refactor: simplify logic for saving results (#1149)

* global bool

* reworked circular to global flag

* cleaner implementation of tiling support in sd cpp

* cleaned rope

* working simplified but still need wraps

* Further clean of rope

* resolve flux conflict

* switch to pad op circular only

* Set ggml to most recent

* Revert ggml temp

* Update ggml to most recent

* Revert unneded flux change

* move circular flag to the GGMLRunnerContext

* Pass through circular param in all places where conv is called

* fix of constant and minor cleanup

* Added back --circular option

* Conv2d circular in vae and various models

* Fix temporal padding for qwen image and other vaes

* Z Image circular tiling

* x and y axis seamless only

* First attempt at chroma seamless x and y

* refactor into pure x and y, almost there

* Fix crash on chroma

* Refactor into cleaner variable choices

* Removed redundant set_circular_enabled

* Sync ggml

* simplify circular parameter

* format code

* no need to perform circular pad on the clip

* simplify circular_axes setting

* unify function naming

* remove unnecessary member variables

* simplify rope

---------

Co-authored-by: Phylliida <phylliidadev@gmail.com>
Co-authored-by: leejet <leejet714@gmail.com>

.github/workflows/build.yml

@@ -163,7 +163,7 @@ jobs:
           - build: "avx512"
             defines: "-DGGML_NATIVE=OFF -DGGML_AVX512=ON -DGGML_AVX=ON -DGGML_AVX2=ON -DSD_BUILD_SHARED_LIBS=ON"
           - build: "cuda12"
-            defines: "-DSD_CUDA=ON -DSD_BUILD_SHARED_LIBS=ON -DCMAKE_CUDA_ARCHITECTURES='61;70;75;80;86;89;90;100;120'"
+            defines: "-DSD_CUDA=ON -DSD_BUILD_SHARED_LIBS=ON -DCMAKE_CUDA_ARCHITECTURES='61;70;75;80;86;89;90;100;120' -DCMAKE_CUDA_FLAGS='-Xcudafe \"--diag_suppress=177\" -Xcudafe \"--diag_suppress=550\"'"
           - build: 'vulkan'
             defines: "-DSD_VULKAN=ON -DSD_BUILD_SHARED_LIBS=ON"
     steps:
@@ -191,13 +191,17 @@ jobs:
           Add-Content $env:GITHUB_ENV "VULKAN_SDK=C:\VulkanSDK\${env:VULKAN_VERSION}"
           Add-Content $env:GITHUB_PATH "C:\VulkanSDK\${env:VULKAN_VERSION}\bin"
 
+      - name: Activate MSVC environment
+        id: msvc_dev_cmd
+        uses: ilammy/msvc-dev-cmd@v1
+
       - name: Build
         id: cmake_build
         run: |
           mkdir build
           cd build
-          cmake .. ${{ matrix.defines }}
+          cmake .. -DCMAKE_CXX_FLAGS='/bigobj' -G Ninja -DCMAKE_C_COMPILER=cl.exe -DCMAKE_CXX_COMPILER=cl.exe ${{ matrix.defines }}
-          cmake --build . --config Release
+          cmake --build .
 
       - name: Check AVX512F support
         id: check_avx512f

Dockerfile

@@ -17,6 +17,6 @@ RUN apt-get update && \
     apt-get install --yes --no-install-recommends libgomp1 && \
     apt-get clean
 
-COPY --from=build /sd.cpp/build/bin/sd /sd
+COPY --from=build /sd.cpp/build/bin/sd-cli /sd-cli
 
-ENTRYPOINT [ "/sd" ]
+ENTRYPOINT [ "/sd-cli" ]

Dockerfile.musa

@@ -18,6 +18,6 @@ RUN mkdir build && cd build && \
 
 FROM mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}-amd64 as runtime
 
-COPY --from=build /sd.cpp/build/bin/sd /sd
+COPY --from=build /sd.cpp/build/bin/sd-cli /sd-cli
 
-ENTRYPOINT [ "/sd" ]
+ENTRYPOINT [ "/sd-cli" ]

Dockerfile.sycl

@@ -14,6 +14,6 @@ RUN mkdir build && cd build && \
 
 FROM intel/oneapi-basekit:${SYCL_VERSION}-devel-ubuntu24.04 AS runtime
 
-COPY --from=build /sd.cpp/build/bin/sd /sd
+COPY --from=build /sd.cpp/build/bin/sd-cli /sd-cli
 
-ENTRYPOINT [ "/sd" ]
+ENTRYPOINT [ "/sd-cli" ]

README.md

@@ -52,7 +52,7 @@ API and command-line option may change frequently.***
     - [Ovis-Image](./docs/ovis_image.md)
   - Image Edit Models
     - [FLUX.1-Kontext-dev](./docs/kontext.md)
-    - [Qwen Image Edit/Qwen Image Edit 2509](./docs/qwen_image_edit.md)
+    - [Qwen Image Edit series](./docs/qwen_image_edit.md)
   - Video Models
     - [Wan2.1/Wan2.2](./docs/wan.md)
   - [PhotoMaker](https://github.com/TencentARC/PhotoMaker) support.
@@ -114,7 +114,7 @@ API and command-line option may change frequently.***
 ### Generate an image with just one command
 
 ```sh
-./bin/sd -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat"
+./bin/sd-cli -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat"
 ```
 
 ***For detailed command-line arguments, check out [cli doc](./examples/cli/README.md).***
@@ -132,7 +132,7 @@ If you want to improve performance or reduce VRAM/RAM usage, please refer to [pe
 - [FLUX.1-Kontext-dev](./docs/kontext.md)
 - [Chroma](./docs/chroma.md)
 - [🔥Qwen Image](./docs/qwen_image.md)
-- [🔥Qwen Image Edit/Qwen Image Edit 2509](./docs/qwen_image_edit.md)
+- [🔥Qwen Image Edit series](./docs/qwen_image_edit.md)
 - [🔥Wan2.1/Wan2.2](./docs/wan.md)
 - [🔥Z-Image](./docs/z_image.md)
 - [Ovis-Image](./docs/ovis_image.md)
@@ -143,6 +143,7 @@ If you want to improve performance or reduce VRAM/RAM usage, please refer to [pe
 - [Using TAESD to faster decoding](./docs/taesd.md)
 - [Docker](./docs/docker.md)
 - [Quantization and GGUF](./docs/quantization_and_gguf.md)
+- [Inference acceleration via caching](./docs/caching.md)
 
 ## Bindings
 

cache_dit.hpp

@@ -0,0 +1,975 @@
+#ifndef __CACHE_DIT_HPP__
+#define __CACHE_DIT_HPP__
+
+#include <algorithm>
+#include <cmath>
+#include <limits>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "ggml_extend.hpp"
+
+struct DBCacheConfig {
+    bool enabled                        = false;
+    int Fn_compute_blocks               = 8;
+    int Bn_compute_blocks               = 0;
+    float residual_diff_threshold       = 0.08f;
+    int max_warmup_steps                = 8;
+    int max_cached_steps                = -1;
+    int max_continuous_cached_steps     = -1;
+    float max_accumulated_residual_diff = -1.0f;
+    std::vector<int> steps_computation_mask;
+    bool scm_policy_dynamic = true;
+};
+
+struct TaylorSeerConfig {
+    bool enabled            = false;
+    int n_derivatives       = 1;
+    int max_warmup_steps    = 2;
+    int skip_interval_steps = 1;
+};
+
+struct CacheDitConfig {
+    DBCacheConfig dbcache;
+    TaylorSeerConfig taylorseer;
+    int double_Fn_blocks = -1;
+    int double_Bn_blocks = -1;
+    int single_Fn_blocks = -1;
+    int single_Bn_blocks = -1;
+};
+
+struct TaylorSeerState {
+    int n_derivatives      = 1;
+    int current_step       = -1;
+    int last_computed_step = -1;
+    std::vector<std::vector<float>> dY_prev;
+    std::vector<std::vector<float>> dY_current;
+
+    void init(int n_deriv, size_t hidden_size) {
+        n_derivatives = n_deriv;
+        int order     = n_derivatives + 1;
+        dY_prev.resize(order);
+        dY_current.resize(order);
+        for (int i = 0; i < order; i++) {
+            dY_prev[i].clear();
+            dY_current[i].clear();
+        }
+        current_step       = -1;
+        last_computed_step = -1;
+    }
+
+    void reset() {
+        for (auto& v : dY_prev)
+            v.clear();
+        for (auto& v : dY_current)
+            v.clear();
+        current_step       = -1;
+        last_computed_step = -1;
+    }
+
+    bool can_approximate() const {
+        return last_computed_step >= n_derivatives && !dY_prev.empty() && !dY_prev[0].empty();
+    }
+
+    void update_derivatives(const float* Y, size_t size, int step) {
+        int order = n_derivatives + 1;
+        dY_prev   = dY_current;
+        dY_current[0].resize(size);
+        for (size_t i = 0; i < size; i++) {
+            dY_current[0][i] = Y[i];
+        }
+
+        int window = step - last_computed_step;
+        if (window <= 0)
+            window = 1;
+
+        for (int d = 0; d < n_derivatives; d++) {
+            if (!dY_prev[d].empty() && dY_prev[d].size() == size) {
+                dY_current[d + 1].resize(size);
+                for (size_t i = 0; i < size; i++) {
+                    dY_current[d + 1][i] = (dY_current[d][i] - dY_prev[d][i]) / static_cast<float>(window);
+                }
+            } else {
+                dY_current[d + 1].clear();
+            }
+        }
+
+        current_step       = step;
+        last_computed_step = step;
+    }
+
+    void approximate(float* output, size_t size, int target_step) const {
+        if (!can_approximate() || dY_prev[0].size() != size) {
+            return;
+        }
+
+        int elapsed = target_step - last_computed_step;
+        if (elapsed <= 0)
+            elapsed = 1;
+
+        std::fill(output, output + size, 0.0f);
+        float factorial = 1.0f;
+        int order       = static_cast<int>(dY_prev.size());
+
+        for (int o = 0; o < order; o++) {
+            if (dY_prev[o].empty() || dY_prev[o].size() != size)
+                continue;
+            if (o > 0)
+                factorial *= static_cast<float>(o);
+            float coeff = std::pow(static_cast<float>(elapsed), o) / factorial;
+            for (size_t i = 0; i < size; i++) {
+                output[i] += coeff * dY_prev[o][i];
+            }
+        }
+    }
+};
+
+struct BlockCacheEntry {
+    std::vector<float> residual_img;
+    std::vector<float> residual_txt;
+    std::vector<float> residual;
+    std::vector<float> prev_img;
+    std::vector<float> prev_txt;
+    std::vector<float> prev_output;
+    bool has_prev = false;
+};
+
+struct CacheDitState {
+    CacheDitConfig config;
+    bool initialized = false;
+
+    int total_double_blocks = 0;
+    int total_single_blocks = 0;
+    size_t hidden_size      = 0;
+
+    int current_step     = -1;
+    int total_steps      = 0;
+    int warmup_remaining = 0;
+    std::vector<int> cached_steps;
+    int continuous_cached_steps     = 0;
+    float accumulated_residual_diff = 0.0f;
+
+    std::vector<BlockCacheEntry> double_block_cache;
+    std::vector<BlockCacheEntry> single_block_cache;
+
+    std::vector<float> Fn_residual_img;
+    std::vector<float> Fn_residual_txt;
+    std::vector<float> prev_Fn_residual_img;
+    std::vector<float> prev_Fn_residual_txt;
+    bool has_prev_Fn_residual = false;
+
+    std::vector<float> Bn_buffer_img;
+    std::vector<float> Bn_buffer_txt;
+    std::vector<float> Bn_buffer;
+    bool has_Bn_buffer = false;
+
+    TaylorSeerState taylor_state;
+
+    bool can_cache_this_step  = false;
+    bool is_caching_this_step = false;
+
+    int total_blocks_computed = 0;
+    int total_blocks_cached   = 0;
+
+    void init(const CacheDitConfig& cfg, int num_double_blocks, int num_single_blocks, size_t h_size) {
+        config              = cfg;
+        total_double_blocks = num_double_blocks;
+        total_single_blocks = num_single_blocks;
+        hidden_size         = h_size;
+
+        initialized = cfg.dbcache.enabled || cfg.taylorseer.enabled;
+
+        if (!initialized)
+            return;
+
+        warmup_remaining = cfg.dbcache.max_warmup_steps;
+        double_block_cache.resize(total_double_blocks);
+        single_block_cache.resize(total_single_blocks);
+
+        if (cfg.taylorseer.enabled) {
+            taylor_state.init(cfg.taylorseer.n_derivatives, h_size);
+        }
+
+        reset_runtime();
+    }
+
+    void reset_runtime() {
+        current_step     = -1;
+        total_steps      = 0;
+        warmup_remaining = config.dbcache.max_warmup_steps;
+        cached_steps.clear();
+        continuous_cached_steps   = 0;
+        accumulated_residual_diff = 0.0f;
+
+        for (auto& entry : double_block_cache) {
+            entry.residual_img.clear();
+            entry.residual_txt.clear();
+            entry.prev_img.clear();
+            entry.prev_txt.clear();
+            entry.has_prev = false;
+        }
+
+        for (auto& entry : single_block_cache) {
+            entry.residual.clear();
+            entry.prev_output.clear();
+            entry.has_prev = false;
+        }
+
+        Fn_residual_img.clear();
+        Fn_residual_txt.clear();
+        prev_Fn_residual_img.clear();
+        prev_Fn_residual_txt.clear();
+        has_prev_Fn_residual = false;
+
+        Bn_buffer_img.clear();
+        Bn_buffer_txt.clear();
+        Bn_buffer.clear();
+        has_Bn_buffer = false;
+
+        taylor_state.reset();
+
+        can_cache_this_step  = false;
+        is_caching_this_step = false;
+
+        total_blocks_computed = 0;
+        total_blocks_cached   = 0;
+    }
+
+    bool enabled() const {
+        return initialized && (config.dbcache.enabled || config.taylorseer.enabled);
+    }
+
+    void begin_step(int step_index, float sigma = 0.0f) {
+        if (!enabled())
+            return;
+        if (step_index == current_step)
+            return;
+
+        current_step = step_index;
+        total_steps++;
+
+        bool in_warmup = warmup_remaining > 0;
+        if (in_warmup) {
+            warmup_remaining--;
+        }
+
+        bool scm_allows_cache = true;
+        if (!config.dbcache.steps_computation_mask.empty()) {
+            if (step_index < static_cast<int>(config.dbcache.steps_computation_mask.size())) {
+                scm_allows_cache = (config.dbcache.steps_computation_mask[step_index] == 0);
+                if (!config.dbcache.scm_policy_dynamic && scm_allows_cache) {
+                    can_cache_this_step  = true;
+                    is_caching_this_step = false;
+                    return;
+                }
+            }
+        }
+
+        bool max_cached_ok = (config.dbcache.max_cached_steps < 0) ||
+                             (static_cast<int>(cached_steps.size()) < config.dbcache.max_cached_steps);
+
+        bool max_cont_ok = (config.dbcache.max_continuous_cached_steps < 0) ||
+                           (continuous_cached_steps < config.dbcache.max_continuous_cached_steps);
+
+        bool accum_ok = (config.dbcache.max_accumulated_residual_diff < 0.0f) ||
+                        (accumulated_residual_diff < config.dbcache.max_accumulated_residual_diff);
+
+        can_cache_this_step  = !in_warmup && scm_allows_cache && max_cached_ok && max_cont_ok && accum_ok && has_prev_Fn_residual;
+        is_caching_this_step = false;
+    }
+
+    void end_step(bool was_cached) {
+        if (was_cached) {
+            cached_steps.push_back(current_step);
+            continuous_cached_steps++;
+        } else {
+            continuous_cached_steps = 0;
+        }
+    }
+
+    static float calculate_residual_diff(const float* prev, const float* curr, size_t size) {
+        if (size == 0)
+            return 0.0f;
+
+        float sum_diff = 0.0f;
+        float sum_abs  = 0.0f;
+
+        for (size_t i = 0; i < size; i++) {
+            sum_diff += std::fabs(prev[i] - curr[i]);
+            sum_abs += std::fabs(prev[i]);
+        }
+
+        return sum_diff / (sum_abs + 1e-6f);
+    }
+
+    static float calculate_residual_diff(const std::vector<float>& prev, const std::vector<float>& curr) {
+        if (prev.size() != curr.size() || prev.empty())
+            return 1.0f;
+        return calculate_residual_diff(prev.data(), curr.data(), prev.size());
+    }
+
+    int get_double_Fn_blocks() const {
+        return (config.double_Fn_blocks >= 0) ? config.double_Fn_blocks : config.dbcache.Fn_compute_blocks;
+    }
+
+    int get_double_Bn_blocks() const {
+        return (config.double_Bn_blocks >= 0) ? config.double_Bn_blocks : config.dbcache.Bn_compute_blocks;
+    }
+
+    int get_single_Fn_blocks() const {
+        return (config.single_Fn_blocks >= 0) ? config.single_Fn_blocks : config.dbcache.Fn_compute_blocks;
+    }
+
+    int get_single_Bn_blocks() const {
+        return (config.single_Bn_blocks >= 0) ? config.single_Bn_blocks : config.dbcache.Bn_compute_blocks;
+    }
+
+    bool is_Fn_double_block(int block_idx) const {
+        return block_idx < get_double_Fn_blocks();
+    }
+
+    bool is_Bn_double_block(int block_idx) const {
+        int Bn = get_double_Bn_blocks();
+        return Bn > 0 && block_idx >= (total_double_blocks - Bn);
+    }
+
+    bool is_Mn_double_block(int block_idx) const {
+        return !is_Fn_double_block(block_idx) && !is_Bn_double_block(block_idx);
+    }
+
+    bool is_Fn_single_block(int block_idx) const {
+        return block_idx < get_single_Fn_blocks();
+    }
+
+    bool is_Bn_single_block(int block_idx) const {
+        int Bn = get_single_Bn_blocks();
+        return Bn > 0 && block_idx >= (total_single_blocks - Bn);
+    }
+
+    bool is_Mn_single_block(int block_idx) const {
+        return !is_Fn_single_block(block_idx) && !is_Bn_single_block(block_idx);
+    }
+
+    void store_Fn_residual(const float* img, const float* txt, size_t img_size, size_t txt_size, const float* input_img, const float* input_txt) {
+        Fn_residual_img.resize(img_size);
+        Fn_residual_txt.resize(txt_size);
+
+        for (size_t i = 0; i < img_size; i++) {
+            Fn_residual_img[i] = img[i] - input_img[i];
+        }
+        for (size_t i = 0; i < txt_size; i++) {
+            Fn_residual_txt[i] = txt[i] - input_txt[i];
+        }
+    }
+
+    bool check_cache_decision() {
+        if (!can_cache_this_step) {
+            is_caching_this_step = false;
+            return false;
+        }
+
+        if (!has_prev_Fn_residual || prev_Fn_residual_img.empty()) {
+            is_caching_this_step = false;
+            return false;
+        }
+
+        float diff_img = calculate_residual_diff(prev_Fn_residual_img, Fn_residual_img);
+        float diff_txt = calculate_residual_diff(prev_Fn_residual_txt, Fn_residual_txt);
+        float diff     = (diff_img + diff_txt) / 2.0f;
+
+        if (diff < config.dbcache.residual_diff_threshold) {
+            is_caching_this_step = true;
+            accumulated_residual_diff += diff;
+            return true;
+        }
+
+        is_caching_this_step = false;
+        return false;
+    }
+
+    void update_prev_Fn_residual() {
+        prev_Fn_residual_img = Fn_residual_img;
+        prev_Fn_residual_txt = Fn_residual_txt;
+        has_prev_Fn_residual = !prev_Fn_residual_img.empty();
+    }
+
+    void store_double_block_residual(int block_idx, const float* img, const float* txt, size_t img_size, size_t txt_size, const float* prev_img, const float* prev_txt) {
+        if (block_idx < 0 || block_idx >= static_cast<int>(double_block_cache.size()))
+            return;
+
+        BlockCacheEntry& entry = double_block_cache[block_idx];
+
+        entry.residual_img.resize(img_size);
+        entry.residual_txt.resize(txt_size);
+        for (size_t i = 0; i < img_size; i++) {
+            entry.residual_img[i] = img[i] - prev_img[i];
+        }
+        for (size_t i = 0; i < txt_size; i++) {
+            entry.residual_txt[i] = txt[i] - prev_txt[i];
+        }
+
+        entry.prev_img.resize(img_size);
+        entry.prev_txt.resize(txt_size);
+        for (size_t i = 0; i < img_size; i++) {
+            entry.prev_img[i] = img[i];
+        }
+        for (size_t i = 0; i < txt_size; i++) {
+            entry.prev_txt[i] = txt[i];
+        }
+        entry.has_prev = true;
+    }
+
+    void apply_double_block_cache(int block_idx, float* img, float* txt, size_t img_size, size_t txt_size) {
+        if (block_idx < 0 || block_idx >= static_cast<int>(double_block_cache.size()))
+            return;
+
+        const BlockCacheEntry& entry = double_block_cache[block_idx];
+        if (entry.residual_img.size() != img_size || entry.residual_txt.size() != txt_size)
+            return;
+
+        for (size_t i = 0; i < img_size; i++) {
+            img[i] += entry.residual_img[i];
+        }
+        for (size_t i = 0; i < txt_size; i++) {
+            txt[i] += entry.residual_txt[i];
+        }
+
+        total_blocks_cached++;
+    }
+
+    void store_single_block_residual(int block_idx, const float* output, size_t size, const float* input) {
+        if (block_idx < 0 || block_idx >= static_cast<int>(single_block_cache.size()))
+            return;
+
+        BlockCacheEntry& entry = single_block_cache[block_idx];
+
+        entry.residual.resize(size);
+        for (size_t i = 0; i < size; i++) {
+            entry.residual[i] = output[i] - input[i];
+        }
+
+        entry.prev_output.resize(size);
+        for (size_t i = 0; i < size; i++) {
+            entry.prev_output[i] = output[i];
+        }
+        entry.has_prev = true;
+    }
+
+    void apply_single_block_cache(int block_idx, float* output, size_t size) {
+        if (block_idx < 0 || block_idx >= static_cast<int>(single_block_cache.size()))
+            return;
+
+        const BlockCacheEntry& entry = single_block_cache[block_idx];
+        if (entry.residual.size() != size)
+            return;
+
+        for (size_t i = 0; i < size; i++) {
+            output[i] += entry.residual[i];
+        }
+
+        total_blocks_cached++;
+    }
+
+    void store_Bn_buffer(const float* img, const float* txt, size_t img_size, size_t txt_size, const float* Bn_start_img, const float* Bn_start_txt) {
+        Bn_buffer_img.resize(img_size);
+        Bn_buffer_txt.resize(txt_size);
+
+        for (size_t i = 0; i < img_size; i++) {
+            Bn_buffer_img[i] = img[i] - Bn_start_img[i];
+        }
+        for (size_t i = 0; i < txt_size; i++) {
+            Bn_buffer_txt[i] = txt[i] - Bn_start_txt[i];
+        }
+        has_Bn_buffer = true;
+    }
+
+    void apply_Bn_buffer(float* img, float* txt, size_t img_size, size_t txt_size) {
+        if (!has_Bn_buffer)
+            return;
+        if (Bn_buffer_img.size() != img_size || Bn_buffer_txt.size() != txt_size)
+            return;
+
+        for (size_t i = 0; i < img_size; i++) {
+            img[i] += Bn_buffer_img[i];
+        }
+        for (size_t i = 0; i < txt_size; i++) {
+            txt[i] += Bn_buffer_txt[i];
+        }
+    }
+
+    void taylor_update(const float* hidden_state, size_t size) {
+        if (!config.taylorseer.enabled)
+            return;
+        taylor_state.update_derivatives(hidden_state, size, current_step);
+    }
+
+    bool taylor_can_approximate() const {
+        return config.taylorseer.enabled && taylor_state.can_approximate();
+    }
+
+    void taylor_approximate(float* output, size_t size) {
+        if (!config.taylorseer.enabled)
+            return;
+        taylor_state.approximate(output, size, current_step);
+    }
+
+    bool should_use_taylor_this_step() const {
+        if (!config.taylorseer.enabled)
+            return false;
+        if (current_step < config.taylorseer.max_warmup_steps)
+            return false;
+
+        int interval = config.taylorseer.skip_interval_steps;
+        if (interval <= 0)
+            interval = 1;
+
+        return (current_step % (interval + 1)) != 0;
+    }
+
+    void log_metrics() const {
+        if (!enabled())
+            return;
+
+        int total_blocks  = total_blocks_computed + total_blocks_cached;
+        float cache_ratio = (total_blocks > 0) ? (static_cast<float>(total_blocks_cached) / total_blocks * 100.0f) : 0.0f;
+
+        float step_cache_ratio = (total_steps > 0) ? (static_cast<float>(cached_steps.size()) / total_steps * 100.0f) : 0.0f;
+
+        LOG_INFO("CacheDIT: steps_cached=%zu/%d (%.1f%%), blocks_cached=%d/%d (%.1f%%), accum_diff=%.4f",
+                 cached_steps.size(), total_steps, step_cache_ratio,
+                 total_blocks_cached, total_blocks, cache_ratio,
+                 accumulated_residual_diff);
+    }
+
+    std::string get_summary() const {
+        char buf[256];
+        snprintf(buf, sizeof(buf),
+                 "CacheDIT[thresh=%.2f]: cached %zu/%d steps, %d/%d blocks",
+                 config.dbcache.residual_diff_threshold,
+                 cached_steps.size(), total_steps,
+                 total_blocks_cached, total_blocks_computed + total_blocks_cached);
+        return std::string(buf);
+    }
+};
+
+inline std::vector<int> parse_scm_mask(const std::string& mask_str) {
+    std::vector<int> mask;
+    if (mask_str.empty())
+        return mask;
+
+    size_t pos   = 0;
+    size_t start = 0;
+    while ((pos = mask_str.find(',', start)) != std::string::npos) {
+        std::string token = mask_str.substr(start, pos - start);
+        mask.push_back(std::stoi(token));
+        start = pos + 1;
+    }
+    if (start < mask_str.length()) {
+        mask.push_back(std::stoi(mask_str.substr(start)));
+    }
+
+    return mask;
+}
+
+inline std::vector<int> generate_scm_mask(
+    const std::vector<int>& compute_bins,
+    const std::vector<int>& cache_bins,
+    int total_steps) {
+    std::vector<int> mask;
+    size_t c_idx = 0, cache_idx = 0;
+
+    while (static_cast<int>(mask.size()) < total_steps) {
+        if (c_idx < compute_bins.size()) {
+            for (int i = 0; i < compute_bins[c_idx] && static_cast<int>(mask.size()) < total_steps; i++) {
+                mask.push_back(1);
+            }
+            c_idx++;
+        }
+        if (cache_idx < cache_bins.size()) {
+            for (int i = 0; i < cache_bins[cache_idx] && static_cast<int>(mask.size()) < total_steps; i++) {
+                mask.push_back(0);
+            }
+            cache_idx++;
+        }
+        if (c_idx >= compute_bins.size() && cache_idx >= cache_bins.size())
+            break;
+    }
+
+    if (!mask.empty()) {
+        mask.back() = 1;
+    }
+
+    return mask;
+}
+
+inline std::vector<int> get_scm_preset(const std::string& preset, int total_steps) {
+    struct Preset {
+        std::vector<int> compute_bins;
+        std::vector<int> cache_bins;
+    };
+
+    Preset slow   = {{8, 3, 3, 2, 1, 1}, {1, 2, 2, 2, 3}};
+    Preset medium = {{6, 2, 2, 2, 2, 1}, {1, 3, 3, 3, 3}};
+    Preset fast   = {{6, 1, 1, 1, 1, 1}, {1, 3, 4, 5, 4}};
+    Preset ultra  = {{4, 1, 1, 1, 1}, {2, 5, 6, 7}};
+
+    Preset* p = nullptr;
+    if (preset == "slow" || preset == "s" || preset == "S")
+        p = &slow;
+    else if (preset == "medium" || preset == "m" || preset == "M")
+        p = &medium;
+    else if (preset == "fast" || preset == "f" || preset == "F")
+        p = &fast;
+    else if (preset == "ultra" || preset == "u" || preset == "U")
+        p = &ultra;
+    else
+        return {};
+
+    if (total_steps != 28 && total_steps > 0) {
+        float scale = static_cast<float>(total_steps) / 28.0f;
+        std::vector<int> scaled_compute, scaled_cache;
+
+        for (int v : p->compute_bins) {
+            scaled_compute.push_back(std::max(1, static_cast<int>(v * scale + 0.5f)));
+        }
+        for (int v : p->cache_bins) {
+            scaled_cache.push_back(std::max(1, static_cast<int>(v * scale + 0.5f)));
+        }
+
+        return generate_scm_mask(scaled_compute, scaled_cache, total_steps);
+    }
+
+    return generate_scm_mask(p->compute_bins, p->cache_bins, total_steps);
+}
+
+inline float get_preset_threshold(const std::string& preset) {
+    if (preset == "slow" || preset == "s" || preset == "S")
+        return 0.20f;
+    if (preset == "medium" || preset == "m" || preset == "M")
+        return 0.25f;
+    if (preset == "fast" || preset == "f" || preset == "F")
+        return 0.30f;
+    if (preset == "ultra" || preset == "u" || preset == "U")
+        return 0.34f;
+    return 0.08f;
+}
+
+inline int get_preset_warmup(const std::string& preset) {
+    if (preset == "slow" || preset == "s" || preset == "S")
+        return 8;
+    if (preset == "medium" || preset == "m" || preset == "M")
+        return 6;
+    if (preset == "fast" || preset == "f" || preset == "F")
+        return 6;
+    if (preset == "ultra" || preset == "u" || preset == "U")
+        return 4;
+    return 8;
+}
+
+inline int get_preset_Fn(const std::string& preset) {
+    if (preset == "slow" || preset == "s" || preset == "S")
+        return 8;
+    if (preset == "medium" || preset == "m" || preset == "M")
+        return 8;
+    if (preset == "fast" || preset == "f" || preset == "F")
+        return 6;
+    if (preset == "ultra" || preset == "u" || preset == "U")
+        return 4;
+    return 8;
+}
+
+inline int get_preset_Bn(const std::string& preset) {
+    (void)preset;
+    return 0;
+}
+
+inline void parse_dbcache_options(const std::string& opts, DBCacheConfig& cfg) {
+    if (opts.empty())
+        return;
+
+    int Fn = 8, Bn = 0, warmup = 8, max_cached = -1, max_cont = -1;
+    float thresh = 0.08f;
+
+    sscanf(opts.c_str(), "%d,%d,%f,%d,%d,%d",
+           &Fn, &Bn, &thresh, &warmup, &max_cached, &max_cont);
+
+    cfg.Fn_compute_blocks           = Fn;
+    cfg.Bn_compute_blocks           = Bn;
+    cfg.residual_diff_threshold     = thresh;
+    cfg.max_warmup_steps            = warmup;
+    cfg.max_cached_steps            = max_cached;
+    cfg.max_continuous_cached_steps = max_cont;
+}
+
+inline void parse_taylorseer_options(const std::string& opts, TaylorSeerConfig& cfg) {
+    if (opts.empty())
+        return;
+
+    int n_deriv = 1, warmup = 2, interval = 1;
+    sscanf(opts.c_str(), "%d,%d,%d", &n_deriv, &warmup, &interval);
+
+    cfg.n_derivatives       = n_deriv;
+    cfg.max_warmup_steps    = warmup;
+    cfg.skip_interval_steps = interval;
+}
+
+struct CacheDitConditionState {
+    DBCacheConfig config;
+    TaylorSeerConfig taylor_config;
+    bool initialized = false;
+
+    int current_step_index = -1;
+    bool step_active       = false;
+    bool skip_current_step = false;
+    bool initial_step      = true;
+    int warmup_remaining   = 0;
+    std::vector<int> cached_steps;
+    int continuous_cached_steps     = 0;
+    float accumulated_residual_diff = 0.0f;
+    int total_steps_skipped         = 0;
+
+    const void* anchor_condition = nullptr;
+
+    struct CacheEntry {
+        std::vector<float> diff;
+        std::vector<float> prev_input;
+        std::vector<float> prev_output;
+        bool has_prev = false;
+    };
+    std::unordered_map<const void*, CacheEntry> cache_diffs;
+
+    TaylorSeerState taylor_state;
+
+    float start_sigma = std::numeric_limits<float>::max();
+    float end_sigma   = 0.0f;
+
+    void reset_runtime() {
+        current_step_index = -1;
+        step_active        = false;
+        skip_current_step  = false;
+        initial_step       = true;
+        warmup_remaining   = config.max_warmup_steps;
+        cached_steps.clear();
+        continuous_cached_steps   = 0;
+        accumulated_residual_diff = 0.0f;
+        total_steps_skipped       = 0;
+        anchor_condition          = nullptr;
+        cache_diffs.clear();
+        taylor_state.reset();
+    }
+
+    void init(const DBCacheConfig& dbcfg, const TaylorSeerConfig& tcfg) {
+        config        = dbcfg;
+        taylor_config = tcfg;
+        initialized   = dbcfg.enabled || tcfg.enabled;
+        reset_runtime();
+
+        if (taylor_config.enabled) {
+            taylor_state.init(taylor_config.n_derivatives, 0);
+        }
+    }
+
+    void set_sigmas(const std::vector<float>& sigmas) {
+        if (!initialized || sigmas.size() < 2)
+            return;
+
+        float start_percent = 0.15f;
+        float end_percent   = 0.95f;
+
+        size_t n_steps    = sigmas.size() - 1;
+        size_t start_step = static_cast<size_t>(start_percent * n_steps);
+        size_t end_step   = static_cast<size_t>(end_percent * n_steps);
+
+        if (start_step >= n_steps)
+            start_step = n_steps - 1;
+        if (end_step >= n_steps)
+            end_step = n_steps - 1;
+
+        start_sigma = sigmas[start_step];
+        end_sigma   = sigmas[end_step];
+
+        if (start_sigma < end_sigma) {
+            std::swap(start_sigma, end_sigma);
+        }
+    }
+
+    bool enabled() const {
+        return initialized && (config.enabled || taylor_config.enabled);
+    }
+
+    void begin_step(int step_index, float sigma) {
+        if (!enabled())
+            return;
+        if (step_index == current_step_index)
+            return;
+
+        current_step_index = step_index;
+        skip_current_step  = false;
+        step_active        = false;
+
+        if (sigma > start_sigma)
+            return;
+        if (!(sigma > end_sigma))
+            return;
+
+        step_active = true;
+
+        if (warmup_remaining > 0) {
+            warmup_remaining--;
+            return;
+        }
+
+        if (!config.steps_computation_mask.empty()) {
+            if (step_index < static_cast<int>(config.steps_computation_mask.size())) {
+                if (config.steps_computation_mask[step_index] == 1) {
+                    return;
+                }
+            }
+        }
+
+        if (config.max_cached_steps >= 0 &&
+            static_cast<int>(cached_steps.size()) >= config.max_cached_steps) {
+            return;
+        }
+
+        if (config.max_continuous_cached_steps >= 0 &&
+            continuous_cached_steps >= config.max_continuous_cached_steps) {
+            return;
+        }
+    }
+
+    bool step_is_active() const {
+        return enabled() && step_active;
+    }
+
+    bool is_step_skipped() const {
+        return enabled() && step_active && skip_current_step;
+    }
+
+    bool has_cache(const void* cond) const {
+        auto it = cache_diffs.find(cond);
+        return it != cache_diffs.end() && !it->second.diff.empty();
+    }
+
+    void update_cache(const void* cond, const float* input, const float* output, size_t size) {
+        CacheEntry& entry = cache_diffs[cond];
+        entry.diff.resize(size);
+        for (size_t i = 0; i < size; i++) {
+            entry.diff[i] = output[i] - input[i];
+        }
+
+        entry.prev_input.resize(size);
+        entry.prev_output.resize(size);
+        for (size_t i = 0; i < size; i++) {
+            entry.prev_input[i]  = input[i];
+            entry.prev_output[i] = output[i];
+        }
+        entry.has_prev = true;
+    }
+
+    void apply_cache(const void* cond, const float* input, float* output, size_t size) {
+        auto it = cache_diffs.find(cond);
+        if (it == cache_diffs.end() || it->second.diff.empty())
+            return;
+        if (it->second.diff.size() != size)
+            return;
+
+        for (size_t i = 0; i < size; i++) {
+            output[i] = input[i] + it->second.diff[i];
+        }
+    }
+
+    bool before_condition(const void* cond, struct ggml_tensor* input, struct ggml_tensor* output, float sigma, int step_index) {
+        if (!enabled() || step_index < 0)
+            return false;
+
+        if (step_index != current_step_index) {
+            begin_step(step_index, sigma);
+        }
+
+        if (!step_active)
+            return false;
+
+        if (initial_step) {
+            anchor_condition = cond;
+            initial_step     = false;
+        }
+
+        bool is_anchor = (cond == anchor_condition);
+
+        if (skip_current_step) {
+            if (has_cache(cond)) {
+                apply_cache(cond, (float*)input->data, (float*)output->data,
+                            static_cast<size_t>(ggml_nelements(output)));
+                return true;
+            }
+            return false;
+        }
+
+        if (!is_anchor)
+            return false;
+
+        auto it = cache_diffs.find(cond);
+        if (it == cache_diffs.end() || !it->second.has_prev)
+            return false;
+
+        size_t ne = static_cast<size_t>(ggml_nelements(input));
+        if (it->second.prev_input.size() != ne)
+            return false;
+
+        float* input_data = (float*)input->data;
+        float diff        = CacheDitState::calculate_residual_diff(
+                   it->second.prev_input.data(), input_data, ne);
+
+        float effective_threshold = config.residual_diff_threshold;
+        if (config.Fn_compute_blocks > 0) {
+            float fn_confidence = 1.0f + 0.02f * (config.Fn_compute_blocks - 8);
+            fn_confidence       = std::max(0.5f, std::min(2.0f, fn_confidence));
+            effective_threshold *= fn_confidence;
+        }
+        if (config.Bn_compute_blocks > 0) {
+            float bn_quality = 1.0f - 0.03f * config.Bn_compute_blocks;
+            bn_quality       = std::max(0.5f, std::min(1.0f, bn_quality));
+            effective_threshold *= bn_quality;
+        }
+
+        if (diff < effective_threshold) {
+            skip_current_step = true;
+            total_steps_skipped++;
+            cached_steps.push_back(current_step_index);
+            continuous_cached_steps++;
+            accumulated_residual_diff += diff;
+            apply_cache(cond, input_data, (float*)output->data, ne);
+            return true;
+        }
+
+        continuous_cached_steps = 0;
+        return false;
+    }
+
+    void after_condition(const void* cond, struct ggml_tensor* input, struct ggml_tensor* output) {
+        if (!step_is_active())
+            return;
+
+        size_t ne = static_cast<size_t>(ggml_nelements(output));
+        update_cache(cond, (float*)input->data, (float*)output->data, ne);
+
+        if (cond == anchor_condition && taylor_config.enabled) {
+            taylor_state.update_derivatives((float*)output->data, ne, current_step_index);
+        }
+    }
+
+    void log_metrics() const {
+        if (!enabled())
+            return;
+
+        LOG_INFO("CacheDIT: steps_skipped=%d/%d (%.1f%%), accum_residual_diff=%.4f",
+                 total_steps_skipped,
+                 current_step_index + 1,
+                 (current_step_index > 0) ? (100.0f * total_steps_skipped / (current_step_index + 1)) : 0.0f,
+                 accumulated_residual_diff);
+    }
+};
+
+#endif

common.hpp

@@ -28,7 +28,7 @@ public:
         if (vae_downsample) {
             auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);
 
-            x = ggml_pad(ctx->ggml_ctx, x, 1, 1, 0, 0);
+            x = ggml_ext_pad(ctx->ggml_ctx, x, 1, 1, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
             x = conv->forward(ctx, x);
         } else {
             auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["op"]);
@@ -194,10 +194,12 @@ public:
         auto proj = std::dynamic_pointer_cast<Linear>(blocks["proj"]);
 
         x          = proj->forward(ctx, x);  // [ne3, ne2, ne1, dim_out*2]
-        auto x_vec = ggml_ext_chunk(ctx->ggml_ctx, x, 2, 0);
+        auto x_vec = ggml_ext_chunk(ctx->ggml_ctx, x, 2, 0, false);
         x          = x_vec[0];  // [ne3, ne2, ne1, dim_out]
         auto gate  = x_vec[1];  // [ne3, ne2, ne1, dim_out]
 
+        gate = ggml_cont(ctx->ggml_ctx, gate);
+
         gate = ggml_gelu_inplace(ctx->ggml_ctx, gate);
 
         x = ggml_mul(ctx->ggml_ctx, x, gate);  // [ne3, ne2, ne1, dim_out]

denoiser.hpp

@@ -347,6 +347,41 @@ struct SmoothStepScheduler : SigmaScheduler {
     }
 };
 
+// Implementation adapted from https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15608
+struct KLOptimalScheduler : SigmaScheduler {
+    std::vector<float> get_sigmas(uint32_t n, float sigma_min, float sigma_max, t_to_sigma_t t_to_sigma) override {
+        std::vector<float> sigmas;
+
+        if (n == 0) {
+            return sigmas;
+        }
+        if (n == 1) {
+            sigmas.push_back(sigma_max);
+            sigmas.push_back(0.0f);
+            return sigmas;
+        }
+
+        float alpha_min = std::atan(sigma_min);
+        float alpha_max = std::atan(sigma_max);
+
+        for (uint32_t i = 0; i < n; ++i) {
+            // t goes from 0.0 to 1.0
+            float t = static_cast<float>(i) / static_cast<float>(n - 1);
+
+            // Interpolate in the angle domain
+            float angle = t * alpha_min + (1.0f - t) * alpha_max;
+
+            // Convert back to sigma
+            sigmas.push_back(std::tan(angle));
+        }
+
+        // Append the final zero to sigma
+        sigmas.push_back(0.0f);
+
+        return sigmas;
+    }
+};
+
 struct Denoiser {
     virtual float sigma_min()                                                                = 0;
     virtual float sigma_max()                                                                = 0;
@@ -392,6 +427,10 @@ struct Denoiser {
                 LOG_INFO("get_sigmas with SmoothStep scheduler");
                 scheduler = std::make_shared<SmoothStepScheduler>();
                 break;
+            case KL_OPTIMAL_SCHEDULER:
+                LOG_INFO("get_sigmas with KL Optimal scheduler");
+                scheduler = std::make_shared<KLOptimalScheduler>();
+                break;
             case LCM_SCHEDULER:
                 LOG_INFO("get_sigmas with LCM scheduler");
                 scheduler = std::make_shared<LCMScheduler>();
@@ -830,7 +869,7 @@ static bool sample_k_diffusion(sample_method_t method,
 
             for (int i = 0; i < steps; i++) {
                 // denoise
-                ggml_tensor* denoised = model(x, sigmas[i], i + 1);
+                ggml_tensor* denoised = model(x, sigmas[i], -(i + 1));
                 if (denoised == nullptr) {
                     return false;
                 }
@@ -888,7 +927,7 @@ static bool sample_k_diffusion(sample_method_t method,
 
             for (int i = 0; i < steps; i++) {
                 // denoise
-                ggml_tensor* denoised = model(x, sigmas[i], i + 1);
+                ggml_tensor* denoised = model(x, sigmas[i], -(i + 1));
                 if (denoised == nullptr) {
                     return false;
                 }

diffusion_model.hpp

@@ -37,8 +37,9 @@ struct DiffusionModel {
     virtual void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) = 0;
     virtual size_t get_params_buffer_size()                                             = 0;
     virtual void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter){};
-    virtual int64_t get_adm_in_channels()             = 0;
+    virtual int64_t get_adm_in_channels()                            = 0;
-    virtual void set_flash_attn_enabled(bool enabled) = 0;
+    virtual void set_flash_attn_enabled(bool enabled)                = 0;
+    virtual void set_circular_axes(bool circular_x, bool circular_y) = 0;
 };
 
 struct UNetModel : public DiffusionModel {
@@ -87,6 +88,10 @@ struct UNetModel : public DiffusionModel {
         unet.set_flash_attention_enabled(enabled);
     }
 
+    void set_circular_axes(bool circular_x, bool circular_y) override {
+        unet.set_circular_axes(circular_x, circular_y);
+    }
+
     bool compute(int n_threads,
                  DiffusionParams diffusion_params,
                  struct ggml_tensor** output     = nullptr,
@@ -148,6 +153,10 @@ struct MMDiTModel : public DiffusionModel {
         mmdit.set_flash_attention_enabled(enabled);
     }
 
+    void set_circular_axes(bool circular_x, bool circular_y) override {
+        mmdit.set_circular_axes(circular_x, circular_y);
+    }
+
     bool compute(int n_threads,
                  DiffusionParams diffusion_params,
                  struct ggml_tensor** output     = nullptr,
@@ -210,6 +219,10 @@ struct FluxModel : public DiffusionModel {
         flux.set_flash_attention_enabled(enabled);
     }
 
+    void set_circular_axes(bool circular_x, bool circular_y) override {
+        flux.set_circular_axes(circular_x, circular_y);
+    }
+
     bool compute(int n_threads,
                  DiffusionParams diffusion_params,
                  struct ggml_tensor** output     = nullptr,
@@ -277,6 +290,10 @@ struct WanModel : public DiffusionModel {
         wan.set_flash_attention_enabled(enabled);
     }
 
+    void set_circular_axes(bool circular_x, bool circular_y) override {
+        wan.set_circular_axes(circular_x, circular_y);
+    }
+
     bool compute(int n_threads,
                  DiffusionParams diffusion_params,
                  struct ggml_tensor** output     = nullptr,
@@ -303,8 +320,9 @@ struct QwenImageModel : public DiffusionModel {
                    bool offload_params_to_cpu,
                    const String2TensorStorage& tensor_storage_map = {},
                    const std::string prefix                       = "model.diffusion_model",
-                   SDVersion version                              = VERSION_QWEN_IMAGE)
+                   SDVersion version                              = VERSION_QWEN_IMAGE,
-        : prefix(prefix), qwen_image(backend, offload_params_to_cpu, tensor_storage_map, prefix, version) {
+                   bool zero_cond_t                               = false)
+        : prefix(prefix), qwen_image(backend, offload_params_to_cpu, tensor_storage_map, prefix, version, zero_cond_t) {
     }
 
     std::string get_desc() override {
@@ -343,6 +361,10 @@ struct QwenImageModel : public DiffusionModel {
         qwen_image.set_flash_attention_enabled(enabled);
     }
 
+    void set_circular_axes(bool circular_x, bool circular_y) override {
+        qwen_image.set_circular_axes(circular_x, circular_y);
+    }
+
     bool compute(int n_threads,
                  DiffusionParams diffusion_params,
                  struct ggml_tensor** output     = nullptr,
@@ -406,6 +428,10 @@ struct ZImageModel : public DiffusionModel {
         z_image.set_flash_attention_enabled(enabled);
     }
 
+    void set_circular_axes(bool circular_x, bool circular_y) override {
+        z_image.set_circular_axes(circular_x, circular_y);
+    }
+
     bool compute(int n_threads,
                  DiffusionParams diffusion_params,
                  struct ggml_tensor** output     = nullptr,

docs/caching.md

@@ -0,0 +1,126 @@
+## Caching
+
+Caching methods accelerate diffusion inference by reusing intermediate computations when changes between steps are small.
+
+### Cache Modes
+
+| Mode | Target | Description |
+|------|--------|-------------|
+| `ucache` | UNET models | Condition-level caching with error tracking |
+| `easycache` | DiT models | Condition-level cache |
+| `dbcache` | DiT models | Block-level L1 residual threshold |
+| `taylorseer` | DiT models | Taylor series approximation |
+| `cache-dit` | DiT models | Combined DBCache + TaylorSeer |
+
+### UCache (UNET Models)
+
+UCache caches the residual difference (output - input) and reuses it when input changes are below threshold.
+
+```bash
+sd-cli -m model.safetensors -p "a cat" --cache-mode ucache --cache-option "threshold=1.5"
+```
+
+#### Parameters
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `threshold` | Error threshold for reuse decision | 1.0 |
+| `start` | Start caching at this percent of steps | 0.15 |
+| `end` | Stop caching at this percent of steps | 0.95 |
+| `decay` | Error decay rate (0-1) | 1.0 |
+| `relative` | Scale threshold by output norm (0/1) | 1 |
+| `reset` | Reset error after computing (0/1) | 1 |
+
+#### Reset Parameter
+
+The `reset` parameter controls error accumulation behavior:
+
+- `reset=1` (default): Resets accumulated error after each computed step. More aggressive caching, works well with most samplers.
+- `reset=0`: Keeps error accumulated. More conservative, recommended for `euler_a` sampler.
+
+### EasyCache (DiT Models)
+
+Condition-level caching for DiT models. Caches and reuses outputs when input changes are below threshold.
+
+```bash
+--cache-mode easycache --cache-option "threshold=0.3"
+```
+
+#### Parameters
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `threshold` | Input change threshold for reuse | 0.2 |
+| `start` | Start caching at this percent of steps | 0.15 |
+| `end` | Stop caching at this percent of steps | 0.95 |
+
+### Cache-DIT (DiT Models)
+
+For DiT models like FLUX and QWEN, use block-level caching modes.
+
+#### DBCache
+
+Caches blocks based on L1 residual difference threshold:
+
+```bash
+--cache-mode dbcache --cache-option "threshold=0.25,warmup=4"
+```
+
+#### TaylorSeer
+
+Uses Taylor series approximation to predict block outputs:
+
+```bash
+--cache-mode taylorseer
+```
+
+#### Cache-DIT (Combined)
+
+Combines DBCache and TaylorSeer:
+
+```bash
+--cache-mode cache-dit --cache-preset fast
+```
+
+#### Parameters
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `Fn` | Front blocks to always compute | 8 |
+| `Bn` | Back blocks to always compute | 0 |
+| `threshold` | L1 residual difference threshold | 0.08 |
+| `warmup` | Steps before caching starts | 8 |
+
+#### Presets
+
+Available presets: `slow`, `medium`, `fast`, `ultra` (or `s`, `m`, `f`, `u`).
+
+```bash
+--cache-mode cache-dit --cache-preset fast
+```
+
+#### SCM Options
+
+Steps Computation Mask controls which steps can be cached:
+
+```bash
+--scm-mask "1,1,1,1,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,1"
+```
+
+Mask values: `1` = compute, `0` = can cache.
+
+| Policy | Description |
+|--------|-------------|
+| `dynamic` | Check threshold before caching |
+| `static` | Always cache on cacheable steps |
+
+```bash
+--scm-policy dynamic
+```
+
+### Performance Tips
+
+- Start with default thresholds and adjust based on output quality
+- Lower threshold = better quality, less speedup
+- Higher threshold = more speedup, potential quality loss
+- More steps generally means more caching opportunities

docs/chroma.md

@@ -15,7 +15,7 @@ You can run Chroma using stable-diffusion.cpp with a GPU that has 6GB or even 4G
 You can download the preconverted gguf weights from [silveroxides/Chroma-GGUF](https://huggingface.co/silveroxides/Chroma-GGUF), this way you don't have to do the conversion yourself.
 
 ```
-.\bin\Release\sd.exe -M convert -m ..\..\ComfyUI\models\unet\chroma-unlocked-v40.safetensors -o ..\models\chroma-unlocked-v40-q8_0.gguf -v --type q8_0
+.\bin\Release\sd-cli.exe -M convert -m ..\..\ComfyUI\models\unet\chroma-unlocked-v40.safetensors -o ..\models\chroma-unlocked-v40-q8_0.gguf -v --type q8_0
 ```
 
 ## Run
@@ -24,7 +24,7 @@ You can download the preconverted gguf weights from [silveroxides/Chroma-GGUF](h
 For example:
 
 ```
- .\bin\Release\sd.exe --diffusion-model  ..\models\chroma-unlocked-v40-q8_0.gguf --vae ..\models\ae.sft --t5xxl ..\models\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'chroma.cpp'" --cfg-scale 4.0 --sampling-method euler -v --chroma-disable-dit-mask --clip-on-cpu
+ .\bin\Release\sd-cli.exe --diffusion-model  ..\models\chroma-unlocked-v40-q8_0.gguf --vae ..\models\ae.sft --t5xxl ..\models\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'chroma.cpp'" --cfg-scale 4.0 --sampling-method euler -v --chroma-disable-dit-mask --clip-on-cpu
 ```
 
 ![](../assets/flux/chroma_v40.png)

docs/chroma_radiance.md

@@ -12,7 +12,7 @@
 ## Examples
 
 ```
-.\bin\Release\sd.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Chroma1-Radiance-v0.4-Q8_0.gguf --t5xxl ..\..\ComfyUI\models\clip\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'chroma  radiance cpp'" --cfg-scale 4.0 --sampling-method euler -v
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Chroma1-Radiance-v0.4-Q8_0.gguf --t5xxl ..\..\ComfyUI\models\clip\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'chroma  radiance cpp'" --cfg-scale 4.0 --sampling-method euler -v
 ```
 
 <img alt="Chroma1-Radiance" src="../assets/flux/chroma1-radiance.png" />

docs/docker.md

@@ -9,7 +9,7 @@ docker build -t sd .
 ### Run
 
 ```shell
-docker run -v /path/to/models:/models -v /path/to/output/:/output sd [args...]
+docker run -v /path/to/models:/models -v /path/to/output/:/output sd-cli [args...]
 # For example
-# docker run -v ./models:/models -v ./build:/output sd -m /models/sd-v1-4.ckpt -p "a lovely cat" -v -o /output/output.png
+# docker run -v ./models:/models -v ./build:/output sd-cli -m /models/sd-v1-4.ckpt -p "a lovely cat" -v -o /output/output.png
 ```

docs/esrgan.md

@@ -5,5 +5,5 @@ You can use ESRGAN to upscale the generated images. At the moment, only the [Rea
 - Specify the model path using the `--upscale-model PATH` parameter. example:
 
 ```bash
-sd -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat" --upscale-model ../models/RealESRGAN_x4plus_anime_6B.pth
+sd-cli -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat" --upscale-model ../models/RealESRGAN_x4plus_anime_6B.pth
 ```

docs/flux.md

@@ -17,7 +17,7 @@ You can download the preconverted gguf weights from [FLUX.1-dev-gguf](https://hu
 
 For example:
 ```
-.\bin\Release\sd.exe -M convert -m ..\..\ComfyUI\models\unet\flux1-dev.sft -o ..\models\flux1-dev-q8_0.gguf -v --type q8_0
+.\bin\Release\sd-cli.exe -M convert -m ..\..\ComfyUI\models\unet\flux1-dev.sft -o ..\models\flux1-dev-q8_0.gguf -v --type q8_0
 ```
 
 ## Run
@@ -28,7 +28,7 @@ For example:
 For example:
 
 ```
- .\bin\Release\sd.exe --diffusion-model  ..\models\flux1-dev-q8_0.gguf --vae ..\models\ae.sft --clip_l ..\models\clip_l.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'flux.cpp'" --cfg-scale 1.0 --sampling-method euler -v --clip-on-cpu
+ .\bin\Release\sd-cli.exe --diffusion-model  ..\models\flux1-dev-q8_0.gguf --vae ..\models\ae.sft --clip_l ..\models\clip_l.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'flux.cpp'" --cfg-scale 1.0 --sampling-method euler -v --clip-on-cpu
 ```
 
 Using formats of different precisions will yield results of varying quality.
@@ -44,7 +44,7 @@ Using formats of different precisions will yield results of varying quality.
 
 
 ```
- .\bin\Release\sd.exe --diffusion-model  ..\models\flux1-schnell-q8_0.gguf --vae ..\models\ae.sft --clip_l ..\models\clip_l.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'flux.cpp'" --cfg-scale 1.0 --sampling-method euler -v --steps 4 --clip-on-cpu
+ .\bin\Release\sd-cli.exe --diffusion-model  ..\models\flux1-schnell-q8_0.gguf --vae ..\models\ae.sft --clip_l ..\models\clip_l.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'flux.cpp'" --cfg-scale 1.0 --sampling-method euler -v --steps 4 --clip-on-cpu
 ```
 
 | q8_0  |
@@ -60,7 +60,7 @@ Since many flux LoRA training libraries have used various LoRA naming formats, i
 - LoRA model from https://huggingface.co/XLabs-AI/flux-lora-collection/tree/main (using comfy converted version!!!)
 
 ```
-.\bin\Release\sd.exe --diffusion-model  ..\models\flux1-dev-q8_0.gguf --vae ...\models\ae.sft --clip_l ..\models\clip_l.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'flux.cpp'<lora:realism_lora_comfy_converted:1>" --cfg-scale 1.0 --sampling-method euler -v --lora-model-dir ../models --clip-on-cpu
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\flux1-dev-q8_0.gguf --vae ...\models\ae.sft --clip_l ..\models\clip_l.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'flux.cpp'<lora:realism_lora_comfy_converted:1>" --cfg-scale 1.0 --sampling-method euler -v --lora-model-dir ../models --clip-on-cpu
 ```
 
 ![output](../assets/flux/flux1-dev-q8_0%20with%20lora.png)

docs/flux2.md

@@ -12,7 +12,7 @@
 ## Examples
 
 ```
-.\bin\Release\sd.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux2-dev-Q4_K_S.gguf --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf -r .\kontext_input.png -p "change 'flux.cpp' to 'flux2-dev.cpp'" --cfg-scale 1.0 --sampling-method euler -v --diffusion-fa --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux2-dev-Q4_K_S.gguf --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf -r .\kontext_input.png -p "change 'flux.cpp' to 'flux2-dev.cpp'" --cfg-scale 1.0 --sampling-method euler -v --diffusion-fa --offload-to-cpu
 ```
 
 <img alt="flux2 example" src="../assets/flux2/example.png" />

docs/hipBLAS_on_Windows.md

@@ -82,4 +82,4 @@ cmake .. -G "Ninja" -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_H
 cmake --build . --config Release
 ```
 
-If everything went OK, `build\bin\sd.exe` file should appear.
+If everything went OK, `build\bin\sd-cli.exe` file should appear.

docs/kontext.md

@@ -16,7 +16,7 @@ You can run Kontext using stable-diffusion.cpp with a GPU that has 6GB or even 4
 You can download the preconverted gguf weights from [FLUX.1-Kontext-dev-GGUF](https://huggingface.co/QuantStack/FLUX.1-Kontext-dev-GGUF), this way you don't have to do the conversion yourself.
 
 ```
-.\bin\Release\sd.exe -M convert -m ..\..\ComfyUI\models\unet\flux1-kontext-dev.safetensors -o ..\models\flux1-kontext-dev-q8_0.gguf -v --type q8_0
+.\bin\Release\sd-cli.exe -M convert -m ..\..\ComfyUI\models\unet\flux1-kontext-dev.safetensors -o ..\models\flux1-kontext-dev-q8_0.gguf -v --type q8_0
 ```
 
 ## Run
@@ -27,7 +27,7 @@ You can download the preconverted gguf weights from [FLUX.1-Kontext-dev-GGUF](ht
 For example:
 
 ```
- .\bin\Release\sd.exe -r .\flux1-dev-q8_0.png --diffusion-model  ..\models\flux1-kontext-dev-q8_0.gguf --vae ..\models\ae.sft --clip_l ..\models\clip_l.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors -p "change 'flux.cpp' to 'kontext.cpp'" --cfg-scale 1.0 --sampling-method euler -v --clip-on-cpu
+ .\bin\Release\sd-cli.exe -r .\flux1-dev-q8_0.png --diffusion-model  ..\models\flux1-kontext-dev-q8_0.gguf --vae ..\models\ae.sft --clip_l ..\models\clip_l.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors -p "change 'flux.cpp' to 'kontext.cpp'" --cfg-scale 1.0 --sampling-method euler -v --clip-on-cpu
 ```
 
 

docs/lcm.md

@@ -7,7 +7,7 @@
 Here's a simple example:
 
 ```
-./bin/sd -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat<lora:lcm-lora-sdv1-5:1>" --steps 4 --lora-model-dir ../models -v --cfg-scale 1
+./bin/sd-cli -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat<lora:lcm-lora-sdv1-5:1>" --steps 4 --lora-model-dir ../models -v --cfg-scale 1
 ```
 
 | without LCM-LoRA (--cfg-scale 7)  | with LCM-LoRA (--cfg-scale 1)  |

docs/lora.md

@@ -7,7 +7,7 @@
 Here's a simple example:
 
 ```
-./bin/sd -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat<lora:marblesh:1>" --lora-model-dir ../models
+./bin/sd-cli -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat<lora:marblesh:1>" --lora-model-dir ../models
 ```
 
 `../models/marblesh.safetensors` or `../models/marblesh.ckpt` will be applied to the model

docs/ovis_image.md

@@ -13,7 +13,7 @@
 ## Examples
 
 ```
-.\bin\Release\sd.exe --diffusion-model  ovis_image-Q4_0.gguf --vae ..\..\ComfyUI\models\vae\ae.sft  --llm ..\..\ComfyUI\models\text_encoders\ovis_2.5.safetensors -p "a lovely cat" --cfg-scale 5.0 -v --offload-to-cpu --diffusion-fa
+.\bin\Release\sd-cli.exe --diffusion-model  ovis_image-Q4_0.gguf --vae ..\..\ComfyUI\models\vae\ae.sft  --llm ..\..\ComfyUI\models\text_encoders\ovis_2.5.safetensors -p "a lovely cat" --cfg-scale 5.0 -v --offload-to-cpu --diffusion-fa
 ```
 
 <img alt="ovis image example" src="../assets/ovis_image/example.png" />

docs/photo_maker.md

@@ -27,7 +27,7 @@ If on low memory GPUs (<= 8GB), recommend running with ```--vae-on-cpu``` option
 Example:
 
 ```bash
-bin/sd -m ../models/sdxlUnstableDiffusers_v11.safetensors  --vae ../models/sdxl_vae.safetensors --photo-maker ../models/photomaker-v1.safetensors --pm-id-images-dir ../assets/photomaker_examples/scarletthead_woman -p "a girl img, retro futurism, retro game art style but extremely beautiful, intricate details, masterpiece, best quality, space-themed, cosmic, celestial, stars, galaxies, nebulas, planets, science fiction, highly detailed" -n "realistic, photo-realistic, worst quality, greyscale, bad anatomy, bad hands, error, text" --cfg-scale 5.0  --sampling-method euler -H 1024 -W 1024 --pm-style-strength 10 --vae-on-cpu --steps 50
+bin/sd-cli -m ../models/sdxlUnstableDiffusers_v11.safetensors  --vae ../models/sdxl_vae.safetensors --photo-maker ../models/photomaker-v1.safetensors --pm-id-images-dir ../assets/photomaker_examples/scarletthead_woman -p "a girl img, retro futurism, retro game art style but extremely beautiful, intricate details, masterpiece, best quality, space-themed, cosmic, celestial, stars, galaxies, nebulas, planets, science fiction, highly detailed" -n "realistic, photo-realistic, worst quality, greyscale, bad anatomy, bad hands, error, text" --cfg-scale 5.0  --sampling-method euler -H 1024 -W 1024 --pm-style-strength 10 --vae-on-cpu --steps 50
 ```
 
 ## PhotoMaker Version 2

docs/quantization_and_gguf.md

@@ -23,5 +23,5 @@ You can also convert weights in the formats `ckpt/safetensors/diffusers` to gguf
 For example:
 
 ```sh
-./bin/sd -M convert -m ../models/v1-5-pruned-emaonly.safetensors -o  ../models/v1-5-pruned-emaonly.q8_0.gguf -v --type q8_0
+./bin/sd-cli -M convert -m ../models/v1-5-pruned-emaonly.safetensors -o  ../models/v1-5-pruned-emaonly.q8_0.gguf -v --type q8_0
 ```

docs/qwen_image.md

@@ -14,7 +14,7 @@
 ## Examples
 
 ```
-.\bin\Release\sd.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\qwen-image-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf  -p '一个穿着"QWEN"标志的T恤的中国美女正拿着黑色的马克笔面相镜头微笑。她身后的玻璃板上手写体写着 “一、Qwen-Image的技术路线： 探索视觉生成基础模型的极限，开创理解与生成一体化的未来。二、Qwen-Image的模型特色：1、复杂文字渲染。支持中英渲染、自动布局； 2、精准图像编辑。支持文字编辑、物体增减、风格变换。三、Qwen-Image的未来愿景：赋能专业内容创作、助力生成式AI发展。”' --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu -H 1024 -W 1024 --diffusion-fa --flow-shift 3
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\qwen-image-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf  -p '一个穿着"QWEN"标志的T恤的中国美女正拿着黑色的马克笔面相镜头微笑。她身后的玻璃板上手写体写着 “一、Qwen-Image的技术路线： 探索视觉生成基础模型的极限，开创理解与生成一体化的未来。二、Qwen-Image的模型特色：1、复杂文字渲染。支持中英渲染、自动布局； 2、精准图像编辑。支持文字编辑、物体增减、风格变换。三、Qwen-Image的未来愿景：赋能专业内容创作、助力生成式AI发展。”' --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu -H 1024 -W 1024 --diffusion-fa --flow-shift 3
 ```
 
 <img alt="qwen example" src="../assets/qwen/example.png" />

docs/qwen_image_edit.md

@@ -9,6 +9,9 @@
     - Qwen Image Edit 2509
         - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/tree/main/split_files/diffusion_models
         - gguf: https://huggingface.co/QuantStack/Qwen-Image-Edit-2509-GGUF/tree/main
+    - Qwen Image Edit 2511
+        - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/tree/main/split_files/diffusion_models
+        - gguf: https://huggingface.co/unsloth/Qwen-Image-Edit-2511-GGUF/tree/main
 - Download vae
     - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/vae
 - Download qwen_2.5_vl 7b
@@ -20,7 +23,7 @@
 ### Qwen Image Edit
 
 ```
-.\bin\Release\sd.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Qwen_Image_Edit-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_2.5_vl_7b.safetensors --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'edit.cpp'" --seed 1118877715456453
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Qwen_Image_Edit-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_2.5_vl_7b.safetensors --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'edit.cpp'" --seed 1118877715456453
 ```
 
 <img alt="qwen_image_edit" src="../assets/qwen/qwen_image_edit.png" />
@@ -29,7 +32,17 @@
 ### Qwen Image Edit 2509
 
 ```
-.\bin\Release\sd.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Qwen-Image-Edit-2509-Q4_K_S.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf --llm_vision ..\..\ComfyUI\models\text_encoders\Qwen2.5-VL-7B-Instruct.mmproj-Q8_0.gguf --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'Qwen Image Edit 2509'"
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Qwen-Image-Edit-2509-Q4_K_S.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf --llm_vision ..\..\ComfyUI\models\text_encoders\Qwen2.5-VL-7B-Instruct.mmproj-Q8_0.gguf --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'Qwen Image Edit 2509'"
 ```
 
-<img alt="qwen_image_edit_2509" src="../assets/qwen/qwen_image_edit_2509.png" />
+<img alt="qwen_image_edit_2509" src="../assets/qwen/qwen_image_edit_2509.png" />
+
+### Qwen Image Edit 2511
+
+To use the new Qwen Image Edit 2511 mode, the  `--qwen-image-zero-cond-t` flag must be enabled; otherwise, image editing quality will degrade significantly.
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\qwen-image-edit-2511-Q4_K_M.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_2.5_vl_7b.safetensors --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'edit.cpp'"  --qwen-image-zero-cond-t
+```
+
+<img alt="qwen_image_edit_2509" src="../assets/qwen/qwen_image_edit_2511.png" />

docs/sd.md

@@ -9,12 +9,12 @@
 ### txt2img example
 
 ```sh
-./bin/sd -m ../models/sd-v1-4.ckpt -p "a lovely cat"
+./bin/sd-cli -m ../models/sd-v1-4.ckpt -p "a lovely cat"
-# ./bin/sd -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat"
+# ./bin/sd-cli -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat"
-# ./bin/sd -m ../models/sd_xl_base_1.0.safetensors --vae ../models/sdxl_vae-fp16-fix.safetensors -H 1024 -W 1024 -p "a lovely cat" -v
+# ./bin/sd-cli -m ../models/sd_xl_base_1.0.safetensors --vae ../models/sdxl_vae-fp16-fix.safetensors -H 1024 -W 1024 -p "a lovely cat" -v
-# ./bin/sd -m ../models/sd3_medium_incl_clips_t5xxlfp16.safetensors -H 1024 -W 1024 -p 'a lovely cat holding a sign says \"Stable Diffusion CPP\"' --cfg-scale 4.5 --sampling-method euler -v --clip-on-cpu
+# ./bin/sd-cli -m ../models/sd3_medium_incl_clips_t5xxlfp16.safetensors -H 1024 -W 1024 -p 'a lovely cat holding a sign says \"Stable Diffusion CPP\"' --cfg-scale 4.5 --sampling-method euler -v --clip-on-cpu
-# ./bin/sd --diffusion-model  ../models/flux1-dev-q3_k.gguf --vae ../models/ae.sft --clip_l ../models/clip_l.safetensors --t5xxl ../models/t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'flux.cpp'" --cfg-scale 1.0 --sampling-method euler -v --clip-on-cpu
+# ./bin/sd-cli --diffusion-model  ../models/flux1-dev-q3_k.gguf --vae ../models/ae.sft --clip_l ../models/clip_l.safetensors --t5xxl ../models/t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'flux.cpp'" --cfg-scale 1.0 --sampling-method euler -v --clip-on-cpu
-# ./bin/sd -m  ..\models\sd3.5_large.safetensors --clip_l ..\models\clip_l.safetensors --clip_g ..\models\clip_g.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors  -H 1024 -W 1024 -p 'a lovely cat holding a sign says \"Stable diffusion 3.5 Large\"' --cfg-scale 4.5 --sampling-method euler -v --clip-on-cpu
+# ./bin/sd-cli -m  ..\models\sd3.5_large.safetensors --clip_l ..\models\clip_l.safetensors --clip_g ..\models\clip_g.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors  -H 1024 -W 1024 -p 'a lovely cat holding a sign says \"Stable diffusion 3.5 Large\"' --cfg-scale 4.5 --sampling-method euler -v --clip-on-cpu
 ```
 
 Using formats of different precisions will yield results of varying quality.
@@ -29,7 +29,7 @@ Using formats of different precisions will yield results of varying quality.
 
 
 ```
-./bin/sd -m ../models/sd-v1-4.ckpt -p "cat with blue eyes" -i ./output.png -o ./img2img_output.png --strength 0.4
+./bin/sd-cli -m ../models/sd-v1-4.ckpt -p "cat with blue eyes" -i ./output.png -o ./img2img_output.png --strength 0.4
 ```
 
 <p align="center">

docs/sd3.md

@@ -14,7 +14,7 @@
 For example:
 
 ```
-.\bin\Release\sd.exe -m  ..\models\sd3.5_large.safetensors --clip_l ..\models\clip_l.safetensors --clip_g ..\models\clip_g.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors  -H 1024 -W 1024 -p 'a lovely cat holding a sign says \"Stable diffusion 3.5 Large\"' --cfg-scale 4.5 --sampling-method euler -v --clip-on-cpu
+.\bin\Release\sd-cli.exe -m  ..\models\sd3.5_large.safetensors --clip_l ..\models\clip_l.safetensors --clip_g ..\models\clip_g.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors  -H 1024 -W 1024 -p 'a lovely cat holding a sign says \"Stable diffusion 3.5 Large\"' --cfg-scale 4.5 --sampling-method euler -v --clip-on-cpu
 ```
 
 ![](../assets/sd3.5_large.png)

docs/taesd.md

@@ -13,5 +13,27 @@ curl -L -O https://huggingface.co/madebyollin/taesd/resolve/main/diffusion_pytor
 - Specify the model path using the `--taesd PATH` parameter. example:
 
 ```bash
-sd -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat" --taesd ../models/diffusion_pytorch_model.safetensors
+sd-cli -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat" --taesd ../models/diffusion_pytorch_model.safetensors
-```
+```
+
+### Qwen-Image and wan (TAEHV)
+
+sd.cpp also supports [TAEHV](https://github.com/madebyollin/taehv) (#937), which can be used for Qwen-Image and wan.
+
+- For **Qwen-Image and wan2.1 and wan2.2-A14B**, download the wan2.1 tae [safetensors weights](https://github.com/madebyollin/taehv/blob/main/safetensors/taew2_1.safetensors)
+  
+  Or curl
+  
+  ```bash
+  curl -L -O https://github.com/madebyollin/taehv/raw/refs/heads/main/safetensors/taew2_1.safetensors
+  ```
+
+- For **wan2.2-TI2V-5B**, use the wan2.2 tae [safetensors weights](https://github.com/madebyollin/taehv/blob/main/safetensors/taew2_2.safetensors)
+  
+  Or curl
+  
+  ```bash
+  curl -L -O https://github.com/madebyollin/taehv/raw/refs/heads/main/safetensors/taew2_2.safetensors
+  ```
+
+Then simply replace the `--vae xxx.safetensors` with `--tae xxx.safetensors` in the commands. If it still out of VRAM, add `--vae-conv-direct` to your command though might be slower.

docs/wan.md

@@ -39,6 +39,9 @@
         - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/vae/wan_2.1_vae.safetensors
     - wan_2.2_vae (for Wan2.2 TI2V 5B only)
         - safetensors: https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/blob/main/split_files/vae/wan2.2_vae.safetensors
+
+    > Wan models vae requires really much VRAM! If you do not have enough VRAM, please try tae instead, though the results may be poorer. For tae usage, please refer to [taesd](taesd.md)
+
 - Download umt5_xxl
     - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/text_encoders/umt5_xxl_fp16.safetensors
     - gguf: https://huggingface.co/city96/umt5-xxl-encoder-gguf/tree/main
@@ -52,7 +55,7 @@
 ### Wan2.1 T2V 1.3B
 
 ```
-.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1_t2v_1.3B_fp16.safetensors --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --video-frames 33 --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1_t2v_1.3B_fp16.safetensors --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --video-frames 33 --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.1_1.3B_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -60,7 +63,7 @@
 ### Wan2.1 T2V 14B
 
 ```
-.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-t2v-14b-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa  --offload-to-cpu --video-frames 33 --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-t2v-14b-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa  --offload-to-cpu --video-frames 33 --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.1_14B_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -70,7 +73,7 @@
 ### Wan2.1 I2V 14B
 
 ```
-.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-i2v-14b-480p-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --clip_vision ..\..\ComfyUI\models\clip_vision\clip_vision_h.safetensors -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa --video-frames 33 --offload-to-cpu -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-i2v-14b-480p-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --clip_vision ..\..\ComfyUI\models\clip_vision\clip_vision_h.safetensors -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa --video-frames 33 --offload-to-cpu -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.1_14B_i2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -78,7 +81,7 @@
 ### Wan2.2 T2V A14B
 
 ```
-.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --offload-to-cpu --video-frames 33 --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --offload-to-cpu --video-frames 33 --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.2_14B_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -86,7 +89,7 @@
 ### Wan2.2 I2V A14B
 
 ```
-.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --offload-to-cpu --video-frames 33 --offload-to-cpu -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --offload-to-cpu --video-frames 33 --offload-to-cpu -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.2_14B_i2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -94,7 +97,7 @@
 ### Wan2.2 T2V A14B T2I
 
 ```
-.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --offload-to-cpu --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --offload-to-cpu --flow-shift 3.0
 ```
 
 <img width="832" height="480" alt="Wan2 2_14B_t2i" src="../assets/wan/Wan2.2_14B_t2i.png" />
@@ -102,7 +105,7 @@
 ### Wan2.2 T2V 14B with Lora
 
 ```
-.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat<lora:wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise:1><lora:|high_noise|wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise:1>" --cfg-scale 3.5 --sampling-method euler --steps 4 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 4 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --offload-to-cpu --lora-model-dir ..\..\ComfyUI\models\loras --video-frames 33 --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat<lora:wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise:1><lora:|high_noise|wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise:1>" --cfg-scale 3.5 --sampling-method euler --steps 4 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 4 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --offload-to-cpu --lora-model-dir ..\..\ComfyUI\models\loras --video-frames 33 --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.2_14B_t2v_lora.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -114,7 +117,7 @@
 #### T2V
 
 ```
-.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.2_ti2v_5B_fp16.safetensors --vae ..\..\ComfyUI\models\vae\wan2.2_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa --offload-to-cpu --video-frames 33 --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.2_ti2v_5B_fp16.safetensors --vae ..\..\ComfyUI\models\vae\wan2.2_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa --offload-to-cpu --video-frames 33 --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.2_5B_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -122,7 +125,7 @@
 #### I2V
 
 ```
-.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.2_ti2v_5B_fp16.safetensors --vae ..\..\ComfyUI\models\vae\wan2.2_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa --offload-to-cpu --video-frames 33 -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.2_ti2v_5B_fp16.safetensors --vae ..\..\ComfyUI\models\vae\wan2.2_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa --offload-to-cpu --video-frames 33 -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.2_5B_i2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -130,7 +133,7 @@
 ### Wan2.1 FLF2V 14B
 
 ```
-.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-flf2v-14b-720p-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --clip_vision ..\..\ComfyUI\models\clip_vision\clip_vision_h.safetensors -p "glass flower blossom" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa --video-frames 33 --offload-to-cpu --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-flf2v-14b-720p-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --clip_vision ..\..\ComfyUI\models\clip_vision\clip_vision_h.safetensors -p "glass flower blossom" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa --video-frames 33 --offload-to-cpu --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png --flow-shift 3.0
 ```
 
 
@@ -139,7 +142,7 @@
 ### Wan2.2 FLF2V 14B
 
 ```
-.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -p "glass flower blossom" -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa --video-frames 33 --offload-to-cpu --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -p "glass flower blossom" -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa --video-frames 33 --offload-to-cpu --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.2_14B_flf2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -149,7 +152,7 @@
 #### T2V
 
 ```
-.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --video-frames 1 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --video-frames 1 --offload-to-cpu
 ```
 
 <video src=../assets/wan/Wan2.1_1.3B_vace_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -158,7 +161,7 @@
 #### R2V
 
 ```
-.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa -i ..\assets\cat_with_sd_cpp_42.png --video-frames 33 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa -i ..\assets\cat_with_sd_cpp_42.png --video-frames 33 --offload-to-cpu
 ```
 
 <video src=../assets/wan/Wan2.1_1.3B_vace_r2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -169,7 +172,7 @@
 ```
 mkdir post+depth
 ffmpeg -i ..\..\ComfyUI\input\post+depth.mp4 -qscale:v 1 -vf fps=8 post+depth\frame_%04d.jpg
-.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "The girl is dancing in a sea of flowers, slowly moving her hands. There is a close - up shot of her upper body. The character is surrounded by other transparent glass flowers in the style of Nicoletta Ceccoli, creating a beautiful, surreal, and emotionally expressive movie scene with a white. transparent feel and a dreamyl atmosphere." --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa -i ..\..\ComfyUI\input\dance_girl.jpg --control-video ./post+depth --video-frames 33 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "The girl is dancing in a sea of flowers, slowly moving her hands. There is a close - up shot of her upper body. The character is surrounded by other transparent glass flowers in the style of Nicoletta Ceccoli, creating a beautiful, surreal, and emotionally expressive movie scene with a white. transparent feel and a dreamyl atmosphere." --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa -i ..\..\ComfyUI\input\dance_girl.jpg --control-video ./post+depth --video-frames 33 --offload-to-cpu
 ```
 
 <video src=../assets/wan/Wan2.1_1.3B_vace_v2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -179,7 +182,7 @@ ffmpeg -i ..\..\ComfyUI\input\post+depth.mp4 -qscale:v 1 -vf fps=8 post+depth\fr
 #### T2V
 
 ```
-.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --video-frames 33 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --video-frames 33 --offload-to-cpu
 ```
 
 <video src=../assets/wan/Wan2.1_14B_vace_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -188,7 +191,7 @@ ffmpeg -i ..\..\ComfyUI\input\post+depth.mp4 -qscale:v 1 -vf fps=8 post+depth\fr
 #### R2V
 
 ```
-.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa -i ..\assets\cat_with_sd_cpp_42.png --video-frames 33 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa -i ..\assets\cat_with_sd_cpp_42.png --video-frames 33 --offload-to-cpu
 ```
 
 <video src=../assets/wan/Wan2.1_14B_vace_r2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -198,7 +201,7 @@ ffmpeg -i ..\..\ComfyUI\input\post+depth.mp4 -qscale:v 1 -vf fps=8 post+depth\fr
 #### V2V
 
 ```
-.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "The girl is dancing in a sea of flowers, slowly moving her hands. There is a close - up shot of her upper body. The character is surrounded by other transparent glass flowers in the style of Nicoletta Ceccoli, creating a beautiful, surreal, and emotionally expressive movie scene with a white. transparent feel and a dreamyl atmosphere." --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa -i ..\..\ComfyUI\input\dance_girl.jpg --control-video ./post+depth --video-frames 33 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "The girl is dancing in a sea of flowers, slowly moving her hands. There is a close - up shot of her upper body. The character is surrounded by other transparent glass flowers in the style of Nicoletta Ceccoli, creating a beautiful, surreal, and emotionally expressive movie scene with a white. transparent feel and a dreamyl atmosphere." --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa -i ..\..\ComfyUI\input\dance_girl.jpg --control-video ./post+depth --video-frames 33 --offload-to-cpu
 ```
 
 <video src=../assets/wan/Wan2.1_14B_vace_v2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>

docs/z_image.md

@@ -16,7 +16,7 @@ You can run Z-Image with stable-diffusion.cpp on GPUs with 4GB of VRAM — or ev
 ## Examples
 
 ```
-.\bin\Release\sd.exe --diffusion-model  z_image_turbo-Q3_K.gguf --vae ..\..\ComfyUI\models\vae\ae.sft  --llm ..\..\ComfyUI\models\text_encoders\Qwen3-4B-Instruct-2507-Q4_K_M.gguf -p "A cinematic, melancholic photograph of a solitary hooded figure walking through a sprawling, rain-slicked metropolis at night. The city lights are a chaotic blur of neon orange and cool blue, reflecting on the wet asphalt. The scene evokes a sense of being a single component in a vast machine. Superimposed over the image in a sleek, modern, slightly glitched font is the philosophical quote: 'THE CITY IS A CIRCUIT BOARD, AND I AM A BROKEN TRANSISTOR.' -- moody, atmospheric, profound, dark academic" --cfg-scale 1.0 -v --offload-to-cpu --diffusion-fa -H 1024 -W 512
+.\bin\Release\sd-cli.exe --diffusion-model  z_image_turbo-Q3_K.gguf --vae ..\..\ComfyUI\models\vae\ae.sft  --llm ..\..\ComfyUI\models\text_encoders\Qwen3-4B-Instruct-2507-Q4_K_M.gguf -p "A cinematic, melancholic photograph of a solitary hooded figure walking through a sprawling, rain-slicked metropolis at night. The city lights are a chaotic blur of neon orange and cool blue, reflecting on the wet asphalt. The scene evokes a sense of being a single component in a vast machine. Superimposed over the image in a sleek, modern, slightly glitched font is the philosophical quote: 'THE CITY IS A CIRCUIT BOARD, AND I AM A BROKEN TRANSISTOR.' -- moody, atmospheric, profound, dark academic" --cfg-scale 1.0 -v --offload-to-cpu --diffusion-fa -H 1024 -W 512
 ```
 
 <img width="256" alt="z-image example" src="../assets/z_image/q3_K.png" />

examples/CMakeLists.txt

@@ -1,3 +1,4 @@
 include_directories(${CMAKE_CURRENT_SOURCE_DIR})
 
-add_subdirectory(cli)
+add_subdirectory(cli)
+add_subdirectory(server)

examples/cli/CMakeLists.txt

@@ -1,4 +1,4 @@
-set(TARGET sd)
+set(TARGET sd-cli)
 
 add_executable(${TARGET} main.cpp)
 install(TARGETS ${TARGET} RUNTIME)

examples/cli/README.md

@@ -1,14 +1,16 @@
 # Run
 
 ```
-usage: ./bin/sd  [options]
+usage: ./bin/sd-cli  [options]
 
 CLI Options:
-  -o, --output <string>       path to write result image to (default: ./output.png)
+  -o, --output <string>       path to write result image to. you can use printf-style %d format specifiers for image sequences (default: ./output.png) (eg. output_%03d.png)
+  --output-begin-idx <int>    starting index for output image sequence, must be non-negative (default 0 if specified %d in output path, 1 otherwise)
   --preview-path <string>     path to write preview image to (default: ./preview.png)
   --preview-interval <int>    interval in denoising steps between consecutive updates of the image preview file (default is 1, meaning updating at
                               every step)
   --canny                     apply canny preprocessor (edge detection)
+  --convert-name              convert tensor name (for convert mode)
   -v, --verbose               print extra info
   --color                     colors the logging tags according to level
   --taesd-preview-only        prevents usage of taesd for decoding the final image. (for use with --preview tae)
@@ -31,6 +33,7 @@ Context Options:
   --high-noise-diffusion-model <string>    path to the standalone high noise diffusion model
   --vae <string>                           path to standalone vae model
   --taesd <string>                         path to taesd. Using Tiny AutoEncoder for fast decoding (low quality)
+  --tae <string>                           alias of --taesd
   --control-net <string>                   path to control net model
   --embd-dir <string>                      embeddings directory
   --lora-model-dir <string>                lora model directory
@@ -45,12 +48,16 @@ Context Options:
   --vae-tiling                             process vae in tiles to reduce memory usage
   --force-sdxl-vae-conv-scale              force use of conv scale on sdxl vae
   --offload-to-cpu                         place the weights in RAM to save VRAM, and automatically load them into VRAM when needed
+  --mmap                                   whether to memory-map model
   --control-net-cpu                        keep controlnet in cpu (for low vram)
   --clip-on-cpu                            keep clip in cpu (for low vram)
   --vae-on-cpu                             keep vae in cpu (for low vram)
   --diffusion-fa                           use flash attention in the diffusion model
   --diffusion-conv-direct                  use ggml_conv2d_direct in the diffusion model
   --vae-conv-direct                        use ggml_conv2d_direct in the vae model
+  --circular                               enable circular padding for convolutions
+  --circularx                              enable circular RoPE wrapping on x-axis (width) only
+  --circulary                              enable circular RoPE wrapping on y-axis (height) only
   --chroma-disable-dit-mask                disable dit mask for chroma
   --chroma-enable-t5-mask                  enable t5 mask for chroma
   --type                                   weight type (examples: f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_K, q3_K, q4_K). If not specified, the default is the
@@ -92,6 +99,7 @@ Generation Options:
   --timestep-shift <int>                   shift timestep for NitroFusion models (default: 0). recommended N for NitroSD-Realism around 250 and 500 for
                                            NitroSD-Vibrant
   --upscale-repeats <int>                  Run the ESRGAN upscaler this many times (default: 1)
+  --upscale-tile-size <int>                tile size for ESRGAN upscaling (default: 128)
   --cfg-scale <float>                      unconditional guidance scale: (default: 7.0)
   --img-cfg-scale <float>                  image guidance scale for inpaint or instruct-pix2pix models: (default: same as --cfg-scale)
   --guidance <float>                       distilled guidance scale for models with guidance input (default: 3.5)
@@ -119,10 +127,17 @@ Generation Options:
                                            tcd] (default: euler for Flux/SD3/Wan, euler_a otherwise)
   --high-noise-sampling-method             (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm,
                                            ddim_trailing, tcd] default: euler for Flux/SD3/Wan, euler_a otherwise
-  --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, lcm],
+  --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple,
-                                           default: discrete
+                                           kl_optimal, lcm], default: discrete
+  --sigmas                                 custom sigma values for the sampler, comma-separated (e.g., "14.61,7.8,3.5,0.0").
   --skip-layers                            layers to skip for SLG steps (default: [7,8,9])
   --high-noise-skip-layers                 (high noise) layers to skip for SLG steps (default: [7,8,9])
   -r, --ref-image                          reference image for Flux Kontext models (can be used multiple times)
-  --easycache                              enable EasyCache for DiT models with optional "threshold,start_percent,end_percent" (default: 0.2,0.15,0.95)
+  --cache-mode                             caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level)
+  --cache-option                           named cache params (key=value format, comma-separated). easycache/ucache:
+                                           threshold=,start=,end=,decay=,relative=,reset=; dbcache/taylorseer/cache-dit: Fn=,Bn=,threshold=,warmup=. Examples:
+                                           "threshold=0.25" or "threshold=1.5,reset=0"
+  --cache-preset                           cache-dit preset: 'slow'/'s', 'medium'/'m', 'fast'/'f', 'ultra'/'u'
+  --scm-mask                               SCM steps mask for cache-dit: comma-separated 0/1 (e.g., "1,1,1,0,0,1,0,0,1,0") - 1=compute, 0=can cache
+  --scm-policy                             SCM policy: 'dynamic' (default) or 'static'
 ```

examples/server/CMakeLists.txt

@@ -0,0 +1,6 @@
+set(TARGET sd-server)
+
+add_executable(${TARGET} main.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE stable-diffusion ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PUBLIC c_std_11 cxx_std_17)

examples/server/README.md

@@ -0,0 +1,135 @@
+# Run
+
+```
+usage: ./bin/sd-server  [options]
+
+Svr Options:
+  -l, --listen-ip <string>    server listen ip (default: 127.0.0.1)
+  --listen-port <int>         server listen port (default: 1234)
+  --serve-html-path <string>  path to HTML file to serve at root (optional)
+  -v, --verbose               print extra info
+  --color                     colors the logging tags according to level
+  -h, --help                  show this help message and exit
+
+Context Options:
+  -m, --model <string>                     path to full model
+  --clip_l <string>                        path to the clip-l text encoder
+  --clip_g <string>                        path to the clip-g text encoder
+  --clip_vision <string>                   path to the clip-vision encoder
+  --t5xxl <string>                         path to the t5xxl text encoder
+  --llm <string>                           path to the llm text encoder. For example: (qwenvl2.5 for qwen-image, mistral-small3.2 for flux2, ...)
+  --llm_vision <string>                    path to the llm vit
+  --qwen2vl <string>                       alias of --llm. Deprecated.
+  --qwen2vl_vision <string>                alias of --llm_vision. Deprecated.
+  --diffusion-model <string>               path to the standalone diffusion model
+  --high-noise-diffusion-model <string>    path to the standalone high noise diffusion model
+  --vae <string>                           path to standalone vae model
+  --taesd <string>                         path to taesd. Using Tiny AutoEncoder for fast decoding (low quality)
+  --tae <string>                           alias of --taesd
+  --control-net <string>                   path to control net model
+  --embd-dir <string>                      embeddings directory
+  --lora-model-dir <string>                lora model directory
+  --tensor-type-rules <string>             weight type per tensor pattern (example: "^vae\.=f16,model\.=q8_0")
+  --photo-maker <string>                   path to PHOTOMAKER model
+  --upscale-model <string>                 path to esrgan model.
+  -t, --threads <int>                      number of threads to use during computation (default: -1). If threads <= 0, then threads will be set to the number of
+                                           CPU physical cores
+  --chroma-t5-mask-pad <int>               t5 mask pad size of chroma
+  --vae-tile-overlap <float>               tile overlap for vae tiling, in fraction of tile size (default: 0.5)
+  --flow-shift <float>                     shift value for Flow models like SD3.x or WAN (default: auto)
+  --vae-tiling                             process vae in tiles to reduce memory usage
+  --force-sdxl-vae-conv-scale              force use of conv scale on sdxl vae
+  --offload-to-cpu                         place the weights in RAM to save VRAM, and automatically load them into VRAM when needed
+  --control-net-cpu                        keep controlnet in cpu (for low vram)
+  --clip-on-cpu                            keep clip in cpu (for low vram)
+  --vae-on-cpu                             keep vae in cpu (for low vram)
+  --mmap                                   whether to memory-map model
+  --diffusion-fa                           use flash attention in the diffusion model
+  --diffusion-conv-direct                  use ggml_conv2d_direct in the diffusion model
+  --vae-conv-direct                        use ggml_conv2d_direct in the vae model
+  --circular                               enable circular padding for convolutions
+  --circularx                              enable circular RoPE wrapping on x-axis (width) only
+  --circulary                              enable circular RoPE wrapping on y-axis (height) only
+  --chroma-disable-dit-mask                disable dit mask for chroma
+  --chroma-enable-t5-mask                  enable t5 mask for chroma
+  --type                                   weight type (examples: f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_K, q3_K, q4_K). If not specified, the default is the
+                                           type of the weight file
+  --rng                                    RNG, one of [std_default, cuda, cpu], default: cuda(sd-webui), cpu(comfyui)
+  --sampler-rng                            sampler RNG, one of [std_default, cuda, cpu]. If not specified, use --rng
+  --prediction                             prediction type override, one of [eps, v, edm_v, sd3_flow, flux_flow, flux2_flow]
+  --lora-apply-mode                        the way to apply LoRA, one of [auto, immediately, at_runtime], default is auto. In auto mode, if the model weights
+                                           contain any quantized parameters, the at_runtime mode will be used; otherwise,
+                                           immediately will be used.The immediately mode may have precision and
+                                           compatibility issues with quantized parameters, but it usually offers faster inference
+                                           speed and, in some cases, lower memory usage. The at_runtime mode, on the
+                                           other hand, is exactly the opposite.
+  --vae-tile-size                          tile size for vae tiling, format [X]x[Y] (default: 32x32)
+  --vae-relative-tile-size                 relative tile size for vae tiling, format [X]x[Y], in fraction of image size if < 1, in number of tiles per dim if >=1
+                                           (overrides --vae-tile-size)
+
+Default Generation Options:
+  -p, --prompt <string>                    the prompt to render
+  -n, --negative-prompt <string>           the negative prompt (default: "")
+  -i, --init-img <string>                  path to the init image
+  --end-img <string>                       path to the end image, required by flf2v
+  --mask <string>                          path to the mask image
+  --control-image <string>                 path to control image, control net
+  --control-video <string>                 path to control video frames, It must be a directory path. The video frames inside should be stored as images in
+                                           lexicographical (character) order. For example, if the control video path is
+                                           `frames`, the directory contain images such as 00.png, 01.png, ... etc.
+  --pm-id-images-dir <string>              path to PHOTOMAKER input id images dir
+  --pm-id-embed-path <string>              path to PHOTOMAKER v2 id embed
+  -H, --height <int>                       image height, in pixel space (default: 512)
+  -W, --width <int>                        image width, in pixel space (default: 512)
+  --steps <int>                            number of sample steps (default: 20)
+  --high-noise-steps <int>                 (high noise) number of sample steps (default: -1 = auto)
+  --clip-skip <int>                        ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer (default: -1). <= 0 represents unspecified,
+                                           will be 1 for SD1.x, 2 for SD2.x
+  -b, --batch-count <int>                  batch count
+  --video-frames <int>                     video frames (default: 1)
+  --fps <int>                              fps (default: 24)
+  --timestep-shift <int>                   shift timestep for NitroFusion models (default: 0). recommended N for NitroSD-Realism around 250 and 500 for
+                                           NitroSD-Vibrant
+  --upscale-repeats <int>                  Run the ESRGAN upscaler this many times (default: 1)
+  --upscale-tile-size <int>                tile size for ESRGAN upscaling (default: 128)
+  --cfg-scale <float>                      unconditional guidance scale: (default: 7.0)
+  --img-cfg-scale <float>                  image guidance scale for inpaint or instruct-pix2pix models: (default: same as --cfg-scale)
+  --guidance <float>                       distilled guidance scale for models with guidance input (default: 3.5)
+  --slg-scale <float>                      skip layer guidance (SLG) scale, only for DiT models: (default: 0). 0 means disabled, a value of 2.5 is nice for sd3.5
+                                           medium
+  --skip-layer-start <float>               SLG enabling point (default: 0.01)
+  --skip-layer-end <float>                 SLG disabling point (default: 0.2)
+  --eta <float>                            eta in DDIM, only for DDIM and TCD (default: 0)
+  --high-noise-cfg-scale <float>           (high noise) unconditional guidance scale: (default: 7.0)
+  --high-noise-img-cfg-scale <float>       (high noise) image guidance scale for inpaint or instruct-pix2pix models (default: same as --cfg-scale)
+  --high-noise-guidance <float>            (high noise) distilled guidance scale for models with guidance input (default: 3.5)
+  --high-noise-slg-scale <float>           (high noise) skip layer guidance (SLG) scale, only for DiT models: (default: 0)
+  --high-noise-skip-layer-start <float>    (high noise) SLG enabling point (default: 0.01)
+  --high-noise-skip-layer-end <float>      (high noise) SLG disabling point (default: 0.2)
+  --high-noise-eta <float>                 (high noise) eta in DDIM, only for DDIM and TCD (default: 0)
+  --strength <float>                       strength for noising/unnoising (default: 0.75)
+  --pm-style-strength <float>
+  --control-strength <float>               strength to apply Control Net (default: 0.9). 1.0 corresponds to full destruction of information in init image
+  --moe-boundary <float>                   timestep boundary for Wan2.2 MoE model. (default: 0.875). Only enabled if `--high-noise-steps` is set to -1
+  --vace-strength <float>                  wan vace strength
+  --increase-ref-index                     automatically increase the indices of references images based on the order they are listed (starting with 1).
+  --disable-auto-resize-ref-image          disable auto resize of ref images
+  -s, --seed                               RNG seed (default: 42, use random seed for < 0)
+  --sampling-method                        sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing,
+                                           tcd] (default: euler for Flux/SD3/Wan, euler_a otherwise)
+  --high-noise-sampling-method             (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm,
+                                           ddim_trailing, tcd] default: euler for Flux/SD3/Wan, euler_a otherwise
+  --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple,
+                                           kl_optimal, lcm], default: discrete
+  --sigmas                                 custom sigma values for the sampler, comma-separated (e.g., "14.61,7.8,3.5,0.0").
+  --skip-layers                            layers to skip for SLG steps (default: [7,8,9])
+  --high-noise-skip-layers                 (high noise) layers to skip for SLG steps (default: [7,8,9])
+  -r, --ref-image                          reference image for Flux Kontext models (can be used multiple times)
+  --cache-mode                             caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level)
+  --cache-option                           named cache params (key=value format, comma-separated). easycache/ucache:
+                                           threshold=,start=,end=,decay=,relative=,reset=; dbcache/taylorseer/cache-dit: Fn=,Bn=,threshold=,warmup=. Examples:
+                                           "threshold=0.25" or "threshold=1.5,reset=0"
+  --cache-preset                           cache-dit preset: 'slow'/'s', 'medium'/'m', 'fast'/'f', 'ultra'/'u'
+  --scm-mask                               SCM steps mask for cache-dit: comma-separated 0/1 (e.g., "1,1,1,0,0,1,0,0,1,0") - 1=compute, 0=can cache
+  --scm-policy                             SCM policy: 'dynamic' (default) or 'static'
+```

examples/server/main.cpp

@@ -0,0 +1,729 @@
+// main.cpp
+#include <chrono>
+#include <filesystem>
+#include <fstream>
+#include <iomanip>
+#include <iostream>
+#include <mutex>
+#include <sstream>
+#include <vector>
+
+#include "httplib.h"
+#include "stable-diffusion.h"
+
+#include "common/common.hpp"
+
+namespace fs = std::filesystem;
+
+// ----------------------- helpers -----------------------
+static const std::string base64_chars =
+    "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
+    "abcdefghijklmnopqrstuvwxyz"
+    "0123456789+/";
+
+std::string base64_encode(const std::vector<uint8_t>& bytes) {
+    std::string ret;
+    int val = 0, valb = -6;
+    for (uint8_t c : bytes) {
+        val = (val << 8) + c;
+        valb += 8;
+        while (valb >= 0) {
+            ret.push_back(base64_chars[(val >> valb) & 0x3F]);
+            valb -= 6;
+        }
+    }
+    if (valb > -6)
+        ret.push_back(base64_chars[((val << 8) >> (valb + 8)) & 0x3F]);
+    while (ret.size() % 4)
+        ret.push_back('=');
+    return ret;
+}
+
+inline bool is_base64(unsigned char c) {
+    return (isalnum(c) || (c == '+') || (c == '/'));
+}
+
+std::vector<uint8_t> base64_decode(const std::string& encoded_string) {
+    int in_len = encoded_string.size();
+    int i      = 0;
+    int j      = 0;
+    int in_    = 0;
+    uint8_t char_array_4[4], char_array_3[3];
+    std::vector<uint8_t> ret;
+
+    while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_])) {
+        char_array_4[i++] = encoded_string[in_];
+        in_++;
+        if (i == 4) {
+            for (i = 0; i < 4; i++)
+                char_array_4[i] = static_cast<uint8_t>(base64_chars.find(char_array_4[i]));
+
+            char_array_3[0] = (char_array_4[0] << 2) + ((char_array_4[1] & 0x30) >> 4);
+            char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
+            char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3];
+
+            for (i = 0; i < 3; i++)
+                ret.push_back(char_array_3[i]);
+            i = 0;
+        }
+    }
+
+    if (i) {
+        for (j = i; j < 4; j++)
+            char_array_4[j] = 0;
+
+        for (j = 0; j < 4; j++)
+            char_array_4[j] = static_cast<uint8_t>(base64_chars.find(char_array_4[j]));
+
+        char_array_3[0] = (char_array_4[0] << 2) + ((char_array_4[1] & 0x30) >> 4);
+        char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
+        char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3];
+
+        for (j = 0; j < i - 1; j++)
+            ret.push_back(char_array_3[j]);
+    }
+
+    return ret;
+}
+
+std::string iso_timestamp_now() {
+    using namespace std::chrono;
+    auto now      = system_clock::now();
+    std::time_t t = system_clock::to_time_t(now);
+    std::tm tm{};
+#ifdef _MSC_VER
+    gmtime_s(&tm, &t);
+#else
+    gmtime_r(&t, &tm);
+#endif
+    std::ostringstream oss;
+    oss << std::put_time(&tm, "%Y-%m-%dT%H:%M:%SZ");
+    return oss.str();
+}
+
+struct SDSvrParams {
+    std::string listen_ip = "127.0.0.1";
+    int listen_port       = 1234;
+    std::string serve_html_path;
+    bool normal_exit = false;
+    bool verbose     = false;
+    bool color       = false;
+
+    ArgOptions get_options() {
+        ArgOptions options;
+
+        options.string_options = {
+            {"-l",
+             "--listen-ip",
+             "server listen ip (default: 127.0.0.1)",
+             &listen_ip},
+            {"",
+             "--serve-html-path",
+             "path to HTML file to serve at root (optional)",
+             &serve_html_path}};
+
+        options.int_options = {
+            {"",
+             "--listen-port",
+             "server listen port (default: 1234)",
+             &listen_port},
+        };
+
+        options.bool_options = {
+            {"-v",
+             "--verbose",
+             "print extra info",
+             true, &verbose},
+            {"",
+             "--color",
+             "colors the logging tags according to level",
+             true, &color},
+        };
+
+        auto on_help_arg = [&](int argc, const char** argv, int index) {
+            normal_exit = true;
+            return -1;
+        };
+
+        options.manual_options = {
+            {"-h",
+             "--help",
+             "show this help message and exit",
+             on_help_arg},
+        };
+        return options;
+    };
+
+    bool process_and_check() {
+        if (listen_ip.empty()) {
+            LOG_ERROR("error: the following arguments are required: listen_ip");
+            return false;
+        }
+
+        if (listen_port < 0 || listen_port > 65535) {
+            LOG_ERROR("error: listen_port should be in the range [0, 65535]");
+            return false;
+        }
+
+        if (!serve_html_path.empty() && !fs::exists(serve_html_path)) {
+            LOG_ERROR("error: serve_html_path file does not exist: %s", serve_html_path.c_str());
+            return false;
+        }
+        return true;
+    }
+
+    std::string to_string() const {
+        std::ostringstream oss;
+        oss << "SDSvrParams {\n"
+            << "  listen_ip: " << listen_ip << ",\n"
+            << "  listen_port: \"" << listen_port << "\",\n"
+            << "  serve_html_path: \"" << serve_html_path << "\",\n"
+            << "}";
+        return oss.str();
+    }
+};
+
+void print_usage(int argc, const char* argv[], const std::vector<ArgOptions>& options_list) {
+    std::cout << version_string() << "\n";
+    std::cout << "Usage: " << argv[0] << " [options]\n\n";
+    std::cout << "Svr Options:\n";
+    options_list[0].print();
+    std::cout << "\nContext Options:\n";
+    options_list[1].print();
+    std::cout << "\nDefault Generation Options:\n";
+    options_list[2].print();
+}
+
+void parse_args(int argc, const char** argv, SDSvrParams& svr_params, SDContextParams& ctx_params, SDGenerationParams& default_gen_params) {
+    std::vector<ArgOptions> options_vec = {svr_params.get_options(), ctx_params.get_options(), default_gen_params.get_options()};
+
+    if (!parse_options(argc, argv, options_vec)) {
+        print_usage(argc, argv, options_vec);
+        exit(svr_params.normal_exit ? 0 : 1);
+    }
+
+    if (!svr_params.process_and_check() ||
+        !ctx_params.process_and_check(IMG_GEN) ||
+        !default_gen_params.process_and_check(IMG_GEN, ctx_params.lora_model_dir)) {
+        print_usage(argc, argv, options_vec);
+        exit(1);
+    }
+}
+
+std::string extract_and_remove_sd_cpp_extra_args(std::string& text) {
+    std::regex re("<sd_cpp_extra_args>(.*?)</sd_cpp_extra_args>");
+    std::smatch match;
+
+    std::string extracted;
+    if (std::regex_search(text, match, re)) {
+        extracted = match[1].str();
+        text      = std::regex_replace(text, re, "");
+    }
+    return extracted;
+}
+
+enum class ImageFormat { JPEG,
+                         PNG };
+
+std::vector<uint8_t> write_image_to_vector(
+    ImageFormat format,
+    const uint8_t* image,
+    int width,
+    int height,
+    int channels,
+    int quality = 90) {
+    std::vector<uint8_t> buffer;
+
+    auto write_func = [&buffer](void* context, void* data, int size) {
+        uint8_t* src = reinterpret_cast<uint8_t*>(data);
+        buffer.insert(buffer.end(), src, src + size);
+    };
+
+    struct ContextWrapper {
+        decltype(write_func)& func;
+    } ctx{write_func};
+
+    auto c_func = [](void* context, void* data, int size) {
+        auto* wrapper = reinterpret_cast<ContextWrapper*>(context);
+        wrapper->func(context, data, size);
+    };
+
+    int result = 0;
+    switch (format) {
+        case ImageFormat::JPEG:
+            result = stbi_write_jpg_to_func(c_func, &ctx, width, height, channels, image, quality);
+            break;
+        case ImageFormat::PNG:
+            result = stbi_write_png_to_func(c_func, &ctx, width, height, channels, image, width * channels);
+            break;
+        default:
+            throw std::runtime_error("invalid image format");
+    }
+
+    if (!result) {
+        throw std::runtime_error("write imgage to mem failed");
+    }
+
+    return buffer;
+}
+
+void sd_log_cb(enum sd_log_level_t level, const char* log, void* data) {
+    SDSvrParams* svr_params = (SDSvrParams*)data;
+    log_print(level, log, svr_params->verbose, svr_params->color);
+}
+
+int main(int argc, const char** argv) {
+    if (argc > 1 && std::string(argv[1]) == "--version") {
+        std::cout << version_string() << "\n";
+        return EXIT_SUCCESS;
+    }
+    SDSvrParams svr_params;
+    SDContextParams ctx_params;
+    SDGenerationParams default_gen_params;
+    parse_args(argc, argv, svr_params, ctx_params, default_gen_params);
+
+    sd_set_log_callback(sd_log_cb, (void*)&svr_params);
+    log_verbose = svr_params.verbose;
+    log_color   = svr_params.color;
+
+    LOG_DEBUG("version: %s", version_string().c_str());
+    LOG_DEBUG("%s", sd_get_system_info());
+    LOG_DEBUG("%s", svr_params.to_string().c_str());
+    LOG_DEBUG("%s", ctx_params.to_string().c_str());
+    LOG_DEBUG("%s", default_gen_params.to_string().c_str());
+
+    sd_ctx_params_t sd_ctx_params = ctx_params.to_sd_ctx_params_t(false, false, false);
+    sd_ctx_t* sd_ctx              = new_sd_ctx(&sd_ctx_params);
+
+    if (sd_ctx == nullptr) {
+        LOG_ERROR("new_sd_ctx_t failed");
+        return 1;
+    }
+
+    std::mutex sd_ctx_mutex;
+
+    httplib::Server svr;
+
+    svr.set_pre_routing_handler([](const httplib::Request& req, httplib::Response& res) {
+        std::string origin = req.get_header_value("Origin");
+        if (origin.empty()) {
+            origin = "*";
+        }
+        res.set_header("Access-Control-Allow-Origin", origin);
+        res.set_header("Access-Control-Allow-Credentials", "true");
+        res.set_header("Access-Control-Allow-Methods", "*");
+        res.set_header("Access-Control-Allow-Headers", "*");
+
+        if (req.method == "OPTIONS") {
+            res.status = 204;
+            return httplib::Server::HandlerResponse::Handled;
+        }
+        return httplib::Server::HandlerResponse::Unhandled;
+    });
+
+    // health
+    svr.Get("/", [&](const httplib::Request&, httplib::Response& res) {
+        if (!svr_params.serve_html_path.empty()) {
+            std::ifstream file(svr_params.serve_html_path);
+            if (file) {
+                std::string content((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
+                res.set_content(content, "text/html");
+            } else {
+                res.status = 500;
+                res.set_content("Error: Unable to read HTML file", "text/plain");
+            }
+        } else {
+            res.set_content("Stable Diffusion Server is running", "text/plain");
+        }
+    });
+
+    // models endpoint (minimal)
+    svr.Get("/v1/models", [&](const httplib::Request&, httplib::Response& res) {
+        json r;
+        r["data"] = json::array();
+        r["data"].push_back({{"id", "sd-cpp-local"}, {"object", "model"}, {"owned_by", "local"}});
+        res.set_content(r.dump(), "application/json");
+    });
+
+    // core endpoint: /v1/images/generations
+    svr.Post("/v1/images/generations", [&](const httplib::Request& req, httplib::Response& res) {
+        try {
+            if (req.body.empty()) {
+                res.status = 400;
+                res.set_content(R"({"error":"empty body"})", "application/json");
+                return;
+            }
+
+            json j                    = json::parse(req.body);
+            std::string prompt        = j.value("prompt", "");
+            int n                     = std::max(1, j.value("n", 1));
+            std::string size          = j.value("size", "");
+            std::string output_format = j.value("output_format", "png");
+            int output_compression    = j.value("output_compression", 100);
+            int width                 = 512;
+            int height                = 512;
+            if (!size.empty()) {
+                auto pos = size.find('x');
+                if (pos != std::string::npos) {
+                    try {
+                        width  = std::stoi(size.substr(0, pos));
+                        height = std::stoi(size.substr(pos + 1));
+                    } catch (...) {
+                    }
+                }
+            }
+
+            if (prompt.empty()) {
+                res.status = 400;
+                res.set_content(R"({"error":"prompt required"})", "application/json");
+                return;
+            }
+
+            std::string sd_cpp_extra_args_str = extract_and_remove_sd_cpp_extra_args(prompt);
+
+            if (output_format != "png" && output_format != "jpeg") {
+                res.status = 400;
+                res.set_content(R"({"error":"invalid output_format, must be one of [png, jpeg]"})", "application/json");
+                return;
+            }
+            if (n <= 0)
+                n = 1;
+            if (n > 8)
+                n = 8;  // safety
+            if (output_compression > 100) {
+                output_compression = 100;
+            }
+            if (output_compression < 0) {
+                output_compression = 0;
+            }
+
+            json out;
+            out["created"]       = iso_timestamp_now();
+            out["data"]          = json::array();
+            out["output_format"] = output_format;
+
+            SDGenerationParams gen_params = default_gen_params;
+            gen_params.prompt             = prompt;
+            gen_params.width              = width;
+            gen_params.height             = height;
+            gen_params.batch_count        = n;
+
+            if (!sd_cpp_extra_args_str.empty() && !gen_params.from_json_str(sd_cpp_extra_args_str)) {
+                res.status = 400;
+                res.set_content(R"({"error":"invalid sd_cpp_extra_args"})", "application/json");
+                return;
+            }
+
+            if (!gen_params.process_and_check(IMG_GEN, "")) {
+                res.status = 400;
+                res.set_content(R"({"error":"invalid params"})", "application/json");
+                return;
+            }
+
+            LOG_DEBUG("%s\n", gen_params.to_string().c_str());
+
+            sd_image_t init_image    = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 3, nullptr};
+            sd_image_t control_image = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 3, nullptr};
+            sd_image_t mask_image    = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 1, nullptr};
+            std::vector<sd_image_t> pmid_images;
+
+            sd_img_gen_params_t img_gen_params = {
+                gen_params.lora_vec.data(),
+                static_cast<uint32_t>(gen_params.lora_vec.size()),
+                gen_params.prompt.c_str(),
+                gen_params.negative_prompt.c_str(),
+                gen_params.clip_skip,
+                init_image,
+                nullptr,
+                0,
+                gen_params.auto_resize_ref_image,
+                gen_params.increase_ref_index,
+                mask_image,
+                gen_params.width,
+                gen_params.height,
+                gen_params.sample_params,
+                gen_params.strength,
+                gen_params.seed,
+                gen_params.batch_count,
+                control_image,
+                gen_params.control_strength,
+                {
+                    pmid_images.data(),
+                    (int)pmid_images.size(),
+                    gen_params.pm_id_embed_path.c_str(),
+                    gen_params.pm_style_strength,
+                },  // pm_params
+                ctx_params.vae_tiling_params,
+                gen_params.cache_params,
+            };
+
+            sd_image_t* results = nullptr;
+            int num_results     = 0;
+
+            {
+                std::lock_guard<std::mutex> lock(sd_ctx_mutex);
+                results     = generate_image(sd_ctx, &img_gen_params);
+                num_results = gen_params.batch_count;
+            }
+
+            for (int i = 0; i < num_results; i++) {
+                if (results[i].data == nullptr) {
+                    continue;
+                }
+                auto image_bytes = write_image_to_vector(output_format == "jpeg" ? ImageFormat::JPEG : ImageFormat::PNG,
+                                                         results[i].data,
+                                                         results[i].width,
+                                                         results[i].height,
+                                                         results[i].channel,
+                                                         output_compression);
+                if (image_bytes.empty()) {
+                    LOG_ERROR("write image to mem failed");
+                    continue;
+                }
+
+                // base64 encode
+                std::string b64 = base64_encode(image_bytes);
+                json item;
+                item["b64_json"] = b64;
+                out["data"].push_back(item);
+            }
+
+            res.set_content(out.dump(), "application/json");
+            res.status = 200;
+
+        } catch (const std::exception& e) {
+            res.status = 500;
+            json err;
+            err["error"]   = "server_error";
+            err["message"] = e.what();
+            res.set_content(err.dump(), "application/json");
+        }
+    });
+
+    svr.Post("/v1/images/edits", [&](const httplib::Request& req, httplib::Response& res) {
+        try {
+            if (!req.is_multipart_form_data()) {
+                res.status = 400;
+                res.set_content(R"({"error":"Content-Type must be multipart/form-data"})", "application/json");
+                return;
+            }
+
+            std::string prompt = req.form.get_field("prompt");
+            if (prompt.empty()) {
+                res.status = 400;
+                res.set_content(R"({"error":"prompt required"})", "application/json");
+                return;
+            }
+
+            std::string sd_cpp_extra_args_str = extract_and_remove_sd_cpp_extra_args(prompt);
+
+            size_t image_count = req.form.get_file_count("image[]");
+            if (image_count == 0) {
+                res.status = 400;
+                res.set_content(R"({"error":"at least one image[] required"})", "application/json");
+                return;
+            }
+
+            std::vector<std::vector<uint8_t>> images_bytes;
+            for (size_t i = 0; i < image_count; i++) {
+                auto file = req.form.get_file("image[]", i);
+                images_bytes.emplace_back(file.content.begin(), file.content.end());
+            }
+
+            std::vector<uint8_t> mask_bytes;
+            if (req.form.has_field("mask")) {
+                auto file = req.form.get_file("mask");
+                mask_bytes.assign(file.content.begin(), file.content.end());
+            }
+
+            int n = 1;
+            if (req.form.has_field("n")) {
+                try {
+                    n = std::stoi(req.form.get_field("n"));
+                } catch (...) {
+                }
+            }
+            n = std::clamp(n, 1, 8);
+
+            std::string size = req.form.get_field("size");
+            int width = 512, height = 512;
+            if (!size.empty()) {
+                auto pos = size.find('x');
+                if (pos != std::string::npos) {
+                    try {
+                        width  = std::stoi(size.substr(0, pos));
+                        height = std::stoi(size.substr(pos + 1));
+                    } catch (...) {
+                    }
+                }
+            }
+
+            std::string output_format = "png";
+            if (req.form.has_field("output_format"))
+                output_format = req.form.get_field("output_format");
+            if (output_format != "png" && output_format != "jpeg") {
+                res.status = 400;
+                res.set_content(R"({"error":"invalid output_format, must be one of [png, jpeg]"})", "application/json");
+                return;
+            }
+
+            std::string output_compression_str = req.form.get_field("output_compression");
+            int output_compression             = 100;
+            try {
+                output_compression = std::stoi(output_compression_str);
+            } catch (...) {
+            }
+            if (output_compression > 100) {
+                output_compression = 100;
+            }
+            if (output_compression < 0) {
+                output_compression = 0;
+            }
+
+            SDGenerationParams gen_params = default_gen_params;
+            gen_params.prompt             = prompt;
+            gen_params.width              = width;
+            gen_params.height             = height;
+            gen_params.batch_count        = n;
+
+            if (!sd_cpp_extra_args_str.empty() && !gen_params.from_json_str(sd_cpp_extra_args_str)) {
+                res.status = 400;
+                res.set_content(R"({"error":"invalid sd_cpp_extra_args"})", "application/json");
+                return;
+            }
+
+            if (!gen_params.process_and_check(IMG_GEN, "")) {
+                res.status = 400;
+                res.set_content(R"({"error":"invalid params"})", "application/json");
+                return;
+            }
+
+            LOG_DEBUG("%s\n", gen_params.to_string().c_str());
+
+            sd_image_t init_image    = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 3, nullptr};
+            sd_image_t control_image = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 3, nullptr};
+            std::vector<sd_image_t> pmid_images;
+
+            std::vector<sd_image_t> ref_images;
+            ref_images.reserve(images_bytes.size());
+            for (auto& bytes : images_bytes) {
+                int img_w           = width;
+                int img_h           = height;
+                uint8_t* raw_pixels = load_image_from_memory(
+                    reinterpret_cast<const char*>(bytes.data()),
+                    bytes.size(),
+                    img_w, img_h,
+                    width, height, 3);
+
+                if (!raw_pixels) {
+                    continue;
+                }
+
+                sd_image_t img{(uint32_t)img_w, (uint32_t)img_h, 3, raw_pixels};
+                ref_images.push_back(img);
+            }
+
+            sd_image_t mask_image = {0};
+            if (!mask_bytes.empty()) {
+                int mask_w        = width;
+                int mask_h        = height;
+                uint8_t* mask_raw = load_image_from_memory(
+                    reinterpret_cast<const char*>(mask_bytes.data()),
+                    mask_bytes.size(),
+                    mask_w, mask_h,
+                    width, height, 1);
+                mask_image = {(uint32_t)mask_w, (uint32_t)mask_h, 1, mask_raw};
+            } else {
+                mask_image.width   = width;
+                mask_image.height  = height;
+                mask_image.channel = 1;
+                mask_image.data    = nullptr;
+            }
+
+            sd_img_gen_params_t img_gen_params = {
+                gen_params.lora_vec.data(),
+                static_cast<uint32_t>(gen_params.lora_vec.size()),
+                gen_params.prompt.c_str(),
+                gen_params.negative_prompt.c_str(),
+                gen_params.clip_skip,
+                init_image,
+                ref_images.data(),
+                (int)ref_images.size(),
+                gen_params.auto_resize_ref_image,
+                gen_params.increase_ref_index,
+                mask_image,
+                gen_params.width,
+                gen_params.height,
+                gen_params.sample_params,
+                gen_params.strength,
+                gen_params.seed,
+                gen_params.batch_count,
+                control_image,
+                gen_params.control_strength,
+                {
+                    pmid_images.data(),
+                    (int)pmid_images.size(),
+                    gen_params.pm_id_embed_path.c_str(),
+                    gen_params.pm_style_strength,
+                },  // pm_params
+                ctx_params.vae_tiling_params,
+                gen_params.cache_params,
+            };
+
+            sd_image_t* results = nullptr;
+            int num_results     = 0;
+
+            {
+                std::lock_guard<std::mutex> lock(sd_ctx_mutex);
+                results     = generate_image(sd_ctx, &img_gen_params);
+                num_results = gen_params.batch_count;
+            }
+
+            json out;
+            out["created"]       = iso_timestamp_now();
+            out["data"]          = json::array();
+            out["output_format"] = output_format;
+
+            for (int i = 0; i < num_results; i++) {
+                if (results[i].data == nullptr)
+                    continue;
+                auto image_bytes = write_image_to_vector(output_format == "jpeg" ? ImageFormat::JPEG : ImageFormat::PNG,
+                                                         results[i].data,
+                                                         results[i].width,
+                                                         results[i].height,
+                                                         results[i].channel,
+                                                         output_compression);
+                std::string b64 = base64_encode(image_bytes);
+                json item;
+                item["b64_json"] = b64;
+                out["data"].push_back(item);
+            }
+
+            res.set_content(out.dump(), "application/json");
+            res.status = 200;
+
+            if (init_image.data) {
+                stbi_image_free(init_image.data);
+            }
+            if (mask_image.data) {
+                stbi_image_free(mask_image.data);
+            }
+            for (auto ref_image : ref_images) {
+                stbi_image_free(ref_image.data);
+            }
+        } catch (const std::exception& e) {
+            res.status = 500;
+            json err;
+            err["error"]   = "server_error";
+            err["message"] = e.what();
+            res.set_content(err.dump(), "application/json");
+        }
+    });
+
+    LOG_INFO("listening on: %s:%d\n", svr_params.listen_ip.c_str(), svr_params.listen_port);
+    svr.listen(svr_params.listen_ip, svr_params.listen_port);
+
+    // cleanup
+    free_sd_ctx(sd_ctx);
+    return 0;
+}

flux.hpp

@@ -233,14 +233,17 @@ namespace Flux {
     __STATIC_INLINE__ struct ggml_tensor* modulate(struct ggml_context* ctx,
                                                    struct ggml_tensor* x,
                                                    struct ggml_tensor* shift,
-                                                   struct ggml_tensor* scale) {
+                                                   struct ggml_tensor* scale,
+                                                   bool skip_reshape = false) {
         // x: [N, L, C]
         // scale: [N, C]
         // shift: [N, C]
-        scale = ggml_reshape_3d(ctx, scale, scale->ne[0], 1, scale->ne[1]);  // [N, 1, C]
+        if (!skip_reshape) {
-        shift = ggml_reshape_3d(ctx, shift, shift->ne[0], 1, shift->ne[1]);  // [N, 1, C]
+            scale = ggml_reshape_3d(ctx, scale, scale->ne[0], 1, scale->ne[1]);  // [N, 1, C]
-        x     = ggml_add(ctx, x, ggml_mul(ctx, x, scale));
+            shift = ggml_reshape_3d(ctx, shift, shift->ne[0], 1, shift->ne[1]);  // [N, 1, C]
-        x     = ggml_add(ctx, x, shift);
+        }
+        x = ggml_add(ctx, x, ggml_mul(ctx, x, scale));
+        x = ggml_add(ctx, x, shift);
         return x;
     }
 
@@ -744,6 +747,8 @@ namespace Flux {
         int64_t nerf_mlp_ratio   = 4;
         int64_t nerf_depth       = 4;
         int64_t nerf_max_freqs   = 8;
+        bool use_x0              = false;
+        bool use_patch_size_32   = false;
     };
 
     struct FluxParams {
@@ -781,7 +786,7 @@ namespace Flux {
         Flux(FluxParams params)
             : params(params) {
             if (params.version == VERSION_CHROMA_RADIANCE) {
-                std::pair<int, int> kernel_size = {(int)params.patch_size, (int)params.patch_size};
+                std::pair<int, int> kernel_size = {16, 16};
                 std::pair<int, int> stride      = kernel_size;
 
                 blocks["img_in_patch"] = std::make_shared<Conv2d>(params.in_channels,
@@ -858,14 +863,14 @@ namespace Flux {
             }
         }
 
-        struct ggml_tensor* pad_to_patch_size(struct ggml_context* ctx,
+        struct ggml_tensor* pad_to_patch_size(GGMLRunnerContext* ctx,
                                               struct ggml_tensor* x) {
             int64_t W = x->ne[0];
             int64_t H = x->ne[1];
 
             int pad_h = (params.patch_size - H % params.patch_size) % params.patch_size;
             int pad_w = (params.patch_size - W % params.patch_size) % params.patch_size;
-            x         = ggml_pad(ctx, x, pad_w, pad_h, 0, 0);  // [N, C, H + pad_h, W + pad_w]
+            x         = ggml_ext_pad(ctx->ggml_ctx, x, pad_w, pad_h, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
             return x;
         }
 
@@ -891,11 +896,11 @@ namespace Flux {
             return x;
         }
 
-        struct ggml_tensor* process_img(struct ggml_context* ctx,
+        struct ggml_tensor* process_img(GGMLRunnerContext* ctx,
                                         struct ggml_tensor* x) {
             // img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
             x = pad_to_patch_size(ctx, x);
-            x = patchify(ctx, x);
+            x = patchify(ctx->ggml_ctx, x);
             return x;
         }
 
@@ -1044,6 +1049,15 @@ namespace Flux {
             return img;
         }
 
+        struct ggml_tensor* _apply_x0_residual(GGMLRunnerContext* ctx,
+                                               struct ggml_tensor* predicted,
+                                               struct ggml_tensor* noisy,
+                                               struct ggml_tensor* timesteps) {
+            auto x = ggml_sub(ctx->ggml_ctx, noisy, predicted);
+            x      = ggml_div(ctx->ggml_ctx, x, timesteps);
+            return x;
+        }
+
         struct ggml_tensor* forward_chroma_radiance(GGMLRunnerContext* ctx,
                                                     struct ggml_tensor* x,
                                                     struct ggml_tensor* timestep,
@@ -1065,9 +1079,16 @@ namespace Flux {
             int pad_h          = (patch_size - H % patch_size) % patch_size;
             int pad_w          = (patch_size - W % patch_size) % patch_size;
 
-            auto img      = pad_to_patch_size(ctx->ggml_ctx, x);
+            auto img      = pad_to_patch_size(ctx, x);
             auto orig_img = img;
 
+            if (params.chroma_radiance_params.use_patch_size_32) {
+                // It's supposed to be using GGML_SCALE_MODE_NEAREST, but this seems more stable
+                // Maybe the implementation of nearest-neighbor interpolation in ggml behaves differently than the one in PyTorch?
+                // img = F.interpolate(img, size=(H//2, W//2), mode="nearest")
+                img = ggml_interpolate(ctx->ggml_ctx, img, W / 2, H / 2, C, x->ne[3], GGML_SCALE_MODE_BILINEAR);
+            }
+
             auto img_in_patch = std::dynamic_pointer_cast<Conv2d>(blocks["img_in_patch"]);
 
             img = img_in_patch->forward(ctx, img);                                                       // [N, hidden_size, H/patch_size, W/patch_size]
@@ -1104,6 +1125,10 @@ namespace Flux {
 
             out = nerf_final_layer_conv->forward(ctx, img_dct);  // [N, C, H, W]
 
+            if (params.chroma_radiance_params.use_x0) {
+                out = _apply_x0_residual(ctx, out, orig_img, timestep);
+            }
+
             return out;
         }
 
@@ -1128,7 +1153,7 @@ namespace Flux {
             int pad_h          = (patch_size - H % patch_size) % patch_size;
             int pad_w          = (patch_size - W % patch_size) % patch_size;
 
-            auto img            = process_img(ctx->ggml_ctx, x);
+            auto img            = process_img(ctx, x);
             uint64_t img_tokens = img->ne[1];
 
             if (params.version == VERSION_FLUX_FILL) {
@@ -1136,8 +1161,8 @@ namespace Flux {
                 ggml_tensor* masked = ggml_view_4d(ctx->ggml_ctx, c_concat, c_concat->ne[0], c_concat->ne[1], C, 1, c_concat->nb[1], c_concat->nb[2], c_concat->nb[3], 0);
                 ggml_tensor* mask   = ggml_view_4d(ctx->ggml_ctx, c_concat, c_concat->ne[0], c_concat->ne[1], 8 * 8, 1, c_concat->nb[1], c_concat->nb[2], c_concat->nb[3], c_concat->nb[2] * C);
 
-                masked = process_img(ctx->ggml_ctx, masked);
+                masked = process_img(ctx, masked);
-                mask   = process_img(ctx->ggml_ctx, mask);
+                mask   = process_img(ctx, mask);
 
                 img = ggml_concat(ctx->ggml_ctx, img, ggml_concat(ctx->ggml_ctx, masked, mask, 0), 0);
             } else if (params.version == VERSION_FLEX_2) {
@@ -1146,21 +1171,21 @@ namespace Flux {
                 ggml_tensor* mask    = ggml_view_4d(ctx->ggml_ctx, c_concat, c_concat->ne[0], c_concat->ne[1], 1, 1, c_concat->nb[1], c_concat->nb[2], c_concat->nb[3], c_concat->nb[2] * C);
                 ggml_tensor* control = ggml_view_4d(ctx->ggml_ctx, c_concat, c_concat->ne[0], c_concat->ne[1], C, 1, c_concat->nb[1], c_concat->nb[2], c_concat->nb[3], c_concat->nb[2] * (C + 1));
 
-                masked  = process_img(ctx->ggml_ctx, masked);
+                masked  = process_img(ctx, masked);
-                mask    = process_img(ctx->ggml_ctx, mask);
+                mask    = process_img(ctx, mask);
-                control = process_img(ctx->ggml_ctx, control);
+                control = process_img(ctx, control);
 
                 img = ggml_concat(ctx->ggml_ctx, img, ggml_concat(ctx->ggml_ctx, ggml_concat(ctx->ggml_ctx, masked, mask, 0), control, 0), 0);
             } else if (params.version == VERSION_FLUX_CONTROLS) {
                 GGML_ASSERT(c_concat != nullptr);
 
-                auto control = process_img(ctx->ggml_ctx, c_concat);
+                auto control = process_img(ctx, c_concat);
                 img          = ggml_concat(ctx->ggml_ctx, img, control, 0);
             }
 
             if (ref_latents.size() > 0) {
                 for (ggml_tensor* ref : ref_latents) {
-                    ref = process_img(ctx->ggml_ctx, ref);
+                    ref = process_img(ctx, ref);
                     img = ggml_concat(ctx->ggml_ctx, img, ref, 1);
                 }
             }
@@ -1290,6 +1315,15 @@ namespace Flux {
                     // not schnell
                     flux_params.guidance_embed = true;
                 }
+                if (tensor_name.find("__x0__") != std::string::npos) {
+                    LOG_DEBUG("using x0 prediction");
+                    flux_params.chroma_radiance_params.use_x0 = true;
+                }
+                if (tensor_name.find("__32x32__") != std::string::npos) {
+                    LOG_DEBUG("using patch size 32 prediction");
+                    flux_params.chroma_radiance_params.use_patch_size_32 = true;
+                    flux_params.patch_size                               = 32;
+                }
                 if (tensor_name.find("distilled_guidance_layer.in_proj.weight") != std::string::npos) {
                     // Chroma
                     flux_params.is_chroma = true;
@@ -1441,6 +1475,8 @@ namespace Flux {
                                             increase_ref_index,
                                             flux_params.ref_index_scale,
                                             flux_params.theta,
+                                            circular_y_enabled,
+                                            circular_x_enabled,
                                             flux_params.axes_dim);
             int pos_len = pe_vec.size() / flux_params.axes_dim_sum / 2;
             // LOG_DEBUG("pos_len %d", pos_len);

format-code.sh

@@ -1,4 +1,4 @@
-for f in *.cpp *.h *.hpp examples/cli/*.cpp examples/cli/*.h; do
+for f in *.cpp *.h *.hpp examples/cli/*.cpp examples/common/*.hpp examples/cli/*.h examples/server/*.cpp; do
   [[ "$f" == vocab* ]] && continue
   echo "formatting '$f'"
   # if [ "$f" != "stable-diffusion.h" ]; then

ggml

@@ -1 +1 @@
-Subproject commit 2d3876d554551d35c06dccc5852be50d5fd2a275
+Subproject commit 3e9f2ba3b934c20b26873b3c60dbf41b116978ff

ggml_extend.hpp

@@ -5,6 +5,7 @@
 #include <inttypes.h>
 #include <stdarg.h>
 #include <algorithm>
+#include <atomic>
 #include <cstring>
 #include <fstream>
 #include <functional>
@@ -732,34 +733,22 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_slice(struct ggml_context* ctx,
 __STATIC_INLINE__ std::vector<struct ggml_tensor*> ggml_ext_chunk(struct ggml_context* ctx,
                                                                   struct ggml_tensor* x,
                                                                   int num,
-                                                                  int64_t dim) {
+                                                                  int64_t dim,
+                                                                  bool cont = true) {
     GGML_ASSERT(dim >= 0 && dim < 4);
     GGML_ASSERT(x->ne[dim] % num == 0);
 
-    int perm[4] = {0, 1, 2, 3};
-    for (int i = dim; i < 3; ++i)
-        perm[i] = perm[i + 1];
-    perm[3] = dim;
-
-    int inv_perm[4];
-    for (int i = 0; i < 4; ++i)
-        inv_perm[perm[i]] = i;
-
-    if (dim != 3) {
-        x = ggml_ext_torch_permute(ctx, x, perm[0], perm[1], perm[2], perm[3]);
-        x = ggml_cont(ctx, x);
-    }
-
     std::vector<struct ggml_tensor*> chunks;
-    int64_t chunk_size = x->ne[3] / num;
+    int64_t chunk_size  = x->ne[dim] / num;
+    int64_t stride      = chunk_size * x->nb[dim];
+    int64_t chunk_ne[4] = {x->ne[0], x->ne[1], x->ne[2], x->ne[3]};
+    chunk_ne[dim]       = chunk_size;
     for (int i = 0; i < num; i++) {
         auto chunk = ggml_view_4d(
             ctx, x,
-            x->ne[0], x->ne[1], x->ne[2], chunk_size,
+            chunk_ne[0], chunk_ne[1], chunk_ne[2], chunk_ne[3],
-            x->nb[1], x->nb[2], x->nb[3], x->nb[3] * i * chunk_size);
+            x->nb[1], x->nb[2], x->nb[3], stride * i);
-
+        if (cont) {
-        if (dim != 3) {
-            chunk = ggml_ext_torch_permute(ctx, chunk, inv_perm[0], inv_perm[1], inv_perm[2], inv_perm[3]);
             chunk = ggml_cont(ctx, chunk);
         }
         chunks.push_back(chunk);
@@ -772,7 +761,7 @@ __STATIC_INLINE__ ggml_tensor* ggml_ext_silu_act(ggml_context* ctx, ggml_tensor*
     // x: [ne3, ne2, ne1, ne0]
     // return: [ne3, ne2, ne1, ne0/2]
 
-    auto x_vec = ggml_ext_chunk(ctx, x, 2, 0);
+    auto x_vec = ggml_ext_chunk(ctx, x, 2, 0, false);
     ggml_tensor* gate;
     if (gate_first) {
         gate = x_vec[0];
@@ -781,7 +770,7 @@ __STATIC_INLINE__ ggml_tensor* ggml_ext_silu_act(ggml_context* ctx, ggml_tensor*
         x    = x_vec[0];
         gate = x_vec[1];
     }
-
+    gate = ggml_cont(ctx, gate);
     gate = ggml_silu_inplace(ctx, gate);
 
     x = ggml_mul(ctx, x, gate);  // [ne3, ne2, ne1, ne0/2]
@@ -860,8 +849,6 @@ __STATIC_INLINE__ void sd_tiling_non_square(ggml_tensor* input,
     LOG_DEBUG("num tiles : %d, %d ", num_tiles_x, num_tiles_y);
     LOG_DEBUG("optimal overlap : %f, %f (targeting %f)", tile_overlap_factor_x, tile_overlap_factor_y, tile_overlap_factor);
 
-    GGML_ASSERT(input_width % 2 == 0 && input_height % 2 == 0 && output_width % 2 == 0 && output_height % 2 == 0);  // should be multiple of 2
-
     int tile_overlap_x     = (int32_t)(p_tile_size_x * tile_overlap_factor_x);
     int non_tile_overlap_x = p_tile_size_x - tile_overlap_x;
 
@@ -1007,6 +994,48 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_linear(struct ggml_context* ctx,
     return x;
 }
 
+__STATIC_INLINE__ struct ggml_tensor* ggml_ext_pad_ext(struct ggml_context* ctx,
+                                                       struct ggml_tensor* x,
+                                                       int lp0,
+                                                       int rp0,
+                                                       int lp1,
+                                                       int rp1,
+                                                       int lp2,
+                                                       int rp2,
+                                                       int lp3,
+                                                       int rp3,
+                                                       bool circular_x = false,
+                                                       bool circular_y = false) {
+    if (circular_x && circular_y) {
+        return ggml_pad_ext_circular(ctx, x, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3);
+    }
+
+    if (circular_x && (lp0 != 0 || rp0 != 0)) {
+        x   = ggml_pad_ext_circular(ctx, x, lp0, rp0, 0, 0, 0, 0, 0, 0);
+        lp0 = rp0 = 0;
+    }
+    if (circular_y && (lp1 != 0 || rp1 != 0)) {
+        x   = ggml_pad_ext_circular(ctx, x, 0, 0, lp1, rp1, 0, 0, 0, 0);
+        lp1 = rp1 = 0;
+    }
+
+    if (lp0 != 0 || rp0 != 0 || lp1 != 0 || rp1 != 0 || lp2 != 0 || rp2 != 0 || lp3 != 0 || rp3 != 0) {
+        x = ggml_pad_ext(ctx, x, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3);
+    }
+    return x;
+}
+
+__STATIC_INLINE__ struct ggml_tensor* ggml_ext_pad(struct ggml_context* ctx,
+                                                   struct ggml_tensor* x,
+                                                   int p0,
+                                                   int p1,
+                                                   int p2          = 0,
+                                                   int p3          = 0,
+                                                   bool circular_x = false,
+                                                   bool circular_y = false) {
+    return ggml_ext_pad_ext(ctx, x, 0, p0, 0, p1, 0, p2, 0, p3, circular_x, circular_y);
+}
+
 // w: [OC，IC, KH, KW]
 // x: [N, IC, IH, IW]
 // b: [OC,]
@@ -1015,20 +1044,29 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_conv_2d(struct ggml_context* ctx,
                                                        struct ggml_tensor* x,
                                                        struct ggml_tensor* w,
                                                        struct ggml_tensor* b,
-                                                       int s0      = 1,
+                                                       int s0          = 1,
-                                                       int s1      = 1,
+                                                       int s1          = 1,
-                                                       int p0      = 0,
+                                                       int p0          = 0,
-                                                       int p1      = 0,
+                                                       int p1          = 0,
-                                                       int d0      = 1,
+                                                       int d0          = 1,
-                                                       int d1      = 1,
+                                                       int d1          = 1,
-                                                       bool direct = false,
+                                                       bool direct     = false,
-                                                       float scale = 1.f) {
+                                                       bool circular_x = false,
+                                                       bool circular_y = false,
+                                                       float scale     = 1.f) {
     if (scale != 1.f) {
         x = ggml_scale(ctx, x, scale);
     }
     if (w->ne[2] != x->ne[2] && ggml_n_dims(w) == 2) {
         w = ggml_reshape_4d(ctx, w, 1, 1, w->ne[0], w->ne[1]);
     }
+
+    if ((p0 != 0 || p1 != 0) && (circular_x || circular_y)) {
+        x  = ggml_ext_pad_ext(ctx, x, p0, p0, p1, p1, 0, 0, 0, 0, circular_x, circular_y);
+        p0 = 0;
+        p1 = 0;
+    }
+
     if (direct) {
         x = ggml_conv_2d_direct(ctx, w, x, s0, s1, p0, p1, d0, d1);
     } else {
@@ -1282,6 +1320,9 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_attention_ext(struct ggml_context
         }
 
         if (mask_in != nullptr) {
+            // the need for padding got removed in ggml 4767bda
+            // ensure we can still use the old version for now
+#ifdef GGML_KQ_MASK_PAD
             int mask_pad = 0;
             if (mask_in->ne[1] % GGML_KQ_MASK_PAD != 0) {
                 mask_pad = GGML_PAD(L_q, GGML_KQ_MASK_PAD) - mask_in->ne[1];
@@ -1289,6 +1330,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_attention_ext(struct ggml_context
             if (mask_pad > 0) {
                 mask_in = ggml_pad(ctx, mask_in, 0, mask_pad, 0, 0);
             }
+#endif
             mask_in = ggml_cast(ctx, mask_in, GGML_TYPE_F16);
         }
 
@@ -1531,14 +1573,16 @@ struct WeightAdapter {
             float scale         = 1.f;
         } linear;
         struct {
-            int s0      = 1;
+            int s0          = 1;
-            int s1      = 1;
+            int s1          = 1;
-            int p0      = 0;
+            int p0          = 0;
-            int p1      = 0;
+            int p1          = 0;
-            int d0      = 1;
+            int d0          = 1;
-            int d1      = 1;
+            int d1          = 1;
-            bool direct = false;
+            bool direct     = false;
-            float scale = 1.f;
+            bool circular_x = false;
+            bool circular_y = false;
+            float scale     = 1.f;
         } conv2d;
     };
     virtual ggml_tensor* patch_weight(ggml_context* ctx, ggml_tensor* weight, const std::string& weight_name) = 0;
@@ -1556,6 +1600,8 @@ struct GGMLRunnerContext {
     ggml_context* ggml_ctx                        = nullptr;
     bool flash_attn_enabled                       = false;
     bool conv2d_direct_enabled                    = false;
+    bool circular_x_enabled                       = false;
+    bool circular_y_enabled                       = false;
     std::shared_ptr<WeightAdapter> weight_adapter = nullptr;
 };
 
@@ -1592,6 +1638,8 @@ protected:
 
     bool flash_attn_enabled    = false;
     bool conv2d_direct_enabled = false;
+    bool circular_x_enabled    = false;
+    bool circular_y_enabled    = false;
 
     void alloc_params_ctx() {
         struct ggml_init_params params;
@@ -1869,6 +1917,8 @@ public:
         runner_ctx.backend               = runtime_backend;
         runner_ctx.flash_attn_enabled    = flash_attn_enabled;
         runner_ctx.conv2d_direct_enabled = conv2d_direct_enabled;
+        runner_ctx.circular_x_enabled    = circular_x_enabled;
+        runner_ctx.circular_y_enabled    = circular_y_enabled;
         runner_ctx.weight_adapter        = weight_adapter;
         return runner_ctx;
     }
@@ -2013,6 +2063,11 @@ public:
         conv2d_direct_enabled = enabled;
     }
 
+    void set_circular_axes(bool circular_x, bool circular_y) {
+        circular_x_enabled = circular_x;
+        circular_y_enabled = circular_y;
+    }
+
     void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) {
         weight_adapter = adapter;
     }
@@ -2276,15 +2331,17 @@ public:
         }
         if (ctx->weight_adapter) {
             WeightAdapter::ForwardParams forward_params;
-            forward_params.op_type       = WeightAdapter::ForwardParams::op_type_t::OP_CONV2D;
+            forward_params.op_type           = WeightAdapter::ForwardParams::op_type_t::OP_CONV2D;
-            forward_params.conv2d.s0     = stride.second;
+            forward_params.conv2d.s0         = stride.second;
-            forward_params.conv2d.s1     = stride.first;
+            forward_params.conv2d.s1         = stride.first;
-            forward_params.conv2d.p0     = padding.second;
+            forward_params.conv2d.p0         = padding.second;
-            forward_params.conv2d.p1     = padding.first;
+            forward_params.conv2d.p1         = padding.first;
-            forward_params.conv2d.d0     = dilation.second;
+            forward_params.conv2d.d0         = dilation.second;
-            forward_params.conv2d.d1     = dilation.first;
+            forward_params.conv2d.d1         = dilation.first;
-            forward_params.conv2d.direct = ctx->conv2d_direct_enabled;
+            forward_params.conv2d.direct     = ctx->conv2d_direct_enabled;
-            forward_params.conv2d.scale  = scale;
+            forward_params.conv2d.circular_x = ctx->circular_x_enabled;
+            forward_params.conv2d.circular_y = ctx->circular_y_enabled;
+            forward_params.conv2d.scale      = scale;
             return ctx->weight_adapter->forward_with_lora(ctx->ggml_ctx, x, w, b, prefix, forward_params);
         }
         return ggml_ext_conv_2d(ctx->ggml_ctx,
@@ -2298,6 +2355,8 @@ public:
                                 dilation.second,
                                 dilation.first,
                                 ctx->conv2d_direct_enabled,
+                                ctx->circular_x_enabled,
+                                ctx->circular_y_enabled,
                                 scale);
     }
 };

lora.hpp

@@ -599,6 +599,8 @@ struct LoraModel : public GGMLRunner {
                                       forward_params.conv2d.d0,
                                       forward_params.conv2d.d1,
                                       forward_params.conv2d.direct,
+                                      forward_params.conv2d.circular_x,
+                                      forward_params.conv2d.circular_y,
                                       forward_params.conv2d.scale);
                 if (lora_mid) {
                     lx = ggml_ext_conv_2d(ctx,
@@ -612,6 +614,8 @@ struct LoraModel : public GGMLRunner {
                                           1,
                                           1,
                                           forward_params.conv2d.direct,
+                                          forward_params.conv2d.circular_x,
+                                          forward_params.conv2d.circular_y,
                                           forward_params.conv2d.scale);
                 }
                 lx = ggml_ext_conv_2d(ctx,
@@ -625,6 +629,8 @@ struct LoraModel : public GGMLRunner {
                                       1,
                                       1,
                                       forward_params.conv2d.direct,
+                                      forward_params.conv2d.circular_x,
+                                      forward_params.conv2d.circular_y,
                                       forward_params.conv2d.scale);
             }
 
@@ -779,6 +785,8 @@ public:
                                    forward_params.conv2d.d0,
                                    forward_params.conv2d.d1,
                                    forward_params.conv2d.direct,
+                                   forward_params.conv2d.circular_x,
+                                   forward_params.conv2d.circular_y,
                                    forward_params.conv2d.scale);
         }
         for (auto& lora_model : lora_models) {

mmdit.hpp

@@ -983,4 +983,4 @@ struct MMDiTRunner : public GGMLRunner {
     }
 };
 
-#endif
+#endif

model.cpp

@@ -1340,7 +1340,7 @@ std::string ModelLoader::load_umt5_tokenizer_json() {
     return json_str;
 }
 
-bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_threads_p) {
+bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_threads_p, bool enable_mmap) {
     int64_t process_time_ms = 0;
     std::atomic<int64_t> read_time_ms(0);
     std::atomic<int64_t> memcpy_time_ms(0);
@@ -1390,6 +1390,15 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
             }
         }
 
+        std::unique_ptr<MmapWrapper> mmapped;
+        if (enable_mmap && !is_zip) {
+            LOG_DEBUG("using mmap for I/O");
+            mmapped = MmapWrapper::create(file_path);
+            if (!mmapped) {
+                LOG_WARN("failed to memory-map '%s'", file_path.c_str());
+            }
+        }
+
         int n_threads = is_zip ? 1 : std::min(num_threads_to_use, (int)file_tensors.size());
         if (n_threads < 1) {
             n_threads = 1;
@@ -1411,7 +1420,7 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
                         failed = true;
                         return;
                     }
-                } else {
+                } else if (!mmapped) {
                     file.open(file_path, std::ios::binary);
                     if (!file.is_open()) {
                         LOG_ERROR("failed to open '%s'", file_path.c_str());
@@ -1464,6 +1473,11 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
                                 zip_entry_noallocread(zip, (void*)buf, n);
                             }
                             zip_entry_close(zip);
+                        } else if (mmapped) {
+                            if (!mmapped->copy_data(buf, n, tensor_storage.offset)) {
+                                LOG_ERROR("read tensor data failed: '%s'", file_path.c_str());
+                                failed = true;
+                            }
                         } else {
                             file.seekg(tensor_storage.offset);
                             file.read(buf, n);
@@ -1520,6 +1534,11 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
                         i64_to_i32_vec((int64_t*)read_buf, (int32_t*)target_buf, tensor_storage.nelements());
                     }
                     if (tensor_storage.type != dst_tensor->type) {
+                        if (convert_buf == nullptr) {
+                            LOG_ERROR("read tensor data failed: too less memory for conversion");
+                            failed = true;
+                            return;
+                        }
                         convert_tensor((void*)target_buf,
                                        tensor_storage.type,
                                        convert_buf,
@@ -1583,7 +1602,8 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
 
 bool ModelLoader::load_tensors(std::map<std::string, struct ggml_tensor*>& tensors,
                                std::set<std::string> ignore_tensors,
-                               int n_threads) {
+                               int n_threads,
+                               bool enable_mmap) {
     std::set<std::string> tensor_names_in_file;
     std::mutex tensor_names_mutex;
     auto on_new_tensor_cb = [&](const TensorStorage& tensor_storage, ggml_tensor** dst_tensor) -> bool {
@@ -1626,7 +1646,7 @@ bool ModelLoader::load_tensors(std::map<std::string, struct ggml_tensor*>& tenso
         return true;
     };
 
-    bool success = load_tensors(on_new_tensor_cb, n_threads);
+    bool success = load_tensors(on_new_tensor_cb, n_threads, enable_mmap);
     if (!success) {
         LOG_ERROR("load tensors from file failed");
         return false;
@@ -1732,6 +1752,13 @@ bool ModelLoader::save_to_gguf_file(const std::string& file_path, ggml_type type
         // tensor_storage.ne[0], tensor_storage.ne[1], tensor_storage.ne[2], tensor_storage.ne[3],
         // tensor->n_dims, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
 
+        if (!tensor->data) {
+            GGML_ASSERT(ggml_nelements(tensor) == 0);
+            // avoid crashing the gguf writer by setting a dummy pointer for zero-sized tensors
+            LOG_DEBUG("setting dummy pointer for zero-sized tensor %s", name.c_str());
+            tensor->data = ggml_get_mem_buffer(ggml_ctx);
+        }
+
         *dst_tensor = tensor;
 
         gguf_add_tensor(gguf_ctx, tensor);
@@ -1771,7 +1798,12 @@ int64_t ModelLoader::get_params_mem_size(ggml_backend_t backend, ggml_type type)
     return mem_size;
 }
 
-bool convert(const char* input_path, const char* vae_path, const char* output_path, sd_type_t output_type, const char* tensor_type_rules) {
+bool convert(const char* input_path,
+             const char* vae_path,
+             const char* output_path,
+             sd_type_t output_type,
+             const char* tensor_type_rules,
+             bool convert_name) {
     ModelLoader model_loader;
 
     if (!model_loader.init_from_file(input_path)) {
@@ -1785,7 +1817,9 @@ bool convert(const char* input_path, const char* vae_path, const char* output_pa
             return false;
         }
     }
-    model_loader.convert_tensors_name();
+    if (convert_name) {
+        model_loader.convert_tensors_name();
+    }
     bool success = model_loader.save_to_gguf_file(output_path, (ggml_type)output_type, tensor_type_rules);
     return success;
 }

model.h

@@ -310,10 +310,11 @@ public:
     std::map<ggml_type, uint32_t> get_vae_wtype_stat();
     String2TensorStorage& get_tensor_storage_map() { return tensor_storage_map; }
     void set_wtype_override(ggml_type wtype, std::string tensor_type_rules = "");
-    bool load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_threads = 0);
+    bool load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_threads = 0, bool use_mmap = false);
     bool load_tensors(std::map<std::string, struct ggml_tensor*>& tensors,
                       std::set<std::string> ignore_tensors = {},
-                      int n_threads                        = 0);
+                      int n_threads                        = 0,
+                      bool use_mmap                        = false);
 
     std::vector<std::string> get_tensor_names() const {
         std::vector<std::string> names;

name_conversion.cpp

@@ -835,6 +835,7 @@ std::string convert_sep_to_dot(std::string name) {
         "proj_out",
         "transformer_blocks",
         "single_transformer_blocks",
+        "single_blocks",
         "diffusion_model",
         "cond_stage_model",
         "first_stage_model",
@@ -876,7 +877,18 @@ std::string convert_sep_to_dot(std::string name) {
         "ff_context",
         "norm_added_q",
         "norm_added_v",
-        "to_add_out"};
+        "to_add_out",
+        "txt_mod",
+        "img_mod",
+        "txt_mlp",
+        "img_mlp",
+        "proj_mlp",
+        "wi_0",
+        "wi_1",
+        "norm1_context",
+        "ff_context",
+        "x_embedder",
+    };
 
     // record the positions of underscores that should NOT be replaced
     std::unordered_set<size_t> protected_positions;
@@ -948,6 +960,7 @@ bool is_first_stage_model_name(const std::string& name) {
 std::string convert_tensor_name(std::string name, SDVersion version) {
     bool is_lora                             = false;
     bool is_lycoris_underline                = false;
+    bool is_underline                        = false;
     std::vector<std::string> lora_prefix_vec = {
         "lora.lora.",
         "lora.lora_",
@@ -955,12 +968,27 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
         "lora.lycoris.",
         "lora.",
     };
+    std::vector<std::string> underline_lora_prefix_vec = {
+        "unet_",
+        "te_",
+        "te1_",
+        "te2_",
+        "te3_",
+        "vae_",
+    };
     for (const auto& prefix : lora_prefix_vec) {
         if (starts_with(name, prefix)) {
             is_lora = true;
             name    = name.substr(prefix.size());
             if (contains(prefix, "lycoris_")) {
                 is_lycoris_underline = true;
+            } else {
+                for (const auto& underline_lora_prefix : underline_lora_prefix_vec) {
+                    if (starts_with(name, underline_lora_prefix)) {
+                        is_underline = true;
+                        break;
+                    }
+                }
             }
             break;
         }
@@ -1020,12 +1048,14 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
             }
         }
 
-        if (sd_version_is_unet(version) || is_lycoris_underline) {
+        // LOG_DEBUG("name %s %d", name.c_str(), version);
+
+        if (sd_version_is_unet(version) || is_underline || is_lycoris_underline) {
             name = convert_sep_to_dot(name);
         }
     }
 
-    std::vector<std::pair<std::string, std::string>> prefix_map = {
+    std::unordered_map<std::string, std::string> prefix_map = {
         {"diffusion_model.", "model.diffusion_model."},
         {"unet.", "model.diffusion_model."},
         {"transformer.", "model.diffusion_model."},  // dit
@@ -1040,8 +1070,13 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
         // {"te2.text_model.encoder.layers.", "cond_stage_model.1.model.transformer.resblocks."},
         {"te2.", "cond_stage_model.1.transformer."},
         {"te1.", "cond_stage_model.transformer."},
+        {"te3.", "text_encoders.t5xxl.transformer."},
     };
 
+    if (sd_version_is_flux(version)) {
+        prefix_map["te1."] = "text_encoders.clip_l.transformer.";
+    }
+
     replace_with_prefix_map(name, prefix_map);
 
     // diffusion model

qwen_image.hpp

@@ -191,11 +191,16 @@ namespace Qwen {
     };
 
     class QwenImageTransformerBlock : public GGMLBlock {
+    protected:
+        bool zero_cond_t;
+
     public:
         QwenImageTransformerBlock(int64_t dim,
                                   int64_t num_attention_heads,
                                   int64_t attention_head_dim,
-                                  float eps = 1e-6) {
+                                  float eps        = 1e-6,
+                                  bool zero_cond_t = false)
+            : zero_cond_t(zero_cond_t) {
             // img_mod.0 is nn.SiLU()
             blocks["img_mod.1"] = std::shared_ptr<GGMLBlock>(new Linear(dim, 6 * dim, true));
 
@@ -220,11 +225,37 @@ namespace Qwen {
                                                                                eps));
         }
 
+        std::vector<ggml_tensor*> get_mod_params_vec(ggml_context* ctx, ggml_tensor* mod_params, ggml_tensor* index = nullptr) {
+            // index: [N, n_img_token]
+            // mod_params: [N, hidden_size * 12]
+            if (index == nullptr) {
+                return ggml_ext_chunk(ctx, mod_params, 6, 0);
+            }
+            mod_params          = ggml_reshape_1d(ctx, mod_params, ggml_nelements(mod_params));
+            auto mod_params_vec = ggml_ext_chunk(ctx, mod_params, 12, 0);
+            index               = ggml_reshape_3d(ctx, index, 1, index->ne[0], index->ne[1]);                                      // [N, n_img_token, 1]
+            index               = ggml_repeat_4d(ctx, index, mod_params_vec[0]->ne[0], index->ne[1], index->ne[2], index->ne[3]);  // [N, n_img_token, hidden_size]
+            std::vector<ggml_tensor*> mod_results;
+            for (int i = 0; i < 6; i++) {
+                auto mod_0 = mod_params_vec[i];
+                auto mod_1 = mod_params_vec[i + 6];
+
+                // mod_result = torch.where(index == 0, mod_0, mod_1)
+                // mod_result = (1 - index)*mod_0 + index*mod_1
+                mod_0           = ggml_sub(ctx, ggml_repeat(ctx, mod_0, index), ggml_mul(ctx, index, mod_0));  // [N, n_img_token, hidden_size]
+                mod_1           = ggml_mul(ctx, index, mod_1);                                                 // [N, n_img_token, hidden_size]
+                auto mod_result = ggml_add(ctx, mod_0, mod_1);
+                mod_results.push_back(mod_result);
+            }
+            return mod_results;
+        }
+
         virtual std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
                                                               struct ggml_tensor* img,
                                                               struct ggml_tensor* txt,
                                                               struct ggml_tensor* t_emb,
-                                                              struct ggml_tensor* pe) {
+                                                              struct ggml_tensor* pe,
+                                                              struct ggml_tensor* modulate_index = nullptr) {
             // img: [N, n_img_token, hidden_size]
             // txt: [N, n_txt_token, hidden_size]
             // pe: [n_img_token + n_txt_token, d_head/2, 2, 2]
@@ -244,14 +275,18 @@ namespace Qwen {
 
             auto img_mod_params    = ggml_silu(ctx->ggml_ctx, t_emb);
             img_mod_params         = img_mod_1->forward(ctx, img_mod_params);
-            auto img_mod_param_vec = ggml_ext_chunk(ctx->ggml_ctx, img_mod_params, 6, 0);
+            auto img_mod_param_vec = get_mod_params_vec(ctx->ggml_ctx, img_mod_params, modulate_index);
+
+            if (zero_cond_t) {
+                t_emb = ggml_ext_chunk(ctx->ggml_ctx, t_emb, 2, 1)[0];
+            }
 
             auto txt_mod_params    = ggml_silu(ctx->ggml_ctx, t_emb);
             txt_mod_params         = txt_mod_1->forward(ctx, txt_mod_params);
-            auto txt_mod_param_vec = ggml_ext_chunk(ctx->ggml_ctx, txt_mod_params, 6, 0);
+            auto txt_mod_param_vec = get_mod_params_vec(ctx->ggml_ctx, txt_mod_params);
 
             auto img_normed    = img_norm1->forward(ctx, img);
-            auto img_modulated = Flux::modulate(ctx->ggml_ctx, img_normed, img_mod_param_vec[0], img_mod_param_vec[1]);
+            auto img_modulated = Flux::modulate(ctx->ggml_ctx, img_normed, img_mod_param_vec[0], img_mod_param_vec[1], modulate_index != nullptr);
             auto img_gate1     = img_mod_param_vec[2];
 
             auto txt_normed    = txt_norm1->forward(ctx, txt);
@@ -264,7 +299,7 @@ namespace Qwen {
             txt = ggml_add(ctx->ggml_ctx, txt, ggml_mul(ctx->ggml_ctx, txt_attn_output, txt_gate1));
 
             auto img_normed2    = img_norm2->forward(ctx, img);
-            auto img_modulated2 = Flux::modulate(ctx->ggml_ctx, img_normed2, img_mod_param_vec[3], img_mod_param_vec[4]);
+            auto img_modulated2 = Flux::modulate(ctx->ggml_ctx, img_normed2, img_mod_param_vec[3], img_mod_param_vec[4], modulate_index != nullptr);
             auto img_gate2      = img_mod_param_vec[5];
 
             auto txt_normed2    = txt_norm2->forward(ctx, txt);
@@ -325,6 +360,7 @@ namespace Qwen {
         float theta                 = 10000;
         std::vector<int> axes_dim   = {16, 56, 56};
         int64_t axes_dim_sum        = 128;
+        bool zero_cond_t            = false;
     };
 
     class QwenImageModel : public GGMLBlock {
@@ -346,7 +382,8 @@ namespace Qwen {
                 auto block                                        = std::shared_ptr<GGMLBlock>(new QwenImageTransformerBlock(inner_dim,
                                                                                                                              params.num_attention_heads,
                                                                                                                              params.attention_head_dim,
-                                                                                                                             1e-6f));
+                                                                                                                             1e-6f,
+                                                                                                                             params.zero_cond_t));
                 blocks["transformer_blocks." + std::to_string(i)] = block;
             }
 
@@ -354,14 +391,14 @@ namespace Qwen {
             blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, params.patch_size * params.patch_size * params.out_channels));
         }
 
-        struct ggml_tensor* pad_to_patch_size(struct ggml_context* ctx,
+        struct ggml_tensor* pad_to_patch_size(GGMLRunnerContext* ctx,
                                               struct ggml_tensor* x) {
             int64_t W = x->ne[0];
             int64_t H = x->ne[1];
 
             int pad_h = (params.patch_size - H % params.patch_size) % params.patch_size;
             int pad_w = (params.patch_size - W % params.patch_size) % params.patch_size;
-            x         = ggml_pad(ctx, x, pad_w, pad_h, 0, 0);  // [N, C, H + pad_h, W + pad_w]
+            x         = ggml_ext_pad(ctx->ggml_ctx, x, pad_w, pad_h, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
             return x;
         }
 
@@ -387,10 +424,10 @@ namespace Qwen {
             return x;
         }
 
-        struct ggml_tensor* process_img(struct ggml_context* ctx,
+        struct ggml_tensor* process_img(GGMLRunnerContext* ctx,
                                         struct ggml_tensor* x) {
             x = pad_to_patch_size(ctx, x);
-            x = patchify(ctx, x);
+            x = patchify(ctx->ggml_ctx, x);
             return x;
         }
 
@@ -421,7 +458,8 @@ namespace Qwen {
                                          struct ggml_tensor* x,
                                          struct ggml_tensor* timestep,
                                          struct ggml_tensor* context,
-                                         struct ggml_tensor* pe) {
+                                         struct ggml_tensor* pe,
+                                         struct ggml_tensor* modulate_index = nullptr) {
             auto time_text_embed = std::dynamic_pointer_cast<QwenTimestepProjEmbeddings>(blocks["time_text_embed"]);
             auto txt_norm        = std::dynamic_pointer_cast<RMSNorm>(blocks["txt_norm"]);
             auto img_in          = std::dynamic_pointer_cast<Linear>(blocks["img_in"]);
@@ -430,18 +468,26 @@ namespace Qwen {
             auto proj_out        = std::dynamic_pointer_cast<Linear>(blocks["proj_out"]);
 
             auto t_emb = time_text_embed->forward(ctx, timestep);
-            auto img   = img_in->forward(ctx, x);
+            if (params.zero_cond_t) {
-            auto txt   = txt_norm->forward(ctx, context);
+                auto t_emb_0 = time_text_embed->forward(ctx, ggml_ext_zeros(ctx->ggml_ctx, timestep->ne[0], timestep->ne[1], timestep->ne[2], timestep->ne[3]));
-            txt        = txt_in->forward(ctx, txt);
+                t_emb        = ggml_concat(ctx->ggml_ctx, t_emb, t_emb_0, 1);
+            }
+            auto img = img_in->forward(ctx, x);
+            auto txt = txt_norm->forward(ctx, context);
+            txt      = txt_in->forward(ctx, txt);
 
             for (int i = 0; i < params.num_layers; i++) {
                 auto block = std::dynamic_pointer_cast<QwenImageTransformerBlock>(blocks["transformer_blocks." + std::to_string(i)]);
 
-                auto result = block->forward(ctx, img, txt, t_emb, pe);
+                auto result = block->forward(ctx, img, txt, t_emb, pe, modulate_index);
                 img         = result.first;
                 txt         = result.second;
             }
 
+            if (params.zero_cond_t) {
+                t_emb = ggml_ext_chunk(ctx->ggml_ctx, t_emb, 2, 1)[0];
+            }
+
             img = norm_out->forward(ctx, img, t_emb);
             img = proj_out->forward(ctx, img);
 
@@ -453,7 +499,8 @@ namespace Qwen {
                                     struct ggml_tensor* timestep,
                                     struct ggml_tensor* context,
                                     struct ggml_tensor* pe,
-                                    std::vector<ggml_tensor*> ref_latents = {}) {
+                                    std::vector<ggml_tensor*> ref_latents = {},
+                                    struct ggml_tensor* modulate_index    = nullptr) {
             // Forward pass of DiT.
             // x: [N, C, H, W]
             // timestep: [N,]
@@ -466,12 +513,12 @@ namespace Qwen {
             int64_t C = x->ne[2];
             int64_t N = x->ne[3];
 
-            auto img            = process_img(ctx->ggml_ctx, x);
+            auto img            = process_img(ctx, x);
             uint64_t img_tokens = img->ne[1];
 
             if (ref_latents.size() > 0) {
                 for (ggml_tensor* ref : ref_latents) {
-                    ref = process_img(ctx->ggml_ctx, ref);
+                    ref = process_img(ctx, ref);
                     img = ggml_concat(ctx->ggml_ctx, img, ref, 1);
                 }
             }
@@ -479,7 +526,7 @@ namespace Qwen {
             int64_t h_len = ((H + (params.patch_size / 2)) / params.patch_size);
             int64_t w_len = ((W + (params.patch_size / 2)) / params.patch_size);
 
-            auto out = forward_orig(ctx, img, timestep, context, pe);  // [N, h_len*w_len, ph*pw*C]
+            auto out = forward_orig(ctx, img, timestep, context, pe, modulate_index);  // [N, h_len*w_len, ph*pw*C]
 
             if (out->ne[1] > img_tokens) {
                 out = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, out, 0, 2, 1, 3));  // [num_tokens, N, C * patch_size * patch_size]
@@ -502,19 +549,25 @@ namespace Qwen {
         QwenImageParams qwen_image_params;
         QwenImageModel qwen_image;
         std::vector<float> pe_vec;
+        std::vector<float> modulate_index_vec;
         SDVersion version;
 
         QwenImageRunner(ggml_backend_t backend,
                         bool offload_params_to_cpu,
                         const String2TensorStorage& tensor_storage_map = {},
                         const std::string prefix                       = "",
-                        SDVersion version                              = VERSION_QWEN_IMAGE)
+                        SDVersion version                              = VERSION_QWEN_IMAGE,
+                        bool zero_cond_t                               = false)
             : GGMLRunner(backend, offload_params_to_cpu) {
-            qwen_image_params.num_layers = 0;
+            qwen_image_params.num_layers  = 0;
+            qwen_image_params.zero_cond_t = zero_cond_t;
             for (auto pair : tensor_storage_map) {
                 std::string tensor_name = pair.first;
                 if (tensor_name.find(prefix) == std::string::npos)
                     continue;
+                if (tensor_name.find("__index_timestep_zero__") != std::string::npos) {
+                    qwen_image_params.zero_cond_t = true;
+                }
                 size_t pos = tensor_name.find("transformer_blocks.");
                 if (pos != std::string::npos) {
                     tensor_name = tensor_name.substr(pos);  // remove prefix
@@ -529,6 +582,9 @@ namespace Qwen {
                 }
             }
             LOG_INFO("qwen_image_params.num_layers: %ld", qwen_image_params.num_layers);
+            if (qwen_image_params.zero_cond_t) {
+                LOG_INFO("use zero_cond_t");
+            }
             qwen_image = QwenImageModel(qwen_image_params);
             qwen_image.init(params_ctx, tensor_storage_map, prefix);
         }
@@ -565,6 +621,8 @@ namespace Qwen {
                                                   ref_latents,
                                                   increase_ref_index,
                                                   qwen_image_params.theta,
+                                                  circular_y_enabled,
+                                                  circular_x_enabled,
                                                   qwen_image_params.axes_dim);
             int pos_len = pe_vec.size() / qwen_image_params.axes_dim_sum / 2;
             // LOG_DEBUG("pos_len %d", pos_len);
@@ -574,6 +632,31 @@ namespace Qwen {
             // pe->data = nullptr;
             set_backend_tensor_data(pe, pe_vec.data());
 
+            ggml_tensor* modulate_index = nullptr;
+            if (qwen_image_params.zero_cond_t) {
+                modulate_index_vec.clear();
+
+                int64_t h_len          = ((x->ne[1] + (qwen_image_params.patch_size / 2)) / qwen_image_params.patch_size);
+                int64_t w_len          = ((x->ne[0] + (qwen_image_params.patch_size / 2)) / qwen_image_params.patch_size);
+                int64_t num_img_tokens = h_len * w_len;
+
+                modulate_index_vec.insert(modulate_index_vec.end(), num_img_tokens, 0.f);
+                int64_t num_ref_img_tokens = 0;
+                for (ggml_tensor* ref : ref_latents) {
+                    int64_t h_len = ((ref->ne[1] + (qwen_image_params.patch_size / 2)) / qwen_image_params.patch_size);
+                    int64_t w_len = ((ref->ne[0] + (qwen_image_params.patch_size / 2)) / qwen_image_params.patch_size);
+
+                    num_ref_img_tokens += h_len * w_len;
+                }
+
+                if (num_ref_img_tokens > 0) {
+                    modulate_index_vec.insert(modulate_index_vec.end(), num_ref_img_tokens, 1.f);
+                }
+
+                modulate_index = ggml_new_tensor_1d(compute_ctx, GGML_TYPE_F32, modulate_index_vec.size());
+                set_backend_tensor_data(modulate_index, modulate_index_vec.data());
+            }
+
             auto runner_ctx = get_context();
 
             struct ggml_tensor* out = qwen_image.forward(&runner_ctx,
@@ -581,7 +664,8 @@ namespace Qwen {
                                                          timesteps,
                                                          context,
                                                          pe,
-                                                         ref_latents);
+                                                         ref_latents,
+                                                         modulate_index);
 
             ggml_build_forward_expand(gf, out);
 
@@ -684,4 +768,4 @@ namespace Qwen {
 
 }  // namespace name
 
-#endif  // __QWEN_IMAGE_HPP__
+#endif  // __QWEN_IMAGE_HPP__

rope.hpp

@@ -1,6 +1,8 @@
 #ifndef __ROPE_HPP__
 #define __ROPE_HPP__
 
+#include <algorithm>
+#include <cmath>
 #include <vector>
 #include "ggml_extend.hpp"
 
@@ -39,7 +41,10 @@ namespace Rope {
         return flat_vec;
     }
 
-    __STATIC_INLINE__ std::vector<std::vector<float>> rope(const std::vector<float>& pos, int dim, int theta) {
+    __STATIC_INLINE__ std::vector<std::vector<float>> rope(const std::vector<float>& pos,
+                                                           int dim,
+                                                           int theta,
+                                                           const std::vector<int>& axis_wrap_dims = {}) {
         assert(dim % 2 == 0);
         int half_dim = dim / 2;
 
@@ -47,14 +52,31 @@ namespace Rope {
 
         std::vector<float> omega(half_dim);
         for (int i = 0; i < half_dim; ++i) {
-            omega[i] = 1.0 / std::pow(theta, scale[i]);
+            omega[i] = 1.0f / std::pow(theta, scale[i]);
         }
 
         int pos_size = pos.size();
         std::vector<std::vector<float>> out(pos_size, std::vector<float>(half_dim));
         for (int i = 0; i < pos_size; ++i) {
             for (int j = 0; j < half_dim; ++j) {
-                out[i][j] = pos[i] * omega[j];
+                float angle = pos[i] * omega[j];
+                if (!axis_wrap_dims.empty()) {
+                    size_t wrap_size = axis_wrap_dims.size();
+                    // mod batch size since we only store this for one item in the batch
+                    size_t wrap_idx = wrap_size > 0 ? (i % wrap_size) : 0;
+                    int wrap_dim    = axis_wrap_dims[wrap_idx];
+                    if (wrap_dim > 0) {
+                        constexpr float TWO_PI = 6.28318530717958647692f;
+                        float cycles           = omega[j] * wrap_dim / TWO_PI;
+                        // closest periodic harmonic, necessary to ensure things neatly tile
+                        // without this round, things don't tile at the boundaries and you end up
+                        // with the model knowing what is "center"
+                        float rounded = std::round(cycles);
+                        angle         = pos[i] * TWO_PI * rounded / wrap_dim;
+                    }
+                }
+
+                out[i][j] = angle;
             }
         }
 
@@ -89,16 +111,25 @@ namespace Rope {
                                                                        int patch_size,
                                                                        int bs,
                                                                        int axes_dim_num,
-                                                                       int index    = 0,
+                                                                       int index       = 0,
-                                                                       int h_offset = 0,
+                                                                       int h_offset    = 0,
-                                                                       int w_offset = 0) {
+                                                                       int w_offset    = 0,
+                                                                       bool scale_rope = false) {
         int h_len = (h + (patch_size / 2)) / patch_size;
         int w_len = (w + (patch_size / 2)) / patch_size;
 
         std::vector<std::vector<float>> img_ids(h_len * w_len, std::vector<float>(axes_dim_num, 0.0));
 
-        std::vector<float> row_ids = linspace<float>(h_offset, h_len - 1 + h_offset, h_len);
+        int h_start = h_offset;
-        std::vector<float> col_ids = linspace<float>(w_offset, w_len - 1 + w_offset, w_len);
+        int w_start = w_offset;
+
+        if (scale_rope) {
+            h_start -= h_len / 2;
+            w_start -= w_len / 2;
+        }
+
+        std::vector<float> row_ids = linspace<float>(h_start, h_start + h_len - 1, h_len);
+        std::vector<float> col_ids = linspace<float>(w_start, w_start + w_len - 1, w_len);
 
         for (int i = 0; i < h_len; ++i) {
             for (int j = 0; j < w_len; ++j) {
@@ -137,7 +168,8 @@ namespace Rope {
     __STATIC_INLINE__ std::vector<float> embed_nd(const std::vector<std::vector<float>>& ids,
                                                   int bs,
                                                   int theta,
-                                                  const std::vector<int>& axes_dim) {
+                                                  const std::vector<int>& axes_dim,
+                                                  const std::vector<std::vector<int>>& wrap_dims = {}) {
         std::vector<std::vector<float>> trans_ids = transpose(ids);
         size_t pos_len                            = ids.size() / bs;
         int num_axes                              = axes_dim.size();
@@ -152,7 +184,12 @@ namespace Rope {
         std::vector<std::vector<float>> emb(bs * pos_len, std::vector<float>(emb_dim * 2 * 2, 0.0));
         int offset = 0;
         for (int i = 0; i < num_axes; ++i) {
-            std::vector<std::vector<float>> rope_emb = rope(trans_ids[i], axes_dim[i], theta);  // [bs*pos_len, axes_dim[i]/2 * 2 * 2]
+            std::vector<int> axis_wrap_dims;
+            if (!wrap_dims.empty() && i < (int)wrap_dims.size()) {
+                axis_wrap_dims = wrap_dims[i];
+            }
+            std::vector<std::vector<float>> rope_emb =
+                rope(trans_ids[i], axes_dim[i], theta, axis_wrap_dims);  // [bs*pos_len, axes_dim[i]/2 * 2 * 2]
             for (int b = 0; b < bs; ++b) {
                 for (int j = 0; j < pos_len; ++j) {
                     for (int k = 0; k < rope_emb[0].size(); ++k) {
@@ -171,7 +208,8 @@ namespace Rope {
                                                                    int axes_dim_num,
                                                                    const std::vector<ggml_tensor*>& ref_latents,
                                                                    bool increase_ref_index,
-                                                                   float ref_index_scale) {
+                                                                   float ref_index_scale,
+                                                                   bool scale_rope) {
         std::vector<std::vector<float>> ids;
         uint64_t curr_h_offset = 0;
         uint64_t curr_w_offset = 0;
@@ -185,6 +223,7 @@ namespace Rope {
                 } else {
                     h_offset = curr_h_offset;
                 }
+                scale_rope = false;
             }
 
             auto ref_ids = gen_flux_img_ids(ref->ne[1],
@@ -194,7 +233,8 @@ namespace Rope {
                                             axes_dim_num,
                                             static_cast<int>(index * ref_index_scale),
                                             h_offset,
-                                            w_offset);
+                                            w_offset,
+                                            scale_rope);
             ids          = concat_ids(ids, ref_ids, bs);
 
             if (increase_ref_index) {
@@ -222,7 +262,7 @@ namespace Rope {
 
         auto ids = concat_ids(txt_ids, img_ids, bs);
         if (ref_latents.size() > 0) {
-            auto refs_ids = gen_refs_ids(patch_size, bs, axes_dim_num, ref_latents, increase_ref_index, ref_index_scale);
+            auto refs_ids = gen_refs_ids(patch_size, bs, axes_dim_num, ref_latents, increase_ref_index, ref_index_scale, false);
             ids           = concat_ids(ids, refs_ids, bs);
         }
         return ids;
@@ -239,6 +279,8 @@ namespace Rope {
                                                      bool increase_ref_index,
                                                      float ref_index_scale,
                                                      int theta,
+                                                     bool circular_h,
+                                                     bool circular_w,
                                                      const std::vector<int>& axes_dim) {
         std::vector<std::vector<float>> ids = gen_flux_ids(h,
                                                            w,
@@ -250,7 +292,47 @@ namespace Rope {
                                                            ref_latents,
                                                            increase_ref_index,
                                                            ref_index_scale);
-        return embed_nd(ids, bs, theta, axes_dim);
+        std::vector<std::vector<int>> wrap_dims;
+        if ((circular_h || circular_w) && bs > 0 && axes_dim.size() >= 3) {
+            int h_len = (h + (patch_size / 2)) / patch_size;
+            int w_len = (w + (patch_size / 2)) / patch_size;
+            if (h_len > 0 && w_len > 0) {
+                size_t pos_len = ids.size() / bs;
+                wrap_dims.assign(axes_dim.size(), std::vector<int>(pos_len, 0));
+                size_t cursor           = context_len;  // text first
+                const size_t img_tokens = static_cast<size_t>(h_len) * static_cast<size_t>(w_len);
+                for (size_t token_i = 0; token_i < img_tokens; ++token_i) {
+                    if (circular_h) {
+                        wrap_dims[1][cursor + token_i] = h_len;
+                    }
+                    if (circular_w) {
+                        wrap_dims[2][cursor + token_i] = w_len;
+                    }
+                }
+                cursor += img_tokens;
+                // reference latents
+                for (ggml_tensor* ref : ref_latents) {
+                    if (ref == nullptr) {
+                        continue;
+                    }
+                    int ref_h         = static_cast<int>(ref->ne[1]);
+                    int ref_w         = static_cast<int>(ref->ne[0]);
+                    int ref_h_l       = (ref_h + (patch_size / 2)) / patch_size;
+                    int ref_w_l       = (ref_w + (patch_size / 2)) / patch_size;
+                    size_t ref_tokens = static_cast<size_t>(ref_h_l) * static_cast<size_t>(ref_w_l);
+                    for (size_t token_i = 0; token_i < ref_tokens; ++token_i) {
+                        if (circular_h) {
+                            wrap_dims[1][cursor + token_i] = ref_h_l;
+                        }
+                        if (circular_w) {
+                            wrap_dims[2][cursor + token_i] = ref_w_l;
+                        }
+                    }
+                    cursor += ref_tokens;
+                }
+            }
+        }
+        return embed_nd(ids, bs, theta, axes_dim, wrap_dims);
     }
 
     __STATIC_INLINE__ std::vector<std::vector<float>> gen_qwen_image_ids(int h,
@@ -271,10 +353,10 @@ namespace Rope {
             }
         }
         int axes_dim_num = 3;
-        auto img_ids     = gen_flux_img_ids(h, w, patch_size, bs, axes_dim_num);
+        auto img_ids     = gen_flux_img_ids(h, w, patch_size, bs, axes_dim_num, 0, 0, 0, true);
         auto ids         = concat_ids(txt_ids_repeated, img_ids, bs);
         if (ref_latents.size() > 0) {
-            auto refs_ids = gen_refs_ids(patch_size, bs, axes_dim_num, ref_latents, increase_ref_index, 1.f);
+            auto refs_ids = gen_refs_ids(patch_size, bs, axes_dim_num, ref_latents, increase_ref_index, 1.f, true);
             ids           = concat_ids(ids, refs_ids, bs);
         }
         return ids;
@@ -289,9 +371,57 @@ namespace Rope {
                                                            const std::vector<ggml_tensor*>& ref_latents,
                                                            bool increase_ref_index,
                                                            int theta,
+                                                           bool circular_h,
+                                                           bool circular_w,
                                                            const std::vector<int>& axes_dim) {
         std::vector<std::vector<float>> ids = gen_qwen_image_ids(h, w, patch_size, bs, context_len, ref_latents, increase_ref_index);
-        return embed_nd(ids, bs, theta, axes_dim);
+        std::vector<std::vector<int>> wrap_dims;
+        // This logic simply stores the (pad and patch_adjusted) sizes of images so we can make sure rope correctly tiles
+        if ((circular_h || circular_w) && bs > 0 && axes_dim.size() >= 3) {
+            int pad_h = (patch_size - (h % patch_size)) % patch_size;
+            int pad_w = (patch_size - (w % patch_size)) % patch_size;
+            int h_len = (h + pad_h) / patch_size;
+            int w_len = (w + pad_w) / patch_size;
+            if (h_len > 0 && w_len > 0) {
+                const size_t total_tokens = ids.size();
+                // Track per-token wrap lengths for the row/column axes so only spatial tokens become periodic.
+                wrap_dims.assign(axes_dim.size(), std::vector<int>(total_tokens / bs, 0));
+                size_t cursor           = context_len;  // ignore text tokens
+                const size_t img_tokens = static_cast<size_t>(h_len) * static_cast<size_t>(w_len);
+                for (size_t token_i = 0; token_i < img_tokens; ++token_i) {
+                    if (circular_h) {
+                        wrap_dims[1][cursor + token_i] = h_len;
+                    }
+                    if (circular_w) {
+                        wrap_dims[2][cursor + token_i] = w_len;
+                    }
+                }
+                cursor += img_tokens;
+                // For each reference image, store wrap sizes as well
+                for (ggml_tensor* ref : ref_latents) {
+                    if (ref == nullptr) {
+                        continue;
+                    }
+                    int ref_h           = static_cast<int>(ref->ne[1]);
+                    int ref_w           = static_cast<int>(ref->ne[0]);
+                    int ref_pad_h       = (patch_size - (ref_h % patch_size)) % patch_size;
+                    int ref_pad_w       = (patch_size - (ref_w % patch_size)) % patch_size;
+                    int ref_h_len       = (ref_h + ref_pad_h) / patch_size;
+                    int ref_w_len       = (ref_w + ref_pad_w) / patch_size;
+                    size_t ref_n_tokens = static_cast<size_t>(ref_h_len) * static_cast<size_t>(ref_w_len);
+                    for (size_t token_i = 0; token_i < ref_n_tokens; ++token_i) {
+                        if (circular_h) {
+                            wrap_dims[1][cursor + token_i] = ref_h_len;
+                        }
+                        if (circular_w) {
+                            wrap_dims[2][cursor + token_i] = ref_w_len;
+                        }
+                    }
+                    cursor += ref_n_tokens;
+                }
+            }
+        }
+        return embed_nd(ids, bs, theta, axes_dim, wrap_dims);
     }
 
     __STATIC_INLINE__ std::vector<std::vector<float>> gen_vid_ids(int t,
@@ -428,9 +558,33 @@ namespace Rope {
                                                         const std::vector<ggml_tensor*>& ref_latents,
                                                         bool increase_ref_index,
                                                         int theta,
+                                                        bool circular_h,
+                                                        bool circular_w,
                                                         const std::vector<int>& axes_dim) {
         std::vector<std::vector<float>> ids = gen_z_image_ids(h, w, patch_size, bs, context_len, seq_multi_of, ref_latents, increase_ref_index);
-        return embed_nd(ids, bs, theta, axes_dim);
+        std::vector<std::vector<int>> wrap_dims;
+        if ((circular_h || circular_w) && bs > 0 && axes_dim.size() >= 3) {
+            int pad_h = (patch_size - (h % patch_size)) % patch_size;
+            int pad_w = (patch_size - (w % patch_size)) % patch_size;
+            int h_len = (h + pad_h) / patch_size;
+            int w_len = (w + pad_w) / patch_size;
+            if (h_len > 0 && w_len > 0) {
+                size_t pos_len = ids.size() / bs;
+                wrap_dims.assign(axes_dim.size(), std::vector<int>(pos_len, 0));
+                size_t cursor     = context_len + bound_mod(context_len, seq_multi_of);  // skip text (and its padding)
+                size_t img_tokens = static_cast<size_t>(h_len) * static_cast<size_t>(w_len);
+                for (size_t token_i = 0; token_i < img_tokens; ++token_i) {
+                    if (circular_h) {
+                        wrap_dims[1][cursor + token_i] = h_len;
+                    }
+                    if (circular_w) {
+                        wrap_dims[2][cursor + token_i] = w_len;
+                    }
+                }
+            }
+        }
+
+        return embed_nd(ids, bs, theta, axes_dim, wrap_dims);
     }
 
     __STATIC_INLINE__ struct ggml_tensor* apply_rope(struct ggml_context* ctx,

stable-diffusion.h

@@ -60,6 +60,7 @@ enum scheduler_t {
     SGM_UNIFORM_SCHEDULER,
     SIMPLE_SCHEDULER,
     SMOOTHSTEP_SCHEDULER,
+    KL_OPTIMAL_SCHEDULER,
     LCM_SCHEDULER,
     SCHEDULER_COUNT
 };
@@ -168,7 +169,6 @@ typedef struct {
     const char* vae_path;
     const char* taesd_path;
     const char* control_net_path;
-    const char* lora_model_dir;
     const sd_embedding_t* embeddings;
     uint32_t embedding_count;
     const char* photo_maker_path;
@@ -182,6 +182,7 @@ typedef struct {
     enum prediction_t prediction;
     enum lora_apply_mode_t lora_apply_mode;
     bool offload_params_to_cpu;
+    bool enable_mmap;
     bool keep_clip_on_cpu;
     bool keep_control_net_on_cpu;
     bool keep_vae_on_cpu;
@@ -189,10 +190,13 @@ typedef struct {
     bool tae_preview_only;
     bool diffusion_conv_direct;
     bool vae_conv_direct;
+    bool circular_x;
+    bool circular_y;
     bool force_sdxl_vae_conv_scale;
     bool chroma_use_dit_mask;
     bool chroma_use_t5_mask;
     int chroma_t5_mask_pad;
+    bool qwen_image_zero_cond_t;
     float flow_shift;
 } sd_ctx_params_t;
 
@@ -225,6 +229,8 @@ typedef struct {
     int sample_steps;
     float eta;
     int shifted_timestep;
+    float* custom_sigmas;
+    int custom_sigmas_count;
 } sd_sample_params_t;
 
 typedef struct {
@@ -234,12 +240,34 @@ typedef struct {
     float style_strength;
 } sd_pm_params_t;  // photo maker
 
+enum sd_cache_mode_t {
+    SD_CACHE_DISABLED = 0,
+    SD_CACHE_EASYCACHE,
+    SD_CACHE_UCACHE,
+    SD_CACHE_DBCACHE,
+    SD_CACHE_TAYLORSEER,
+    SD_CACHE_CACHE_DIT,
+};
+
 typedef struct {
-    bool enabled;
+    enum sd_cache_mode_t mode;
     float reuse_threshold;
     float start_percent;
     float end_percent;
-} sd_easycache_params_t;
+    float error_decay_rate;
+    bool use_relative_threshold;
+    bool reset_error_on_compute;
+    int Fn_compute_blocks;
+    int Bn_compute_blocks;
+    float residual_diff_threshold;
+    int max_warmup_steps;
+    int max_cached_steps;
+    int max_continuous_cached_steps;
+    int taylorseer_n_derivatives;
+    int taylorseer_skip_interval;
+    const char* scm_mask;
+    bool scm_policy_dynamic;
+} sd_cache_params_t;
 
 typedef struct {
     bool is_high_noise;
@@ -269,7 +297,7 @@ typedef struct {
     float control_strength;
     sd_pm_params_t pm_params;
     sd_tiling_params_t vae_tiling_params;
-    sd_easycache_params_t easycache;
+    sd_cache_params_t cache;
 } sd_img_gen_params_t;
 
 typedef struct {
@@ -291,7 +319,7 @@ typedef struct {
     int64_t seed;
     int video_frames;
     float vace_strength;
-    sd_easycache_params_t easycache;
+    sd_cache_params_t cache;
 } sd_vid_gen_params_t;
 
 typedef struct sd_ctx_t sd_ctx_t;
@@ -321,7 +349,7 @@ SD_API enum preview_t str_to_preview(const char* str);
 SD_API const char* sd_lora_apply_mode_name(enum lora_apply_mode_t mode);
 SD_API enum lora_apply_mode_t str_to_lora_apply_mode(const char* str);
 
-SD_API void sd_easycache_params_init(sd_easycache_params_t* easycache_params);
+SD_API void sd_cache_params_init(sd_cache_params_t* cache_params);
 
 SD_API void sd_ctx_params_init(sd_ctx_params_t* sd_ctx_params);
 SD_API char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params);
@@ -333,7 +361,7 @@ SD_API void sd_sample_params_init(sd_sample_params_t* sample_params);
 SD_API char* sd_sample_params_to_str(const sd_sample_params_t* sample_params);
 
 SD_API enum sample_method_t sd_get_default_sample_method(const sd_ctx_t* sd_ctx);
-SD_API enum scheduler_t sd_get_default_scheduler(const sd_ctx_t* sd_ctx);
+SD_API enum scheduler_t sd_get_default_scheduler(const sd_ctx_t* sd_ctx, enum sample_method_t sample_method);
 
 SD_API void sd_img_gen_params_init(sd_img_gen_params_t* sd_img_gen_params);
 SD_API char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params);
@@ -361,7 +389,8 @@ SD_API bool convert(const char* input_path,
                     const char* vae_path,
                     const char* output_path,
                     enum sd_type_t output_type,
-                    const char* tensor_type_rules);
+                    const char* tensor_type_rules,
+                    bool convert_name);
 
 SD_API bool preprocess_canny(sd_image_t image,
                              float high_threshold,

tae.hpp

@@ -162,6 +162,311 @@ public:
     }
 };
 
+class TPool : public UnaryBlock {
+    int stride;
+
+public:
+    TPool(int channels, int stride)
+        : stride(stride) {
+        blocks["conv"] = std::shared_ptr<GGMLBlock>(new Conv2d(channels * stride, channels, {1, 1}, {1, 1}, {0, 0}, {1, 1}, false));
+    }
+
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+        auto conv = std::dynamic_pointer_cast<UnaryBlock>(blocks["conv"]);
+        auto h    = x;
+        if (stride != 1) {
+            h = ggml_reshape_4d(ctx->ggml_ctx, h, h->ne[0], h->ne[1], h->ne[2] * stride, h->ne[3] / stride);
+        }
+        h = conv->forward(ctx, h);
+        return h;
+    }
+};
+
+class TGrow : public UnaryBlock {
+    int stride;
+
+public:
+    TGrow(int channels, int stride)
+        : stride(stride) {
+        blocks["conv"] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, channels * stride, {1, 1}, {1, 1}, {0, 0}, {1, 1}, false));
+    }
+
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+        auto conv = std::dynamic_pointer_cast<UnaryBlock>(blocks["conv"]);
+        auto h    = conv->forward(ctx, x);
+        if (stride != 1) {
+            h = ggml_reshape_4d(ctx->ggml_ctx, h, h->ne[0], h->ne[1], h->ne[2] / stride, h->ne[3] * stride);
+        }
+        return h;
+    }
+};
+
+class MemBlock : public GGMLBlock {
+    bool has_skip_conv = false;
+
+public:
+    MemBlock(int channels, int out_channels)
+        : has_skip_conv(channels != out_channels) {
+        blocks["conv.0"] = std::shared_ptr<GGMLBlock>(new Conv2d(channels * 2, out_channels, {3, 3}, {1, 1}, {1, 1}));
+        blocks["conv.2"] = std::shared_ptr<GGMLBlock>(new Conv2d(out_channels, out_channels, {3, 3}, {1, 1}, {1, 1}));
+        blocks["conv.4"] = std::shared_ptr<GGMLBlock>(new Conv2d(out_channels, out_channels, {3, 3}, {1, 1}, {1, 1}));
+        if (has_skip_conv) {
+            blocks["skip"] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, out_channels, {1, 1}, {1, 1}, {0, 0}, {1, 1}, false));
+        }
+    }
+
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x, struct ggml_tensor* past) {
+        // x: [n, channels, h, w]
+        auto conv0 = std::dynamic_pointer_cast<Conv2d>(blocks["conv.0"]);
+        auto conv1 = std::dynamic_pointer_cast<Conv2d>(blocks["conv.2"]);
+        auto conv2 = std::dynamic_pointer_cast<Conv2d>(blocks["conv.4"]);
+
+        auto h = ggml_concat(ctx->ggml_ctx, x, past, 2);
+        h      = conv0->forward(ctx, h);
+        h      = ggml_relu_inplace(ctx->ggml_ctx, h);
+        h      = conv1->forward(ctx, h);
+        h      = ggml_relu_inplace(ctx->ggml_ctx, h);
+        h      = conv2->forward(ctx, h);
+
+        auto skip = x;
+        if (has_skip_conv) {
+            auto skip_conv = std::dynamic_pointer_cast<Conv2d>(blocks["skip"]);
+            skip           = skip_conv->forward(ctx, x);
+        }
+        h = ggml_add_inplace(ctx->ggml_ctx, h, skip);
+        h = ggml_relu_inplace(ctx->ggml_ctx, h);
+        return h;
+    }
+};
+
+struct ggml_tensor* patchify(struct ggml_context* ctx,
+                             struct ggml_tensor* x,
+                             int64_t patch_size,
+                             int64_t b = 1) {
+    // x: [f, b*c, h*q, w*r]
+    // return: [f, b*c*r*q, h, w]
+    if (patch_size == 1) {
+        return x;
+    }
+    int64_t r = patch_size;
+    int64_t q = patch_size;
+
+    int64_t W = x->ne[0];
+    int64_t H = x->ne[1];
+    int64_t C = x->ne[2];
+    int64_t f = x->ne[3];
+
+    int64_t w = W / r;
+    int64_t h = H / q;
+
+    x = ggml_reshape_4d(ctx, x, W, q, h, C * f);                         // [W, q, h, C*f]
+    x = ggml_ext_cont(ctx, ggml_ext_torch_permute(ctx, x, 0, 2, 1, 3));  // [W, h, q, C*f]
+    x = ggml_reshape_4d(ctx, x, r, w, h, q * C * f);                     // [r, w, h, q*C*f]
+    x = ggml_ext_cont(ctx, ggml_ext_torch_permute(ctx, x, 1, 2, 0, 3));  // [w, h, r, q*C*f]
+    x = ggml_reshape_4d(ctx, x, w, h, r * q * C, f);                     // [f, b*c*r*q, h, w]
+
+    return x;
+}
+
+struct ggml_tensor* unpatchify(struct ggml_context* ctx,
+                               struct ggml_tensor* x,
+                               int64_t patch_size,
+                               int64_t b = 1) {
+    // x: [f, b*c*r*q, h, w]
+    // return: [f, b*c, h*q, w*r]
+    if (patch_size == 1) {
+        return x;
+    }
+    int64_t r = patch_size;
+    int64_t q = patch_size;
+    int64_t c = x->ne[2] / b / q / r;
+    int64_t f = x->ne[3];
+    int64_t h = x->ne[1];
+    int64_t w = x->ne[0];
+
+    x = ggml_reshape_4d(ctx, x, w, h, r, q * c * b * f);                 // [q*c*b*f, r, h, w]
+    x = ggml_ext_cont(ctx, ggml_ext_torch_permute(ctx, x, 2, 0, 1, 3));  // [r, w, h, q*c*b*f]
+    x = ggml_reshape_4d(ctx, x, r * w, h, q, c * b * f);                 // [c*b*f, q, h, r*w]
+    x = ggml_ext_cont(ctx, ggml_ext_torch_permute(ctx, x, 0, 2, 1, 3));  // [r*w, q, h, c*b*f]
+    x = ggml_reshape_4d(ctx, x, r * w, q * h, c * b, f);
+
+    return x;
+}
+
+class TinyVideoEncoder : public UnaryBlock {
+    int in_channels = 3;
+    int hidden      = 64;
+    int z_channels  = 4;
+    int num_blocks  = 3;
+    int num_layers  = 3;
+    int patch_size  = 1;
+
+public:
+    TinyVideoEncoder(int z_channels = 4, int patch_size = 1)
+        : z_channels(z_channels), patch_size(patch_size) {
+        int index                       = 0;
+        blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels * patch_size * patch_size, hidden, {3, 3}, {1, 1}, {1, 1}));
+        index++;  // nn.ReLU()
+        for (int i = 0; i < num_layers; i++) {
+            int stride                      = i == num_layers - 1 ? 1 : 2;
+            blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new TPool(hidden, stride));
+            blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(hidden, hidden, {3, 3}, {2, 2}, {1, 1}, {1, 1}, false));
+            for (int j = 0; j < num_blocks; j++) {
+                blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new MemBlock(hidden, hidden));
+            }
+        }
+        blocks[std::to_string(index)] = std::shared_ptr<GGMLBlock>(new Conv2d(hidden, z_channels, {3, 3}, {1, 1}, {1, 1}));
+    }
+
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* z) override {
+        auto first_conv = std::dynamic_pointer_cast<Conv2d>(blocks["0"]);
+
+        if (patch_size > 1) {
+            z = patchify(ctx->ggml_ctx, z, patch_size, 1);
+        }
+
+        auto h = first_conv->forward(ctx, z);
+        h      = ggml_relu_inplace(ctx->ggml_ctx, h);
+
+        int index = 2;
+        for (int i = 0; i < num_layers; i++) {
+            auto pool = std::dynamic_pointer_cast<UnaryBlock>(blocks[std::to_string(index++)]);
+            auto conv = std::dynamic_pointer_cast<UnaryBlock>(blocks[std::to_string(index++)]);
+
+            h = pool->forward(ctx, h);
+            h = conv->forward(ctx, h);
+            for (int j = 0; j < num_blocks; j++) {
+                auto block = std::dynamic_pointer_cast<MemBlock>(blocks[std::to_string(index++)]);
+                auto mem   = ggml_pad_ext(ctx->ggml_ctx, h, 0, 0, 0, 0, 0, 0, 1, 0);
+                mem        = ggml_view_4d(ctx->ggml_ctx, mem, h->ne[0], h->ne[1], h->ne[2], h->ne[3], h->nb[1], h->nb[2], h->nb[3], 0);
+                h          = block->forward(ctx, h, mem);
+            }
+        }
+        auto last_conv = std::dynamic_pointer_cast<Conv2d>(blocks[std::to_string(index)]);
+        h              = last_conv->forward(ctx, h);
+        return h;
+    }
+};
+
+class TinyVideoDecoder : public UnaryBlock {
+    int z_channels               = 4;
+    int out_channels             = 3;
+    int num_blocks               = 3;
+    static const int num_layers  = 3;
+    int channels[num_layers + 1] = {256, 128, 64, 64};
+    int patch_size               = 1;
+
+public:
+    TinyVideoDecoder(int z_channels = 4, int patch_size = 1)
+        : z_channels(z_channels), patch_size(patch_size) {
+        int index                       = 1;  // Clamp()
+        blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(z_channels, channels[0], {3, 3}, {1, 1}, {1, 1}));
+        index++;  // nn.ReLU()
+        for (int i = 0; i < num_layers; i++) {
+            int stride = i == 0 ? 1 : 2;
+            for (int j = 0; j < num_blocks; j++) {
+                blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new MemBlock(channels[i], channels[i]));
+            }
+            index++;  // nn.Upsample()
+            blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new TGrow(channels[i], stride));
+            blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(channels[i], channels[i + 1], {3, 3}, {1, 1}, {1, 1}, {1, 1}, false));
+        }
+        index++;  // nn.ReLU()
+        blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(channels[num_layers], out_channels * patch_size * patch_size, {3, 3}, {1, 1}, {1, 1}));
+    }
+
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* z) override {
+        auto first_conv = std::dynamic_pointer_cast<Conv2d>(blocks["1"]);
+
+        // Clamp()
+        auto h = ggml_scale_inplace(ctx->ggml_ctx,
+                                    ggml_tanh_inplace(ctx->ggml_ctx,
+                                                      ggml_scale(ctx->ggml_ctx, z, 1.0f / 3.0f)),
+                                    3.0f);
+
+        h         = first_conv->forward(ctx, h);
+        h         = ggml_relu_inplace(ctx->ggml_ctx, h);
+        int index = 3;
+        for (int i = 0; i < num_layers; i++) {
+            for (int j = 0; j < num_blocks; j++) {
+                auto block = std::dynamic_pointer_cast<MemBlock>(blocks[std::to_string(index++)]);
+                auto mem   = ggml_pad_ext(ctx->ggml_ctx, h, 0, 0, 0, 0, 0, 0, 1, 0);
+                mem        = ggml_view_4d(ctx->ggml_ctx, mem, h->ne[0], h->ne[1], h->ne[2], h->ne[3], h->nb[1], h->nb[2], h->nb[3], 0);
+                h          = block->forward(ctx, h, mem);
+            }
+            // upsample
+            index++;
+            h          = ggml_upscale(ctx->ggml_ctx, h, 2, GGML_SCALE_MODE_NEAREST);
+            auto block = std::dynamic_pointer_cast<UnaryBlock>(blocks[std::to_string(index++)]);
+            h          = block->forward(ctx, h);
+            block      = std::dynamic_pointer_cast<UnaryBlock>(blocks[std::to_string(index++)]);
+            h          = block->forward(ctx, h);
+        }
+        h = ggml_relu_inplace(ctx->ggml_ctx, h);
+
+        auto last_conv = std::dynamic_pointer_cast<Conv2d>(blocks[std::to_string(++index)]);
+        h              = last_conv->forward(ctx, h);
+        if (patch_size > 1) {
+            h = unpatchify(ctx->ggml_ctx, h, patch_size, 1);
+        }
+        // shape(W, H, 3, 3 + T) => shape(W, H, 3, T)
+        h = ggml_view_4d(ctx->ggml_ctx, h, h->ne[0], h->ne[1], h->ne[2], h->ne[3] - 3, h->nb[1], h->nb[2], h->nb[3], 3 * h->nb[3]);
+        return h;
+    }
+};
+
+class TAEHV : public GGMLBlock {
+protected:
+    bool decode_only;
+    SDVersion version;
+
+public:
+    TAEHV(bool decode_only = true, SDVersion version = VERSION_WAN2)
+        : decode_only(decode_only), version(version) {
+        int z_channels = 16;
+        int patch      = 1;
+        if (version == VERSION_WAN2_2_TI2V) {
+            z_channels = 48;
+            patch      = 2;
+        }
+        blocks["decoder"] = std::shared_ptr<GGMLBlock>(new TinyVideoDecoder(z_channels, patch));
+        if (!decode_only) {
+            blocks["encoder"] = std::shared_ptr<GGMLBlock>(new TinyVideoEncoder(z_channels, patch));
+        }
+    }
+
+    struct ggml_tensor* decode(GGMLRunnerContext* ctx, struct ggml_tensor* z) {
+        auto decoder = std::dynamic_pointer_cast<TinyVideoDecoder>(blocks["decoder"]);
+        if (sd_version_is_wan(version)) {
+            // (W, H, C, T) -> (W, H, T, C)
+            z = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, z, 0, 1, 3, 2));
+        }
+        auto result = decoder->forward(ctx, z);
+        if (sd_version_is_wan(version)) {
+            // (W, H, C, T) -> (W, H, T, C)
+            result = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, result, 0, 1, 3, 2));
+        }
+        return result;
+    }
+
+    struct ggml_tensor* encode(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        auto encoder = std::dynamic_pointer_cast<TinyVideoEncoder>(blocks["encoder"]);
+        // (W, H, T, C) -> (W, H, C, T)
+        x                  = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 1, 3, 2));
+        int64_t num_frames = x->ne[3];
+        if (num_frames % 4) {
+            // pad to multiple of 4 at the end
+            auto last_frame = ggml_view_4d(ctx->ggml_ctx, x, x->ne[0], x->ne[1], x->ne[2], 1, x->nb[1], x->nb[2], x->nb[3], (num_frames - 1) * x->nb[3]);
+            for (int i = 0; i < 4 - num_frames % 4; i++) {
+                x = ggml_concat(ctx->ggml_ctx, x, last_frame, 3);
+            }
+        }
+        x = encoder->forward(ctx, x);
+        x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 1, 3, 2));
+        return x;
+    }
+};
+
 class TAESD : public GGMLBlock {
 protected:
     bool decode_only;
@@ -192,18 +497,30 @@ public:
 };
 
 struct TinyAutoEncoder : public GGMLRunner {
+    TinyAutoEncoder(ggml_backend_t backend, bool offload_params_to_cpu)
+        : GGMLRunner(backend, offload_params_to_cpu) {}
+    virtual bool compute(const int n_threads,
+                         struct ggml_tensor* z,
+                         bool decode_graph,
+                         struct ggml_tensor** output,
+                         struct ggml_context* output_ctx = nullptr) = 0;
+
+    virtual bool load_from_file(const std::string& file_path, int n_threads) = 0;
+};
+
+struct TinyImageAutoEncoder : public TinyAutoEncoder {
     TAESD taesd;
     bool decode_only = false;
 
-    TinyAutoEncoder(ggml_backend_t backend,
+    TinyImageAutoEncoder(ggml_backend_t backend,
-                    bool offload_params_to_cpu,
+                         bool offload_params_to_cpu,
-                    const String2TensorStorage& tensor_storage_map,
+                         const String2TensorStorage& tensor_storage_map,
-                    const std::string prefix,
+                         const std::string prefix,
-                    bool decoder_only = true,
+                         bool decoder_only = true,
-                    SDVersion version = VERSION_SD1)
+                         SDVersion version = VERSION_SD1)
         : decode_only(decoder_only),
           taesd(decoder_only, version),
-          GGMLRunner(backend, offload_params_to_cpu) {
+          TinyAutoEncoder(backend, offload_params_to_cpu) {
         taesd.init(params_ctx, tensor_storage_map, prefix);
     }
 
@@ -260,4 +577,73 @@ struct TinyAutoEncoder : public GGMLRunner {
     }
 };
 
+struct TinyVideoAutoEncoder : public TinyAutoEncoder {
+    TAEHV taehv;
+    bool decode_only = false;
+
+    TinyVideoAutoEncoder(ggml_backend_t backend,
+                         bool offload_params_to_cpu,
+                         const String2TensorStorage& tensor_storage_map,
+                         const std::string prefix,
+                         bool decoder_only = true,
+                         SDVersion version = VERSION_WAN2)
+        : decode_only(decoder_only),
+          taehv(decoder_only, version),
+          TinyAutoEncoder(backend, offload_params_to_cpu) {
+        taehv.init(params_ctx, tensor_storage_map, prefix);
+    }
+
+    std::string get_desc() override {
+        return "taehv";
+    }
+
+    bool load_from_file(const std::string& file_path, int n_threads) {
+        LOG_INFO("loading taehv from '%s', decode_only = %s", file_path.c_str(), decode_only ? "true" : "false");
+        alloc_params_buffer();
+        std::map<std::string, ggml_tensor*> taehv_tensors;
+        taehv.get_param_tensors(taehv_tensors);
+        std::set<std::string> ignore_tensors;
+        if (decode_only) {
+            ignore_tensors.insert("encoder.");
+        }
+
+        ModelLoader model_loader;
+        if (!model_loader.init_from_file(file_path)) {
+            LOG_ERROR("init taehv model loader from file failed: '%s'", file_path.c_str());
+            return false;
+        }
+
+        bool success = model_loader.load_tensors(taehv_tensors, ignore_tensors, n_threads);
+
+        if (!success) {
+            LOG_ERROR("load tae tensors from model loader failed");
+            return false;
+        }
+
+        LOG_INFO("taehv model loaded");
+        return success;
+    }
+
+    struct ggml_cgraph* build_graph(struct ggml_tensor* z, bool decode_graph) {
+        struct ggml_cgraph* gf  = ggml_new_graph(compute_ctx);
+        z                       = to_backend(z);
+        auto runner_ctx         = get_context();
+        struct ggml_tensor* out = decode_graph ? taehv.decode(&runner_ctx, z) : taehv.encode(&runner_ctx, z);
+        ggml_build_forward_expand(gf, out);
+        return gf;
+    }
+
+    bool compute(const int n_threads,
+                 struct ggml_tensor* z,
+                 bool decode_graph,
+                 struct ggml_tensor** output,
+                 struct ggml_context* output_ctx = nullptr) {
+        auto get_graph = [&]() -> struct ggml_cgraph* {
+            return build_graph(z, decode_graph);
+        };
+
+        return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+    }
+};
+
 #endif  // __TAE_HPP__

thirdparty/README.md

@@ -1,3 +1,10 @@
 - json.hpp library from: https://github.com/nlohmann/json
+    - LICENSE: https://github.com/nlohmann/json/blob/develop/LICENSE.MIT
 - ZIP Library from: https://github.com/kuba--/zip
-- darts.h from: https://github.com/google/sentencepiece/tree/master/third_party/darts_clone
+    LICENSE: https://github.com/kuba--/zip/blob/master/LICENSE.txt
+- darts.h from: https://github.com/google/sentencepiece/tree/master/third_party/darts_clone
+    - LICENSE: https://github.com/google/sentencepiece/blob/master/third_party/darts_clone/LICENSE
+- httplib.h from: https://github.com/yhirose/cpp-httplib/blob/master/httplib.h
+    - LICENSE: https://github.com/yhirose/cpp-httplib/blob/master/LICENSE
+- stb_image.h/stb_image_resize.h/stb_image_write.h from: https://github.com/nothings/stb
+    - LICENSE: https://github.com/nothings/stb/blob/master/LICENSE

ucache.hpp

@@ -0,0 +1,404 @@
+#ifndef __UCACHE_HPP__
+#define __UCACHE_HPP__
+
+#include <cmath>
+#include <limits>
+#include <unordered_map>
+#include <vector>
+
+#include "denoiser.hpp"
+#include "ggml_extend.hpp"
+
+struct UCacheConfig {
+    bool enabled                = false;
+    float reuse_threshold       = 1.0f;
+    float start_percent         = 0.15f;
+    float end_percent           = 0.95f;
+    float error_decay_rate      = 1.0f;
+    bool use_relative_threshold = true;
+    bool adaptive_threshold     = true;
+    float early_step_multiplier = 0.5f;
+    float late_step_multiplier  = 1.5f;
+    bool reset_error_on_compute = true;
+};
+
+struct UCacheCacheEntry {
+    std::vector<float> diff;
+};
+
+struct UCacheState {
+    UCacheConfig config;
+    Denoiser* denoiser                  = nullptr;
+    float start_sigma                   = std::numeric_limits<float>::max();
+    float end_sigma                     = 0.0f;
+    bool initialized                    = false;
+    bool initial_step                   = true;
+    bool skip_current_step              = false;
+    bool step_active                    = false;
+    const SDCondition* anchor_condition = nullptr;
+    std::unordered_map<const SDCondition*, UCacheCacheEntry> cache_diffs;
+    std::vector<float> prev_input;
+    std::vector<float> prev_output;
+    float output_prev_norm                = 0.0f;
+    bool has_prev_input                   = false;
+    bool has_prev_output                  = false;
+    bool has_output_prev_norm             = false;
+    bool has_relative_transformation_rate = false;
+    float relative_transformation_rate    = 0.0f;
+    float cumulative_change_rate          = 0.0f;
+    float last_input_change               = 0.0f;
+    bool has_last_input_change            = false;
+    int total_steps_skipped               = 0;
+    int current_step_index                = -1;
+    int steps_computed_since_active       = 0;
+    float accumulated_error               = 0.0f;
+    float reference_output_norm           = 0.0f;
+
+    struct BlockMetrics {
+        float sum_transformation_rate = 0.0f;
+        float sum_output_norm         = 0.0f;
+        int sample_count              = 0;
+        float min_change_rate         = std::numeric_limits<float>::max();
+        float max_change_rate         = 0.0f;
+
+        void reset() {
+            sum_transformation_rate = 0.0f;
+            sum_output_norm         = 0.0f;
+            sample_count            = 0;
+            min_change_rate         = std::numeric_limits<float>::max();
+            max_change_rate         = 0.0f;
+        }
+
+        void record(float change_rate, float output_norm) {
+            if (std::isfinite(change_rate) && change_rate > 0.0f) {
+                sum_transformation_rate += change_rate;
+                sum_output_norm += output_norm;
+                sample_count++;
+                if (change_rate < min_change_rate)
+                    min_change_rate = change_rate;
+                if (change_rate > max_change_rate)
+                    max_change_rate = change_rate;
+            }
+        }
+
+        float avg_transformation_rate() const {
+            return (sample_count > 0) ? (sum_transformation_rate / sample_count) : 0.0f;
+        }
+
+        float avg_output_norm() const {
+            return (sample_count > 0) ? (sum_output_norm / sample_count) : 0.0f;
+        }
+    };
+    BlockMetrics block_metrics;
+    int total_active_steps = 0;
+
+    void reset_runtime() {
+        initial_step      = true;
+        skip_current_step = false;
+        step_active       = false;
+        anchor_condition  = nullptr;
+        cache_diffs.clear();
+        prev_input.clear();
+        prev_output.clear();
+        output_prev_norm                 = 0.0f;
+        has_prev_input                   = false;
+        has_prev_output                  = false;
+        has_output_prev_norm             = false;
+        has_relative_transformation_rate = false;
+        relative_transformation_rate     = 0.0f;
+        cumulative_change_rate           = 0.0f;
+        last_input_change                = 0.0f;
+        has_last_input_change            = false;
+        total_steps_skipped              = 0;
+        current_step_index               = -1;
+        steps_computed_since_active      = 0;
+        accumulated_error                = 0.0f;
+        reference_output_norm            = 0.0f;
+        block_metrics.reset();
+        total_active_steps = 0;
+    }
+
+    void init(const UCacheConfig& cfg, Denoiser* d) {
+        config      = cfg;
+        denoiser    = d;
+        initialized = cfg.enabled && d != nullptr;
+        reset_runtime();
+        if (initialized) {
+            start_sigma = percent_to_sigma(config.start_percent);
+            end_sigma   = percent_to_sigma(config.end_percent);
+        }
+    }
+
+    void set_sigmas(const std::vector<float>& sigmas) {
+        if (!initialized || sigmas.size() < 2) {
+            return;
+        }
+        size_t n_steps = sigmas.size() - 1;
+
+        size_t start_step = static_cast<size_t>(config.start_percent * n_steps);
+        size_t end_step   = static_cast<size_t>(config.end_percent * n_steps);
+
+        if (start_step >= n_steps)
+            start_step = n_steps - 1;
+        if (end_step >= n_steps)
+            end_step = n_steps - 1;
+
+        start_sigma = sigmas[start_step];
+        end_sigma   = sigmas[end_step];
+
+        if (start_sigma < end_sigma) {
+            std::swap(start_sigma, end_sigma);
+        }
+    }
+
+    bool enabled() const {
+        return initialized && config.enabled;
+    }
+
+    float percent_to_sigma(float percent) const {
+        if (!denoiser) {
+            return 0.0f;
+        }
+        if (percent <= 0.0f) {
+            return std::numeric_limits<float>::max();
+        }
+        if (percent >= 1.0f) {
+            return 0.0f;
+        }
+        float t = (1.0f - percent) * (TIMESTEPS - 1);
+        return denoiser->t_to_sigma(t);
+    }
+
+    void begin_step(int step_index, float sigma) {
+        if (!enabled()) {
+            return;
+        }
+        if (step_index == current_step_index) {
+            return;
+        }
+        current_step_index    = step_index;
+        skip_current_step     = false;
+        has_last_input_change = false;
+        step_active           = false;
+
+        if (sigma > start_sigma) {
+            return;
+        }
+        if (!(sigma > end_sigma)) {
+            return;
+        }
+        step_active = true;
+        total_active_steps++;
+    }
+
+    bool step_is_active() const {
+        return enabled() && step_active;
+    }
+
+    bool is_step_skipped() const {
+        return enabled() && step_active && skip_current_step;
+    }
+
+    float get_adaptive_threshold(int estimated_total_steps = 0) const {
+        float base_threshold = config.reuse_threshold;
+
+        if (!config.adaptive_threshold) {
+            return base_threshold;
+        }
+
+        int effective_total = estimated_total_steps;
+        if (effective_total <= 0) {
+            effective_total = std::max(20, steps_computed_since_active * 2);
+        }
+
+        float progress = (effective_total > 0) ? (static_cast<float>(steps_computed_since_active) / effective_total) : 0.0f;
+
+        float multiplier = 1.0f;
+        if (progress < 0.2f) {
+            multiplier = config.early_step_multiplier;
+        } else if (progress > 0.8f) {
+            multiplier = config.late_step_multiplier;
+        }
+
+        return base_threshold * multiplier;
+    }
+
+    bool has_cache(const SDCondition* cond) const {
+        auto it = cache_diffs.find(cond);
+        return it != cache_diffs.end() && !it->second.diff.empty();
+    }
+
+    void update_cache(const SDCondition* cond, ggml_tensor* input, ggml_tensor* output) {
+        UCacheCacheEntry& entry = cache_diffs[cond];
+        size_t ne               = static_cast<size_t>(ggml_nelements(output));
+        entry.diff.resize(ne);
+        float* out_data = (float*)output->data;
+        float* in_data  = (float*)input->data;
+
+        for (size_t i = 0; i < ne; ++i) {
+            entry.diff[i] = out_data[i] - in_data[i];
+        }
+    }
+
+    void apply_cache(const SDCondition* cond, ggml_tensor* input, ggml_tensor* output) {
+        auto it = cache_diffs.find(cond);
+        if (it == cache_diffs.end() || it->second.diff.empty()) {
+            return;
+        }
+
+        copy_ggml_tensor(output, input);
+        float* out_data                = (float*)output->data;
+        const std::vector<float>& diff = it->second.diff;
+        for (size_t i = 0; i < diff.size(); ++i) {
+            out_data[i] += diff[i];
+        }
+    }
+
+    bool before_condition(const SDCondition* cond,
+                          ggml_tensor* input,
+                          ggml_tensor* output,
+                          float sigma,
+                          int step_index) {
+        if (!enabled() || step_index < 0) {
+            return false;
+        }
+        if (step_index != current_step_index) {
+            begin_step(step_index, sigma);
+        }
+        if (!step_active) {
+            return false;
+        }
+
+        if (initial_step) {
+            anchor_condition = cond;
+            initial_step     = false;
+        }
+
+        bool is_anchor = (cond == anchor_condition);
+
+        if (skip_current_step) {
+            if (has_cache(cond)) {
+                apply_cache(cond, input, output);
+                return true;
+            }
+            return false;
+        }
+
+        if (!is_anchor) {
+            return false;
+        }
+
+        if (!has_prev_input || !has_prev_output || !has_cache(cond)) {
+            return false;
+        }
+
+        size_t ne = static_cast<size_t>(ggml_nelements(input));
+        if (prev_input.size() != ne) {
+            return false;
+        }
+
+        float* input_data = (float*)input->data;
+        last_input_change = 0.0f;
+        for (size_t i = 0; i < ne; ++i) {
+            last_input_change += std::fabs(input_data[i] - prev_input[i]);
+        }
+        if (ne > 0) {
+            last_input_change /= static_cast<float>(ne);
+        }
+        has_last_input_change = true;
+
+        if (has_output_prev_norm && has_relative_transformation_rate &&
+            last_input_change > 0.0f && output_prev_norm > 0.0f) {
+            float approx_output_change_rate = (relative_transformation_rate * last_input_change) / output_prev_norm;
+            accumulated_error               = accumulated_error * config.error_decay_rate + approx_output_change_rate;
+
+            float effective_threshold = get_adaptive_threshold();
+            if (config.use_relative_threshold && reference_output_norm > 0.0f) {
+                effective_threshold = effective_threshold * reference_output_norm;
+            }
+
+            if (accumulated_error < effective_threshold) {
+                skip_current_step = true;
+                total_steps_skipped++;
+                apply_cache(cond, input, output);
+                return true;
+            } else if (config.reset_error_on_compute) {
+                accumulated_error = 0.0f;
+            }
+        }
+
+        return false;
+    }
+
+    void after_condition(const SDCondition* cond, ggml_tensor* input, ggml_tensor* output) {
+        if (!step_is_active()) {
+            return;
+        }
+
+        update_cache(cond, input, output);
+
+        if (cond != anchor_condition) {
+            return;
+        }
+
+        size_t ne      = static_cast<size_t>(ggml_nelements(input));
+        float* in_data = (float*)input->data;
+        prev_input.resize(ne);
+        for (size_t i = 0; i < ne; ++i) {
+            prev_input[i] = in_data[i];
+        }
+        has_prev_input = true;
+
+        float* out_data     = (float*)output->data;
+        float output_change = 0.0f;
+        if (has_prev_output && prev_output.size() == ne) {
+            for (size_t i = 0; i < ne; ++i) {
+                output_change += std::fabs(out_data[i] - prev_output[i]);
+            }
+            if (ne > 0) {
+                output_change /= static_cast<float>(ne);
+            }
+        }
+
+        prev_output.resize(ne);
+        for (size_t i = 0; i < ne; ++i) {
+            prev_output[i] = out_data[i];
+        }
+        has_prev_output = true;
+
+        float mean_abs = 0.0f;
+        for (size_t i = 0; i < ne; ++i) {
+            mean_abs += std::fabs(out_data[i]);
+        }
+        output_prev_norm     = (ne > 0) ? (mean_abs / static_cast<float>(ne)) : 0.0f;
+        has_output_prev_norm = output_prev_norm > 0.0f;
+
+        if (reference_output_norm == 0.0f) {
+            reference_output_norm = output_prev_norm;
+        }
+
+        if (has_last_input_change && last_input_change > 0.0f && output_change > 0.0f) {
+            float rate = output_change / last_input_change;
+            if (std::isfinite(rate)) {
+                relative_transformation_rate     = rate;
+                has_relative_transformation_rate = true;
+                block_metrics.record(rate, output_prev_norm);
+            }
+        }
+
+        has_last_input_change = false;
+    }
+
+    void log_block_metrics() const {
+        if (block_metrics.sample_count > 0) {
+            LOG_INFO("UCacheBlockMetrics: samples=%d, avg_rate=%.4f, min=%.4f, max=%.4f, avg_norm=%.4f",
+                     block_metrics.sample_count,
+                     block_metrics.avg_transformation_rate(),
+                     block_metrics.min_change_rate,
+                     block_metrics.max_change_rate,
+                     block_metrics.avg_output_norm());
+        }
+    }
+};
+
+#endif  // __UCACHE_HPP__

util.cpp

@@ -95,9 +95,71 @@ bool is_directory(const std::string& path) {
     return (attributes != INVALID_FILE_ATTRIBUTES && (attributes & FILE_ATTRIBUTE_DIRECTORY));
 }
 
+class MmapWrapperImpl : public MmapWrapper {
+public:
+    MmapWrapperImpl(void* data, size_t size, HANDLE hfile, HANDLE hmapping)
+        : MmapWrapper(data, size), hfile_(hfile), hmapping_(hmapping) {}
+
+    ~MmapWrapperImpl() override {
+        UnmapViewOfFile(data_);
+        CloseHandle(hmapping_);
+        CloseHandle(hfile_);
+    }
+
+private:
+    HANDLE hfile_;
+    HANDLE hmapping_;
+};
+
+std::unique_ptr<MmapWrapper> MmapWrapper::create(const std::string& filename) {
+    void* mapped_data = nullptr;
+    size_t file_size  = 0;
+
+    HANDLE file_handle = CreateFileA(
+        filename.c_str(),
+        GENERIC_READ,
+        FILE_SHARE_READ,
+        NULL,
+        OPEN_EXISTING,
+        FILE_ATTRIBUTE_NORMAL,
+        NULL);
+
+    if (file_handle == INVALID_HANDLE_VALUE) {
+        return nullptr;
+    }
+
+    LARGE_INTEGER size;
+    if (!GetFileSizeEx(file_handle, &size)) {
+        CloseHandle(file_handle);
+        return nullptr;
+    }
+
+    file_size = static_cast<size_t>(size.QuadPart);
+
+    HANDLE mapping_handle = CreateFileMapping(file_handle, NULL, PAGE_READONLY, 0, 0, NULL);
+
+    if (mapping_handle == NULL) {
+        CloseHandle(file_handle);
+        return nullptr;
+    }
+
+    mapped_data = MapViewOfFile(mapping_handle, FILE_MAP_READ, 0, 0, file_size);
+
+    if (mapped_data == NULL) {
+        CloseHandle(mapping_handle);
+        CloseHandle(file_handle);
+        return nullptr;
+    }
+
+    return std::make_unique<MmapWrapperImpl>(mapped_data, file_size, file_handle, mapping_handle);
+}
+
 #else  // Unix
 #include <dirent.h>
+#include <fcntl.h>
+#include <sys/mman.h>
 #include <sys/stat.h>
+#include <unistd.h>
 
 bool file_exists(const std::string& filename) {
     struct stat buffer;
@@ -109,8 +171,64 @@ bool is_directory(const std::string& path) {
     return (stat(path.c_str(), &buffer) == 0 && S_ISDIR(buffer.st_mode));
 }
 
+class MmapWrapperImpl : public MmapWrapper {
+public:
+    MmapWrapperImpl(void* data, size_t size)
+        : MmapWrapper(data, size) {}
+
+    ~MmapWrapperImpl() override {
+        munmap(data_, size_);
+    }
+};
+
+std::unique_ptr<MmapWrapper> MmapWrapper::create(const std::string& filename) {
+    int file_descriptor = open(filename.c_str(), O_RDONLY);
+    if (file_descriptor == -1) {
+        return nullptr;
+    }
+
+    int mmap_flags = MAP_PRIVATE;
+
+#ifdef __linux__
+    // performance flags used by llama.cpp
+    // posix_fadvise(file_descriptor, 0, 0, POSIX_FADV_SEQUENTIAL);
+    // mmap_flags |= MAP_POPULATE;
 #endif
 
+    struct stat sb;
+    if (fstat(file_descriptor, &sb) == -1) {
+        close(file_descriptor);
+        return nullptr;
+    }
+
+    size_t file_size = sb.st_size;
+
+    void* mapped_data = mmap(NULL, file_size, PROT_READ, mmap_flags, file_descriptor, 0);
+
+    close(file_descriptor);
+
+    if (mapped_data == MAP_FAILED) {
+        return nullptr;
+    }
+
+#ifdef __linux__
+    // performance flags used by llama.cpp
+    // posix_madvise(mapped_data, file_size, POSIX_MADV_WILLNEED);
+#endif
+
+    return std::make_unique<MmapWrapperImpl>(mapped_data, file_size);
+}
+
+#endif
+
+bool MmapWrapper::copy_data(void* buf, size_t n, size_t offset) const {
+    if (offset >= size_ || n > (size_ - offset)) {
+        return false;
+    }
+    std::memcpy(buf, data() + offset, n);
+    return true;
+}
+
 // get_num_physical_cores is copy from
 // https://github.com/ggerganov/llama.cpp/blob/master/examples/common.cpp
 // LICENSE: https://github.com/ggerganov/llama.cpp/blob/master/LICENSE

util.h

@@ -2,6 +2,7 @@
 #define __UTIL_H__
 
 #include <cstdint>
+#include <memory>
 #include <string>
 #include <vector>
 
@@ -43,6 +44,28 @@ sd_image_f32_t resize_sd_image_f32_t(sd_image_f32_t image, int target_width, int
 
 sd_image_f32_t clip_preprocess(sd_image_f32_t image, int target_width, int target_height);
 
+class MmapWrapper {
+public:
+    static std::unique_ptr<MmapWrapper> create(const std::string& filename);
+
+    virtual ~MmapWrapper() = default;
+
+    MmapWrapper(const MmapWrapper&)            = delete;
+    MmapWrapper& operator=(const MmapWrapper&) = delete;
+    MmapWrapper(MmapWrapper&&)                 = delete;
+    MmapWrapper& operator=(MmapWrapper&&)      = delete;
+
+    const uint8_t* data() const { return static_cast<uint8_t*>(data_); }
+    size_t size() const { return size_; }
+    bool copy_data(void* buf, size_t n, size_t offset) const;
+
+protected:
+    MmapWrapper(void* data, size_t size)
+        : data_(data), size_(size) {}
+    void* data_  = nullptr;
+    size_t size_ = 0;
+};
+
 std::string path_join(const std::string& p1, const std::string& p2);
 std::vector<std::string> split_string(const std::string& str, char delimiter);
 void pretty_progress(int step, int steps, float time);

wan.hpp

@@ -75,7 +75,7 @@ namespace WAN {
                 lp2 -= (int)cache_x->ne[2];
             }
 
-            x = ggml_pad_ext(ctx->ggml_ctx, x, lp0, rp0, lp1, rp1, lp2, rp2, 0, 0);
+            x = ggml_ext_pad_ext(ctx->ggml_ctx, x, lp0, rp0, lp1, rp1, lp2, rp2, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
             return ggml_ext_conv_3d(ctx->ggml_ctx, x, w, b, in_channels,
                                     std::get<2>(stride), std::get<1>(stride), std::get<0>(stride),
                                     0, 0, 0,
@@ -206,9 +206,9 @@ namespace WAN {
                 } else if (mode == "upsample3d") {
                     x = ggml_upscale(ctx->ggml_ctx, x, 2, GGML_SCALE_MODE_NEAREST);
                 } else if (mode == "downsample2d") {
-                    x = ggml_pad(ctx->ggml_ctx, x, 1, 1, 0, 0);
+                    x = ggml_ext_pad(ctx->ggml_ctx, x, 1, 1, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
                 } else if (mode == "downsample3d") {
-                    x = ggml_pad(ctx->ggml_ctx, x, 1, 1, 0, 0);
+                    x = ggml_ext_pad(ctx->ggml_ctx, x, 1, 1, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
                 }
                 x = resample_1->forward(ctx, x);
                 x = ggml_ext_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, x, 0, 1, 3, 2));  // (c, t, h, w)
@@ -1826,7 +1826,7 @@ namespace WAN {
             }
         }
 
-        struct ggml_tensor* pad_to_patch_size(struct ggml_context* ctx,
+        struct ggml_tensor* pad_to_patch_size(GGMLRunnerContext* ctx,
                                               struct ggml_tensor* x) {
             int64_t W = x->ne[0];
             int64_t H = x->ne[1];
@@ -1835,8 +1835,7 @@ namespace WAN {
             int pad_t = (std::get<0>(params.patch_size) - T % std::get<0>(params.patch_size)) % std::get<0>(params.patch_size);
             int pad_h = (std::get<1>(params.patch_size) - H % std::get<1>(params.patch_size)) % std::get<1>(params.patch_size);
             int pad_w = (std::get<2>(params.patch_size) - W % std::get<2>(params.patch_size)) % std::get<2>(params.patch_size);
-            x         = ggml_pad(ctx, x, pad_w, pad_h, pad_t, 0);  // [N*C, T + pad_t, H + pad_h, W + pad_w]
+            ggml_ext_pad(ctx->ggml_ctx, x, pad_w, pad_h, pad_t, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
-
             return x;
         }
 
@@ -1986,14 +1985,14 @@ namespace WAN {
             int64_t T = x->ne[2];
             int64_t C = x->ne[3];
 
-            x = pad_to_patch_size(ctx->ggml_ctx, x);
+            x = pad_to_patch_size(ctx, x);
 
             int64_t t_len = ((T + (std::get<0>(params.patch_size) / 2)) / std::get<0>(params.patch_size));
             int64_t h_len = ((H + (std::get<1>(params.patch_size) / 2)) / std::get<1>(params.patch_size));
             int64_t w_len = ((W + (std::get<2>(params.patch_size) / 2)) / std::get<2>(params.patch_size));
 
             if (time_dim_concat != nullptr) {
-                time_dim_concat = pad_to_patch_size(ctx->ggml_ctx, time_dim_concat);
+                time_dim_concat = pad_to_patch_size(ctx, time_dim_concat);
                 x               = ggml_concat(ctx->ggml_ctx, x, time_dim_concat, 2);  // [N*C, (T+pad_t) + (T2+pad_t2), H + pad_h, W + pad_w]
                 t_len           = ((x->ne[2] + (std::get<0>(params.patch_size) / 2)) / std::get<0>(params.patch_size));
             }

z_image.hpp

@@ -324,14 +324,14 @@ namespace ZImage {
             blocks["final_layer"] = std::make_shared<FinalLayer>(z_image_params.hidden_size, z_image_params.patch_size, z_image_params.out_channels);
         }
 
-        struct ggml_tensor* pad_to_patch_size(struct ggml_context* ctx,
+        struct ggml_tensor* pad_to_patch_size(GGMLRunnerContext* ctx,
                                               struct ggml_tensor* x) {
             int64_t W = x->ne[0];
             int64_t H = x->ne[1];
 
             int pad_h = (z_image_params.patch_size - H % z_image_params.patch_size) % z_image_params.patch_size;
             int pad_w = (z_image_params.patch_size - W % z_image_params.patch_size) % z_image_params.patch_size;
-            x         = ggml_pad(ctx, x, pad_w, pad_h, 0, 0);  // [N, C, H + pad_h, W + pad_w]
+            x         = ggml_ext_pad(ctx->ggml_ctx, x, pad_w, pad_h, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
             return x;
         }
 
@@ -357,10 +357,10 @@ namespace ZImage {
             return x;
         }
 
-        struct ggml_tensor* process_img(struct ggml_context* ctx,
+        struct ggml_tensor* process_img(GGMLRunnerContext* ctx,
                                         struct ggml_tensor* x) {
             x = pad_to_patch_size(ctx, x);
-            x = patchify(ctx, x);
+            x = patchify(ctx->ggml_ctx, x);
             return x;
         }
 
@@ -473,12 +473,12 @@ namespace ZImage {
             int64_t C = x->ne[2];
             int64_t N = x->ne[3];
 
-            auto img             = process_img(ctx->ggml_ctx, x);
+            auto img             = process_img(ctx, x);
             uint64_t n_img_token = img->ne[1];
 
             if (ref_latents.size() > 0) {
                 for (ggml_tensor* ref : ref_latents) {
-                    ref = process_img(ctx->ggml_ctx, ref);
+                    ref = process_img(ctx, ref);
                     img = ggml_concat(ctx->ggml_ctx, img, ref, 1);
                 }
             }
@@ -552,6 +552,8 @@ namespace ZImage {
                                                ref_latents,
                                                increase_ref_index,
                                                z_image_params.theta,
+                                               circular_y_enabled,
+                                               circular_x_enabled,
                                                z_image_params.axes_dim);
             int pos_len = pe_vec.size() / z_image_params.axes_dim_sum / 2;
             // LOG_DEBUG("pos_len %d", pos_len);