diff --git a/examples/cli/main.cpp b/examples/cli/main.cpp
index a5b0037b6..35dd0413a 100644
--- a/examples/cli/main.cpp
+++ b/examples/cli/main.cpp
@@ -278,7 +278,9 @@ void parse_args(int argc, const char** argv, SDCliParams& cli_params, SDContextP
     bool valid = cli_params.resolve_and_validate();
     if (valid && cli_params.mode != METADATA) {
         valid = ctx_params.resolve_and_validate(cli_params.mode) &&
-                gen_params.resolve_and_validate(cli_params.mode, ctx_params.lora_model_dir);
+                gen_params.resolve_and_validate(cli_params.mode,
+                                                ctx_params.lora_model_dir,
+                                                ctx_params.hires_upscalers_dir);
     }
 
     if (!valid) {
@@ -688,6 +690,10 @@ int main(int argc, const char* argv[]) {
         vae_decode_only = false;
     }
 
+    if (gen_params.hires_enabled && !gen_params.hires_upscaler_model_path.empty()) {
+        vae_decode_only = false;
+    }
+
     sd_ctx_params_t sd_ctx_params = ctx_params.to_sd_ctx_params_t(vae_decode_only, true, cli_params.taesd_preview);
 
     SDImageVec results;
diff --git a/examples/common/common.cpp b/examples/common/common.cpp
index 0235c53de..ab770a0b5 100644
--- a/examples/common/common.cpp
+++ b/examples/common/common.cpp
@@ -351,7 +351,10 @@ ArgOptions SDContextParams::get_options() {
          "--lora-model-dir",
          "lora model directory",
          &lora_model_dir},
-
+        {"",
+         "--hires-upscalers-dir",
+         "highres fix upscaler model directory",
+         &hires_upscalers_dir},
         {"",
          "--tensor-type-rules",
          "weight type per tensor pattern (example: \"^vae\\.=f16,model\\.=q8_0\")",
@@ -649,6 +652,7 @@ std::string SDContextParams::to_string() const {
         << "  wtype: " << sd_type_name(wtype) << ",\n"
         << "  tensor_type_rules: \"" << tensor_type_rules << "\",\n"
         << "  lora_model_dir: \"" << lora_model_dir << "\",\n"
+        << "  hires_upscalers_dir: \"" << hires_upscalers_dir << "\",\n"
         << "  photo_maker_path: \"" << photo_maker_path << "\",\n"
         << "  rng_type: " << sd_rng_type_name(rng_type) << ",\n"
         << "  sampler_rng_type: " << sd_rng_type_name(sampler_rng_type) << ",\n"
@@ -777,6 +781,10 @@ ArgOptions SDGenerationParams::get_options() {
          "--pm-id-embed-path",
          "path to PHOTOMAKER v2 id embed",
          &pm_id_embed_path},
+        {"",
+         "--hires-upscaler",
+         "highres fix upscaler, Latent (nearest) or a model name/path under --hires-upscalers-dir (default: Latent (nearest))",
+         &hires_upscaler},
     };
 
     options.int_options = {
@@ -826,6 +834,22 @@ ArgOptions SDGenerationParams::get_options() {
          "--upscale-tile-size",
          "tile size for ESRGAN upscaling (default: 128)",
          &upscale_tile_size},
+        {"",
+         "--hires-width",
+         "highres fix target width, 0 to use --hires-scale (default: 0)",
+         &hires_width},
+        {"",
+         "--hires-height",
+         "highres fix target height, 0 to use --hires-scale (default: 0)",
+         &hires_height},
+        {"",
+         "--hires-steps",
+         "highres fix second pass sample steps, 0 to reuse --steps (default: 0)",
+         &hires_steps},
+        {"",
+         "--hires-upscale-tile-size",
+         "highres fix upscaler tile size, reserved for model-backed upscalers (default: 128)",
+         &hires_upscale_tile_size},
     };
 
     options.float_options = {
@@ -913,6 +937,14 @@ ArgOptions SDGenerationParams::get_options() {
          "--vae-tile-overlap",
          "tile overlap for vae tiling, in fraction of tile size (default: 0.5)",
          &vae_tiling_params.target_overlap},
+        {"",
+         "--hires-scale",
+         "highres fix scale when target size is not set (default: 2.0)",
+         &hires_scale},
+        {"",
+         "--hires-denoising-strength",
+         "highres fix second pass denoising strength (default: 0.7)",
+         &hires_denoising_strength},
     };
 
     options.bool_options = {
@@ -936,6 +968,11 @@ ArgOptions SDGenerationParams::get_options() {
          "process vae in tiles to reduce memory usage",
          true,
          &vae_tiling_params.enabled},
+        {"",
+         "--hires",
+         "enable highres fix",
+         true,
+         &hires_enabled},
     };
 
     auto on_seed_arg = [&](int argc, const char** argv, int index) {
@@ -1424,6 +1461,37 @@ static bool parse_lora_json_field(const json& parent,
     return true;
 }
 
+static bool resolve_model_file_from_dir(const std::string& model_name,
+                                        const std::string& model_dir,
+                                        const std::vector<std::string>& valid_ext,
+                                        const char* label,
+                                        std::string& resolved_path) {
+    if (model_dir.empty()) {
+        LOG_ERROR("%s directory is empty", label);
+        return false;
+    }
+    if (model_name.empty() ||
+        model_name.find('/') != std::string::npos ||
+        model_name.find('\\') != std::string::npos ||
+        fs::path(model_name).has_root_path() ||
+        fs::path(model_name).has_extension()) {
+        LOG_ERROR("%s must be a model name without path or extension: %s", label, model_name.c_str());
+        return false;
+    }
+
+    fs::path model_dir_path = model_dir;
+    for (const auto& ext : valid_ext) {
+        fs::path try_path = model_dir_path / (model_name + ext);
+        if (fs::exists(try_path) && fs::is_regular_file(try_path)) {
+            resolved_path = try_path.lexically_normal().string();
+            return true;
+        }
+    }
+
+    LOG_ERROR("can not find %s %s in %s", label, model_name.c_str(), model_dir_path.lexically_normal().string().c_str());
+    return false;
+}
+
 bool SDGenerationParams::from_json_str(
     const std::string& json_str,
     const std::function<std::string(const std::string&)>& lora_path_resolver) {
@@ -1487,6 +1555,34 @@ bool SDGenerationParams::from_json_str(
     load_if_exists("increase_ref_index", increase_ref_index);
     load_if_exists("embed_image_metadata", embed_image_metadata);
 
+    if (j.contains("hires") && j["hires"].is_object()) {
+        const json& hires_json = j["hires"];
+        if (hires_json.contains("enabled") && hires_json["enabled"].is_boolean()) {
+            hires_enabled = hires_json["enabled"];
+        }
+        if (hires_json.contains("upscaler") && hires_json["upscaler"].is_string()) {
+            hires_upscaler = hires_json["upscaler"];
+        }
+        if (hires_json.contains("scale") && hires_json["scale"].is_number()) {
+            hires_scale = hires_json["scale"];
+        }
+        if (hires_json.contains("target_width") && hires_json["target_width"].is_number_integer()) {
+            hires_width = hires_json["target_width"];
+        }
+        if (hires_json.contains("target_height") && hires_json["target_height"].is_number_integer()) {
+            hires_height = hires_json["target_height"];
+        }
+        if (hires_json.contains("steps") && hires_json["steps"].is_number_integer()) {
+            hires_steps = hires_json["steps"];
+        }
+        if (hires_json.contains("denoising_strength") && hires_json["denoising_strength"].is_number()) {
+            hires_denoising_strength = hires_json["denoising_strength"];
+        }
+        if (hires_json.contains("upscale_tile_size") && hires_json["upscale_tile_size"].is_number_integer()) {
+            hires_upscale_tile_size = hires_json["upscale_tile_size"];
+        }
+    }
+
     auto parse_sample_params_json = [&](const json& sample_json,
                                         sd_sample_params_t& target_params,
                                         std::vector<int>& target_skip_layers,
@@ -1800,7 +1896,7 @@ bool SDGenerationParams::initialize_cache_params() {
     return true;
 }
 
-bool SDGenerationParams::resolve(const std::string& lora_model_dir, bool strict) {
+bool SDGenerationParams::resolve(const std::string& lora_model_dir, const std::string& hires_upscalers_dir, bool strict) {
     if (high_noise_sample_params.sample_steps <= 0) {
         high_noise_sample_params.sample_steps = -1;
     }
@@ -1819,6 +1915,27 @@ bool SDGenerationParams::resolve(const std::string& lora_model_dir, bool strict)
         sample_params.sample_steps = std::clamp(sample_params.sample_steps, 1, 100);
     }
 
+    hires_upscaler_model_path.clear();
+    if (hires_enabled) {
+        if (hires_upscaler.empty()) {
+            hires_upscaler = "Latent (nearest)";
+        }
+        resolved_hires_upscaler = str_to_sd_hires_upscaler(hires_upscaler.c_str());
+        if (resolved_hires_upscaler == SD_HIRES_UPSCALER_NONE) {
+            hires_enabled = false;
+        } else if (resolved_hires_upscaler == SD_HIRES_UPSCALER_COUNT) {
+            static const std::vector<std::string> valid_ext = {".gguf", ".safetensors", ".pt", ".pth"};
+            if (!resolve_model_file_from_dir(hires_upscaler,
+                                             hires_upscalers_dir,
+                                             valid_ext,
+                                             "hires upscaler",
+                                             hires_upscaler_model_path)) {
+                return false;
+            }
+            resolved_hires_upscaler = SD_HIRES_UPSCALER_MODEL;
+        }
+    }
+
     prompt_with_lora = prompt;
     if (!lora_model_dir.empty()) {
         extract_and_remove_lora(lora_model_dir);
@@ -1883,6 +2000,29 @@ bool SDGenerationParams::validate(SDMode mode) {
         return false;
     }
 
+    if (hires_enabled) {
+        if (hires_width < 0 || hires_height < 0) {
+            LOG_ERROR("error: hires target width and height must be >= 0");
+            return false;
+        }
+        if (hires_scale <= 0.f && hires_width <= 0 && hires_height <= 0) {
+            LOG_ERROR("error: hires scale must be positive when target size is not set");
+            return false;
+        }
+        if (hires_steps < 0) {
+            LOG_ERROR("error: hires steps must be >= 0");
+            return false;
+        }
+        if (hires_denoising_strength <= 0.f || hires_denoising_strength > 1.f) {
+            LOG_ERROR("error: hires denoising strength must be in (0.0, 1.0]");
+            return false;
+        }
+        if (hires_upscale_tile_size < 1) {
+            LOG_ERROR("error: hires upscale tile size must be positive");
+            return false;
+        }
+    }
+
     if (mode == UPSCALE) {
         if (init_image_path.length() == 0) {
             LOG_ERROR("error: upscale mode needs an init image (--init-img)\n");
@@ -1893,8 +2033,11 @@ bool SDGenerationParams::validate(SDMode mode) {
     return true;
 }
 
-bool SDGenerationParams::resolve_and_validate(SDMode mode, const std::string& lora_model_dir, bool strict) {
-    if (!resolve(lora_model_dir, strict)) {
+bool SDGenerationParams::resolve_and_validate(SDMode mode,
+                                              const std::string& lora_model_dir,
+                                              const std::string& hires_upscalers_dir,
+                                              bool strict) {
+    if (!resolve(lora_model_dir, hires_upscalers_dir, strict)) {
         return false;
     }
     if (!validate(mode)) {
@@ -1965,6 +2108,16 @@ sd_img_gen_params_t SDGenerationParams::to_sd_img_gen_params_t() {
     params.pm_params             = pm_params;
     params.vae_tiling_params     = vae_tiling_params;
     params.cache                 = cache_params;
+
+    params.hires.enabled            = hires_enabled;
+    params.hires.upscaler           = resolved_hires_upscaler;
+    params.hires.model_path         = hires_upscaler_model_path.empty() ? nullptr : hires_upscaler_model_path.c_str();
+    params.hires.scale              = hires_scale;
+    params.hires.target_width       = hires_width;
+    params.hires.target_height      = hires_height;
+    params.hires.steps              = hires_steps;
+    params.hires.denoising_strength = hires_denoising_strength;
+    params.hires.upscale_tile_size  = hires_upscale_tile_size;
     return params;
 }
 
@@ -2089,6 +2242,15 @@ std::string SDGenerationParams::to_string() const {
         << "  seed: " << seed << ",\n"
         << "  upscale_repeats: " << upscale_repeats << ",\n"
         << "  upscale_tile_size: " << upscale_tile_size << ",\n"
+        << "  hires: { enabled: " << (hires_enabled ? "true" : "false")
+        << ", upscaler: \"" << hires_upscaler << "\""
+        << ", model_path: \"" << hires_upscaler_model_path << "\""
+        << ", scale: " << hires_scale
+        << ", target_width: " << hires_width
+        << ", target_height: " << hires_height
+        << ", steps: " << hires_steps
+        << ", denoising_strength: " << hires_denoising_strength
+        << ", upscale_tile_size: " << hires_upscale_tile_size << " },\n"
         << "  vae_tiling_params: { "
         << vae_tiling_params.enabled << ", "
         << vae_tiling_params.tile_size_x << ", "
@@ -2162,6 +2324,13 @@ std::string get_image_params(const SDContextParams& ctx_params, const SDGenerati
     if (gen_params.clip_skip != -1) {
         parameter_string += "Clip skip: " + std::to_string(gen_params.clip_skip) + ", ";
     }
+    if (gen_params.hires_enabled) {
+        parameter_string += "Hires upscale: " + gen_params.hires_upscaler + ", ";
+        parameter_string += "Hires scale: " + std::to_string(gen_params.hires_scale) + ", ";
+        parameter_string += "Hires resize: " + std::to_string(gen_params.hires_width) + "x" + std::to_string(gen_params.hires_height) + ", ";
+        parameter_string += "Hires steps: " + std::to_string(gen_params.hires_steps) + ", ";
+        parameter_string += "Denoising strength: " + std::to_string(gen_params.hires_denoising_strength) + ", ";
+    }
     parameter_string += "Version: stable-diffusion.cpp";
     return parameter_string;
 }
diff --git a/examples/common/common.h b/examples/common/common.h
index 5afe89b34..a754f42be 100644
--- a/examples/common/common.h
+++ b/examples/common/common.h
@@ -101,6 +101,7 @@ struct SDContextParams {
     sd_type_t wtype = SD_TYPE_COUNT;
     std::string tensor_type_rules;
     std::string lora_model_dir = ".";
+    std::string hires_upscalers_dir;
 
     std::map<std::string, std::string> embedding_map;
     std::vector<sd_embedding_t> embedding_vec;
@@ -190,12 +191,23 @@ struct SDGenerationParams {
     int upscale_repeats   = 1;
     int upscale_tile_size = 128;
 
+    bool hires_enabled         = false;
+    std::string hires_upscaler = "Latent (nearest)";
+    std::string hires_upscaler_model_path;
+    float hires_scale              = 2.f;
+    int hires_width                = 0;
+    int hires_height               = 0;
+    int hires_steps                = 0;
+    float hires_denoising_strength = 0.7f;
+    int hires_upscale_tile_size    = 128;
+
     std::map<std::string, float> lora_map;
     std::map<std::string, float> high_noise_lora_map;
 
     // Derived and normalized fields.
     std::string prompt_with_lora;  // for metadata record only
     std::vector<sd_lora_t> lora_vec;
+    sd_hires_upscaler_t resolved_hires_upscaler;
 
     // Owned execution payload.
     SDImageOwner init_image;
@@ -225,9 +237,12 @@ struct SDGenerationParams {
     void set_width_and_height_if_unset(int w, int h);
     int get_resolved_width() const;
     int get_resolved_height() const;
-    bool resolve(const std::string& lora_model_dir, bool strict = false);
+    bool resolve(const std::string& lora_model_dir, const std::string& hires_upscalers_dir, bool strict = false);
     bool validate(SDMode mode);
-    bool resolve_and_validate(SDMode mode, const std::string& lora_model_dir, bool strict = false);
+    bool resolve_and_validate(SDMode mode,
+                              const std::string& lora_model_dir,
+                              const std::string& hires_upscalers_dir,
+                              bool strict = false);
     sd_img_gen_params_t to_sd_img_gen_params_t();
     sd_vid_gen_params_t to_sd_vid_gen_params_t();
     std::string to_string() const;
diff --git a/examples/server/api.md b/examples/server/api.md
index 39744dbed..03df0908b 100644
--- a/examples/server/api.md
+++ b/examples/server/api.md
@@ -38,6 +38,8 @@ Current generation-related endpoints include:
 - `POST /sdapi/v1/txt2img`
 - `POST /sdapi/v1/img2img`
 - `GET /sdapi/v1/loras`
+- `GET /sdapi/v1/upscalers`
+- `GET /sdapi/v1/latent-upscale-modes`
 - `GET /sdapi/v1/samplers`
 - `GET /sdapi/v1/schedulers`
 - `GET /sdapi/v1/sd-models`
@@ -216,6 +218,13 @@ Currently supported request fields:
 | `scheduler` | `string` | Scheduler name |
 | `lora` | `array<object>` | Structured LoRA list |
 | `extra_images` | `array<string>` | Base64 or data URL images |
+| `enable_hr` | `boolean` | Enable highres fix for `txt2img` |
+| `hr_upscaler` | `string` | `Latent (nearest)` or an upscaler model name from `/sdapi/v1/upscalers` |
+| `hr_scale` | `number` | Highres scale when resize target is not set |
+| `hr_resize_x` | `integer` | Highres target width, `0` to use scale |
+| `hr_resize_y` | `integer` | Highres target height, `0` to use scale |
+| `hr_steps` | `integer` | Highres second-pass sample steps, `0` to reuse `steps` |
+| `denoising_strength` | `number` | Highres denoising strength for `txt2img` |
 
 Native extension fields:
 
@@ -241,6 +250,8 @@ Currently supported request fields:
 | `inpainting_mask_invert` | `integer` or `boolean` | Treated as invert flag |
 | `denoising_strength` | `number` | Clamped to `0.0..1.0` |
 
+Highres fix fields are currently handled for `txt2img`; `img2img` uses `denoising_strength` as image-to-image strength.
+
 Native extension fields:
 
 - any `sdcpp API` fields embedded through `sd_cpp_extra_args` inside `prompt`
@@ -258,6 +269,8 @@ Response fields:
 Currently exposed:
 
 - `GET /sdapi/v1/loras`
+- `GET /sdapi/v1/upscalers`
+- `GET /sdapi/v1/latent-upscale-modes`
 - `GET /sdapi/v1/samplers`
 - `GET /sdapi/v1/schedulers`
 - `GET /sdapi/v1/sd-models`
@@ -272,6 +285,24 @@ Response fields:
 | `[].name` | `string` | Display name derived from file stem |
 | `[].path` | `string` | Relative path under the configured LoRA directory |
 
+`GET /sdapi/v1/upscalers`
+
+| Field | Type | Notes |
+| --- | --- | --- |
+| `[].name` | `string` | Built-in name or model stem |
+| `[].model_name` | `string \| null` | Model family label for model-backed upscalers |
+| `[].model_path` | `string \| null` | Absolute model path for model-backed upscalers |
+| `[].model_url` | `string \| null` | Currently always null |
+| `[].scale` | `integer` | Currently `4` |
+
+Built-in entries include `None`, `Lanczos`, and `Nearest`. Model-backed entries are scanned from the top level of `--hires-upscalers-dir`; subdirectories are not scanned.
+
+`GET /sdapi/v1/latent-upscale-modes`
+
+| Field | Type | Notes |
+| --- | --- | --- |
+| `[].name` | `string` | WebUI-compatible latent upscale mode name |
+
 `GET /sdapi/v1/samplers`
 
 | Field | Type | Notes |
@@ -388,6 +419,7 @@ Top-level fields:
 | `samplers` | `array<string>` | Available sampling methods |
 | `schedulers` | `array<string>` | Available schedulers |
 | `loras` | `array<object>` | Available LoRA entries |
+| `upscalers` | `array<object>` | Available model-backed highres upscalers |
 | `limits` | `object` | Shared queue and size limits |
 
 `model`
@@ -424,6 +456,14 @@ Shared nested fields:
 | `loras[].name` | `string` |
 | `loras[].path` | `string` |
 
+`upscalers`
+
+| Field | Type | Notes |
+| --- | --- | --- |
+| `upscalers[].name` | `string` | Built-in name or model stem; use this value in `hires.upscaler` |
+
+Built-in entries include `None` and `Latent (nearest)`. Model-backed entries are scanned from the top level of `--hires-upscalers-dir`; subdirectories are not scanned.
+
 `limits`
 
 | Field | Type |
@@ -482,6 +522,15 @@ Shared default fields used by both `img_gen` and `vid_gen`:
 | `auto_resize_ref_image` | `boolean` |
 | `increase_ref_index` | `boolean` |
 | `control_strength` | `number` |
+| `hires` | `object` |
+| `hires.enabled` | `boolean` |
+| `hires.upscaler` | `string` |
+| `hires.scale` | `number` |
+| `hires.target_width` | `integer` |
+| `hires.target_height` | `integer` |
+| `hires.steps` | `integer` |
+| `hires.denoising_strength` | `number` |
+| `hires.upscale_tile_size` | `integer` |
 
 `vid_gen`-specific default fields:
 
@@ -514,6 +563,7 @@ Fields returned in `features_by_mode.img_gen`:
 - `ref_images`
 - `lora`
 - `vae_tiling`
+- `hires`
 - `cache`
 - `cancel_queued`
 - `cancel_generating`
@@ -625,6 +675,16 @@ Example:
   },
 
   "lora": [],
+  "hires": {
+    "enabled": false,
+    "upscaler": "Latent (nearest)",
+    "scale": 2.0,
+    "target_width": 0,
+    "target_height": 0,
+    "steps": 0,
+    "denoising_strength": 0.7,
+    "upscale_tile_size": 128
+  },
 
   "vae_tiling_params": {
     "enabled": false,
@@ -729,12 +789,23 @@ Other native fields:
 
 | Field | Type |
 | --- | --- |
+| `hires` | `object` |
+| `hires.enabled` | `boolean` |
+| `hires.upscaler` | `string` |
+| `hires.scale` | `number` |
+| `hires.target_width` | `integer` |
+| `hires.target_height` | `integer` |
+| `hires.steps` | `integer` |
+| `hires.denoising_strength` | `number` |
+| `hires.upscale_tile_size` | `integer` |
 | `vae_tiling_params` | `object` |
 | `cache_mode` | `string` |
 | `cache_option` | `string` |
 | `scm_mask` | `string` |
 | `scm_policy_dynamic` | `boolean` |
 
+For `hires.upscaler`, use `Latent (nearest)` for latent upscale or an `upscalers[].name` value from `GET /sdcpp/v1/capabilities`. Model-backed upscalers are resolved as `--hires-upscalers-dir / (name + ext)` and must live directly in that directory.
+
 HTTP-only output fields:
 
 | Field | Type |
diff --git a/examples/server/main.cpp b/examples/server/main.cpp
index 11a334d5f..114d526a8 100644
--- a/examples/server/main.cpp
+++ b/examples/server/main.cpp
@@ -48,7 +48,9 @@ static void parse_args(int argc,
 
     if (!svr_params.resolve_and_validate() ||
         !ctx_params.resolve_and_validate(IMG_GEN) ||
-        !default_gen_params.resolve_and_validate(IMG_GEN, ctx_params.lora_model_dir)) {
+        !default_gen_params.resolve_and_validate(IMG_GEN,
+                                                 ctx_params.lora_model_dir,
+                                                 ctx_params.hires_upscalers_dir)) {
         print_usage(argv[0], options_vec);
         exit(1);
     }
@@ -95,6 +97,8 @@ int main(int argc, const char** argv) {
 
     std::vector<LoraEntry> lora_cache;
     std::mutex lora_mutex;
+    std::vector<UpscalerEntry> upscaler_cache;
+    std::mutex upscaler_mutex;
     AsyncJobManager async_job_manager;
     ServerRuntime runtime = {
         sd_ctx.get(),
@@ -104,6 +108,8 @@ int main(int argc, const char** argv) {
         &default_gen_params,
         &lora_cache,
         &lora_mutex,
+        &upscaler_cache,
+        &upscaler_mutex,
         &async_job_manager,
     };
 
diff --git a/examples/server/routes_openai.cpp b/examples/server/routes_openai.cpp
index ce6215d1e..a24383d67 100644
--- a/examples/server/routes_openai.cpp
+++ b/examples/server/routes_openai.cpp
@@ -70,7 +70,7 @@ static bool build_openai_generation_request(const httplib::Request& req,
     }
 
     // Intentionally disable prompt-embedded LoRA tag parsing for server APIs.
-    if (!request.gen_params.resolve_and_validate(IMG_GEN, "", true)) {
+    if (!request.gen_params.resolve_and_validate(IMG_GEN, "", runtime.ctx_params->hires_upscalers_dir, true)) {
         error_message = "invalid params";
         return false;
     }
@@ -212,7 +212,7 @@ static bool build_openai_edit_request(const httplib::Request& req,
     }
 
     // Intentionally disable prompt-embedded LoRA tag parsing for server APIs.
-    if (!request.gen_params.resolve_and_validate(IMG_GEN, "", true)) {
+    if (!request.gen_params.resolve_and_validate(IMG_GEN, "", runtime.ctx_params->hires_upscalers_dir, true)) {
         error_message = "invalid params";
         return false;
     }
diff --git a/examples/server/routes_sdapi.cpp b/examples/server/routes_sdapi.cpp
index 63c89ec8b..74a6b3219 100644
--- a/examples/server/routes_sdapi.cpp
+++ b/examples/server/routes_sdapi.cpp
@@ -1,6 +1,7 @@
 #include "routes.h"
 
 #include <algorithm>
+#include <cctype>
 #include <cstring>
 #include <regex>
 #include <string_view>
@@ -35,14 +36,20 @@ static fs::path resolve_display_model_path(const ServerRuntime& runtime) {
     return {};
 }
 
+static std::string lower_ascii(std::string value) {
+    std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c) {
+        return static_cast<char>(std::tolower(c));
+    });
+    return value;
+}
+
 static enum sample_method_t get_sdapi_sample_method(std::string name) {
     enum sample_method_t result = str_to_sample_method(name.c_str());
     if (result != SAMPLE_METHOD_COUNT) {
         return result;
     }
 
-    std::transform(name.begin(), name.end(), name.begin(),
-                   [](unsigned char c) { return static_cast<char>(std::tolower(c)); });
+    name = lower_ascii(name);
     static const std::unordered_map<std::string_view, sample_method_t> hardcoded{
         {"euler a", EULER_A_SAMPLE_METHOD},
         {"k_euler_a", EULER_A_SAMPLE_METHOD},
@@ -114,6 +121,18 @@ static bool build_sdapi_img_gen_request(const json& j,
     request.gen_params.width                          = j.value("width", -1);
     request.gen_params.height                         = j.value("height", -1);
 
+    if (!img2img && j.value("enable_hr", false)) {
+        request.gen_params.hires_enabled = true;
+        request.gen_params.hires_scale   = j.value("hr_scale", request.gen_params.hires_scale);
+        request.gen_params.hires_width   = j.value("hr_resize_x", request.gen_params.hires_width);
+        request.gen_params.hires_height  = j.value("hr_resize_y", request.gen_params.hires_height);
+        request.gen_params.hires_steps   = j.value("hr_steps", request.gen_params.hires_steps);
+        request.gen_params.hires_denoising_strength =
+            j.value("denoising_strength", request.gen_params.hires_denoising_strength);
+
+        request.gen_params.hires_upscaler = j.value("hr_upscaler", request.gen_params.hires_upscaler);
+    }
+
     std::string sd_cpp_extra_args_str = extract_and_remove_sd_cpp_extra_args(request.gen_params.prompt);
     if (!sd_cpp_extra_args_str.empty() && !request.gen_params.from_json_str(sd_cpp_extra_args_str)) {
         error_message = "invalid sd_cpp_extra_args";
@@ -228,7 +247,7 @@ static bool build_sdapi_img_gen_request(const json& j,
     }
 
     // Intentionally disable prompt-embedded LoRA tag parsing for server APIs.
-    if (!request.gen_params.resolve_and_validate(IMG_GEN, "", true)) {
+    if (!request.gen_params.resolve_and_validate(IMG_GEN, "", runtime.ctx_params->hires_upscalers_dir, true)) {
         error_message = "invalid params";
         return false;
     }
@@ -347,6 +366,45 @@ void register_sdapi_endpoints(httplib::Server& svr, ServerRuntime& rt) {
         res.set_content(result.dump(), "application/json");
     });
 
+    svr.Get("/sdapi/v1/upscalers", [runtime](const httplib::Request&, httplib::Response& res) {
+        refresh_upscaler_cache(*runtime);
+
+        auto make_builtin = [](const char* name) {
+            json item;
+            item["name"]       = name;
+            item["model_name"] = nullptr;
+            item["model_path"] = nullptr;
+            item["model_url"]  = nullptr;
+            item["scale"]      = 4;
+            return item;
+        };
+
+        json result = json::array();
+        result.push_back(make_builtin("None"));
+
+        {
+            std::lock_guard<std::mutex> lock(*runtime->upscaler_mutex);
+            for (const auto& e : *runtime->upscaler_cache) {
+                json item;
+                item["name"]       = e.name;
+                item["model_name"] = e.model_name;
+                item["model_path"] = e.fullpath;
+                item["model_url"]  = nullptr;
+                item["scale"]      = e.scale;
+                result.push_back(item);
+            }
+        }
+
+        res.set_content(result.dump(), "application/json");
+    });
+
+    svr.Get("/sdapi/v1/latent-upscale-modes", [](const httplib::Request&, httplib::Response& res) {
+        json result = json::array({
+            {{"name", "Latent (nearest)"}},
+        });
+        res.set_content(result.dump(), "application/json");
+    });
+
     svr.Get("/sdapi/v1/samplers", [runtime](const httplib::Request&, httplib::Response& res) {
         std::vector<std::string> sampler_names;
         sampler_names.push_back("default");
diff --git a/examples/server/routes_sdcpp.cpp b/examples/server/routes_sdcpp.cpp
index 8119136a4..c314eb0fa 100644
--- a/examples/server/routes_sdcpp.cpp
+++ b/examples/server/routes_sdcpp.cpp
@@ -114,6 +114,17 @@ static json make_img_gen_defaults_json(const SDGenerationParams& defaults, const
         {"increase_ref_index", defaults.increase_ref_index},
         {"control_strength", defaults.control_strength},
         {"sample_params", make_sample_params_json(defaults.sample_params, defaults.skip_layers)},
+        {"hires",
+         {
+             {"enabled", defaults.hires_enabled},
+             {"upscaler", defaults.hires_upscaler},
+             {"scale", defaults.hires_scale},
+             {"target_width", defaults.hires_width},
+             {"target_height", defaults.hires_height},
+             {"steps", defaults.hires_steps},
+             {"denoising_strength", defaults.hires_denoising_strength},
+             {"upscale_tile_size", defaults.hires_upscale_tile_size},
+         }},
         {"vae_tiling_params", make_vae_tiling_json(defaults.vae_tiling_params)},
         {"cache_mode", defaults.cache_mode},
         {"cache_option", defaults.cache_option},
@@ -157,6 +168,7 @@ static json make_img_gen_features_json() {
         {"ref_images", true},
         {"lora", true},
         {"vae_tiling", true},
+        {"hires", true},
         {"cache", true},
         {"cancel_queued", true},
         {"cancel_generating", false},
@@ -179,6 +191,7 @@ static json make_vid_gen_features_json() {
 
 static json make_capabilities_json(ServerRuntime& runtime) {
     refresh_lora_cache(runtime);
+    refresh_upscaler_cache(runtime);
 
     AsyncJobManager& manager  = *runtime.async_job_manager;
     const auto& defaults      = *runtime.default_gen_params;
@@ -190,6 +203,7 @@ static json make_capabilities_json(ServerRuntime& runtime) {
     json image_output_formats = supported_img_output_formats();
     json video_output_formats = supported_vid_output_formats();
     json available_loras      = json::array();
+    json available_upscalers  = json::array();
     json supported_modes      = json::array();
 
     for (int i = 0; i < SAMPLE_METHOD_COUNT; ++i) {
@@ -210,6 +224,21 @@ static json make_capabilities_json(ServerRuntime& runtime) {
         }
     }
 
+    available_upscalers.push_back({
+        {"name", "None"},
+    });
+    available_upscalers.push_back({
+        {"name", "Latent (nearest)"},
+    });
+    {
+        std::lock_guard<std::mutex> lock(*runtime.upscaler_mutex);
+        for (const auto& entry : *runtime.upscaler_cache) {
+            available_upscalers.push_back({
+                {"name", entry.name},
+            });
+        }
+    }
+
     if (supports_img) {
         supported_modes.push_back("img_gen");
     }
@@ -284,6 +313,7 @@ static json make_capabilities_json(ServerRuntime& runtime) {
     result["features"]               = top_level_features;
     result["features_by_mode"]       = features_by_mode;
     result["loras"]                  = available_loras;
+    result["upscalers"]              = available_upscalers;
     return result;
 }
 
@@ -307,7 +337,7 @@ static bool parse_img_gen_request(const json& body,
         return false;
     }
     // Intentionally disable prompt-embedded LoRA tag parsing for server APIs.
-    if (!request.gen_params.resolve_and_validate(IMG_GEN, "", true)) {
+    if (!request.gen_params.resolve_and_validate(IMG_GEN, "", runtime.ctx_params->hires_upscalers_dir, true)) {
         error_message = "invalid generation parameters";
         return false;
     }
@@ -334,7 +364,7 @@ static bool parse_vid_gen_request(const json& body,
         return false;
     }
     // Intentionally disable prompt-embedded LoRA tag parsing for server APIs.
-    if (!request.gen_params.resolve_and_validate(VID_GEN, "", true)) {
+    if (!request.gen_params.resolve_and_validate(VID_GEN, "", runtime.ctx_params->hires_upscalers_dir, true)) {
         error_message = "invalid generation parameters";
         return false;
     }
diff --git a/examples/server/runtime.cpp b/examples/server/runtime.cpp
index 39880a182..afadb62ae 100644
--- a/examples/server/runtime.cpp
+++ b/examples/server/runtime.cpp
@@ -1,6 +1,7 @@
 #include "runtime.h"
 
 #include <algorithm>
+#include <cctype>
 #include <chrono>
 #include <cstdlib>
 #include <filesystem>
@@ -13,6 +14,18 @@
 
 namespace fs = std::filesystem;
 
+static std::string lower_ascii(std::string value) {
+    std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c) {
+        return static_cast<char>(std::tolower(c));
+    });
+    return value;
+}
+
+static bool is_supported_model_ext(const fs::path& p) {
+    auto ext = lower_ascii(p.extension().string());
+    return ext == ".gguf" || ext == ".pt" || ext == ".pth" || ext == ".safetensors";
+}
+
 static const std::string k_base64_chars =
     "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
     "abcdefghijklmnopqrstuvwxyz"
@@ -241,20 +254,12 @@ void refresh_lora_cache(ServerRuntime& rt) {
 
     fs::path lora_dir = rt.ctx_params->lora_model_dir;
     if (fs::exists(lora_dir) && fs::is_directory(lora_dir)) {
-        auto is_lora_ext = [](const fs::path& p) {
-            auto ext = p.extension().string();
-            std::transform(ext.begin(), ext.end(), ext.begin(), [](unsigned char c) {
-                return static_cast<char>(std::tolower(c));
-            });
-            return ext == ".gguf" || ext == ".pt" || ext == ".pth" || ext == ".safetensors";
-        };
-
         for (auto& entry : fs::recursive_directory_iterator(lora_dir)) {
             if (!entry.is_regular_file()) {
                 continue;
             }
             const fs::path& p = entry.path();
-            if (!is_lora_ext(p)) {
+            if (!is_supported_model_ext(p)) {
                 continue;
             }
 
@@ -286,6 +291,40 @@ std::string get_lora_full_path(ServerRuntime& rt, const std::string& path) {
     return it != rt.lora_cache->end() ? it->fullpath : "";
 }
 
+void refresh_upscaler_cache(ServerRuntime& rt) {
+    std::vector<UpscalerEntry> new_cache;
+
+    fs::path upscaler_dir = rt.ctx_params->hires_upscalers_dir;
+    if (fs::exists(upscaler_dir) && fs::is_directory(upscaler_dir)) {
+        for (auto& entry : fs::directory_iterator(upscaler_dir)) {
+            if (!entry.is_regular_file()) {
+                continue;
+            }
+            const fs::path& p = entry.path();
+            if (!is_supported_model_ext(p)) {
+                continue;
+            }
+
+            UpscalerEntry upscaler_entry;
+            upscaler_entry.name       = p.stem().u8string();
+            upscaler_entry.fullpath   = fs::absolute(p).lexically_normal().u8string();
+            upscaler_entry.model_name = "ESRGAN_4x";
+            upscaler_entry.path       = p.filename().u8string();
+
+            new_cache.push_back(std::move(upscaler_entry));
+        }
+    }
+
+    std::sort(new_cache.begin(), new_cache.end(), [](const UpscalerEntry& a, const UpscalerEntry& b) {
+        return a.name < b.name;
+    });
+
+    {
+        std::lock_guard<std::mutex> lock(*rt.upscaler_mutex);
+        *rt.upscaler_cache = std::move(new_cache);
+    }
+}
+
 int64_t unix_timestamp_now() {
     return std::chrono::duration_cast<std::chrono::seconds>(
                std::chrono::system_clock::now().time_since_epoch())
diff --git a/examples/server/runtime.h b/examples/server/runtime.h
index 1970e7dbc..5c5f2d480 100644
--- a/examples/server/runtime.h
+++ b/examples/server/runtime.h
@@ -37,6 +37,14 @@ struct LoraEntry {
     std::string fullpath;
 };
 
+struct UpscalerEntry {
+    std::string name;
+    std::string path;
+    std::string fullpath;
+    std::string model_name;
+    int scale = 4;
+};
+
 struct ServerRuntime {
     sd_ctx_t* sd_ctx;
     std::mutex* sd_ctx_mutex;
@@ -45,6 +53,8 @@ struct ServerRuntime {
     const SDGenerationParams* default_gen_params;
     std::vector<LoraEntry>* lora_cache;
     std::mutex* lora_mutex;
+    std::vector<UpscalerEntry>* upscaler_cache;
+    std::mutex* upscaler_mutex;
     AsyncJobManager* async_job_manager;
 };
 
@@ -86,4 +96,5 @@ bool runtime_supports_generation_mode(const ServerRuntime& runtime, SDMode mode)
 std::string unsupported_generation_mode_error(SDMode mode);
 void refresh_lora_cache(ServerRuntime& rt);
 std::string get_lora_full_path(ServerRuntime& rt, const std::string& path);
+void refresh_upscaler_cache(ServerRuntime& rt);
 int64_t unix_timestamp_now();
diff --git a/include/stable-diffusion.h b/include/stable-diffusion.h
index a99b10450..fba5c1b77 100644
--- a/include/stable-diffusion.h
+++ b/include/stable-diffusion.h
@@ -289,6 +289,25 @@ typedef struct {
     const char* path;
 } sd_lora_t;
 
+enum sd_hires_upscaler_t {
+    SD_HIRES_UPSCALER_NONE,
+    SD_HIRES_UPSCALER_LATENT_NEAREST,
+    SD_HIRES_UPSCALER_MODEL,
+    SD_HIRES_UPSCALER_COUNT,
+};
+
+typedef struct {
+    bool enabled;
+    enum sd_hires_upscaler_t upscaler;
+    const char* model_path;
+    float scale;
+    int target_width;
+    int target_height;
+    int steps;
+    float denoising_strength;
+    int upscale_tile_size;
+} sd_hires_params_t;
+
 typedef struct {
     const sd_lora_t* loras;
     uint32_t lora_count;
@@ -312,6 +331,7 @@ typedef struct {
     sd_pm_params_t pm_params;
     sd_tiling_params_t vae_tiling_params;
     sd_cache_params_t cache;
+    sd_hires_params_t hires;
 } sd_img_gen_params_t;
 
 typedef struct {
@@ -365,8 +385,11 @@ SD_API const char* sd_preview_name(enum preview_t preview);
 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 const char* sd_hires_upscaler_name(enum sd_hires_upscaler_t upscaler);
+SD_API enum sd_hires_upscaler_t str_to_sd_hires_upscaler(const char* str);
 
 SD_API void sd_cache_params_init(sd_cache_params_t* cache_params);
+SD_API void sd_hires_params_init(sd_hires_params_t* hires_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);
diff --git a/src/stable-diffusion.cpp b/src/stable-diffusion.cpp
index b9d3e9af1..8ae6bb504 100644
--- a/src/stable-diffusion.cpp
+++ b/src/stable-diffusion.cpp
@@ -17,6 +17,7 @@
 #include "pmid.hpp"
 #include "sample-cache.h"
 #include "tae.hpp"
+#include "upscaler.h"
 #include "vae.hpp"
 
 #include "latent-preview.h"
@@ -2113,6 +2114,28 @@ enum lora_apply_mode_t str_to_lora_apply_mode(const char* str) {
     return LORA_APPLY_MODE_COUNT;
 }
 
+const char* hires_upscaler_to_str[] = {
+    "None",
+    "Latent (nearest)",
+    "Model",
+};
+
+const char* sd_hires_upscaler_name(enum sd_hires_upscaler_t upscaler) {
+    if (upscaler < SD_HIRES_UPSCALER_COUNT) {
+        return hires_upscaler_to_str[upscaler];
+    }
+    return NONE_STR;
+}
+
+enum sd_hires_upscaler_t str_to_sd_hires_upscaler(const char* str) {
+    for (int i = 0; i < SD_HIRES_UPSCALER_COUNT; i++) {
+        if (!strcmp(str, hires_upscaler_to_str[i])) {
+            return (enum sd_hires_upscaler_t)i;
+        }
+    }
+    return SD_HIRES_UPSCALER_COUNT;
+}
+
 void sd_cache_params_init(sd_cache_params_t* cache_params) {
     *cache_params                             = {};
     cache_params->mode                        = SD_CACHE_DISABLED;
@@ -2141,6 +2164,19 @@ void sd_cache_params_init(sd_cache_params_t* cache_params) {
     cache_params->spectrum_stop_percent       = 0.9f;
 }
 
+void sd_hires_params_init(sd_hires_params_t* hires_params) {
+    *hires_params                    = {};
+    hires_params->enabled            = false;
+    hires_params->upscaler           = SD_HIRES_UPSCALER_LATENT_NEAREST;
+    hires_params->model_path         = nullptr;
+    hires_params->scale              = 2.0f;
+    hires_params->target_width       = 0;
+    hires_params->target_height      = 0;
+    hires_params->steps              = 0;
+    hires_params->denoising_strength = 0.7f;
+    hires_params->upscale_tile_size  = 128;
+}
+
 void sd_ctx_params_init(sd_ctx_params_t* sd_ctx_params) {
     *sd_ctx_params                         = {};
     sd_ctx_params->vae_decode_only         = true;
@@ -2310,6 +2346,7 @@ void sd_img_gen_params_init(sd_img_gen_params_t* sd_img_gen_params) {
     sd_img_gen_params->pm_params         = {nullptr, 0, nullptr, 20.f};
     sd_img_gen_params->vae_tiling_params = {false, 0, 0, 0.5f, 0.0f, 0.0f};
     sd_cache_params_init(&sd_img_gen_params->cache);
+    sd_hires_params_init(&sd_img_gen_params->hires);
 }
 
 char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params) {
@@ -2336,7 +2373,8 @@ char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params) {
              "increase_ref_index: %s\n"
              "control_strength: %.2f\n"
              "photo maker: {style_strength = %.2f, id_images_count = %d, id_embed_path = %s}\n"
-             "VAE tiling: %s\n",
+             "VAE tiling: %s\n"
+             "hires: {enabled=%s, upscaler=%s, model_path=%s, scale=%.2f, target=%dx%d, steps=%d, denoising_strength=%.2f}\n",
              SAFE_STR(sd_img_gen_params->prompt),
              SAFE_STR(sd_img_gen_params->negative_prompt),
              sd_img_gen_params->clip_skip,
@@ -2353,7 +2391,15 @@ char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params) {
              sd_img_gen_params->pm_params.style_strength,
              sd_img_gen_params->pm_params.id_images_count,
              SAFE_STR(sd_img_gen_params->pm_params.id_embed_path),
-             BOOL_STR(sd_img_gen_params->vae_tiling_params.enabled));
+             BOOL_STR(sd_img_gen_params->vae_tiling_params.enabled),
+             BOOL_STR(sd_img_gen_params->hires.enabled),
+             sd_hires_upscaler_name(sd_img_gen_params->hires.upscaler),
+             SAFE_STR(sd_img_gen_params->hires.model_path),
+             sd_img_gen_params->hires.scale,
+             sd_img_gen_params->hires.target_width,
+             sd_img_gen_params->hires.target_height,
+             sd_img_gen_params->hires.steps,
+             sd_img_gen_params->hires.denoising_strength);
     const char* cache_mode_str = "disabled";
     if (sd_img_gen_params->cache.mode == SD_CACHE_EASYCACHE) {
         cache_mode_str = "easycache";
@@ -2534,6 +2580,7 @@ struct GenerationRequest {
     sd_guidance_params_t guidance            = {};
     sd_guidance_params_t high_noise_guidance = {};
     sd_pm_params_t pm_params                 = {};
+    sd_hires_params_t hires                  = {};
     int frames                               = -1;
     float vace_strength                      = 1.f;
 
@@ -2555,6 +2602,7 @@ struct GenerationRequest {
         auto_resize_ref_image       = sd_img_gen_params->auto_resize_ref_image;
         guidance                    = sd_img_gen_params->sample_params.guidance;
         pm_params                   = sd_img_gen_params->pm_params;
+        hires                       = sd_img_gen_params->hires;
         cache_params                = &sd_img_gen_params->cache;
         resolve(sd_ctx);
     }
@@ -2577,26 +2625,76 @@ struct GenerationRequest {
     }
 
     void align_generation_request_size() {
+        align_image_size(&width, &height, "generation request");
+    }
+
+    void align_image_size(int* target_width, int* target_height, const char* label) {
         int spatial_multiple = vae_scale_factor * diffusion_model_down_factor;
-        int width_offset     = align_up_offset(width, spatial_multiple);
-        int height_offset    = align_up_offset(height, spatial_multiple);
+        int width_offset     = align_up_offset(*target_width, spatial_multiple);
+        int height_offset    = align_up_offset(*target_height, spatial_multiple);
         if (width_offset <= 0 && height_offset <= 0) {
             return;
         }
 
-        int original_width  = width;
-        int original_height = height;
+        int original_width  = *target_width;
+        int original_height = *target_height;
 
-        width += width_offset;
-        height += height_offset;
-        LOG_WARN("align up %dx%d to %dx%d (multiple=%d)",
+        *target_width += width_offset;
+        *target_height += height_offset;
+        LOG_WARN("align %s up %dx%d to %dx%d (multiple=%d)",
+                 label,
                  original_width,
                  original_height,
-                 width,
-                 height,
+                 *target_width,
+                 *target_height,
                  spatial_multiple);
     }
 
+    void resolve_hires() {
+        if (!hires.enabled) {
+            return;
+        }
+        if (hires.upscaler == SD_HIRES_UPSCALER_NONE) {
+            hires.enabled = false;
+            return;
+        }
+        if (hires.upscaler < SD_HIRES_UPSCALER_NONE && hires.upscaler >= SD_HIRES_UPSCALER_COUNT) {
+            LOG_WARN("hires upscaler '%d' is invalid, disabling hires", hires.upscaler);
+            hires.enabled = false;
+            return;
+        }
+        if (hires.upscaler == SD_HIRES_UPSCALER_MODEL && strlen(SAFE_STR(hires.model_path)) == 0) {
+            LOG_WARN("hires model upscaler requires a model path, disabling hires");
+            hires.enabled = false;
+            return;
+        }
+        if (hires.scale <= 0.f && hires.target_width <= 0 && hires.target_height <= 0) {
+            LOG_WARN("hires scale must be positive when no target size is set, disabling hires");
+            hires.enabled = false;
+            return;
+        }
+        hires.denoising_strength = std::clamp(hires.denoising_strength, 0.0001f, 1.f);
+        hires.steps              = std::max(0, hires.steps);
+
+        if (hires.target_width > 0 && hires.target_height > 0) {
+            // pass
+        } else if (hires.target_width > 0) {
+            hires.target_height = hires.target_width;
+        } else if (hires.target_height > 0) {
+            hires.target_width = hires.target_height;
+        } else {
+            hires.target_width  = static_cast<int>(std::round(width * hires.scale));
+            hires.target_height = static_cast<int>(std::round(height * hires.scale));
+        }
+
+        if (hires.target_width <= 0 || hires.target_height <= 0) {
+            LOG_WARN("hires target size is not positive, disabling hires");
+            hires.enabled = false;
+            return;
+        }
+        align_image_size(&hires.target_width, &hires.target_height, "hires target");
+    }
+
     static void resolve_guidance(sd_ctx_t* sd_ctx,
                                  sd_guidance_params_t* guidance,
                                  bool* use_uncond,
@@ -2637,6 +2735,7 @@ struct GenerationRequest {
 
     void resolve(sd_ctx_t* sd_ctx) {
         align_generation_request_size();
+        resolve_hires();
         seed = resolve_seed(seed);
 
         resolve_guidance(sd_ctx, &guidance, &use_uncond, &use_img_cond);
@@ -3149,6 +3248,67 @@ static sd_image_t* decode_image_outputs(sd_ctx_t* sd_ctx,
     return result_images;
 }
 
+static sd::Tensor<float> upscale_hires_latent(sd_ctx_t* sd_ctx,
+                                              const sd::Tensor<float>& latent,
+                                              const GenerationRequest& request,
+                                              UpscalerGGML* upscaler) {
+    if (request.hires.upscaler == SD_HIRES_UPSCALER_LATENT_NEAREST) {
+        std::vector<int64_t> target_shape = latent.shape();
+        if (target_shape.size() < 2) {
+            LOG_ERROR("latent has invalid shape for hires upscale");
+            return {};
+        }
+        target_shape[0] = request.hires.target_width / request.vae_scale_factor;
+        target_shape[1] = request.hires.target_height / request.vae_scale_factor;
+
+        LOG_INFO("hires latent upscale %" PRId64 "x%" PRId64 " -> %" PRId64 "x%" PRId64,
+                 latent.shape()[0],
+                 latent.shape()[1],
+                 target_shape[0],
+                 target_shape[1]);
+        return sd::ops::interpolate(latent, target_shape, sd::ops::InterpolateMode::Nearest);
+    } else if (request.hires.upscaler == SD_HIRES_UPSCALER_MODEL) {
+        if (upscaler == nullptr) {
+            LOG_ERROR("hires model upscaler context is null");
+            return {};
+        }
+        if (sd_ctx->sd->vae_decode_only) {
+            LOG_ERROR("hires model upscaler requires VAE encoder weights; create the context with vae_decode_only=false");
+            return {};
+        }
+
+        sd::Tensor<float> decoded = sd_ctx->sd->decode_first_stage(latent);
+        if (decoded.empty()) {
+            LOG_ERROR("decode_first_stage failed before hires model upscale");
+            return {};
+        }
+
+        sd::Tensor<float> upscaled_tensor = upscaler->upscale_tensor(decoded);
+        if (upscaled_tensor.empty()) {
+            LOG_ERROR("hires model upscale failed");
+            return {};
+        }
+
+        if (upscaled_tensor.shape()[0] != request.hires.target_width ||
+            upscaled_tensor.shape()[1] != request.hires.target_height) {
+            upscaled_tensor = sd::ops::interpolate(upscaled_tensor,
+                                                   {request.hires.target_width,
+                                                    request.hires.target_height,
+                                                    upscaled_tensor.shape()[2],
+                                                    upscaled_tensor.shape()[3]});
+        }
+
+        sd::Tensor<float> upscaled_latent = sd_ctx->sd->encode_first_stage(upscaled_tensor);
+        if (upscaled_latent.empty()) {
+            LOG_ERROR("encode_first_stage failed after hires model upscale");
+        }
+        return upscaled_latent;
+    }
+
+    LOG_ERROR("unsupported hires upscaler '%s'", sd_hires_upscaler_name(request.hires.upscaler));
+    return {};
+}
+
 SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_gen_params) {
     if (sd_ctx == nullptr || sd_img_gen_params == nullptr) {
         return nullptr;
@@ -3236,7 +3396,7 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
         }
         return nullptr;
     }
-    if (sd_ctx->sd->free_params_immediately) {
+    if (sd_ctx->sd->free_params_immediately && !request.hires.enabled) {
         sd_ctx->sd->diffusion_model->free_params_buffer();
     }
     int64_t denoise_end = ggml_time_ms();
@@ -3244,6 +3404,131 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
              final_latents.size(),
              (denoise_end - denoise_start) * 1.0f / 1000);
 
+    if (request.hires.enabled && request.hires.target_width > 0) {
+        LOG_INFO("hires fix: upscaling to %dx%d", request.hires.target_width, request.hires.target_height);
+
+        std::unique_ptr<UpscalerGGML> hires_upscaler;
+        if (request.hires.upscaler == SD_HIRES_UPSCALER_MODEL) {
+            LOG_INFO("hires fix: loading model upscaler from '%s'", request.hires.model_path);
+            hires_upscaler = std::make_unique<UpscalerGGML>(sd_ctx->sd->n_threads,
+                                                            false,
+                                                            request.hires.upscale_tile_size);
+            if (!hires_upscaler->load_from_file(request.hires.model_path,
+                                                sd_ctx->sd->offload_params_to_cpu,
+                                                sd_ctx->sd->n_threads)) {
+                LOG_ERROR("load hires model upscaler failed");
+                if (sd_ctx->sd->free_params_immediately) {
+                    sd_ctx->sd->diffusion_model->free_params_buffer();
+                }
+                return nullptr;
+            }
+        }
+
+        int hires_steps = request.hires.steps > 0 ? request.hires.steps : plan.sample_steps;
+
+        // sd-webui behavior: scale up total steps so trimming by denoising_strength yields exactly hires_steps effective steps,
+        // unlike img2img which trims from a fixed step count
+        hires_steps = static_cast<int>(hires_steps / request.hires.denoising_strength);
+
+        std::vector<float> hires_sigmas = sd_ctx->sd->denoiser->get_sigmas(
+            hires_steps,
+            sd_ctx->sd->get_image_seq_len(request.hires.target_height, request.hires.target_width),
+            sd_img_gen_params->sample_params.scheduler,
+            sd_ctx->sd->version);
+
+        size_t t_enc = static_cast<size_t>(hires_steps * request.hires.denoising_strength);
+        if (t_enc >= static_cast<size_t>(hires_steps)) {
+            t_enc = static_cast<size_t>(hires_steps) - 1;
+        }
+        std::vector<float> hires_sigma_sched(hires_sigmas.begin() + hires_steps - static_cast<int>(t_enc) - 1,
+                                             hires_sigmas.end());
+        LOG_INFO("hires fix: %d steps, denoising_strength=%.2f, sigma_sched_size=%zu",
+                 hires_steps,
+                 request.hires.denoising_strength,
+                 hires_sigma_sched.size());
+
+        std::vector<sd::Tensor<float>> hires_final_latents;
+        int64_t hires_denoise_start = ggml_time_ms();
+        for (int b = 0; b < (int)final_latents.size(); b++) {
+            int64_t cur_seed = request.seed + b;
+            sd_ctx->sd->rng->manual_seed(cur_seed);
+            sd_ctx->sd->sampler_rng->manual_seed(cur_seed);
+
+            sd::Tensor<float> upscaled = upscale_hires_latent(sd_ctx,
+                                                              final_latents[b],
+                                                              request,
+                                                              hires_upscaler.get());
+            if (upscaled.empty()) {
+                if (sd_ctx->sd->free_params_immediately) {
+                    sd_ctx->sd->diffusion_model->free_params_buffer();
+                }
+                return nullptr;
+            }
+
+            sd::Tensor<float> noise = sd::randn_like<float>(upscaled, sd_ctx->sd->rng);
+
+            sd::Tensor<float> hires_denoise_mask;
+            if (!latents.denoise_mask.empty()) {
+                std::vector<int64_t> mask_shape = latents.denoise_mask.shape();
+                mask_shape[0]                   = upscaled.shape()[0];
+                mask_shape[1]                   = upscaled.shape()[1];
+                hires_denoise_mask              = sd::ops::interpolate(latents.denoise_mask,
+                                                                       mask_shape,
+                                                                       sd::ops::InterpolateMode::NearestMax);
+            }
+
+            int64_t hires_sample_start = ggml_time_ms();
+            sd::Tensor<float> x_0      = sd_ctx->sd->sample(sd_ctx->sd->diffusion_model,
+                                                            true,
+                                                            upscaled,
+                                                            std::move(noise),
+                                                            embeds.cond,
+                                                            embeds.uncond,
+                                                            embeds.img_cond,
+                                                            embeds.id_cond,
+                                                            latents.control_image,
+                                                            request.control_strength,
+                                                            request.guidance,
+                                                            plan.eta,
+                                                            request.shifted_timestep,
+                                                            plan.sample_method,
+                                                            sd_ctx->sd->is_flow_denoiser(),
+                                                            hires_sigma_sched,
+                                                            plan.start_merge_step,
+                                                            latents.ref_latents,
+                                                            request.increase_ref_index,
+                                                            hires_denoise_mask,
+                                                            sd::Tensor<float>(),
+                                                            1.f,
+                                                            request.cache_params);
+            int64_t hires_sample_end   = ggml_time_ms();
+            if (!x_0.empty()) {
+                LOG_INFO("hires sampling %d/%d completed, taking %.2fs",
+                         b + 1,
+                         (int)final_latents.size(),
+                         (hires_sample_end - hires_sample_start) * 1.0f / 1000);
+                hires_final_latents.push_back(std::move(x_0));
+                continue;
+            }
+
+            LOG_ERROR("hires sampling for image %d/%d failed after %.2fs",
+                      b + 1,
+                      (int)final_latents.size(),
+                      (hires_sample_end - hires_sample_start) * 1.0f / 1000);
+            if (sd_ctx->sd->free_params_immediately) {
+                sd_ctx->sd->diffusion_model->free_params_buffer();
+            }
+            return nullptr;
+        }
+        if (sd_ctx->sd->free_params_immediately) {
+            sd_ctx->sd->diffusion_model->free_params_buffer();
+        }
+        int64_t hires_denoise_end = ggml_time_ms();
+        LOG_INFO("hires fix completed, taking %.2fs", (hires_denoise_end - hires_denoise_start) * 1.0f / 1000);
+
+        final_latents = std::move(hires_final_latents);
+    }
+
     auto result = decode_image_outputs(sd_ctx, request, final_latents);
     if (result == nullptr) {
         return nullptr;
diff --git a/src/upscaler.cpp b/src/upscaler.cpp
index 03f7714e5..ed7bb89a0 100644
--- a/src/upscaler.cpp
+++ b/src/upscaler.cpp
@@ -1,125 +1,115 @@
-#include "esrgan.hpp"
+#include "upscaler.h"
 #include "ggml_extend.hpp"
 #include "model.h"
 #include "stable-diffusion.h"
 #include "util.h"
 
-struct UpscalerGGML {
-    ggml_backend_t backend    = nullptr;  // general backend
-    ggml_type model_data_type = GGML_TYPE_F16;
-    std::shared_ptr<ESRGAN> esrgan_upscaler;
-    std::string esrgan_path;
-    int n_threads;
-    bool direct   = false;
-    int tile_size = 128;
-
-    UpscalerGGML(int n_threads,
-                 bool direct   = false,
-                 int tile_size = 128)
-        : n_threads(n_threads),
-          direct(direct),
-          tile_size(tile_size) {
-    }
+UpscalerGGML::UpscalerGGML(int n_threads,
+                           bool direct,
+                           int tile_size)
+    : n_threads(n_threads),
+      direct(direct),
+      tile_size(tile_size) {
+}
 
-    bool load_from_file(const std::string& esrgan_path,
-                        bool offload_params_to_cpu,
-                        int n_threads) {
-        ggml_log_set(ggml_log_callback_default, nullptr);
+bool UpscalerGGML::load_from_file(const std::string& esrgan_path,
+                                  bool offload_params_to_cpu,
+                                  int n_threads) {
+    ggml_log_set(ggml_log_callback_default, nullptr);
 #ifdef SD_USE_CUDA
-        LOG_DEBUG("Using CUDA backend");
-        backend = ggml_backend_cuda_init(0);
+    LOG_DEBUG("Using CUDA backend");
+    backend = ggml_backend_cuda_init(0);
 #endif
 #ifdef SD_USE_METAL
-        LOG_DEBUG("Using Metal backend");
-        backend = ggml_backend_metal_init();
+    LOG_DEBUG("Using Metal backend");
+    backend = ggml_backend_metal_init();
 #endif
 #ifdef SD_USE_VULKAN
-        LOG_DEBUG("Using Vulkan backend");
-        backend = ggml_backend_vk_init(0);
+    LOG_DEBUG("Using Vulkan backend");
+    backend = ggml_backend_vk_init(0);
 #endif
 #ifdef SD_USE_OPENCL
-        LOG_DEBUG("Using OpenCL backend");
-        backend = ggml_backend_opencl_init();
+    LOG_DEBUG("Using OpenCL backend");
+    backend = ggml_backend_opencl_init();
 #endif
 #ifdef SD_USE_SYCL
-        LOG_DEBUG("Using SYCL backend");
-        backend = ggml_backend_sycl_init(0);
+    LOG_DEBUG("Using SYCL backend");
+    backend = ggml_backend_sycl_init(0);
 #endif
-        ModelLoader model_loader;
-        if (!model_loader.init_from_file_and_convert_name(esrgan_path)) {
-            LOG_ERROR("init model loader from file failed: '%s'", esrgan_path.c_str());
-        }
-        model_loader.set_wtype_override(model_data_type);
-        if (!backend) {
-            LOG_DEBUG("Using CPU backend");
-            backend = ggml_backend_cpu_init();
-        }
-        LOG_INFO("Upscaler weight type: %s", ggml_type_name(model_data_type));
-        esrgan_upscaler = std::make_shared<ESRGAN>(backend, offload_params_to_cpu, tile_size, model_loader.get_tensor_storage_map());
-        if (direct) {
-            esrgan_upscaler->set_conv2d_direct_enabled(true);
-        }
-        if (!esrgan_upscaler->load_from_file(esrgan_path, n_threads)) {
-            return false;
-        }
-        return true;
+    ModelLoader model_loader;
+    if (!model_loader.init_from_file_and_convert_name(esrgan_path)) {
+        LOG_ERROR("init model loader from file failed: '%s'", esrgan_path.c_str());
+    }
+    model_loader.set_wtype_override(model_data_type);
+    if (!backend) {
+        LOG_DEBUG("Using CPU backend");
+        backend = ggml_backend_cpu_init();
+    }
+    LOG_INFO("Upscaler weight type: %s", ggml_type_name(model_data_type));
+    esrgan_upscaler = std::make_shared<ESRGAN>(backend, offload_params_to_cpu, tile_size, model_loader.get_tensor_storage_map());
+    if (direct) {
+        esrgan_upscaler->set_conv2d_direct_enabled(true);
+    }
+    if (!esrgan_upscaler->load_from_file(esrgan_path, n_threads)) {
+        return false;
     }
+    return true;
+}
 
-    sd::Tensor<float> upscale_tensor(const sd::Tensor<float>& input_tensor) {
-        sd::Tensor<float> upscaled;
-        if (tile_size <= 0 || (input_tensor.shape()[0] <= tile_size && input_tensor.shape()[1] <= tile_size)) {
-            upscaled = esrgan_upscaler->compute(n_threads, input_tensor);
-        } else {
-            auto on_processing = [&](const sd::Tensor<float>& input_tile) -> sd::Tensor<float> {
-                auto output_tile = esrgan_upscaler->compute(n_threads, input_tile);
-                if (output_tile.empty()) {
-                    LOG_ERROR("esrgan compute failed while processing a tile");
-                    return {};
-                }
-                return output_tile;
-            };
+sd::Tensor<float> UpscalerGGML::upscale_tensor(const sd::Tensor<float>& input_tensor) {
+    sd::Tensor<float> upscaled;
+    if (tile_size <= 0 || (input_tensor.shape()[0] <= tile_size && input_tensor.shape()[1] <= tile_size)) {
+        upscaled = esrgan_upscaler->compute(n_threads, input_tensor);
+    } else {
+        auto on_processing = [&](const sd::Tensor<float>& input_tile) -> sd::Tensor<float> {
+            auto output_tile = esrgan_upscaler->compute(n_threads, input_tile);
+            if (output_tile.empty()) {
+                LOG_ERROR("esrgan compute failed while processing a tile");
+                return {};
+            }
+            return output_tile;
+        };
 
-            upscaled = process_tiles_2d(input_tensor,
-                                        static_cast<int>(input_tensor.shape()[0] * esrgan_upscaler->scale),
-                                        static_cast<int>(input_tensor.shape()[1] * esrgan_upscaler->scale),
-                                        esrgan_upscaler->scale,
-                                        tile_size,
-                                        tile_size,
-                                        0.25f,
-                                        false,
-                                        false,
-                                        on_processing);
-        }
-        esrgan_upscaler->free_compute_buffer();
-        if (upscaled.empty()) {
-            LOG_ERROR("esrgan compute failed");
-            return {};
-        }
-        return upscaled;
+        upscaled = process_tiles_2d(input_tensor,
+                                    static_cast<int>(input_tensor.shape()[0] * esrgan_upscaler->scale),
+                                    static_cast<int>(input_tensor.shape()[1] * esrgan_upscaler->scale),
+                                    esrgan_upscaler->scale,
+                                    tile_size,
+                                    tile_size,
+                                    0.25f,
+                                    false,
+                                    false,
+                                    on_processing);
+    }
+    esrgan_upscaler->free_compute_buffer();
+    if (upscaled.empty()) {
+        LOG_ERROR("esrgan compute failed");
+        return {};
     }
+    return upscaled;
+}
 
-    sd_image_t upscale(sd_image_t input_image, uint32_t upscale_factor) {
-        // upscale_factor, unused for RealESRGAN_x4plus_anime_6B.pth
-        sd_image_t upscaled_image = {0, 0, 0, nullptr};
-        int output_width          = (int)input_image.width * esrgan_upscaler->scale;
-        int output_height         = (int)input_image.height * esrgan_upscaler->scale;
-        LOG_INFO("upscaling from (%i x %i) to (%i x %i)",
-                 input_image.width, input_image.height, output_width, output_height);
+sd_image_t UpscalerGGML::upscale(sd_image_t input_image, uint32_t upscale_factor) {
+    // upscale_factor, unused for RealESRGAN_x4plus_anime_6B.pth
+    sd_image_t upscaled_image = {0, 0, 0, nullptr};
+    int output_width          = (int)input_image.width * esrgan_upscaler->scale;
+    int output_height         = (int)input_image.height * esrgan_upscaler->scale;
+    LOG_INFO("upscaling from (%i x %i) to (%i x %i)",
+             input_image.width, input_image.height, output_width, output_height);
 
-        sd::Tensor<float> input_tensor = sd_image_to_tensor(input_image);
-        sd::Tensor<float> upscaled;
-        int64_t t0 = ggml_time_ms();
-        upscaled   = upscale_tensor(input_tensor);
-        if (upscaled.empty()) {
-            return upscaled_image;
-        }
-        sd_image_t upscaled_data = tensor_to_sd_image(upscaled);
-        int64_t t3               = ggml_time_ms();
-        LOG_INFO("input_image_tensor upscaled, taking %.2fs", (t3 - t0) / 1000.0f);
-        upscaled_image = upscaled_data;
+    sd::Tensor<float> input_tensor = sd_image_to_tensor(input_image);
+    sd::Tensor<float> upscaled;
+    int64_t t0 = ggml_time_ms();
+    upscaled   = upscale_tensor(input_tensor);
+    if (upscaled.empty()) {
         return upscaled_image;
     }
-};
+    sd_image_t upscaled_data = tensor_to_sd_image(upscaled);
+    int64_t t3               = ggml_time_ms();
+    LOG_INFO("input_image_tensor upscaled, taking %.2fs", (t3 - t0) / 1000.0f);
+    upscaled_image = upscaled_data;
+    return upscaled_image;
+}
 
 struct upscaler_ctx_t {
     UpscalerGGML* upscaler = nullptr;
diff --git a/src/upscaler.h b/src/upscaler.h
new file mode 100644
index 000000000..b11f004a6
--- /dev/null
+++ b/src/upscaler.h
@@ -0,0 +1,31 @@
+#ifndef __SD_UPSCALER_H__
+#define __SD_UPSCALER_H__
+
+#include "esrgan.hpp"
+#include "stable-diffusion.h"
+#include "tensor.hpp"
+
+#include <memory>
+#include <string>
+
+struct UpscalerGGML {
+    ggml_backend_t backend    = nullptr;  // general backend
+    ggml_type model_data_type = GGML_TYPE_F16;
+    std::shared_ptr<ESRGAN> esrgan_upscaler;
+    std::string esrgan_path;
+    int n_threads;
+    bool direct   = false;
+    int tile_size = 128;
+
+    UpscalerGGML(int n_threads,
+                 bool direct   = false,
+                 int tile_size = 128);
+
+    bool load_from_file(const std::string& esrgan_path,
+                        bool offload_params_to_cpu,
+                        int n_threads);
+    sd::Tensor<float> upscale_tensor(const sd::Tensor<float>& input_tensor);
+    sd_image_t upscale(sd_image_t input_image, uint32_t upscale_factor);
+};
+
+#endif  // __SD_UPSCALER_H__