fix: make tiled VAE reuse the compute buffer (#821)

.gitignore

@@ -1,13 +1,14 @@
 build*/
+cmake-build-*/
 test/
 .vscode/
+.idea/
 .cache/
 *.swp
-.vscode/
 *.bat
 *.bin
 *.exe
 *.gguf
 output*.png
 models*
-*.log
+*.log

CMakeLists.txt

@@ -149,3 +149,7 @@ if (SD_BUILD_EXAMPLES)
     add_subdirectory(examples)
 endif()
 
+set(SD_PUBLIC_HEADERS stable-diffusion.h)
+set_target_properties(${SD_LIB} PROPERTIES PUBLIC_HEADER "${SD_PUBLIC_HEADERS}")
+
+install(TARGETS ${SD_LIB} LIBRARY PUBLIC_HEADER)

README.md

@@ -137,7 +137,9 @@ This provides BLAS acceleration using the ROCm cores of your AMD GPU. Make sure
 Windows User Refer to [docs/hipBLAS_on_Windows.md](docs%2FhipBLAS_on_Windows.md) for a comprehensive guide.
 
 ```
-cmake .. -G "Ninja" -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DAMDGPU_TARGETS=gfx1100 -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON
+export GFX_NAME=$(rocminfo | grep -m 1 -E "gfx[^0]{1}" | sed -e 's/ *Name: *//' | awk '{$1=$1; print}' || echo "rocminfo missing")
+echo $GFX_NAME
+cmake .. -G "Ninja" -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DGPU_TARGETS=$GFX_NAME -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON
 cmake --build . --config Release
 ```
 

denoiser.hpp

@@ -251,6 +251,35 @@ struct KarrasSchedule : SigmaSchedule {
     }
 };
 
+// Close to Beta Schedule, but increadably simple in code.
+struct SmoothStepSchedule : SigmaSchedule {
+    static constexpr float smoothstep(float x) {
+        return x * x * (3.0f - 2.0f * x);
+    }
+
+    std::vector<float> get_sigmas(uint32_t n, float /*sigma_min*/, float /*sigma_max*/, t_to_sigma_t t_to_sigma) override {
+        std::vector<float> result;
+        result.reserve(n + 1);
+
+        const int t_max = TIMESTEPS - 1;
+        if (n == 0) {
+            return result;
+        } else if (n == 1) {
+            result.push_back(t_to_sigma((float)t_max));
+            result.push_back(0.f);
+            return result;
+        }
+
+        for (uint32_t i = 0; i < n; i++) {
+            float u = 1.f - float(i) / float(n);
+            result.push_back(t_to_sigma(std::round(smoothstep(u) * t_max)));
+        }
+
+        result.push_back(0.f);
+        return result;
+    }
+};
+
 struct Denoiser {
     std::shared_ptr<SigmaSchedule> scheduler                                                 = std::make_shared<DiscreteSchedule>();
     virtual float sigma_min()                                                                = 0;

diffusion_model.hpp

@@ -95,8 +95,9 @@ struct MMDiTModel : public DiffusionModel {
 
     MMDiTModel(ggml_backend_t backend,
                bool offload_params_to_cpu,
+               bool flash_attn                     = false,
                const String2GGMLType& tensor_types = {})
-        : mmdit(backend, offload_params_to_cpu, tensor_types, "model.diffusion_model") {
+        : mmdit(backend, offload_params_to_cpu, flash_attn, tensor_types, "model.diffusion_model") {
     }
 
     std::string get_desc() {

docs/lora.md

@@ -10,4 +10,30 @@ Here's a simple example:
 ./bin/sd -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat<lora:marblesh:1>" --lora-model-dir ../models
 ```
 
-`../models/marblesh.safetensors` or `../models/marblesh.ckpt` will be applied to the model
+`../models/marblesh.safetensors` or `../models/marblesh.ckpt` will be applied to the model
+
+# Support matrix
+
+> ℹ️ CUDA `get_rows` support is defined here:  
+> [ggml-org/ggml/src/ggml-cuda/getrows.cu#L156](https://github.com/ggml-org/ggml/blob/7dee1d6a1e7611f238d09be96738388da97c88ed/src/ggml-cuda/getrows.cu#L156)  
+> Currently only the basic types + Q4/Q5/Q8 are implemented. K-quants are **not** supported.
+
+NOTE: The other backends may have different support.
+
+| Quant / Type | CUDA |
+|--------------|------|
+| F32          | ✔️   |
+| F16          | ✔️   |
+| BF16         | ✔️   |
+| I32          | ✔️   |
+| Q4_0         | ✔️   |
+| Q4_1         | ✔️   |
+| Q5_0         | ✔️   |
+| Q5_1         | ✔️   |
+| Q8_0         | ✔️   |
+| Q2_K         | ❌   |
+| Q3_K         | ❌   |
+| Q4_K         | ❌   |
+| Q5_K         | ❌   |
+| Q6_K         | ❌   |
+| Q8_K         | ❌   |

examples/cli/main.cpp

@@ -1,6 +1,7 @@
 #include <stdio.h>
 #include <string.h>
 #include <time.h>
+#include <filesystem>
 #include <functional>
 #include <iostream>
 #include <map>
@@ -237,7 +238,7 @@ void print_usage(int argc, const char* argv[]) {
     printf("  --skip-layers LAYERS               Layers to skip for SLG steps: (default: [7,8,9])\n");
     printf("  --skip-layer-start START           SLG enabling point: (default: 0.01)\n");
     printf("  --skip-layer-end END               SLG disabling point: (default: 0.2)\n");
-    printf("  --scheduler {discrete, karras, exponential, ays, gits} Denoiser sigma scheduler (default: discrete)\n");
+    printf("  --scheduler {discrete, karras, exponential, ays, gits, smoothstep} Denoiser sigma scheduler (default: discrete)\n");
     printf("  --sampling-method {euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd}\n");
     printf("                                     sampling method (default: \"euler_a\")\n");
     printf("  --steps  STEPS                     number of sample steps (default: 20)\n");
@@ -250,7 +251,7 @@ void print_usage(int argc, const char* argv[]) {
     printf("  --high-noise-skip-layers LAYERS    (high noise) Layers to skip for SLG steps: (default: [7,8,9])\n");
     printf("  --high-noise-skip-layer-start      (high noise) SLG enabling point: (default: 0.01)\n");
     printf("  --high-noise-skip-layer-end END    (high noise) SLG disabling point: (default: 0.2)\n");
-    printf("  --high-noise-scheduler {discrete, karras, exponential, ays, gits} Denoiser sigma scheduler (default: discrete)\n");
+    printf("  --high-noise-scheduler {discrete, karras, exponential, ays, gits, smoothstep} Denoiser sigma scheduler (default: discrete)\n");
     printf("  --high-noise-sampling-method {euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd}\n");
     printf("                                     (high noise) sampling method (default: \"euler_a\")\n");
     printf("  --high-noise-steps  STEPS          (high noise) number of sample steps (default: -1 = auto)\n");
@@ -1283,6 +1284,21 @@ int main(int argc, const char* argv[]) {
         }
     }
 
+    // create directory if not exists
+    {
+        namespace fs            = std::filesystem;
+        const fs::path out_path = params.output_path;
+        if (const fs::path out_dir = out_path.parent_path(); !out_dir.empty()) {
+            std::error_code ec;
+            fs::create_directories(out_dir, ec);  // OK if already exists
+            if (ec) {
+                fprintf(stderr, "failed to create directory '%s': %s\n",
+                        out_dir.string().c_str(), ec.message().c_str());
+                return 1;
+            }
+        }
+    }
+
     std::string base_path;
     std::string file_ext;
     std::string file_ext_lower;

ggml_extend.hpp

@@ -56,6 +56,25 @@
 #define __STATIC_INLINE__ static inline
 #endif
 
+__STATIC_INLINE__ void ggml_log_callback_default(ggml_log_level level, const char* text, void*) {
+    switch (level) {
+        case GGML_LOG_LEVEL_DEBUG:
+            LOG_DEBUG(text);
+            break;
+        case GGML_LOG_LEVEL_INFO:
+            LOG_INFO(text);
+            break;
+        case GGML_LOG_LEVEL_WARN:
+            LOG_WARN(text);
+            break;
+        case GGML_LOG_LEVEL_ERROR:
+            LOG_ERROR(text);
+            break;
+        default:
+            LOG_DEBUG(text);
+    }
+}
+
 static_assert(GGML_MAX_NAME >= 128, "GGML_MAX_NAME must be at least 128");
 
 // n-mode trensor-matrix product
@@ -124,13 +143,6 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_kronecker(ggml_context* ctx, struct g
                     b);
 }
 
-__STATIC_INLINE__ void ggml_log_callback_default(ggml_log_level level, const char* text, void* user_data) {
-    (void)level;
-    (void)user_data;
-    fputs(text, stderr);
-    fflush(stderr);
-}
-
 __STATIC_INLINE__ void ggml_tensor_set_f32_randn(struct ggml_tensor* tensor, std::shared_ptr<RNG> rng) {
     uint32_t n                        = (uint32_t)ggml_nelements(tensor);
     std::vector<float> random_numbers = rng->randn(n);

mmdit.hpp

@@ -147,14 +147,16 @@ public:
     int64_t num_heads;
     bool pre_only;
     std::string qk_norm;
+    bool flash_attn;
 
 public:
     SelfAttention(int64_t dim,
                   int64_t num_heads   = 8,
                   std::string qk_norm = "",
                   bool qkv_bias       = false,
-                  bool pre_only       = false)
-        : num_heads(num_heads), pre_only(pre_only), qk_norm(qk_norm) {
+                  bool pre_only       = false,
+                  bool flash_attn     = false)
+        : num_heads(num_heads), pre_only(pre_only), qk_norm(qk_norm), flash_attn(flash_attn) {
         int64_t d_head = dim / num_heads;
         blocks["qkv"]  = std::shared_ptr<GGMLBlock>(new Linear(dim, dim * 3, qkv_bias));
         if (!pre_only) {
@@ -206,8 +208,8 @@ public:
                                 ggml_backend_t backend,
                                 struct ggml_tensor* x) {
         auto qkv = pre_attention(ctx, x);
-        x        = ggml_nn_attention_ext(ctx, backend, qkv[0], qkv[1], qkv[2], num_heads);  // [N, n_token, dim]
-        x        = post_attention(ctx, x);                                                  // [N, n_token, dim]
+        x        = ggml_nn_attention_ext(ctx, backend, qkv[0], qkv[1], qkv[2], num_heads, NULL, false, false, true);  // [N, n_token, dim]
+        x        = post_attention(ctx, x);                                                                            // [N, n_token, dim]
         return x;
     }
 };
@@ -232,6 +234,7 @@ public:
     int64_t num_heads;
     bool pre_only;
     bool self_attn;
+    bool flash_attn;
 
 public:
     DismantledBlock(int64_t hidden_size,
@@ -240,16 +243,17 @@ public:
                     std::string qk_norm = "",
                     bool qkv_bias       = false,
                     bool pre_only       = false,
-                    bool self_attn      = false)
+                    bool self_attn      = false,
+                    bool flash_attn     = false)
         : num_heads(num_heads), pre_only(pre_only), self_attn(self_attn) {
         // rmsnorm is always Flase
         // scale_mod_only is always Flase
         // swiglu is always Flase
         blocks["norm1"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-06f, false));
-        blocks["attn"]  = std::shared_ptr<GGMLBlock>(new SelfAttention(hidden_size, num_heads, qk_norm, qkv_bias, pre_only));
+        blocks["attn"]  = std::shared_ptr<GGMLBlock>(new SelfAttention(hidden_size, num_heads, qk_norm, qkv_bias, pre_only, flash_attn));
 
         if (self_attn) {
-            blocks["attn2"] = std::shared_ptr<GGMLBlock>(new SelfAttention(hidden_size, num_heads, qk_norm, qkv_bias, false));
+            blocks["attn2"] = std::shared_ptr<GGMLBlock>(new SelfAttention(hidden_size, num_heads, qk_norm, qkv_bias, false, flash_attn));
         }
 
         if (!pre_only) {
@@ -435,8 +439,8 @@ public:
             auto qkv2          = std::get<1>(qkv_intermediates);
             auto intermediates = std::get<2>(qkv_intermediates);
 
-            auto attn_out  = ggml_nn_attention_ext(ctx, backend, qkv[0], qkv[1], qkv[2], num_heads);     // [N, n_token, dim]
-            auto attn2_out = ggml_nn_attention_ext(ctx, backend, qkv2[0], qkv2[1], qkv2[2], num_heads);  // [N, n_token, dim]
+            auto attn_out  = ggml_nn_attention_ext(ctx, backend, qkv[0], qkv[1], qkv[2], num_heads, NULL, false, false, flash_attn);     // [N, n_token, dim]
+            auto attn2_out = ggml_nn_attention_ext(ctx, backend, qkv2[0], qkv2[1], qkv2[2], num_heads, NULL, false, false, flash_attn);  // [N, n_token, dim]
             x              = post_attention_x(ctx,
                                               attn_out,
                                               attn2_out,
@@ -452,7 +456,7 @@ public:
             auto qkv               = qkv_intermediates.first;
             auto intermediates     = qkv_intermediates.second;
 
-            auto attn_out = ggml_nn_attention_ext(ctx, backend, qkv[0], qkv[1], qkv[2], num_heads);  // [N, n_token, dim]
+            auto attn_out = ggml_nn_attention_ext(ctx, backend, qkv[0], qkv[1], qkv[2], num_heads, NULL, false, false, flash_attn);  // [N, n_token, dim]
             x             = post_attention(ctx,
                                            attn_out,
                                            intermediates[0],
@@ -468,6 +472,7 @@ public:
 __STATIC_INLINE__ std::pair<struct ggml_tensor*, struct ggml_tensor*>
 block_mixing(struct ggml_context* ctx,
              ggml_backend_t backend,
+             bool flash_attn,
              struct ggml_tensor* context,
              struct ggml_tensor* x,
              struct ggml_tensor* c,
@@ -497,8 +502,8 @@ block_mixing(struct ggml_context* ctx,
         qkv.push_back(ggml_concat(ctx, context_qkv[i], x_qkv[i], 1));
     }
 
-    auto attn         = ggml_nn_attention_ext(ctx, backend, qkv[0], qkv[1], qkv[2], x_block->num_heads);  // [N, n_context + n_token, hidden_size]
-    attn              = ggml_cont(ctx, ggml_permute(ctx, attn, 0, 2, 1, 3));                              // [n_context + n_token, N, hidden_size]
+    auto attn         = ggml_nn_attention_ext(ctx, backend, qkv[0], qkv[1], qkv[2], x_block->num_heads, NULL, false, false, flash_attn);  // [N, n_context + n_token, hidden_size]
+    attn              = ggml_cont(ctx, ggml_permute(ctx, attn, 0, 2, 1, 3));                                                              // [n_context + n_token, N, hidden_size]
     auto context_attn = ggml_view_3d(ctx,
                                      attn,
                                      attn->ne[0],
@@ -556,6 +561,8 @@ block_mixing(struct ggml_context* ctx,
 }
 
 struct JointBlock : public GGMLBlock {
+    bool flash_attn;
+
 public:
     JointBlock(int64_t hidden_size,
                int64_t num_heads,
@@ -563,9 +570,11 @@ public:
                std::string qk_norm = "",
                bool qkv_bias       = false,
                bool pre_only       = false,
-               bool self_attn_x    = false) {
-        blocks["context_block"] = std::shared_ptr<GGMLBlock>(new DismantledBlock(hidden_size, num_heads, mlp_ratio, qk_norm, qkv_bias, pre_only));
-        blocks["x_block"]       = std::shared_ptr<GGMLBlock>(new DismantledBlock(hidden_size, num_heads, mlp_ratio, qk_norm, qkv_bias, false, self_attn_x));
+               bool self_attn_x    = false,
+               bool flash_attn     = false)
+        : flash_attn(flash_attn) {
+        blocks["context_block"] = std::shared_ptr<GGMLBlock>(new DismantledBlock(hidden_size, num_heads, mlp_ratio, qk_norm, qkv_bias, pre_only, false, flash_attn));
+        blocks["x_block"]       = std::shared_ptr<GGMLBlock>(new DismantledBlock(hidden_size, num_heads, mlp_ratio, qk_norm, qkv_bias, false, self_attn_x, flash_attn));
     }
 
     std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(struct ggml_context* ctx,
@@ -576,7 +585,7 @@ public:
         auto context_block = std::dynamic_pointer_cast<DismantledBlock>(blocks["context_block"]);
         auto x_block       = std::dynamic_pointer_cast<DismantledBlock>(blocks["x_block"]);
 
-        return block_mixing(ctx, backend, context, x, c, context_block, x_block);
+        return block_mixing(ctx, backend, flash_attn, context, x, c, context_block, x_block);
     }
 };
 
@@ -634,6 +643,7 @@ protected:
     int64_t context_embedder_out_dim = 1536;
     int64_t hidden_size;
     std::string qk_norm;
+    bool flash_attn = false;
 
     void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types = {}, std::string prefix = "") {
         enum ggml_type wtype = GGML_TYPE_F32;
@@ -641,7 +651,8 @@ protected:
     }
 
 public:
-    MMDiT(const String2GGMLType& tensor_types = {}) {
+    MMDiT(bool flash_attn = false, const String2GGMLType& tensor_types = {})
+        : flash_attn(flash_attn) {
         // input_size is always None
         // learn_sigma is always False
         // register_length is alwalys 0
@@ -709,7 +720,8 @@ public:
                                                                                                     qk_norm,
                                                                                                     true,
                                                                                                     i == depth - 1,
-                                                                                                    i <= d_self));
+                                                                                                    i <= d_self,
+                                                                                                    flash_attn));
         }
 
         blocks["final_layer"] = std::shared_ptr<GGMLBlock>(new FinalLayer(hidden_size, patch_size, out_channels));
@@ -856,9 +868,10 @@ struct MMDiTRunner : public GGMLRunner {
 
     MMDiTRunner(ggml_backend_t backend,
                 bool offload_params_to_cpu,
+                bool flash_attn,
                 const String2GGMLType& tensor_types = {},
                 const std::string prefix            = "")
-        : GGMLRunner(backend, offload_params_to_cpu), mmdit(tensor_types) {
+        : GGMLRunner(backend, offload_params_to_cpu), mmdit(flash_attn, tensor_types) {
         mmdit.init(params_ctx, tensor_types, prefix);
     }
 
@@ -957,7 +970,7 @@ struct MMDiTRunner : public GGMLRunner {
         // ggml_backend_t backend    = ggml_backend_cuda_init(0);
         ggml_backend_t backend             = ggml_backend_cpu_init();
         ggml_type model_data_type          = GGML_TYPE_F16;
-        std::shared_ptr<MMDiTRunner> mmdit = std::shared_ptr<MMDiTRunner>(new MMDiTRunner(backend, false));
+        std::shared_ptr<MMDiTRunner> mmdit = std::shared_ptr<MMDiTRunner>(new MMDiTRunner(backend, false, false));
         {
             LOG_INFO("loading from '%s'", file_path.c_str());
 

stable-diffusion.cpp

@@ -145,7 +145,6 @@ public:
 #endif
 #ifdef SD_USE_METAL
         LOG_DEBUG("Using Metal backend");
-        ggml_log_set(ggml_log_callback_default, nullptr);
         backend = ggml_backend_metal_init();
 #endif
 #ifdef SD_USE_VULKAN
@@ -192,6 +191,8 @@ public:
             rng = std::make_shared<PhiloxRNG>();
         }
 
+        ggml_log_set(ggml_log_callback_default, nullptr);
+
         init_backend();
 
         ModelLoader model_loader;
@@ -330,7 +331,7 @@ public:
             if (sd_version_is_dit(version)) {
                 use_t5xxl = true;
             }
-            if (!ggml_backend_is_cpu(backend) && use_t5xxl) {
+            if (!clip_on_cpu && !ggml_backend_is_cpu(backend) && use_t5xxl) {
                 LOG_WARN(
                     "!!!It appears that you are using the T5 model. Some backends may encounter issues with it."
                     "If you notice that the generated images are completely black,"
@@ -344,14 +345,12 @@ public:
                 LOG_INFO("Using flash attention in the diffusion model");
             }
             if (sd_version_is_sd3(version)) {
-                if (sd_ctx_params->diffusion_flash_attn) {
-                    LOG_WARN("flash attention in this diffusion model is currently unsupported!");
-                }
                 cond_stage_model = std::make_shared<SD3CLIPEmbedder>(clip_backend,
                                                                      offload_params_to_cpu,
                                                                      model_loader.tensor_storages_types);
                 diffusion_model  = std::make_shared<MMDiTModel>(backend,
                                                                offload_params_to_cpu,
+                                                               sd_ctx_params->diffusion_flash_attn,
                                                                model_loader.tensor_storages_types);
             } else if (sd_version_is_flux(version)) {
                 bool is_chroma = false;
@@ -362,6 +361,15 @@ public:
                     }
                 }
                 if (is_chroma) {
+                    if (sd_ctx_params->diffusion_flash_attn && sd_ctx_params->chroma_use_dit_mask) {
+                        LOG_WARN(
+                            "!!!It looks like you are using Chroma with flash attention. "
+                            "This is currently unsupported. "
+                            "If you find that the generated images are broken, "
+                            "try either disabling flash attention or specifying "
+                            "--chroma-disable-dit-mask as a workaround.");
+                    }
+
                     cond_stage_model = std::make_shared<T5CLIPEmbedder>(clip_backend,
                                                                         offload_params_to_cpu,
                                                                         model_loader.tensor_storages_types,
@@ -744,6 +752,10 @@ public:
                 denoiser->scheduler          = std::make_shared<GITSSchedule>();
                 denoiser->scheduler->version = version;
                 break;
+            case SMOOTHSTEP:
+                LOG_INFO("Running with SmoothStep scheduler");
+                denoiser->scheduler = std::make_shared<SmoothStepSchedule>();
+                break;
             case DEFAULT:
                 // Don't touch anything.
                 break;
@@ -1527,6 +1539,7 @@ const char* schedule_to_str[] = {
     "exponential",
     "ays",
     "gits",
+    "smoothstep",
 };
 
 const char* sd_schedule_name(enum scheduler_t scheduler) {
@@ -1546,7 +1559,7 @@ enum scheduler_t str_to_schedule(const char* str) {
 }
 
 void sd_ctx_params_init(sd_ctx_params_t* sd_ctx_params) {
-    memset((void*)sd_ctx_params, 0, sizeof(sd_ctx_params_t));
+    *sd_ctx_params                         = {};
     sd_ctx_params->vae_decode_only         = true;
     sd_ctx_params->vae_tiling              = false;
     sd_ctx_params->free_params_immediately = true;
@@ -1630,6 +1643,7 @@ char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params) {
 }
 
 void sd_sample_params_init(sd_sample_params_t* sample_params) {
+    *sample_params                             = {};
     sample_params->guidance.txt_cfg            = 7.0f;
     sample_params->guidance.img_cfg            = INFINITY;
     sample_params->guidance.distilled_guidance = 3.5f;
@@ -1676,9 +1690,9 @@ char* sd_sample_params_to_str(const sd_sample_params_t* sample_params) {
 }
 
 void sd_img_gen_params_init(sd_img_gen_params_t* sd_img_gen_params) {
-    memset((void*)sd_img_gen_params, 0, sizeof(sd_img_gen_params_t));
-    sd_img_gen_params->clip_skip = -1;
+    *sd_img_gen_params = {};
     sd_sample_params_init(&sd_img_gen_params->sample_params);
+    sd_img_gen_params->clip_skip        = -1;
     sd_img_gen_params->ref_images_count = 0;
     sd_img_gen_params->width            = 512;
     sd_img_gen_params->height           = 512;
@@ -1735,7 +1749,7 @@ char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params) {
 }
 
 void sd_vid_gen_params_init(sd_vid_gen_params_t* sd_vid_gen_params) {
-    memset((void*)sd_vid_gen_params, 0, sizeof(sd_vid_gen_params_t));
+    *sd_vid_gen_params = {};
     sd_sample_params_init(&sd_vid_gen_params->sample_params);
     sd_sample_params_init(&sd_vid_gen_params->high_noise_sample_params);
     sd_vid_gen_params->high_noise_sample_params.sample_steps = -1;
@@ -1759,6 +1773,7 @@ sd_ctx_t* new_sd_ctx(const sd_ctx_params_t* sd_ctx_params) {
 
     sd_ctx->sd = new StableDiffusionGGML();
     if (sd_ctx->sd == NULL) {
+        free(sd_ctx);
         return NULL;
     }
 
@@ -2361,7 +2376,7 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_g
                                                         sd_img_gen_params->control_strength,
                                                         sd_img_gen_params->style_strength,
                                                         sd_img_gen_params->normalize_input,
-                                                        sd_img_gen_params->input_id_images_path,
+                                                        SAFE_STR(sd_img_gen_params->input_id_images_path),
                                                         ref_latents,
                                                         sd_img_gen_params->increase_ref_index,
                                                         concat_latent,

stable-diffusion.h

@@ -57,6 +57,7 @@ enum scheduler_t {
     EXPONENTIAL,
     AYS,
     GITS,
+    SMOOTHSTEP,
     SCHEDULE_COUNT
 };
 

upscaler.cpp

@@ -19,13 +19,13 @@ struct UpscalerGGML {
 
     bool load_from_file(const std::string& esrgan_path,
                         bool offload_params_to_cpu) {
+        ggml_log_set(ggml_log_callback_default, nullptr);
 #ifdef SD_USE_CUDA
         LOG_DEBUG("Using CUDA backend");
         backend = ggml_backend_cuda_init(0);
 #endif
 #ifdef SD_USE_METAL
         LOG_DEBUG("Using Metal backend");
-        ggml_log_set(ggml_log_callback_default, nullptr);
         backend = ggml_backend_metal_init();
 #endif
 #ifdef SD_USE_VULKAN

util.cpp

@@ -414,7 +414,10 @@ void log_printf(sd_log_level_t level, const char* file, int line, const char* fo
     if (written >= 0 && written < LOG_BUFFER_SIZE) {
         vsnprintf(log_buffer + written, LOG_BUFFER_SIZE - written, format, args);
     }
-    strncat(log_buffer, "\n", LOG_BUFFER_SIZE - strlen(log_buffer));
+    size_t len = strlen(log_buffer);
+    if (log_buffer[len - 1] != '\n') {
+        strncat(log_buffer, "\n", LOG_BUFFER_SIZE - len);
+    }
 
     if (sd_log_cb) {
         sd_log_cb(level, log_buffer, sd_log_cb_data);

vae.hpp

@@ -588,7 +588,7 @@ struct AutoEncoderKL : public VAE {
         };
         // ggml_set_f32(z, 0.5f);
         // print_ggml_tensor(z);
-        GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
+        GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
     }
 
     void test() {