,6 +14,7 @@
 #include "ggml.h"
 #include "json.hpp"
 #include "zip.h"
+#include "gguf.h"
 
 #define SD_MAX_DIMS 5
 

stable-diffusion.cpp

@@ -47,6 +47,8 @@ const char* sampling_methods_str[] = {
     "iPNDM",
     "iPNDM_v",
     "LCM",
+    "DDIM \"trailing\"",
+    "TCD"
 };
 
 /*================================================== Helper Functions ================================================*/
@@ -159,7 +161,7 @@ public:
                         bool vae_on_cpu,
                         bool diffusion_flash_attn) {
         use_tiny_autoencoder = taesd_path.size() > 0;
-#ifdef SD_USE_CUBLAS
+#ifdef SD_USE_CUDA
         LOG_DEBUG("Using CUDA backend");
         backend = ggml_backend_cuda_init(0);
 #endif
@@ -360,7 +362,7 @@ public:
                 first_stage_model->alloc_params_buffer();
                 first_stage_model->get_param_tensors(tensors, "first_stage_model");
             } else {
-                tae_first_stage = std::make_shared<TinyAutoEncoder>(backend, model_loader.tensor_storages_types, "decoder.layers", vae_decode_only);
+                tae_first_stage = std::make_shared<TinyAutoEncoder>(backend, model_loader.tensor_storages_types, "decoder.layers", vae_decode_only, version);
             }
             // first_stage_model->get_param_tensors(tensors, "first_stage_model.");
 
@@ -525,6 +527,10 @@ public:
             if (is_using_v_parameterization_for_sd2(ctx, sd_version_is_inpaint(version))) {
                 is_using_v_parameterization = true;
             }
+        } else if (sd_version_is_sdxl(version)) {
+            if (model_loader.tensor_storages_types.find("v_pred") != model_loader.tensor_storages_types.end()) {
+                is_using_v_parameterization = true;
+            }
         } else if (version == VERSION_SVD) {
             // TODO: V_PREDICTION_EDM
             is_using_v_parameterization = true;
@@ -606,7 +612,9 @@ public:
         ggml_set_f32(timesteps, 999);
 
         struct ggml_tensor* concat = is_inpaint ? ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 8, 8, 5, 1) : NULL;
-        ggml_set_f32(concat, 0);
+        if (concat != NULL) {
+            ggml_set_f32(concat, 0);
+        }
 
         int64_t t0              = ggml_time_ms();
         struct ggml_tensor* out = ggml_dup_tensor(work_ctx, x_t);
@@ -667,19 +675,20 @@ public:
         for (auto& kv : lora_state) {
             const std::string& lora_name = kv.first;
             float multiplier             = kv.second;
-
-            if (curr_lora_state.find(lora_name) != curr_lora_state.end()) {
-                float curr_multiplier = curr_lora_state[lora_name];
-                float multiplier_diff = multiplier - curr_multiplier;
-                if (multiplier_diff != 0.f) {
-                    lora_state_diff[lora_name] = multiplier_diff;
-                }
-            } else {
-                lora_state_diff[lora_name] = multiplier;
-            }
+            lora_state_diff[lora_name] += multiplier;
+        }
+        for (auto& kv : curr_lora_state) {
+            const std::string& lora_name = kv.first;
+            float curr_multiplier        = kv.second;
+            lora_state_diff[lora_name] -= curr_multiplier;
+        }
+        
+        size_t rm = lora_state_diff.size() - lora_state.size();
+        if (rm != 0) {
+            LOG_INFO("Attempting to apply %lu LoRAs (removing %lu applied LoRAs)", lora_state.size(), rm);
+        } else {
+            LOG_INFO("Attempting to apply %lu LoRAs", lora_state.size());
         }
-
-        LOG_INFO("Attempting to apply %lu LoRAs", lora_state.size());
 
         for (auto& kv : lora_state_diff) {
             apply_lora(kv.first, kv.second);
@@ -786,6 +795,7 @@ public:
                         float min_cfg,
                         float cfg_scale,
                         float guidance,
+                        float eta,
                         sample_method_t method,
                         const std::vector<float>& sigmas,
                         int start_merge_step,
@@ -981,7 +991,7 @@ public:
             return denoised;
         };
 
-        sample_k_diffusion(method, denoise, work_ctx, x, sigmas, rng);
+        sample_k_diffusion(method, denoise, work_ctx, x, sigmas, rng, eta);
 
         x = denoiser->inverse_noise_scaling(sigmas[sigmas.size() - 1], x);
 
@@ -1187,6 +1197,7 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx,
                            int clip_skip,
                            float cfg_scale,
                            float guidance,
+                           float eta,
                            int width,
                            int height,
                            enum sample_method_t sample_method,
@@ -1450,6 +1461,7 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx,
                                                      cfg_scale,
                                                      cfg_scale,
                                                      guidance,
+                                                     eta,
                                                      sample_method,
                                                      sigmas,
                                                      start_merge_step,
@@ -1515,6 +1527,7 @@ sd_image_t* txt2img(sd_ctx_t* sd_ctx,
                     int clip_skip,
                     float cfg_scale,
                     float guidance,
+                    float eta,
                     int width,
                     int height,
                     enum sample_method_t sample_method,
@@ -1593,6 +1606,7 @@ sd_image_t* txt2img(sd_ctx_t* sd_ctx,
                                                clip_skip,
                                                cfg_scale,
                                                guidance,
+                                               eta,
                                                width,
                                                height,
                                                sample_method,
@@ -1624,6 +1638,7 @@ sd_image_t* img2img(sd_ctx_t* sd_ctx,
                     int clip_skip,
                     float cfg_scale,
                     float guidance,
+                    float eta,
                     int width,
                     int height,
                     sample_method_t sample_method,
@@ -1771,6 +1786,7 @@ sd_image_t* img2img(sd_ctx_t* sd_ctx,
                                                clip_skip,
                                                cfg_scale,
                                                guidance,
+                                               eta,
                                                width,
                                                height,
                                                sample_method,
@@ -1790,7 +1806,7 @@ sd_image_t* img2img(sd_ctx_t* sd_ctx,
 
     size_t t2 = ggml_time_ms();
 
-    LOG_INFO("img2img completed in %.2fs", (t1 - t0) * 1.0f / 1000);
+    LOG_INFO("img2img completed in %.2fs", (t2 - t0) * 1.0f / 1000);
 
     return result_images;
 }
@@ -1884,6 +1900,7 @@ SD_API sd_image_t* img2vid(sd_ctx_t* sd_ctx,
                                                  min_cfg,
                                                  cfg_scale,
                                                  0.f,
+                                                 0.f,
                                                  sample_method,
                                                  sigmas,
                                                  -1,

stable-diffusion.h

@@ -44,6 +44,8 @@ enum sample_method_t {
     IPNDM,
     IPNDM_V,
     LCM,
+    DDIM_TRAILING,
+    TCD,
     N_SAMPLE_METHODS
 };
 
@@ -59,43 +61,46 @@ enum schedule_t {
 
 // same as enum ggml_type
 enum sd_type_t {
-    SD_TYPE_F32  = 0,
-    SD_TYPE_F16  = 1,
-    SD_TYPE_Q4_0 = 2,
-    SD_TYPE_Q4_1 = 3,
+    SD_TYPE_F32     = 0,
+    SD_TYPE_F16     = 1,
+    SD_TYPE_Q4_0    = 2,
+    SD_TYPE_Q4_1    = 3,
     // SD_TYPE_Q4_2 = 4, support has been removed
     // SD_TYPE_Q4_3 = 5, support has been removed
-    SD_TYPE_Q5_0     = 6,
-    SD_TYPE_Q5_1     = 7,
-    SD_TYPE_Q8_0     = 8,
-    SD_TYPE_Q8_1     = 9,
-    SD_TYPE_Q2_K     = 10,
-    SD_TYPE_Q3_K     = 11,
-    SD_TYPE_Q4_K     = 12,
-    SD_TYPE_Q5_K     = 13,
-    SD_TYPE_Q6_K     = 14,
-    SD_TYPE_Q8_K     = 15,
-    SD_TYPE_IQ2_XXS  = 16,
-    SD_TYPE_IQ2_XS   = 17,
-    SD_TYPE_IQ3_XXS  = 18,
-    SD_TYPE_IQ1_S    = 19,
-    SD_TYPE_IQ4_NL   = 20,
-    SD_TYPE_IQ3_S    = 21,
-    SD_TYPE_IQ2_S    = 22,
-    SD_TYPE_IQ4_XS   = 23,
-    SD_TYPE_I8       = 24,
-    SD_TYPE_I16      = 25,
-    SD_TYPE_I32      = 26,
-    SD_TYPE_I64      = 27,
-    SD_TYPE_F64      = 28,
-    SD_TYPE_IQ1_M    = 29,
-    SD_TYPE_BF16     = 30,
-    SD_TYPE_Q4_0_4_4 = 31,
-    SD_TYPE_Q4_0_4_8 = 32,
-    SD_TYPE_Q4_0_8_8 = 33,
-    SD_TYPE_TQ1_0    = 34,
-    SD_TYPE_TQ2_0    = 35,
-    SD_TYPE_COUNT,
+    SD_TYPE_Q5_0    = 6,
+    SD_TYPE_Q5_1    = 7,
+    SD_TYPE_Q8_0    = 8,
+    SD_TYPE_Q8_1    = 9,
+    SD_TYPE_Q2_K    = 10,
+    SD_TYPE_Q3_K    = 11,
+    SD_TYPE_Q4_K    = 12,
+    SD_TYPE_Q5_K    = 13,
+    SD_TYPE_Q6_K    = 14,
+    SD_TYPE_Q8_K    = 15,
+    SD_TYPE_IQ2_XXS = 16,
+    SD_TYPE_IQ2_XS  = 17,
+    SD_TYPE_IQ3_XXS = 18,
+    SD_TYPE_IQ1_S   = 19,
+    SD_TYPE_IQ4_NL  = 20,
+    SD_TYPE_IQ3_S   = 21,
+    SD_TYPE_IQ2_S   = 22,
+    SD_TYPE_IQ4_XS  = 23,
+    SD_TYPE_I8      = 24,
+    SD_TYPE_I16     = 25,
+    SD_TYPE_I32     = 26,
+    SD_TYPE_I64     = 27,
+    SD_TYPE_F64     = 28,
+    SD_TYPE_IQ1_M   = 29,
+    SD_TYPE_BF16    = 30,
+    // SD_TYPE_Q4_0_4_4 = 31, support has been removed from gguf files
+    // SD_TYPE_Q4_0_4_8 = 32,
+    // SD_TYPE_Q4_0_8_8 = 33,
+    SD_TYPE_TQ1_0   = 34,
+    SD_TYPE_TQ2_0   = 35,
+    // SD_TYPE_IQ4_NL_4_4 = 36,
+    // SD_TYPE_IQ4_NL_4_8 = 37,
+    // SD_TYPE_IQ4_NL_8_8 = 38,
+    SD_TYPE_COUNT   = 39,
 };
 
 SD_API const char* sd_type_name(enum sd_type_t type);
@@ -155,6 +160,7 @@ SD_API sd_image_t* txt2img(sd_ctx_t* sd_ctx,
                            int clip_skip,
                            float cfg_scale,
                            float guidance,
+                           float eta,
                            int width,
                            int height,
                            enum sample_method_t sample_method,
@@ -180,6 +186,7 @@ SD_API sd_image_t* img2img(sd_ctx_t* sd_ctx,
                            int clip_skip,
                            float cfg_scale,
                            float guidance,
+                           float eta,
                            int width,
                            int height,
                            enum sample_method_t sample_method,

tae.hpp

@@ -62,7 +62,8 @@ class TinyEncoder : public UnaryBlock {
     int num_blocks  = 3;
 
 public:
-    TinyEncoder() {
+    TinyEncoder(int z_channels = 4)
+        : z_channels(z_channels) {
         int index                       = 0;
         blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, channels, {3, 3}, {1, 1}, {1, 1}));
         blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new TAEBlock(channels, channels));
@@ -106,7 +107,10 @@ class TinyDecoder : public UnaryBlock {
     int num_blocks   = 3;
 
 public:
-    TinyDecoder(int index = 0) {
+    TinyDecoder(int z_channels = 4)
+        : z_channels(z_channels) {
+        int index = 0;
+
         blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(z_channels, channels, {3, 3}, {1, 1}, {1, 1}));
         index++;  // nn.ReLU()
 
@@ -163,12 +167,16 @@ protected:
     bool decode_only;
 
 public:
-    TAESD(bool decode_only = true)
+    TAESD(bool decode_only = true, SDVersion version = VERSION_SD1)
         : decode_only(decode_only) {
-        blocks["decoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyDecoder());
+        int z_channels = 4;
+        if (sd_version_is_dit(version)) {
+            z_channels = 16;
+        }
+        blocks["decoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyDecoder(z_channels));
 
         if (!decode_only) {
-            blocks["encoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyEncoder());
+            blocks["encoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyEncoder(z_channels));
         }
     }
 
@@ -190,9 +198,10 @@ struct TinyAutoEncoder : public GGMLRunner {
     TinyAutoEncoder(ggml_backend_t backend,
                     std::map<std::string, enum ggml_type>& tensor_types,
                     const std::string prefix,
-                    bool decoder_only = true)
+                    bool decoder_only = true,
+                    SDVersion version = VERSION_SD1)
         : decode_only(decoder_only),
-          taesd(decode_only),
+          taesd(decoder_only, version),
           GGMLRunner(backend) {
         taesd.init(params_ctx, tensor_types, prefix);
     }

thirdparty/stb_image_write.h

@@ -1412,7 +1412,7 @@ static int stbiw__jpg_processDU(stbi__write_context *s, int *bitBuf, int *bitCnt
    return DU[0];
 }
 
-static int stbi_write_jpg_core(stbi__write_context *s, int width, int height, int comp, const void* data, int quality) {
+static int stbi_write_jpg_core(stbi__write_context *s, int width, int height, int comp, const void* data, int quality, const char* parameters) {
    // Constants that don't pollute global namespace
    static const unsigned char std_dc_luminance_nrcodes[] = {0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0};
    static const unsigned char std_dc_luminance_values[] = {0,1,2,3,4,5,6,7,8,9,10,11};
@@ -1521,6 +1521,20 @@ static int stbi_write_jpg_core(stbi__write_context *s, int width, int height, in
       s->func(s->context, (void*)YTable, sizeof(YTable));
       stbiw__putc(s, 1);
       s->func(s->context, UVTable, sizeof(UVTable));
+
+      // comment block with parameters of generation
+      if(parameters != NULL) {
+         stbiw__putc(s, 0xFF /* comnent */ );
+         stbiw__putc(s, 0xFE /* marker  */ );
+         size_t param_length = std::min(2 + strlen("parameters") + 1 + strlen(parameters) + 1, (size_t) 0xFFFF);
+         stbiw__putc(s, param_length >> 8); // no need to mask, length < 65536
+         stbiw__putc(s, param_length & 0xFF);
+         s->func(s->context, (void*)"parameters", strlen("parameters") + 1); // std::string is zero-terminated
+         s->func(s->context, (void*)parameters, std::min(param_length, (size_t) 65534) - 2 - strlen("parameters") - 1);
+         if(param_length > 65534) stbiw__putc(s, 0); // always zero-terminate for safety
+         if(param_length & 1) stbiw__putc(s, 0xFF); // pad to even length
+      }
+
       s->func(s->context, (void*)head1, sizeof(head1));
       s->func(s->context, (void*)(std_dc_luminance_nrcodes+1), sizeof(std_dc_luminance_nrcodes)-1);
       s->func(s->context, (void*)std_dc_luminance_values, sizeof(std_dc_luminance_values));
@@ -1625,16 +1639,16 @@ STBIWDEF int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x,
 {
    stbi__write_context s = { 0 };
    stbi__start_write_callbacks(&s, func, context);
-   return stbi_write_jpg_core(&s, x, y, comp, (void *) data, quality);
+   return stbi_write_jpg_core(&s, x, y, comp, (void *) data, quality, NULL);
 }
 
 
 #ifndef STBI_WRITE_NO_STDIO
-STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality)
+STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality, const char* parameters)
 {
    stbi__write_context s = { 0 };
    if (stbi__start_write_file(&s,filename)) {
-      int r = stbi_write_jpg_core(&s, x, y, comp, data, quality);
+      int r = stbi_write_jpg_core(&s, x, y, comp, data, quality, parameters);
       stbi__end_write_file(&s);
       return r;
    } else

upscaler.cpp

@@ -15,7 +15,7 @@ struct UpscalerGGML {
     }
 
     bool load_from_file(const std::string& esrgan_path) {
-#ifdef SD_USE_CUBLAS
+#ifdef SD_USE_CUDA
         LOG_DEBUG("Using CUDA backend");
         backend = ggml_backend_cuda_init(0);
 #endif

util.cpp

@@ -113,18 +113,31 @@ std::vector<std::string> get_files_from_dir(const std::string& dir) {
 
     // Find the first file in the directory
     hFind = FindFirstFile(directoryPath, &findFileData);
-
+    bool isAbsolutePath = false;
     // Check if the directory was found
     if (hFind == INVALID_HANDLE_VALUE) {
-        printf("Unable to find directory.\n");
-        return files;
+        printf("Unable to find directory. Try with original path \n");
+
+        char directoryPathAbsolute[MAX_PATH];
+        sprintf(directoryPathAbsolute, "%s*", dir.c_str());
+
+        hFind = FindFirstFile(directoryPathAbsolute, &findFileData);
+        isAbsolutePath = true;
+        if (hFind == INVALID_HANDLE_VALUE) {
+            printf("Absolute path was also wrong.\n");
+            return files;
+        }
     }
 
     // Loop through all files in the directory
     do {
         // Check if the found file is a regular file (not a directory)
         if (!(findFileData.dwFileAttributes & FILE_ATTRIBUTE_DIRECTORY)) {
-            files.push_back(std::string(currentDirectory) + "\\" + dir + "\\" + std::string(findFileData.cFileName));
+            if (isAbsolutePath) {
+                files.push_back(dir + "\\" + std::string(findFileData.cFileName));
+            } else {
+                files.push_back(std::string(currentDirectory) + "\\" + dir + "\\" + std::string(findFileData.cFileName));
+            }
         }
     } while (FindNextFile(hFind, &findFileData) != 0);
 
@@ -348,7 +361,7 @@ void pretty_progress(int step, int steps, float time) {
         }
     }
     progress += "|";
-    printf(time > 1.0f ? "\r%s %i/%i - %.2fs/it" : "\r%s %i/%i - %.2fit/s",
+    printf(time > 1.0f ? "\r%s %i/%i - %.2fs/it" : "\r%s %i/%i - %.2fit/s\033[K",
            progress.c_str(), step, steps,
            time > 1.0f || time == 0 ? time : (1.0f / time));
     fflush(stdout);  // for linux