fix(cmake): build HIP backend with PIC so the static-lib PIE link succeeds (#1593)

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

CMakeLists.txt

@@ -125,6 +125,12 @@ endif ()
 if (SD_HIPBLAS)
     message("-- Use HIPBLAS as backend stable-diffusion")
     set(GGML_HIP ON)
+    # ggml-hip's device-stub objects must be position-independent, or the
+    # default-PIE sd-cli link fails with `relocation R_X86_64_32 ... cannot be
+    # used when making a PIE object` on distros that default to PIE
+    # (Ubuntu 24.04, Fedora 40+, Debian 12+). The shared-library branch below
+    # already sets this; the static build (the HIP default) did not.
+    set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 endif ()
 
 if(SD_MUSA)

examples/cli/README.md

@@ -129,8 +129,8 @@ Generation Options:
   --hires-upscale-tile-size <int>          highres fix upscaler tile size, reserved for model-backed upscalers (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)
+  --img-cfg-scale <float>                  image guidance scale for inpaint or image edit models: (default: same as
+                                           --cfg-scale)
   --guidance <float>                       distilled guidance scale for models with guidance input (default: 3.5)
   --slg-scale <float>                      skip layer guidance (SLG) scale, only for DiT models: (default: 0). 0 means
                                            disabled, a value of 2.5 is nice for sd3.5 medium
@@ -140,8 +140,8 @@ Generation Options:
                                            res_2s; 1 for euler_a, er_sde and dpm++2s_a)
   --flow-shift <float>                     shift value for Flow models like SD3.x or WAN (default: auto)
   --high-noise-cfg-scale <float>           (high noise) unconditional guidance scale: (default: 7.0)
-  --high-noise-img-cfg-scale <float>       (high noise) image guidance scale for inpaint or instruct-pix2pix models
-                                           (default: same as --cfg-scale)
+  --high-noise-img-cfg-scale <float>       (high noise) image guidance scale for inpaint or image edit models (default:
+                                           same as --cfg-scale)
   --high-noise-guidance <float>            (high noise) distilled guidance scale for models with guidance input
                                            (default: 3.5)
   --high-noise-slg-scale <float>           (high noise) skip layer guidance (SLG) scale, only for DiT models: (default:

examples/common/common.cpp

@@ -940,7 +940,7 @@ ArgOptions SDGenerationParams::get_options() {
          &sample_params.guidance.txt_cfg},
         {"",
          "--img-cfg-scale",
-         "image guidance scale for inpaint or instruct-pix2pix models: (default: same as --cfg-scale)",
+         "image guidance scale for inpaint or image edit models: (default: same as --cfg-scale)",
          &sample_params.guidance.img_cfg},
         {"",
          "--guidance",
@@ -972,7 +972,7 @@ ArgOptions SDGenerationParams::get_options() {
          &high_noise_sample_params.guidance.txt_cfg},
         {"",
          "--high-noise-img-cfg-scale",
-         "(high noise) image guidance scale for inpaint or instruct-pix2pix models (default: same as --cfg-scale)",
+         "(high noise) image guidance scale for inpaint or image edit models (default: same as --cfg-scale)",
          &high_noise_sample_params.guidance.img_cfg},
         {"",
          "--high-noise-guidance",

examples/server/README.md

@@ -231,8 +231,8 @@ Default Generation Options:
   --hires-upscale-tile-size <int>          highres fix upscaler tile size, reserved for model-backed upscalers (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)
+  --img-cfg-scale <float>                  image guidance scale for inpaint or image edit models: (default: same as
+                                           --cfg-scale)
   --guidance <float>                       distilled guidance scale for models with guidance input (default: 3.5)
   --slg-scale <float>                      skip layer guidance (SLG) scale, only for DiT models: (default: 0). 0 means
                                            disabled, a value of 2.5 is nice for sd3.5 medium
@@ -242,8 +242,8 @@ Default Generation Options:
                                            res_2s; 1 for euler_a, er_sde and dpm++2s_a)
   --flow-shift <float>                     shift value for Flow models like SD3.x or WAN (default: auto)
   --high-noise-cfg-scale <float>           (high noise) unconditional guidance scale: (default: 7.0)
-  --high-noise-img-cfg-scale <float>       (high noise) image guidance scale for inpaint or instruct-pix2pix models
-                                           (default: same as --cfg-scale)
+  --high-noise-img-cfg-scale <float>       (high noise) image guidance scale for inpaint or image edit models (default:
+                                           same as --cfg-scale)
   --high-noise-guidance <float>            (high noise) distilled guidance scale for models with guidance input
                                            (default: 3.5)
   --high-noise-slg-scale <float>           (high noise) skip layer guidance (SLG) scale, only for DiT models: (default:

src/convert.cpp

@@ -8,7 +8,7 @@
 #include "model_io/safetensors_io.h"
 #include "util.h"
 
-#include "ggml-cpu.h"
+#include "ggml_extend_backend.h"
 
 static ggml_type get_export_tensor_type(ModelLoader& model_loader,
                                         const TensorStorage& tensor_storage,
@@ -103,7 +103,7 @@ bool convert(const char* input_path,
     bool output_is_safetensors = ends_with(output_path, ".safetensors");
     TensorTypeRules type_rules = parse_tensor_type_rules(tensor_type_rules);
 
-    auto backend    = ggml_backend_cpu_init();
+    auto backend    = sd_backend_cpu_init();
     size_t mem_size = 1 * 1024 * 1024;  // for padding
     mem_size += model_loader.get_tensor_storage_map().size() * ggml_tensor_overhead();
     mem_size += model_loader.get_params_mem_size(backend, type);

src/flux.hpp

@@ -1567,7 +1567,7 @@ namespace Flux {
 
         static void load_from_file_and_test(const std::string& file_path) {
             // ggml_backend_t backend = ggml_backend_cuda_init(0);
-            ggml_backend_t backend    = ggml_backend_cpu_init();
+            ggml_backend_t backend    = sd_backend_cpu_init();
             ggml_type model_data_type = GGML_TYPE_COUNT;
 
             ModelLoader model_loader;

src/ggml_extend.hpp

@@ -1442,7 +1442,7 @@ __STATIC_INLINE__ ggml_tensor* ggml_ext_group_norm(ggml_context* ctx,
 
 __STATIC_INLINE__ void ggml_ext_backend_tensor_get_and_sync(ggml_backend_t backend, const ggml_tensor* tensor, void* data, size_t offset, size_t size) {
     if ((sd_backend_is(backend, "ROCm") || sd_backend_is(backend, "CUDA") || sd_backend_is(backend, "SYCL")) &&
-        !ggml_backend_is_cpu(backend)) {
+        !sd_backend_is_cpu(backend)) {
         ggml_backend_tensor_get_async(backend, tensor, data, offset, size);
         ggml_backend_synchronize(backend);
         return;
@@ -1899,7 +1899,7 @@ protected:
         LOG_DEBUG("%s compute buffer size: %.2f MB(%s)",
                   get_desc().c_str(),
                   compute_buffer_size / 1024.0 / 1024.0,
-                  ggml_backend_is_cpu(runtime_backend) ? "RAM" : "VRAM");
+                  sd_backend_is_cpu(runtime_backend) ? "RAM" : "VRAM");
         return true;
     }
 
@@ -1986,7 +1986,7 @@ protected:
         LOG_DEBUG("%s cache backend buffer size = % 6.2f MB(%s) (%i tensors)",
                   get_desc().c_str(),
                   cache_buffer_size / (1024.f * 1024.f),
-                  ggml_backend_is_cpu(runtime_backend) ? "RAM" : "VRAM",
+                  sd_backend_is_cpu(runtime_backend) ? "RAM" : "VRAM",
                   num_tensors);
         if (old_cache_buffer != nullptr) {
             ggml_backend_buffer_free(old_cache_buffer);
@@ -2293,13 +2293,13 @@ protected:
                max_graph_vram_bytes > 0 &&
                plan.segments.size() > 1 &&
                params_backend != runtime_backend &&
-               !ggml_backend_is_cpu(runtime_backend);
+               !sd_backend_is_cpu(runtime_backend);
     }
 
     bool can_attempt_graph_cut_segmented_compute() const {
         return max_graph_vram_bytes > 0 &&
                params_backend != runtime_backend &&
-               !ggml_backend_is_cpu(runtime_backend);
+               !sd_backend_is_cpu(runtime_backend);
     }
 
     bool resolve_graph_cut_plan(ggml_cgraph* gf,
@@ -2436,8 +2436,8 @@ protected:
         int64_t t_copy_begin = ggml_time_ms();
         copy_data_to_backend_tensor(gf, !preserve_backend_tensor_data_map);
         int64_t t_copy_end = ggml_time_ms();
-        if (ggml_backend_is_cpu(runtime_backend)) {
-            ggml_backend_cpu_set_n_threads(runtime_backend, n_threads);
+        if (sd_backend_is_cpu(runtime_backend)) {
+            sd_backend_cpu_set_n_threads(runtime_backend, n_threads);
         }
 
         int64_t t_compute_begin = ggml_time_ms();
@@ -2679,7 +2679,7 @@ public:
         LOG_DEBUG("%s params backend buffer size = % 6.2f MB(%s) (%i tensors)",
                   get_desc().c_str(),
                   params_buffer_size / (1024.f * 1024.f),
-                  ggml_backend_is_cpu(params_backend) ? "RAM" : "VRAM",
+                  sd_backend_is_cpu(params_backend) ? "RAM" : "VRAM",
                   num_tensors);
         return true;
     }
@@ -2746,7 +2746,7 @@ public:
             return nullptr;
         }
         // it's performing a compute, check if backend isn't cpu
-        if (!ggml_backend_is_cpu(runtime_backend) && (tensor->buffer == nullptr || ggml_backend_buffer_is_host(tensor->buffer))) {
+        if (!sd_backend_is_cpu(runtime_backend) && (tensor->buffer == nullptr || ggml_backend_buffer_is_host(tensor->buffer))) {
             // pass input tensors to gpu memory
             auto backend_tensor = ggml_dup_tensor(compute_ctx, tensor);
 

src/ggml_extend_backend.cpp

@@ -8,6 +8,7 @@
 #include <stdexcept>
 #include <vector>
 
+#include "stable-diffusion.h"
 #include "util.h"
 
 static std::string trim_copy(const std::string& value) {
@@ -300,6 +301,61 @@ static ggml_backend_t init_named_backend(const std::string& name) {
     return ggml_backend_init_by_name(resolved.c_str(), nullptr);
 }
 
+bool sd_backend_is_cpu(ggml_backend_t backend) {
+    if (backend == nullptr) {
+        return false;
+    }
+    auto dev = ggml_backend_get_device(backend);
+    return dev != nullptr && ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU;
+}
+
+ggml_backend_t sd_backend_cpu_init() {
+    ggml_backend_load_all_once();
+    return ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, nullptr);
+}
+
+bool sd_backend_cpu_set_n_threads(ggml_backend_t backend, int n_threads) {
+    if (backend == nullptr) {
+        return false;
+    }
+    auto dev = ggml_backend_get_device(backend);
+    if (dev != nullptr && ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) {
+        auto reg                           = ggml_backend_dev_backend_reg(dev);
+        auto ggml_backend_set_n_threads_fn = (ggml_backend_set_n_threads_t)ggml_backend_reg_get_proc_address(reg, "ggml_backend_set_n_threads");
+        if (ggml_backend_set_n_threads_fn != nullptr) {
+            ggml_backend_set_n_threads_fn(backend, n_threads);
+            return true;
+        }
+    }
+    return false;
+}
+
+const char* sd_get_system_info() {
+    static std::string cache_info = []() -> std::string {
+        ggml_backend_load_all_once();
+        std::stringstream ss;
+        ss << "System Info: \n";
+        auto dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
+        if (dev != nullptr) {
+            auto reg                          = ggml_backend_dev_backend_reg(dev);
+            auto ggml_backend_get_features_fn = (ggml_backend_get_features_t)ggml_backend_reg_get_proc_address(reg, "ggml_backend_get_features");
+            if (ggml_backend_get_features_fn != nullptr) {
+                ggml_backend_feature* feat = ggml_backend_get_features_fn(reg);
+                while (feat->name && feat->value) {
+                    ss << "   " << feat->name << " = " << feat->value << " | ";
+                    feat++;
+                }
+            } else {
+                LOG_WARN("unable to get CPU features");
+            }
+        } else {
+            LOG_WARN("unable to get CPU features");
+        }
+        return ss.str();
+    }();
+    return cache_info.c_str();
+}
+
 static ggml_backend_t sd_get_default_backend() {
     ggml_backend_load_all_once();
     static std::once_flag once;
@@ -349,10 +405,10 @@ static ggml_backend_t sd_get_default_backend() {
 
     if (!backend) {
         LOG_WARN("loading CPU backend");
-        backend = ggml_backend_cpu_init();
+        backend = sd_backend_cpu_init();
     }
 
-    if (ggml_backend_is_cpu(backend)) {
+    if (sd_backend_is_cpu(backend)) {
         LOG_DEBUG("Using CPU backend");
     }
 
@@ -452,11 +508,11 @@ ggml_backend_t SDBackendManager::params_backend(SDBackendModule module) {
 }
 
 bool SDBackendManager::runtime_backend_is_cpu(SDBackendModule module) {
-    return ggml_backend_is_cpu(runtime_backend(module));
+    return sd_backend_is_cpu(runtime_backend(module));
 }
 
 bool SDBackendManager::params_backend_is_cpu(SDBackendModule module) {
-    return ggml_backend_is_cpu(params_backend(module));
+    return sd_backend_is_cpu(params_backend(module));
 }
 
 bool SDBackendManager::runtime_backend_supports_host_buffer(SDBackendModule module) {
@@ -464,7 +520,7 @@ bool SDBackendManager::runtime_backend_supports_host_buffer(SDBackendModule modu
     if (backend == nullptr) {
         return false;
     }
-    if (ggml_backend_is_cpu(backend)) {
+    if (sd_backend_is_cpu(backend)) {
         return true;
     }
     ggml_backend_dev_t dev = ggml_backend_get_device(backend);

src/ggml_extend_backend.h

@@ -8,7 +8,6 @@
 #include <unordered_map>
 
 #include "ggml-backend.h"
-#include "ggml-cpu.h"
 #include "ggml.h"
 
 enum class SDBackendModule {
@@ -72,6 +71,9 @@ private:
 };
 
 bool sd_backend_is(ggml_backend_t backend, const std::string& name);
+bool sd_backend_is_cpu(ggml_backend_t backend);
+ggml_backend_t sd_backend_cpu_init();
+bool sd_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads);
 const char* sd_backend_module_name(SDBackendModule module);
 void ggml_ext_im_set_f32_1d(const struct ggml_tensor* tensor, int i, float value);
 #endif

src/llm.hpp

@@ -2041,7 +2041,7 @@ namespace LLM {
         static void load_from_file_and_test(const std::string& file_path) {
             // cpu f16: pass
             // ggml_backend_t backend = ggml_backend_cuda_init(0);
-            ggml_backend_t backend    = ggml_backend_cpu_init();
+            ggml_backend_t backend    = sd_backend_cpu_init();
             ggml_type model_data_type = GGML_TYPE_COUNT;
 
             ModelLoader model_loader;

src/lora.hpp

@@ -772,7 +772,7 @@ struct LoraModel : public GGMLRunner {
             }
 
             ggml_tensor* original_tensor = model_tensor;
-            if (!ggml_backend_is_cpu(runtime_backend) && ggml_backend_buffer_is_host(original_tensor->buffer)) {
+            if (!sd_backend_is_cpu(runtime_backend) && ggml_backend_buffer_is_host(original_tensor->buffer)) {
                 model_tensor = ggml_dup_tensor(compute_ctx, model_tensor);
                 set_backend_tensor_data(model_tensor, original_tensor->data);
             }
@@ -786,7 +786,7 @@ struct LoraModel : public GGMLRunner {
                 final_tensor = ggml_add_inplace(compute_ctx, model_tensor, diff);
             }
             ggml_build_forward_expand(gf, final_tensor);
-            if (!ggml_backend_is_cpu(runtime_backend) && ggml_backend_buffer_is_host(original_tensor->buffer)) {
+            if (!sd_backend_is_cpu(runtime_backend) && ggml_backend_buffer_is_host(original_tensor->buffer)) {
                 original_tensor_to_final_tensor[original_tensor] = final_tensor;
             }
         }

src/ltx_audio_vae.h

@@ -1052,7 +1052,7 @@ namespace LTXV {
         static void load_from_file_and_test(const std::string& model_path,
                                             const std::string& input_path,
                                             const std::string& prefix = "") {
-            ggml_backend_t backend = ggml_backend_cpu_init();
+            ggml_backend_t backend = sd_backend_cpu_init();
             // ggml_backend_t backend = ggml_backend_cuda_init(0);
             LOG_INFO("loading ltx audio vae from '%s'", model_path.c_str());
 

src/ltx_vae.hpp

@@ -1517,7 +1517,7 @@ struct LTXVideoVAE : public VAE {
     static void load_from_file_and_test(const std::string& model_path,
                                         const std::string& input_path) {
         // ggml_backend_t backend = ggml_backend_cuda_init(0);
-        ggml_backend_t backend = ggml_backend_cpu_init();
+        ggml_backend_t backend = sd_backend_cpu_init();
         LOG_INFO("loading ltx vae from '%s'", model_path.c_str());
 
         ModelLoader model_loader;

src/ltxv.hpp

@@ -1995,7 +1995,7 @@ namespace LTXV {
                                             const std::string& audio_x_path         = "",
                                             const std::string& audio_timesteps_path = "") {
             // ggml_backend_t backend = ggml_backend_cuda_init(0);
-            ggml_backend_t backend = ggml_backend_cpu_init();
+            ggml_backend_t backend = sd_backend_cpu_init();
             LOG_INFO("loading ltxav from '%s'", model_path.c_str());
 
             ModelLoader model_loader;

src/mmdit.hpp

@@ -947,7 +947,7 @@ struct MMDiTRunner : public DiffusionModelRunner {
 
     static void load_from_file_and_test(const std::string& file_path) {
         // ggml_backend_t backend    = ggml_backend_cuda_init(0);
-        ggml_backend_t backend             = ggml_backend_cpu_init();
+        ggml_backend_t backend             = sd_backend_cpu_init();
         ggml_type model_data_type          = GGML_TYPE_F16;
         std::shared_ptr<MMDiTRunner> mmdit = std::make_shared<MMDiTRunner>(backend, backend);
         {

src/qwen_image.hpp

@@ -683,7 +683,7 @@ namespace Qwen {
             // cuda q8: pass
             // cuda q8 fa: pass
             // ggml_backend_t backend    = ggml_backend_cuda_init(0);
-            ggml_backend_t backend    = ggml_backend_cpu_init();
+            ggml_backend_t backend    = sd_backend_cpu_init();
             ggml_type model_data_type = GGML_TYPE_Q8_0;
 
             ModelLoader model_loader;

src/stable-diffusion.cpp

@@ -109,6 +109,19 @@ const char* sampling_methods_str[] = {
 
 /*================================================== Helper Functions ================================================*/
 
+static bool sd_version_supports_ref_latent_img_cfg(SDVersion version) {
+    return version == VERSION_FLUX ||
+           sd_version_is_flux2(version) ||
+           sd_version_is_qwen_image(version) ||
+           sd_version_is_longcat(version) ||
+           sd_version_is_z_image(version);
+}
+
+static bool sd_version_supports_img_cfg(SDVersion version, bool has_ref_images) {
+    return sd_version_is_inpaint_or_unet_edit(version) ||
+           (has_ref_images && sd_version_supports_ref_latent_img_cfg(version));
+}
+
 void calculate_alphas_cumprod(float* alphas_cumprod,
                               float linear_start = 0.00085f,
                               float linear_end   = 0.0120f,
@@ -1095,7 +1108,7 @@ public:
                 if (module_backend == nullptr) {
                     return false;
                 }
-                if (ggml_backend_is_cpu(module_backend)) {
+                if (sd_backend_is_cpu(module_backend)) {
                     total_params_ram_size += size;
                 } else {
                     total_params_vram_size += size;
@@ -1110,7 +1123,7 @@ public:
                 if (module_backend == nullptr) {
                     return "N/A";
                 }
-                return ggml_backend_is_cpu(module_backend) ? "RAM" : "VRAM";
+                return sd_backend_is_cpu(module_backend) ? "RAM" : "VRAM";
             };
 
             if (!add_params_memory(clip_params_mem_size, SDBackendModule::TE) ||
@@ -2059,13 +2072,19 @@ public:
                                     cond,
                                     &controls);
 
+            static const std::vector<sd::Tensor<float>> empty_ref_latents;
+            bool uncond_without_ref_latents = !img_cond.empty() &&
+                                              !ref_latents.empty() &&
+                                              sd_version_supports_ref_latent_img_cfg(version);
+
             auto run_condition = [&](const SDCondition& condition,
-                                     const sd::Tensor<float>* c_concat_override = nullptr,
-                                     const std::vector<int>* local_skip_layers  = nullptr) -> sd::Tensor<float> {
+                                     const sd::Tensor<float>* c_concat_override                 = nullptr,
+                                     const std::vector<int>* local_skip_layers                  = nullptr,
+                                     const std::vector<sd::Tensor<float>>* ref_latents_override = nullptr) -> sd::Tensor<float> {
                 diffusion_params.context     = condition.c_crossattn.empty() ? nullptr : &condition.c_crossattn;
                 diffusion_params.c_concat    = c_concat_override != nullptr ? c_concat_override : (condition.c_concat.empty() ? nullptr : &condition.c_concat);
                 diffusion_params.y           = condition.c_vector.empty() ? nullptr : &condition.c_vector;
-                diffusion_params.ref_latents = condition.c_ref_images.empty() ? &ref_latents : &condition.c_ref_images;
+                diffusion_params.ref_latents = ref_latents_override != nullptr ? ref_latents_override : (condition.c_ref_images.empty() ? &ref_latents : &condition.c_ref_images);
 
                 if (sd_version_is_unet(version)) {
                     diffusion_params.extra = UNetDiffusionExtra{-1, &controls, control_strength};
@@ -2140,7 +2159,10 @@ public:
                     LOG_DEBUG("Skipping layers at uncond step %d\n", step);
                     uncond_skip_layers = &skip_layer_guidance.layers();
                 }
-                uncond_out = run_condition(uncond, nullptr, uncond_skip_layers);
+                uncond_out = run_condition(uncond,
+                                           nullptr,
+                                           uncond_skip_layers,
+                                           uncond_without_ref_latents ? &empty_ref_latents : nullptr);
                 if (uncond_out.empty()) {
                     return {};
                 }
@@ -3149,6 +3171,7 @@ struct GenerationRequest {
     bool use_img_cond                        = false;
     bool use_high_noise_uncond               = false;
     bool use_high_noise_img_cond             = false;
+    bool has_ref_images                      = false;
     const sd_cache_params_t* cache_params    = nullptr;
     int batch_count                          = 1;
     int shifted_timestep                     = 0;
@@ -3182,6 +3205,7 @@ struct GenerationRequest {
         eta                         = sd_img_gen_params->sample_params.eta;
         increase_ref_index          = sd_img_gen_params->increase_ref_index;
         auto_resize_ref_image       = sd_img_gen_params->auto_resize_ref_image;
+        has_ref_images              = sd_img_gen_params->ref_images_count > 0;
         guidance                    = sd_img_gen_params->sample_params.guidance;
         pm_params                   = sd_img_gen_params->pm_params;
         hires                       = sd_img_gen_params->hires;
@@ -3305,17 +3329,22 @@ struct GenerationRequest {
                                  sd_guidance_params_t* guidance,
                                  bool* use_uncond,
                                  bool* use_img_cond,
+                                 bool has_ref_images,
                                  const char* stage_name = nullptr) {
         GGML_ASSERT(guidance != nullptr);
         GGML_ASSERT(use_uncond != nullptr);
         GGML_ASSERT(use_img_cond != nullptr);
         // out_uncond + text_cfg_scale * (out_cond - out_img_cond) + image_cfg_scale * (out_img_cond - out_uncond)
         // img_cfg == txt_cfg means that img_cfg is not used
+        bool img_cfg_was_unset = !std::isfinite(guidance->img_cfg);
         if (!std::isfinite(guidance->img_cfg)) {
             guidance->img_cfg = guidance->txt_cfg;
         }
 
-        if (!sd_version_is_inpaint_or_unet_edit(sd_ctx->sd->version)) {
+        if (!sd_version_supports_img_cfg(sd_ctx->sd->version, has_ref_images)) {
+            if (!img_cfg_was_unset && guidance->img_cfg != guidance->txt_cfg) {
+                LOG_WARN("2-conditioning CFG is not supported with this model, disabling it for better performance");
+            }
             guidance->img_cfg = guidance->txt_cfg;
         }
 
@@ -3344,12 +3373,13 @@ struct GenerationRequest {
         resolve_hires();
         seed = resolve_seed(seed);
 
-        resolve_guidance(sd_ctx, &guidance, &use_uncond, &use_img_cond);
+        resolve_guidance(sd_ctx, &guidance, &use_uncond, &use_img_cond, has_ref_images);
         if (sd_ctx->sd->high_noise_diffusion_model) {
             resolve_guidance(sd_ctx,
                              &high_noise_guidance,
                              &use_high_noise_uncond,
                              &use_high_noise_img_cond,
+                             has_ref_images,
                              "high noise: ");
         }
 
@@ -3949,6 +3979,7 @@ static std::optional<ImageGenerationLatents> prepare_image_generation_latents(sd
         LOG_WARN("This model needs at least one reference image; using an empty reference");
         ref_images.push_back(sd::zeros<float>({request->width, request->height, 3, 1}));
         request->guidance.img_cfg = request->guidance.txt_cfg;
+        request->use_img_cond     = false;
     }
 
     if (!ref_images.empty()) {
@@ -4104,6 +4135,10 @@ static std::optional<ImageGenerationEmbeds> prepare_image_generation_embeds(sd_c
         cond.c_concat = latents->concat_latent;  // TODO: optimize
     }
 
+    bool use_ref_latent_img_cfg = request->use_img_cond &&
+                                  !latents->ref_images.empty() &&
+                                  sd_version_supports_ref_latent_img_cfg(sd_ctx->sd->version);
+
     SDCondition uncond;
     if (request->use_uncond || request->use_high_noise_uncond) {
         bool zero_out_masked = false;
@@ -4121,6 +4156,23 @@ static std::optional<ImageGenerationEmbeds> prepare_image_generation_embeds(sd_c
         }
     }
 
+    SDCondition img_cond;
+    if (request->use_img_cond) {
+        if (use_ref_latent_img_cfg) {
+            img_cond = uncond;
+
+            std::vector<sd::Tensor<float>> empty_ref_images;
+            condition_params.ref_images = &empty_ref_images;
+            uncond                      = sd_ctx->sd->cond_stage_model->get_learned_condition(sd_ctx->sd->n_threads,
+                                                                         condition_params);
+            if (uncond.c_concat.empty()) {
+                uncond.c_concat = latents->uncond_concat_latent;  // TODO: optimize
+            }
+        } else {
+            img_cond = SDCondition(uncond.c_crossattn, uncond.c_vector, cond.c_concat);
+        }
+    }
+
     int64_t t1 = ggml_time_ms();
     LOG_INFO("get_learned_condition completed, taking %.2fs", (t1 - prepare_start_ms) * 1.0f / 1000);
 
@@ -4129,9 +4181,7 @@ static std::optional<ImageGenerationEmbeds> prepare_image_generation_embeds(sd_c
     }
 
     ImageGenerationEmbeds embeds;
-    if (request->use_img_cond) {
-        embeds.img_cond = SDCondition(uncond.c_crossattn, uncond.c_vector, cond.c_concat);
-    }
+    embeds.img_cond = std::move(img_cond);
     embeds.cond    = std::move(cond);
     embeds.uncond  = std::move(uncond);
     embeds.id_cond = std::move(id_cond);

src/t5.hpp

@@ -563,7 +563,7 @@ struct T5Embedder {
         // cuda f32: pass
         // cuda q8_0: pass
         // ggml_backend_t backend = ggml_backend_cuda_init(0);
-        ggml_backend_t backend    = ggml_backend_cpu_init();
+        ggml_backend_t backend    = sd_backend_cpu_init();
         ggml_type model_data_type = GGML_TYPE_F16;
 
         ModelLoader model_loader;

src/util.cpp

@@ -25,9 +25,7 @@
 #include <unistd.h>
 #endif
 
-#include "ggml-backend.h"
 #include "ggml.h"
-#include "ggml_extend_backend.h"
 #include "stable-diffusion.h"
 
 bool ends_with(const std::string& str, const std::string& ending) {
@@ -974,30 +972,3 @@ std::vector<std::pair<std::string, float>> split_quotation_attention(
     }
     return result;
 }
-
-// namespace is needed to avoid conflicts with ggml_backend_extend.hpp
-namespace ggml_cpu {
-#include "ggml-cpu.h"
-}
-
-const char* sd_get_system_info() {
-    using namespace ggml_cpu;
-    static char buffer[1024];
-    std::stringstream ss;
-    ss << "System Info: \n";
-    ss << "    SSE3 = " << ggml_cpu_has_sse3() << " | ";
-    ss << "    AVX = " << ggml_cpu_has_avx() << " | ";
-    ss << "    AVX2 = " << ggml_cpu_has_avx2() << " | ";
-    ss << "    AVX512 = " << ggml_cpu_has_avx512() << " | ";
-    ss << "    AVX512_VBMI = " << ggml_cpu_has_avx512_vbmi() << " | ";
-    ss << "    AVX512_VNNI = " << ggml_cpu_has_avx512_vnni() << " | ";
-    ss << "    FMA = " << ggml_cpu_has_fma() << " | ";
-    ss << "    NEON = " << ggml_cpu_has_neon() << " | ";
-    ss << "    ARM_FMA = " << ggml_cpu_has_arm_fma() << " | ";
-    ss << "    F16C = " << ggml_cpu_has_f16c() << " | ";
-    ss << "    FP16_VA = " << ggml_cpu_has_fp16_va() << " | ";
-    ss << "    WASM_SIMD = " << ggml_cpu_has_wasm_simd() << " | ";
-    ss << "    VSX = " << ggml_cpu_has_vsx() << " | ";
-    snprintf(buffer, sizeof(buffer), "%s", ss.str().c_str());
-    return buffer;
-}

src/wan.hpp

@@ -1328,7 +1328,7 @@ namespace WAN {
 
         static void load_from_file_and_test(const std::string& file_path) {
             // ggml_backend_t backend = ggml_backend_cuda_init(0);
-            ggml_backend_t backend            = ggml_backend_cpu_init();
+            ggml_backend_t backend            = sd_backend_cpu_init();
             ggml_type model_data_type         = GGML_TYPE_F16;
             std::shared_ptr<WanVAERunner> vae = std::make_shared<WanVAERunner>(backend, backend, String2TensorStorage{}, "", false, VERSION_WAN2_2_TI2V);
             {
@@ -2348,7 +2348,7 @@ namespace WAN {
 
         static void load_from_file_and_test(const std::string& file_path) {
             // ggml_backend_t backend = ggml_backend_cuda_init(0);
-            ggml_backend_t backend    = ggml_backend_cpu_init();
+            ggml_backend_t backend    = sd_backend_cpu_init();
             ggml_type model_data_type = GGML_TYPE_F16;
             LOG_INFO("loading from '%s'", file_path.c_str());
 

src/z_image.hpp

@@ -615,7 +615,7 @@ namespace ZImage {
             // cuda q8: pass
             // cuda q8 fa: pass
             // ggml_backend_t backend = ggml_backend_cuda_init(0);
-            ggml_backend_t backend    = ggml_backend_cpu_init();
+            ggml_backend_t backend    = sd_backend_cpu_init();
             ggml_type model_data_type = GGML_TYPE_Q8_0;
 
             ModelLoader model_loader;