refactor: simplify logic for saving results (#1149 )

chore: reformat named cache params description into single line
feat: support mmap for model loading (#1059 )
2026-10-03 19:00:50 +08:00 · 2025-12-28 23:27:27 +08:00 · 2025-12-28 22:53:07 +08:00 · 2025-12-28 22:38:29 +08:00 · 2025-12-27 16:48:15 +08:00 · 2025-12-27 15:54:18 +08:00
33 changed files with 3288 additions and 536 deletions
@@ -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 --build . --config Release
+          cmake .. -DCMAKE_CXX_FLAGS='/bigobj' -G Ninja -DCMAKE_C_COMPILER=cl.exe -DCMAKE_CXX_COMPILER=cl.exe ${{ matrix.defines }}
+          cmake --build .

      - name: Check AVX512F support
        id: check_avx512f
@@ -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.
@@ -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

@@ -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
@@ -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"]);
@@ -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;
                }
@@ -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 void set_flash_attn_enabled(bool enabled) = 0;
+    virtual int64_t get_adm_in_channels()                            = 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)
-        : prefix(prefix), qwen_image(backend, offload_params_to_cpu, tensor_storage_map, prefix, version) {
+                   SDVersion version                              = VERSION_QWEN_IMAGE,
+                   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,
@@ -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
@@ -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
@@ -32,4 +35,14 @@
 .\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" />
@@ -14,4 +14,26 @@ curl -L -O https://huggingface.co/madebyollin/taesd/resolve/main/diffusion_pytor

 ```bash
 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.
@@ -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
@@ -4,11 +4,13 @@
 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)
@@ -46,12 +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
@@ -93,6 +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)
@@ -120,11 +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],
-                                           default: discrete
+  --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)
-  --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'
 ```
@@ -26,13 +26,16 @@ const char* previews_str[] = {
    "vae",
 };

+std::regex format_specifier_regex("(?:[^%]|^)(?:%%)*(%\\d{0,3}d)");
+
 struct SDCliParams {
    SDMode mode             = IMG_GEN;
    std::string output_path = "output.png";
+    int output_begin_idx    = -1;

    bool verbose          = false;
-    bool version          = false;
    bool canny_preprocess = false;
+    bool convert_name     = false;

    preview_t preview_method = PREVIEW_NONE;
    int preview_interval     = 1;
@@ -50,7 +53,7 @@ struct SDCliParams {
        options.string_options = {
            {"-o",
             "--output",
-             "path to write result image to (default: ./output.png)",
+             "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_path},
            {"",
             "--preview-path",
@@ -63,6 +66,10 @@ struct SDCliParams {
             "--preview-interval",
             "interval in denoising steps between consecutive updates of the image preview file (default is 1, meaning updating at every step)",
             &preview_interval},
+            {"",
+             "--output-begin-idx",
+             "starting index for output image sequence, must be non-negative (default 0 if specified %d in output path, 1 otherwise)",
+             &output_begin_idx},
        };

        options.bool_options = {
@@ -70,14 +77,14 @@ struct SDCliParams {
             "--canny",
             "apply canny preprocessor (edge detection)",
             true, &canny_preprocess},
+            {"",
+             "--convert-name",
+             "convert tensor name (for convert mode)",
+             true, &convert_name},
            {"-v",
             "--verbose",
             "print extra info",
             true, &verbose},
-            {"",
-             "--version",
-             "print stable-diffusion.cpp version",
-             true, &version},
            {"",
             "--color",
             "colors the logging tags according to level",
@@ -179,6 +186,7 @@ struct SDCliParams {
            << "  verbose: " << (verbose ? "true" : "false") << ",\n"
            << "  color: " << (color ? "true" : "false") << ",\n"
            << "  canny_preprocess: " << (canny_preprocess ? "true" : "false") << ",\n"
+            << "  convert_name: " << (convert_name ? "true" : "false") << ",\n"
            << "  preview_method: " << previews_str[preview_method] << ",\n"
            << "  preview_interval: " << preview_interval << ",\n"
            << "  preview_path: \"" << preview_path << "\",\n"
@@ -343,6 +351,114 @@ void step_callback(int step, int frame_count, sd_image_t* image, bool is_noisy,
    }
 }

+std::string format_frame_idx(std::string pattern, int frame_idx) {
+    std::smatch match;
+    std::string result = pattern;
+    while (std::regex_search(result, match, format_specifier_regex)) {
+        std::string specifier = match.str(1);
+        char buffer[32];
+        snprintf(buffer, sizeof(buffer), specifier.c_str(), frame_idx);
+        result.replace(match.position(1), match.length(1), buffer);
+    }
+
+    // Then replace all '%%' with '%'
+    size_t pos = 0;
+    while ((pos = result.find("%%", pos)) != std::string::npos) {
+        result.replace(pos, 2, "%");
+        pos += 1;
+    }
+    return result;
+}
+
+bool save_results(const SDCliParams& cli_params,
+                  const SDContextParams& ctx_params,
+                  const SDGenerationParams& gen_params,
+                  sd_image_t* results,
+                  int num_results) {
+    if (results == nullptr || num_results <= 0) {
+        return false;
+    }
+
+    namespace fs      = std::filesystem;
+    fs::path out_path = cli_params.output_path;
+
+    if (!out_path.parent_path().empty()) {
+        std::error_code ec;
+        fs::create_directories(out_path.parent_path(), ec);
+        if (ec) {
+            LOG_ERROR("failed to create directory '%s': %s",
+                      out_path.parent_path().string().c_str(), ec.message().c_str());
+            return false;
+        }
+    }
+
+    fs::path base_path = out_path;
+    fs::path ext       = out_path.has_extension() ? out_path.extension() : fs::path{};
+    if (!ext.empty())
+        base_path.replace_extension();
+
+    std::string ext_lower = ext.string();
+    std::transform(ext_lower.begin(), ext_lower.end(), ext_lower.begin(), ::tolower);
+    bool is_jpg = (ext_lower == ".jpg" || ext_lower == ".jpeg" || ext_lower == ".jpe");
+
+    int output_begin_idx = cli_params.output_begin_idx;
+    if (output_begin_idx < 0) {
+        output_begin_idx = 0;
+    }
+
+    auto write_image = [&](const fs::path& path, int idx) {
+        const sd_image_t& img = results[idx];
+        if (!img.data)
+            return;
+
+        std::string params = get_image_params(cli_params, ctx_params, gen_params, gen_params.seed + idx);
+        int ok             = 0;
+        if (is_jpg) {
+            ok = stbi_write_jpg(path.string().c_str(), img.width, img.height, img.channel, img.data, 90, params.c_str());
+        } else {
+            ok = stbi_write_png(path.string().c_str(), img.width, img.height, img.channel, img.data, 0, params.c_str());
+        }
+        LOG_INFO("save result image %d to '%s' (%s)", idx, path.string().c_str(), ok ? "success" : "failure");
+    };
+
+    if (std::regex_search(cli_params.output_path, format_specifier_regex)) {
+        if (!is_jpg && ext_lower != ".png")
+            ext = ".png";
+        fs::path pattern = base_path;
+        pattern += ext;
+
+        for (int i = 0; i < num_results; ++i) {
+            fs::path img_path = format_frame_idx(pattern.string(), output_begin_idx + i);
+            write_image(img_path, i);
+        }
+        return true;
+    }
+
+    if (cli_params.mode == VID_GEN && num_results > 1) {
+        if (ext_lower != ".avi")
+            ext = ".avi";
+        fs::path video_path = base_path;
+        video_path += ext;
+        create_mjpg_avi_from_sd_images(video_path.string().c_str(), results, num_results, gen_params.fps);
+        LOG_INFO("save result MJPG AVI video to '%s'", video_path.string().c_str());
+        return true;
+    }
+
+    if (!is_jpg && ext_lower != ".png")
+        ext = ".png";
+
+    for (int i = 0; i < num_results; ++i) {
+        fs::path img_path = base_path;
+        if (num_results > 1) {
+            img_path += "_" + std::to_string(output_begin_idx + i);
+        }
+        img_path += ext;
+        write_image(img_path, i);
+    }
+
+    return true;
+}
+
 int main(int argc, const char* argv[]) {
    if (argc > 1 && std::string(argv[1]) == "--version") {
        std::cout << version_string() << "\n";
@@ -354,9 +470,6 @@ int main(int argc, const char* argv[]) {
    SDGenerationParams gen_params;

    parse_args(argc, argv, cli_params, ctx_params, gen_params);
-    if (cli_params.verbose || cli_params.version) {
-        std::cout << version_string() << "\n";
-    }
    if (gen_params.video_frames > 4) {
        size_t last_dot_pos   = cli_params.preview_path.find_last_of(".");
        std::string base_path = cli_params.preview_path;
@@ -384,19 +497,19 @@ int main(int argc, const char* argv[]) {
                            cli_params.preview_noisy,
                            (void*)&cli_params);

-    if (cli_params.verbose) {
-        LOG_INFO("%s", sd_get_system_info());
-        LOG_INFO("%s", cli_params.to_string().c_str());
-        LOG_INFO("%s", ctx_params.to_string().c_str());
-        LOG_INFO("%s", gen_params.to_string().c_str());
-    }
+    LOG_DEBUG("version: %s", version_string().c_str());
+    LOG_DEBUG("%s", sd_get_system_info());
+    LOG_DEBUG("%s", cli_params.to_string().c_str());
+    LOG_DEBUG("%s", ctx_params.to_string().c_str());
+    LOG_DEBUG("%s", gen_params.to_string().c_str());

    if (cli_params.mode == CONVERT) {
        bool success = convert(ctx_params.model_path.c_str(),
                               ctx_params.vae_path.c_str(),
                               cli_params.output_path.c_str(),
                               ctx_params.wtype,
-                               ctx_params.tensor_type_rules.c_str());
+                               ctx_params.tensor_type_rules.c_str(),
+                               cli_params.convert_name);
        if (!success) {
            LOG_ERROR("convert '%s'/'%s' to '%s' failed",
                      ctx_params.model_path.c_str(),
@@ -588,7 +701,7 @@ int main(int argc, const char* argv[]) {
        }

        if (gen_params.sample_params.scheduler == SCHEDULER_COUNT) {
-            gen_params.sample_params.scheduler = sd_get_default_scheduler(sd_ctx);
+            gen_params.sample_params.scheduler = sd_get_default_scheduler(sd_ctx, gen_params.sample_params.sample_method);
        }

        if (cli_params.mode == IMG_GEN) {
@@ -619,7 +732,7 @@ int main(int argc, const char* argv[]) {
                    gen_params.pm_style_strength,
                },  // pm_params
                ctx_params.vae_tiling_params,
-                gen_params.easycache_params,
+                gen_params.cache_params,
            };

            results     = generate_image(sd_ctx, &img_gen_params);
@@ -644,7 +757,7 @@ int main(int argc, const char* argv[]) {
                gen_params.seed,
                gen_params.video_frames,
                gen_params.vace_strength,
-                gen_params.easycache_params,
+                gen_params.cache_params,
            };

            results = generate_video(sd_ctx, &vid_gen_params, &num_results);
@@ -689,67 +802,8 @@ int main(int argc, const char* argv[]) {
        }
    }

-    // create directory if not exists
-    {
-        const fs::path out_path = cli_params.output_path;
-        if (const fs::path out_dir = out_path.parent_path(); !out_dir.empty()) {
-            std::error_code ec;
-            fs::create_directories(out_dir, ec);  // OK if already exists
-            if (ec) {
-                LOG_ERROR("failed to create directory '%s': %s",
-                          out_dir.string().c_str(), ec.message().c_str());
-                return 1;
-            }
-        }
-    }
-
-    std::string base_path;
-    std::string file_ext;
-    std::string file_ext_lower;
-    bool is_jpg;
-    size_t last_dot_pos   = cli_params.output_path.find_last_of(".");
-    size_t last_slash_pos = std::min(cli_params.output_path.find_last_of("/"),
-                                     cli_params.output_path.find_last_of("\\"));
-    if (last_dot_pos != std::string::npos && (last_slash_pos == std::string::npos || last_dot_pos > last_slash_pos)) {  // filename has extension
-        base_path = cli_params.output_path.substr(0, last_dot_pos);
-        file_ext = file_ext_lower = cli_params.output_path.substr(last_dot_pos);
-        std::transform(file_ext.begin(), file_ext.end(), file_ext_lower.begin(), ::tolower);
-        is_jpg = (file_ext_lower == ".jpg" || file_ext_lower == ".jpeg" || file_ext_lower == ".jpe");
-    } else {
-        base_path = cli_params.output_path;
-        file_ext = file_ext_lower = "";
-        is_jpg                    = false;
-    }
-
-    if (cli_params.mode == VID_GEN && num_results > 1) {
-        std::string vid_output_path = cli_params.output_path;
-        if (file_ext_lower == ".png") {
-            vid_output_path = base_path + ".avi";
-        }
-        create_mjpg_avi_from_sd_images(vid_output_path.c_str(), results, num_results, gen_params.fps);
-        LOG_INFO("save result MJPG AVI video to '%s'\n", vid_output_path.c_str());
-    } else {
-        // appending ".png" to absent or unknown extension
-        if (!is_jpg && file_ext_lower != ".png") {
-            base_path += file_ext;
-            file_ext = ".png";
-        }
-        for (int i = 0; i < num_results; i++) {
-            if (results[i].data == nullptr) {
-                continue;
-            }
-            int write_ok;
-            std::string final_image_path = i > 0 ? base_path + "_" + std::to_string(i + 1) + file_ext : base_path + file_ext;
-            if (is_jpg) {
-                write_ok = stbi_write_jpg(final_image_path.c_str(), results[i].width, results[i].height, results[i].channel,
-                                          results[i].data, 90, get_image_params(cli_params, ctx_params, gen_params, gen_params.seed + i).c_str());
-                LOG_INFO("save result JPEG image to '%s' (%s)", final_image_path.c_str(), write_ok == 0 ? "failure" : "success");
-            } else {
-                write_ok = stbi_write_png(final_image_path.c_str(), results[i].width, results[i].height, results[i].channel,
-                                          results[i].data, 0, get_image_params(cli_params, ctx_params, gen_params, gen_params.seed + i).c_str());
-                LOG_INFO("save result PNG image to '%s' (%s)", final_image_path.c_str(), write_ok == 0 ? "failure" : "success");
-            }
-        }
+    if (!save_results(cli_params, ctx_params, gen_params, results, num_results)) {
+        return 1;
    }

    for (int i = 0; i < num_results; i++) {
@@ -761,4 +815,4 @@ int main(int argc, const char* argv[]) {
    release_all_resources();

    return 0;
-}
+}
@@ -95,17 +95,28 @@ static void print_utf8(FILE* stream, const char* utf8) {
                   ? GetStdHandle(STD_ERROR_HANDLE)
                   : GetStdHandle(STD_OUTPUT_HANDLE);

-    int wlen = MultiByteToWideChar(CP_UTF8, 0, utf8, -1, NULL, 0);
-    if (wlen <= 0)
-        return;
+    DWORD mode;
+    BOOL is_console = GetConsoleMode(h, &mode);

-    wchar_t* wbuf = (wchar_t*)malloc(wlen * sizeof(wchar_t));
-    MultiByteToWideChar(CP_UTF8, 0, utf8, -1, wbuf, wlen);
+    if (is_console) {
+        int wlen = MultiByteToWideChar(CP_UTF8, 0, utf8, -1, NULL, 0);
+        if (wlen <= 0)
+            return;

-    DWORD written;
-    WriteConsoleW(h, wbuf, wlen - 1, &written, NULL);
+        wchar_t* wbuf = (wchar_t*)malloc(wlen * sizeof(wchar_t));
+        if (!wbuf)
+            return;

-    free(wbuf);
+        MultiByteToWideChar(CP_UTF8, 0, utf8, -1, wbuf, wlen);
+
+        DWORD written;
+        WriteConsoleW(h, wbuf, wlen - 1, &written, NULL);
+
+        free(wbuf);
+    } else {
+        DWORD written;
+        WriteFile(h, utf8, (DWORD)strlen(utf8), &written, NULL);
+    }
 #else
    fputs(utf8, stream);
 #endif
@@ -442,6 +453,7 @@ struct SDContextParams {
    rng_type_t rng_type         = CUDA_RNG;
    rng_type_t sampler_rng_type = RNG_TYPE_COUNT;
    bool offload_params_to_cpu  = false;
+    bool enable_mmap            = false;
    bool control_net_cpu        = false;
    bool clip_on_cpu            = false;
    bool vae_on_cpu             = false;
@@ -449,10 +461,16 @@ struct SDContextParams {
    bool diffusion_conv_direct  = false;
    bool vae_conv_direct        = false;

+    bool circular   = false;
+    bool circular_x = false;
+    bool circular_y = false;
+
    bool chroma_use_dit_mask = true;
    bool chroma_use_t5_mask  = false;
    int chroma_t5_mask_pad   = 1;

+    bool qwen_image_zero_cond_t = false;
+
    prediction_t prediction           = PREDICTION_COUNT;
    lora_apply_mode_t lora_apply_mode = LORA_APPLY_AUTO;

@@ -581,6 +599,10 @@ struct SDContextParams {
             "--offload-to-cpu",
             "place the weights in RAM to save VRAM, and automatically load them into VRAM when needed",
             true, &offload_params_to_cpu},
+            {"",
+             "--mmap",
+             "whether to memory-map model",
+             true, &enable_mmap},
            {"",
             "--control-net-cpu",
             "keep controlnet in cpu (for low vram)",
@@ -605,10 +627,26 @@ struct SDContextParams {
             "--vae-conv-direct",
             "use ggml_conv2d_direct in the vae model",
             true, &vae_conv_direct},
+            {"",
+             "--circular",
+             "enable circular padding for convolutions",
+             true, &circular},
+            {"",
+             "--circularx",
+             "enable circular RoPE wrapping on x-axis (width) only",
+             true, &circular_x},
+            {"",
+             "--circulary",
+             "enable circular RoPE wrapping on y-axis (height) only",
+             true, &circular_y},
            {"",
             "--chroma-disable-dit-mask",
             "disable dit mask for chroma",
             false, &chroma_use_dit_mask},
+            {"",
+             "--qwen-image-zero-cond-t",
+             "enable zero_cond_t for qwen image",
+             true, &qwen_image_zero_cond_t},
            {"",
             "--chroma-enable-t5-mask",
             "enable t5 mask for chroma",
@@ -862,13 +900,18 @@ struct SDContextParams {
            << "  sampler_rng_type: " << sd_rng_type_name(sampler_rng_type) << ",\n"
            << "  flow_shift: " << (std::isinf(flow_shift) ? "INF" : std::to_string(flow_shift)) << "\n"
            << "  offload_params_to_cpu: " << (offload_params_to_cpu ? "true" : "false") << ",\n"
+            << "  enable_mmap: " << (enable_mmap ? "true" : "false") << ",\n"
            << "  control_net_cpu: " << (control_net_cpu ? "true" : "false") << ",\n"
            << "  clip_on_cpu: " << (clip_on_cpu ? "true" : "false") << ",\n"
            << "  vae_on_cpu: " << (vae_on_cpu ? "true" : "false") << ",\n"
            << "  diffusion_flash_attn: " << (diffusion_flash_attn ? "true" : "false") << ",\n"
            << "  diffusion_conv_direct: " << (diffusion_conv_direct ? "true" : "false") << ",\n"
            << "  vae_conv_direct: " << (vae_conv_direct ? "true" : "false") << ",\n"
+            << "  circular: " << (circular ? "true" : "false") << ",\n"
+            << "  circular_x: " << (circular_x ? "true" : "false") << ",\n"
+            << "  circular_y: " << (circular_y ? "true" : "false") << ",\n"
            << "  chroma_use_dit_mask: " << (chroma_use_dit_mask ? "true" : "false") << ",\n"
+            << "  qwen_image_zero_cond_t: " << (qwen_image_zero_cond_t ? "true" : "false") << ",\n"
            << "  chroma_use_t5_mask: " << (chroma_use_t5_mask ? "true" : "false") << ",\n"
            << "  chroma_t5_mask_pad: " << chroma_t5_mask_pad << ",\n"
            << "  prediction: " << sd_prediction_name(prediction) << ",\n"
@@ -921,6 +964,7 @@ struct SDContextParams {
            prediction,
            lora_apply_mode,
            offload_params_to_cpu,
+            enable_mmap,
            clip_on_cpu,
            control_net_cpu,
            vae_on_cpu,
@@ -928,10 +972,13 @@ struct SDContextParams {
            taesd_preview,
            diffusion_conv_direct,
            vae_conv_direct,
+            circular || circular_x,
+            circular || circular_y,
            force_sdxl_vae_conv_scale,
            chroma_use_dit_mask,
            chroma_use_t5_mask,
            chroma_t5_mask_pad,
+            qwen_image_zero_cond_t,
            flow_shift,
        };
        return sd_ctx_params;
@@ -997,8 +1044,12 @@ struct SDGenerationParams {

    std::vector<float> custom_sigmas;

-    std::string easycache_option;
-    sd_easycache_params_t easycache_params;
+    std::string cache_mode;
+    std::string cache_option;
+    std::string cache_preset;
+    std::string scm_mask;
+    bool scm_policy_dynamic = true;
+    sd_cache_params_t cache_params{};

    float moe_boundary  = 0.875f;
    int video_frames    = 1;
@@ -1360,36 +1411,64 @@ struct SDGenerationParams {
            return 1;
        };

-        auto on_easycache_arg = [&](int argc, const char** argv, int index) {
-            const std::string default_values = "0.2,0.15,0.95";
-            auto looks_like_value            = [](const std::string& token) {
-                if (token.empty()) {
-                    return false;
-                }
-                if (token[0] != '-') {
-                    return true;
-                }
-                if (token.size() == 1) {
-                    return false;
-                }
-                unsigned char next = static_cast<unsigned char>(token[1]);
-                return std::isdigit(next) || token[1] == '.';
-            };
+        auto on_cache_mode_arg = [&](int argc, const char** argv, int index) {
+            if (++index >= argc) {
+                return -1;
+            }
+            cache_mode = argv_to_utf8(index, argv);
+            if (cache_mode != "easycache" && cache_mode != "ucache" &&
+                cache_mode != "dbcache" && cache_mode != "taylorseer" && cache_mode != "cache-dit") {
+                fprintf(stderr, "error: invalid cache mode '%s', must be 'easycache', 'ucache', 'dbcache', 'taylorseer', or 'cache-dit'\n", cache_mode.c_str());
+                return -1;
+            }
+            return 1;
+        };

-            std::string option_value;
-            int consumed = 0;
-            if (index + 1 < argc) {
-                std::string next_arg = argv[index + 1];
-                if (looks_like_value(next_arg)) {
-                    option_value = argv_to_utf8(index + 1, argv);
-                    consumed     = 1;
-                }
+        auto on_cache_option_arg = [&](int argc, const char** argv, int index) {
+            if (++index >= argc) {
+                return -1;
            }
-            if (option_value.empty()) {
-                option_value = default_values;
+            cache_option = argv_to_utf8(index, argv);
+            return 1;
+        };
+
+        auto on_scm_mask_arg = [&](int argc, const char** argv, int index) {
+            if (++index >= argc) {
+                return -1;
            }
-            easycache_option = option_value;
-            return consumed;
+            scm_mask = argv_to_utf8(index, argv);
+            return 1;
+        };
+
+        auto on_scm_policy_arg = [&](int argc, const char** argv, int index) {
+            if (++index >= argc) {
+                return -1;
+            }
+            std::string policy = argv_to_utf8(index, argv);
+            if (policy == "dynamic") {
+                scm_policy_dynamic = true;
+            } else if (policy == "static") {
+                scm_policy_dynamic = false;
+            } else {
+                fprintf(stderr, "error: invalid scm policy '%s', must be 'dynamic' or 'static'\n", policy.c_str());
+                return -1;
+            }
+            return 1;
+        };
+
+        auto on_cache_preset_arg = [&](int argc, const char** argv, int index) {
+            if (++index >= argc) {
+                return -1;
+            }
+            cache_preset = argv_to_utf8(index, argv);
+            if (cache_preset != "slow" && cache_preset != "s" && cache_preset != "S" &&
+                cache_preset != "medium" && cache_preset != "m" && cache_preset != "M" &&
+                cache_preset != "fast" && cache_preset != "f" && cache_preset != "F" &&
+                cache_preset != "ultra" && cache_preset != "u" && cache_preset != "U") {
+                fprintf(stderr, "error: invalid cache preset '%s', must be 'slow'/'s', 'medium'/'m', 'fast'/'f', or 'ultra'/'u'\n", cache_preset.c_str());
+                return -1;
+            }
+            return 1;
        };

        options.manual_options = {
@@ -1409,7 +1488,7 @@ struct SDGenerationParams {
             on_high_noise_sample_method_arg},
            {"",
             "--scheduler",
-             "denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, lcm], default: discrete",
+             "denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, kl_optimal, lcm], default: discrete",
             on_scheduler_arg},
            {"",
             "--sigmas",
@@ -1428,9 +1507,25 @@ struct SDGenerationParams {
             "reference image for Flux Kontext models (can be used multiple times)",
             on_ref_image_arg},
            {"",
-             "--easycache",
-             "enable EasyCache for DiT models with optional \"threshold,start_percent,end_percent\" (default: 0.2,0.15,0.95)",
-             on_easycache_arg},
+             "--cache-mode",
+             "caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level)",
+             on_cache_mode_arg},
+            {"",
+             "--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\"",
+             on_cache_option_arg},
+            {"",
+             "--cache-preset",
+             "cache-dit preset: 'slow'/'s', 'medium'/'m', 'fast'/'f', 'ultra'/'u'",
+             on_cache_preset_arg},
+            {"",
+             "--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",
+             on_scm_mask_arg},
+            {"",
+             "--scm-policy",
+             "SCM policy: 'dynamic' (default) or 'static'",
+             on_scm_policy_arg},

        };

@@ -1473,7 +1568,10 @@ struct SDGenerationParams {

        load_if_exists("prompt", prompt);
        load_if_exists("negative_prompt", negative_prompt);
-        load_if_exists("easycache_option", easycache_option);
+        load_if_exists("cache_mode", cache_mode);
+        load_if_exists("cache_option", cache_option);
+        load_if_exists("cache_preset", cache_preset);
+        load_if_exists("scm_mask", scm_mask);

        load_if_exists("clip_skip", clip_skip);
        load_if_exists("width", width);
@@ -1613,57 +1711,118 @@ struct SDGenerationParams {
            return false;
        }

-        if (!easycache_option.empty()) {
-            float values[3] = {0.0f, 0.0f, 0.0f};
-            std::stringstream ss(easycache_option);
+        sd_cache_params_init(&cache_params);
+
+        auto parse_named_params = [&](const std::string& opt_str) -> bool {
+            std::stringstream ss(opt_str);
            std::string token;
-            int idx = 0;
            while (std::getline(ss, token, ',')) {
-                auto trim = [](std::string& s) {
-                    const char* whitespace = " \t\r\n";
-                    auto start             = s.find_first_not_of(whitespace);
-                    if (start == std::string::npos) {
-                        s.clear();
-                        return;
-                    }
-                    auto end = s.find_last_not_of(whitespace);
-                    s        = s.substr(start, end - start + 1);
-                };
-                trim(token);
-                if (token.empty()) {
-                    LOG_ERROR("error: invalid easycache option '%s'", easycache_option.c_str());
-                    return false;
-                }
-                if (idx >= 3) {
-                    LOG_ERROR("error: easycache expects exactly 3 comma-separated values (threshold,start,end)\n");
+                size_t eq_pos = token.find('=');
+                if (eq_pos == std::string::npos) {
+                    LOG_ERROR("error: cache option '%s' missing '=' separator", token.c_str());
                    return false;
                }
+                std::string key = token.substr(0, eq_pos);
+                std::string val = token.substr(eq_pos + 1);
                try {
-                    values[idx] = std::stof(token);
+                    if (key == "threshold") {
+                        if (cache_mode == "easycache" || cache_mode == "ucache") {
+                            cache_params.reuse_threshold = std::stof(val);
+                        } else {
+                            cache_params.residual_diff_threshold = std::stof(val);
+                        }
+                    } else if (key == "start") {
+                        cache_params.start_percent = std::stof(val);
+                    } else if (key == "end") {
+                        cache_params.end_percent = std::stof(val);
+                    } else if (key == "decay") {
+                        cache_params.error_decay_rate = std::stof(val);
+                    } else if (key == "relative") {
+                        cache_params.use_relative_threshold = (std::stof(val) != 0.0f);
+                    } else if (key == "reset") {
+                        cache_params.reset_error_on_compute = (std::stof(val) != 0.0f);
+                    } else if (key == "Fn" || key == "fn") {
+                        cache_params.Fn_compute_blocks = std::stoi(val);
+                    } else if (key == "Bn" || key == "bn") {
+                        cache_params.Bn_compute_blocks = std::stoi(val);
+                    } else if (key == "warmup") {
+                        cache_params.max_warmup_steps = std::stoi(val);
+                    } else {
+                        LOG_ERROR("error: unknown cache parameter '%s'", key.c_str());
+                        return false;
+                    }
                } catch (const std::exception&) {
-                    LOG_ERROR("error: invalid easycache value '%s'", token.c_str());
+                    LOG_ERROR("error: invalid value '%s' for parameter '%s'", val.c_str(), key.c_str());
                    return false;
                }
-                idx++;
            }
-            if (idx != 3) {
-                LOG_ERROR("error: easycache expects exactly 3 comma-separated values (threshold,start,end)\n");
-                return false;
+            return true;
+        };
+
+        if (!cache_mode.empty()) {
+            if (cache_mode == "easycache") {
+                cache_params.mode                   = SD_CACHE_EASYCACHE;
+                cache_params.reuse_threshold        = 0.2f;
+                cache_params.start_percent          = 0.15f;
+                cache_params.end_percent            = 0.95f;
+                cache_params.error_decay_rate       = 1.0f;
+                cache_params.use_relative_threshold = true;
+                cache_params.reset_error_on_compute = true;
+            } else if (cache_mode == "ucache") {
+                cache_params.mode                   = SD_CACHE_UCACHE;
+                cache_params.reuse_threshold        = 1.0f;
+                cache_params.start_percent          = 0.15f;
+                cache_params.end_percent            = 0.95f;
+                cache_params.error_decay_rate       = 1.0f;
+                cache_params.use_relative_threshold = true;
+                cache_params.reset_error_on_compute = true;
+            } else if (cache_mode == "dbcache") {
+                cache_params.mode                    = SD_CACHE_DBCACHE;
+                cache_params.Fn_compute_blocks       = 8;
+                cache_params.Bn_compute_blocks       = 0;
+                cache_params.residual_diff_threshold = 0.08f;
+                cache_params.max_warmup_steps        = 8;
+            } else if (cache_mode == "taylorseer") {
+                cache_params.mode                    = SD_CACHE_TAYLORSEER;
+                cache_params.Fn_compute_blocks       = 8;
+                cache_params.Bn_compute_blocks       = 0;
+                cache_params.residual_diff_threshold = 0.08f;
+                cache_params.max_warmup_steps        = 8;
+            } else if (cache_mode == "cache-dit") {
+                cache_params.mode                    = SD_CACHE_CACHE_DIT;
+                cache_params.Fn_compute_blocks       = 8;
+                cache_params.Bn_compute_blocks       = 0;
+                cache_params.residual_diff_threshold = 0.08f;
+                cache_params.max_warmup_steps        = 8;
            }
-            if (values[0] < 0.0f) {
-                LOG_ERROR("error: easycache threshold must be non-negative\n");
-                return false;
+
+            if (!cache_option.empty()) {
+                if (!parse_named_params(cache_option)) {
+                    return false;
+                }
            }
-            if (values[1] < 0.0f || values[1] >= 1.0f || values[2] <= 0.0f || values[2] > 1.0f || values[1] >= values[2]) {
-                LOG_ERROR("error: easycache start/end percents must satisfy 0.0 <= start < end <= 1.0\n");
-                return false;
+
+            if (cache_mode == "easycache" || cache_mode == "ucache") {
+                if (cache_params.reuse_threshold < 0.0f) {
+                    LOG_ERROR("error: cache threshold must be non-negative");
+                    return false;
+                }
+                if (cache_params.start_percent < 0.0f || cache_params.start_percent >= 1.0f ||
+                    cache_params.end_percent <= 0.0f || cache_params.end_percent > 1.0f ||
+                    cache_params.start_percent >= cache_params.end_percent) {
+                    LOG_ERROR("error: cache start/end percents must satisfy 0.0 <= start < end <= 1.0");
+                    return false;
+                }
            }
-            easycache_params.enabled         = true;
-            easycache_params.reuse_threshold = values[0];
-            easycache_params.start_percent   = values[1];
-            easycache_params.end_percent     = values[2];
-        } else {
-            easycache_params.enabled = false;
+        }
+
+        if (cache_params.mode == SD_CACHE_DBCACHE ||
+            cache_params.mode == SD_CACHE_TAYLORSEER ||
+            cache_params.mode == SD_CACHE_CACHE_DIT) {
+            if (!scm_mask.empty()) {
+                cache_params.scm_mask = scm_mask.c_str();
+            }
+            cache_params.scm_policy_dynamic = scm_policy_dynamic;
        }

        sample_params.guidance.slg.layers                 = skip_layers.data();
@@ -1765,12 +1924,13 @@ struct SDGenerationParams {
            << "  high_noise_skip_layers: " << vec_to_string(high_noise_skip_layers) << ",\n"
            << "  high_noise_sample_params: " << high_noise_sample_params_str << ",\n"
            << "  custom_sigmas: " << vec_to_string(custom_sigmas) << ",\n"
-            << "  easycache_option: \"" << easycache_option << "\",\n"
-            << "  easycache: "
-            << (easycache_params.enabled ? "enabled" : "disabled")
-            << " (threshold=" << easycache_params.reuse_threshold
-            << ", start=" << easycache_params.start_percent
-            << ", end=" << easycache_params.end_percent << "),\n"
+            << "  cache_mode: \"" << cache_mode << "\",\n"
+            << "  cache_option: \"" << cache_option << "\",\n"
+            << "  cache: "
+            << (cache_params.mode != SD_CACHE_DISABLED ? "enabled" : "disabled")
+            << " (threshold=" << cache_params.reuse_threshold
+            << ", start=" << cache_params.start_percent
+            << ", end=" << cache_params.end_percent << "),\n"
            << "  moe_boundary: " << moe_boundary << ",\n"
            << "  video_frames: " << video_frames << ",\n"
            << "  fps: " << fps << ",\n"
@@ -1911,4 +2071,4 @@ uint8_t* load_image_from_memory(const char* image_bytes,
                                int expected_height  = 0,
                                int expected_channel = 3) {
    return load_image_common(true, image_bytes, len, width, height, expected_width, expected_height, expected_channel);
-}
+}
@@ -6,6 +6,7 @@ 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
@@ -42,9 +43,13 @@ Context Options:
  --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
@@ -114,11 +119,17 @@ Default 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],
-                                           default: discrete
+  --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)
-  --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'
+```
@@ -104,9 +104,10 @@ std::string iso_timestamp_now() {
 struct SDSvrParams {
    std::string listen_ip = "127.0.0.1";
    int listen_port       = 1234;
-    bool normal_exit      = false;
-    bool verbose          = false;
-    bool color            = false;
+    std::string serve_html_path;
+    bool normal_exit = false;
+    bool verbose     = false;
+    bool color       = false;

    ArgOptions get_options() {
        ArgOptions options;
@@ -115,7 +116,11 @@ struct SDSvrParams {
            {"-l",
             "--listen-ip",
             "server listen ip (default: 127.0.0.1)",
-             &listen_ip}};
+             &listen_ip},
+            {"",
+             "--serve-html-path",
+             "path to HTML file to serve at root (optional)",
+             &serve_html_path}};

        options.int_options = {
            {"",
@@ -159,6 +164,11 @@ struct SDSvrParams {
            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;
    }

@@ -167,6 +177,7 @@ struct SDSvrParams {
        oss << "SDSvrParams {\n"
            << "  listen_ip: " << listen_ip << ",\n"
            << "  listen_port: \"" << listen_port << "\",\n"
+            << "  serve_html_path: \"" << serve_html_path << "\",\n"
            << "}";
        return oss.str();
    }
@@ -262,6 +273,10 @@ void sd_log_cb(enum sd_log_level_t level, const char* log, void* data) {
 }

 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;
@@ -271,12 +286,11 @@ int main(int argc, const char** argv) {
    log_verbose = svr_params.verbose;
    log_color   = svr_params.color;

-    if (svr_params.verbose) {
-        LOG_INFO("%s", sd_get_system_info());
-        LOG_INFO("%s", svr_params.to_string().c_str());
-        LOG_INFO("%s", ctx_params.to_string().c_str());
-        LOG_INFO("%s", default_gen_params.to_string().c_str());
-    }
+    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);
@@ -309,7 +323,18 @@ int main(int argc, const char** argv) {

    // health
    svr.Get("/", [&](const httplib::Request&, httplib::Response& res) {
-        res.set_content(R"({"ok":true,"service":"sd-cpp-http"})", "application/json");
+        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)
@@ -429,7 +454,7 @@ int main(int argc, const char** argv) {
                    gen_params.pm_style_strength,
                },  // pm_params
                ctx_params.vae_tiling_params,
-                gen_params.easycache_params,
+                gen_params.cache_params,
            };

            sd_image_t* results = nullptr;
@@ -642,7 +667,7 @@ int main(int argc, const char** argv) {
                    gen_params.pm_style_strength,
                },  // pm_params
                ctx_params.vae_tiling_params,
-                gen_params.easycache_params,
+                gen_params.cache_params,
            };

            sd_image_t* results = nullptr;
@@ -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]
-        shift = ggml_reshape_3d(ctx, shift, shift->ne[0], 1, shift->ne[1]);  // [N, 1, C]
-        x     = ggml_add(ctx, x, ggml_mul(ctx, x, scale));
-        x     = ggml_add(ctx, x, shift);
+        if (!skip_reshape) {
+            scale = ggml_reshape_3d(ctx, scale, scale->ne[0], 1, scale->ne[1]);  // [N, 1, C]
+            shift = ggml_reshape_3d(ctx, shift, shift->ne[0], 1, shift->ne[1]);  // [N, 1, C]
+        }
+        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);
-                mask   = process_img(ctx->ggml_ctx, mask);
+                masked = process_img(ctx, masked);
+                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);
-                mask    = process_img(ctx->ggml_ctx, mask);
-                control = process_img(ctx->ggml_ctx, control);
+                masked  = process_img(ctx, masked);
+                mask    = process_img(ctx, mask);
+                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);
@@ -5,6 +5,7 @@
 #include <inttypes.h>
 #include <stdarg.h>
 #include <algorithm>
+#include <atomic>
 #include <cstring>
 #include <fstream>
 #include <functional>
@@ -993,6 +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,]
@@ -1001,20 +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 s1      = 1,
-                                                       int p0      = 0,
-                                                       int p1      = 0,
-                                                       int d0      = 1,
-                                                       int d1      = 1,
-                                                       bool direct = false,
-                                                       float scale = 1.f) {
+                                                       int s0          = 1,
+                                                       int s1          = 1,
+                                                       int p0          = 0,
+                                                       int p1          = 0,
+                                                       int d0          = 1,
+                                                       int d1          = 1,
+                                                       bool direct     = false,
+                                                       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 {
@@ -1521,14 +1573,16 @@ struct WeightAdapter {
            float scale         = 1.f;
        } linear;
        struct {
-            int s0      = 1;
-            int s1      = 1;
-            int p0      = 0;
-            int p1      = 0;
-            int d0      = 1;
-            int d1      = 1;
-            bool direct = false;
-            float scale = 1.f;
+            int s0          = 1;
+            int s1          = 1;
+            int p0          = 0;
+            int p1          = 0;
+            int d0          = 1;
+            int d1          = 1;
+            bool direct     = false;
+            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;
@@ -1546,6 +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;
 };

@@ -1582,6 +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;
@@ -1859,6 +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;
    }
@@ -2003,6 +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;
    }
@@ -2266,15 +2331,17 @@ public:
        }
        if (ctx->weight_adapter) {
            WeightAdapter::ForwardParams forward_params;
-            forward_params.op_type       = WeightAdapter::ForwardParams::op_type_t::OP_CONV2D;
-            forward_params.conv2d.s0     = stride.second;
-            forward_params.conv2d.s1     = stride.first;
-            forward_params.conv2d.p0     = padding.second;
-            forward_params.conv2d.p1     = padding.first;
-            forward_params.conv2d.d0     = dilation.second;
-            forward_params.conv2d.d1     = dilation.first;
-            forward_params.conv2d.direct = ctx->conv2d_direct_enabled;
-            forward_params.conv2d.scale  = scale;
+            forward_params.op_type           = WeightAdapter::ForwardParams::op_type_t::OP_CONV2D;
+            forward_params.conv2d.s0         = stride.second;
+            forward_params.conv2d.s1         = stride.first;
+            forward_params.conv2d.p0         = padding.second;
+            forward_params.conv2d.p1         = padding.first;
+            forward_params.conv2d.d0         = dilation.second;
+            forward_params.conv2d.d1         = dilation.first;
+            forward_params.conv2d.direct     = ctx->conv2d_direct_enabled;
+            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,
@@ -2288,6 +2355,8 @@ public:
                                dilation.second,
                                dilation.first,
                                ctx->conv2d_direct_enabled,
+                                ctx->circular_x_enabled,
+                                ctx->circular_y_enabled,
                                scale);
    }
 };
@@ -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) {
@@ -983,4 +983,4 @@ struct MMDiTRunner : public GGMLRunner {
    }
 };

-#endif
+#endif
@@ -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);
@@ -1588,7 +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 {
@@ -1631,7 +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;
@@ -1737,6 +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);
@@ -1776,7 +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)) {
@@ -1790,7 +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;
 }
@@ -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;
@@ -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
@@ -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);
-            auto txt   = txt_norm->forward(ctx, context);
-            txt        = txt_in->forward(ctx, txt);
+            if (params.zero_cond_t) {
+                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]));
+                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__
@@ -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 h_offset = 0,
-                                                                       int w_offset = 0) {
+                                                                       int index       = 0,
+                                                                       int h_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);
-        std::vector<float> col_ids = linspace<float>(w_offset, w_len - 1 + w_offset, w_len);
+        int h_start = h_offset;
+        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,
@@ -60,6 +60,7 @@ enum scheduler_t {
    SGM_UNIFORM_SCHEDULER,
    SIMPLE_SCHEDULER,
    SMOOTHSTEP_SCHEDULER,
+    KL_OPTIMAL_SCHEDULER,
    LCM_SCHEDULER,
    SCHEDULER_COUNT
 };
@@ -181,6 +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;
@@ -188,10 +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;

@@ -235,12 +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;
@@ -270,7 +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 {
@@ -292,7 +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;
@@ -322,7 +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);
@@ -334,7 +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);
@@ -362,7 +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,
@@ -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__
@@ -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
@@ -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);
@@ -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));
            }
@@ -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);
Author	SHA1	Message	Date
leejet	4ff2c8c74b	refactor: simplify logic for saving results (#1149 )	2025-12-28 23:27:27 +08:00
leejet	51bd9c8004	chore: reformat named cache params description into single line	2025-12-28 22:53:07 +08:00
Wagner Bruna	d0d836ae74	feat: support mmap for model loading (#1059 )	2025-12-28 22:38:29 +08:00
leejet	a2d83dd0c8	refactor: move pmid condition logic into get_pmid_condition (#1148 )	2025-12-27 16:48:15 +08:00
Wagner Bruna	cc107714d7	fix: consistently pass 2nd-order samplers half steps as negatives (#1095 )	2025-12-27 15:54:18 +08:00
leejet	37c9860b79	fix: handle redirected UTF-8 output correctly on Windows (#1147 )	2025-12-27 15:43:19 +08:00
leejet	ccb6b0ac9d	feat: add __index_timestep_zero__ support (#1146 )	2025-12-26 22:07:40 +08:00
Weiqi Gao	df4efe26bd	feat: add png sequence output for vid_gen (#1117 )	2025-12-26 22:06:13 +08:00
leejet	860a78e248	fix: avoid crash when using taesd for preview only (#1141 )	2025-12-24 23:30:12 +08:00
leejet	a0adcfb148	feat: add support for qwen image edit 2511 (#1096 )	2025-12-24 23:00:08 +08:00
leejet	3d5fdd7b37	feat: add support for more underline loras (#1135 )	2025-12-24 22:59:23 +08:00
Weiqi Gao	3e6c428c27	chore: use Ninja on Windows to speed up build process (#1120 )	2025-12-24 22:53:17 +08:00
张春乔	96fcb13fc0	feat: add --serve-html-path option to example server (#1123 )	2025-12-24 22:43:09 +08:00
leejet	3e812460cf	fix: correct ggml_pad_ext (#1133 )	2025-12-23 21:37:07 +08:00
leejet	98916e8256	docs: update README.md	2025-12-22 23:58:28 +08:00
rmatif	298b11069f	feat: add more caching methods (#1066 )	2025-12-22 23:52:11 +08:00
leejet	30a91138f8	fix: add the missing }	2025-12-21 21:53:38 +08:00
leejet	c6937ba44a	fix: correct the parsing of --convert-name opotion	2025-12-21 21:47:50 +08:00
leejet	ca5b1969a8	feat: do not convert tensor names by default in convert mode (#1122 )	2025-12-21 18:40:10 +08:00
Phylliida Dev Phylliida leejet	50ff966445	feat: add seamless texture generation support (#914 ) * 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>	2025-12-21 18:06:47 +08:00
leejet	88ec9d30b1	feat: add scale_rope support (#1121 )	2025-12-21 15:40:21 +08:00
stduhpf	60abda56e0	feat: select vulkan device with env variable (#629 )	2025-12-21 15:35:38 +08:00
stduhpfandleejet	23fce0bd84	feat: add support for Chroma Radiance x0 (#1091 ) * Add x0 Flux pred (+prepare for others) * Fix convert models with empty tensors * patch_32 exp support attempt * improve support for patch_32 * follow official pipeline --------- Co-authored-by: leejet <leejet714@gmail.com>	2025-12-20 00:55:57 +08:00
Wagner Bruna	7c88c4765c	chore: give feedback about cfg values smaller than 1 (#1088 )	2025-12-19 23:41:52 +08:00
Weiqi Gao	1f77545cf8	docs: document usage of tae for VRAM reduction using wan (#1108 )	2025-12-19 23:31:09 +08:00
leejet	8e9f3a4d9e	feat: add support for underline style lora of flux (#1103 ) * feat: add support for underline style lora of flux * add support for underline style lora of t5 * add more protected tokens	2025-12-18 21:44:16 +08:00
Wagner Brunaandleejet	78e15bd4af	feat: default to LCM scheduler for LCM sampling (#1109 ) * feat: default to LCM scheduler for LCM sampling * fix bug and attempt to get default scheduler for vid_gen when none is set --------- Co-authored-by: leejet <leejet714@gmail.com>	2025-12-18 21:43:39 +08:00
Daniele	97cf2efe45	feat: add KL Optimal scheduler (#1098 )	2025-12-18 21:02:55 +08:00
leejet	bda7fab9f2	chore: remove unused debug code	2025-12-17 23:43:37 +08:00
leejet	c2e18c86e8	fix: make flash attn work with high noise diffusion model (#1111 )	2025-12-17 23:28:59 +08:00
leejet	c3ad6a13e1	refactor: optimize the printing of version log (#1102 )	2025-12-16 23:11:27 +08:00