feat: auto-detect max VRAM budget with --max-vram -1 (#1498)

CMakeLists.txt

@@ -69,6 +69,12 @@ option(SD_BUILD_SHARED_GGML_LIB      "sd: build ggml as a separate shared lib" O
 option(SD_USE_SYSTEM_GGML            "sd: use system-installed GGML library" OFF)
 #option(SD_BUILD_SERVER               "sd: build server example"                           ON)
 
+set(CMAKE_C_STANDARD 11)
+set(CMAKE_C_STANDARD_REQUIRED true)
+
+set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CXX_STANDARD_REQUIRED true)
+
 if(SD_CUDA)
     message("-- Use CUDA as backend stable-diffusion")
     set(GGML_CUDA ON)

examples/cli/README.md

@@ -55,7 +55,7 @@ Context Options:
                                            then threads will be set to the number of CPU physical cores
   --chroma-t5-mask-pad <int>               t5 mask pad size of chroma
   --max-vram <float>                       maximum VRAM budget in GiB for graph-cut segmented execution. 0 disables
-                                           graph splitting
+                                           graph splitting; -1 auto-detects free VRAM minus 1 GiB
   --force-sdxl-vae-conv-scale              force use of conv scale on sdxl vae
   --offload-to-cpu                         place the weights in RAM to save VRAM, and automatically load them into VRAM
                                            when needed

examples/common/common.cpp

@@ -397,7 +397,7 @@ ArgOptions SDContextParams::get_options() {
     options.float_options = {
         {"",
          "--max-vram",
-         "maximum VRAM budget in GiB for graph-cut segmented execution. 0 disables graph splitting",
+         "maximum VRAM budget in GiB for graph-cut segmented execution. 0 disables graph splitting; -1 auto-detects free VRAM minus 1 GiB",
          &max_vram},
     };
 

examples/server/README.md

@@ -157,7 +157,7 @@ Context Options:
                                            then threads will be set to the number of CPU physical cores
   --chroma-t5-mask-pad <int>               t5 mask pad size of chroma
   --max-vram <float>                       maximum VRAM budget in GiB for graph-cut segmented execution. 0 disables
-                                           graph splitting
+                                           graph splitting; -1 auto-detects free VRAM minus 1 GiB
   --force-sdxl-vae-conv-scale              force use of conv scale on sdxl vae
   --offload-to-cpu                         place the weights in RAM to save VRAM, and automatically load them into VRAM
                                            when needed

include/stable-diffusion.h

@@ -205,7 +205,7 @@ typedef struct {
     bool chroma_use_t5_mask;
     int chroma_t5_mask_pad;
     bool qwen_image_zero_cond_t;
-    float max_vram;
+    float max_vram;  // GiB budget for graph-cut segmented param offload (0 = disabled, -1 = auto free VRAM minus 1 GiB)
 } sd_ctx_params_t;
 
 typedef struct {

src/denoiser.hpp

@@ -824,45 +824,33 @@ static std::tuple<float, float, float> get_ancestral_step(float sigma_from,
 static sd::Tensor<float> sample_euler_ancestral(denoise_cb_t model,
                                                 sd::Tensor<float> x,
                                                 const std::vector<float>& sigmas,
-                                                std::shared_ptr<RNG> rng,
+                                                std::shared_ptr<RNG> rng = nullptr,
-                                                float eta) {
+                                                bool is_flow_denoiser    = false,
+                                                float eta                = 0.f) {
     int steps = static_cast<int>(sigmas.size()) - 1;
     for (int i = 0; i < steps; i++) {
         float sigma       = sigmas[i];
+        float sigma_to    = sigmas[i + 1];
         auto denoised_opt = model(x, sigma, i + 1, nullptr);
         if (denoised_opt.empty()) {
             return {};
         }
         sd::Tensor<float> denoised = std::move(denoised_opt);
-        sd::Tensor<float> d         = (x - denoised) / sigma;
+        if (sigma_to == 0.f) {
-        auto [sigma_down, sigma_up] = get_ancestral_step(sigmas[i], sigmas[i + 1], eta);
+            x = denoised;
-        x += d * (sigma_down - sigmas[i]);
+        } else if (eta == 0.f) {
-        if (sigmas[i + 1] > 0) {
+            float sigma_ratio = sigma_to / sigma;
-            x += sd::Tensor<float>::randn_like(x, rng) * sigma_up;
+            x                 = sigma_ratio * x + (1.0 - sigma_ratio) * denoised;
-        }
+        } else {
-    }
+            auto [sigma_down, sigma_up, alpha_scale] = get_ancestral_step(sigma, sigma_to, eta, is_flow_denoiser);
-    return x;
-}
-
-static sd::Tensor<float> sample_euler_flow(denoise_cb_t model,
-                                           sd::Tensor<float> x,
-                                           const std::vector<float>& sigmas,
-                                           std::shared_ptr<RNG> rng,
-                                           float eta) {
-    int steps = static_cast<int>(sigmas.size()) - 1;
-    for (int i = 0; i < steps; i++) {
-        float sigma       = sigmas[i];
-        auto denoised_opt = model(x, sigma, i + 1, nullptr);
-        if (denoised_opt.empty()) {
-            return {};
-        }
-        sd::Tensor<float> denoised               = std::move(denoised_opt);
-        auto [sigma_down, sigma_up, alpha_scale] = get_ancestral_step_flow(sigma, sigmas[i + 1], eta);
             float sigma_ratio                        = sigma_down / sigma;
             x                                        = sigma_ratio * x + (1.0f - sigma_ratio) * denoised;
-
+            if (sigma_up > 0.f) {
-        if (sigma_up > 0.0f) {
+                if (is_flow_denoiser) {
-            x = alpha_scale * x + sd::Tensor<float>::randn_like(x, rng) * sigma_up;
+                    x *= alpha_scale;
+                }
+                x += sd::Tensor<float>::randn_like(x, rng) * sigma_up;
+            }
         }
     }
     return x;
@@ -1633,46 +1621,6 @@ static sd::Tensor<float> sample_er_sde(denoise_cb_t model,
     return x;
 }
 
-static sd::Tensor<float> sample_ddim_trailing(denoise_cb_t model,
-                                              sd::Tensor<float> x,
-                                              const std::vector<float>& sigmas,
-                                              std::shared_ptr<RNG> rng,
-                                              float eta) {
-    int steps = static_cast<int>(sigmas.size()) - 1;
-    for (int i = 0; i < steps; i++) {
-        float sigma    = sigmas[i];
-        float sigma_to = sigmas[i + 1];
-
-        auto model_output_opt = model(x, sigma, i + 1, nullptr);
-        if (model_output_opt.empty()) {
-            return {};
-        }
-        sd::Tensor<float> model_output = std::move(model_output_opt);
-        model_output                   = (x - model_output) * (1.0f / sigma);
-
-        float alpha_prod_t      = 1.0f / (sigma * sigma + 1.0f);
-        float alpha_prod_t_prev = 1.0f / (sigma_to * sigma_to + 1.0f);
-        float beta_prod_t       = 1.0f - alpha_prod_t;
-
-        sd::Tensor<float> pred_original_sample = ((x / std::sqrt(sigma * sigma + 1)) -
-                                                  std::sqrt(beta_prod_t) * model_output) *
-                                                 (1.0f / std::sqrt(alpha_prod_t));
-
-        float beta_prod_t_prev = 1.0f - alpha_prod_t_prev;
-        float variance         = (beta_prod_t_prev / beta_prod_t) *
-                         (1.0f - alpha_prod_t / alpha_prod_t_prev);
-        float std_dev_t = eta * std::sqrt(variance);
-
-        x = pred_original_sample +
-            std::sqrt((1.0f - alpha_prod_t_prev - std::pow(std_dev_t, 2)) / alpha_prod_t_prev) * model_output;
-
-        if (eta > 0) {
-            x += std_dev_t / std::sqrt(alpha_prod_t_prev) * sd::Tensor<float>::randn_like(x, rng);
-        }
-    }
-    return x;
-}
-
 static sd::Tensor<float> sample_tcd(denoise_cb_t model,
                                     sd::Tensor<float> x,
                                     const std::vector<float>& sigmas,
@@ -1715,12 +1663,12 @@ static sd::Tensor<float> sample_tcd(denoise_cb_t model,
         int timestep_s    = (int)floor((1 - eta) * prev_timestep);
         float sigma       = sigmas[i];
 
-        auto model_output_opt = model(x, sigma, i + 1, nullptr);
+        auto denoised_opt = model(x, sigma, i + 1, nullptr);
-        if (model_output_opt.empty()) {
+        if (denoised_opt.empty()) {
             return {};
         }
-        sd::Tensor<float> model_output = std::move(model_output_opt);
+        sd::Tensor<float> denoised = std::move(denoised_opt);
-        model_output                   = (x - model_output) * (1.0f / sigma);
+        sd::Tensor<float> d        = (x - denoised) / sigma;
 
         float alpha_prod_t      = 1.0f / (sigma * sigma + 1.0f);
         float beta_prod_t       = 1.0f - alpha_prod_t;
@@ -1728,12 +1676,8 @@ static sd::Tensor<float> sample_tcd(denoise_cb_t model,
         float alpha_prod_s      = static_cast<float>(alphas_cumprod[timestep_s]);
         float beta_prod_s       = 1.0f - alpha_prod_s;
 
-        sd::Tensor<float> pred_original_sample = ((x / std::sqrt(sigma * sigma + 1)) -
+        x = std::sqrt(alpha_prod_s / alpha_prod_t_prev) * denoised +
-                                                  std::sqrt(beta_prod_t) * model_output) *
+            std::sqrt(beta_prod_s / alpha_prod_t_prev) * d;
-                                                 (1.0f / std::sqrt(alpha_prod_t));
-
-        x = std::sqrt(alpha_prod_s / alpha_prod_t_prev) * pred_original_sample +
-            std::sqrt(beta_prod_s / alpha_prod_t_prev) * model_output;
 
         if (eta > 0 && sigma_to > 0.0f) {
             x = std::sqrt(alpha_prod_t_prev / alpha_prod_s) * x +
@@ -1804,10 +1748,7 @@ static sd::Tensor<float> sample_k_diffusion(sample_method_t method,
                                             const char* extra_sample_args) {
     switch (method) {
         case EULER_A_SAMPLE_METHOD:
-            if (is_flow_denoiser)
+            return sample_euler_ancestral(model, std::move(x), sigmas, rng, is_flow_denoiser, eta);
-                return sample_euler_flow(model, std::move(x), sigmas, rng, eta);
-            else
-                return sample_euler_ancestral(model, std::move(x), sigmas, rng, eta);
         case EULER_SAMPLE_METHOD:
             return sample_euler(model, std::move(x), sigmas);
         case HEUN_SAMPLE_METHOD:
@@ -1836,7 +1777,8 @@ static sd::Tensor<float> sample_k_diffusion(sample_method_t method,
         case ER_SDE_SAMPLE_METHOD:
             return sample_er_sde(model, std::move(x), sigmas, rng, is_flow_denoiser, eta);
         case DDIM_TRAILING_SAMPLE_METHOD:
-            return sample_ddim_trailing(model, std::move(x), sigmas, rng, eta);
+            // DDIM is equivalent to Euler Ancestral with the Simple scheduler
+            return sample_euler_ancestral(model, std::move(x), sigmas, rng, is_flow_denoiser, eta);
         case TCD_SAMPLE_METHOD:
             return sample_tcd(model, std::move(x), sigmas, rng, eta);
         case EULER_CFG_PP_SAMPLE_METHOD:

src/ggml_extend.hpp

@@ -2732,6 +2732,9 @@ public:
                 rebuild_params_tensor_set();
                 return true;
             }
+        } else {
+            LOG_DEBUG("%s skipping params allocation (no tensors)", get_desc().c_str());
+            return true;
         }
         params_buffer = ggml_backend_alloc_ctx_tensors(params_ctx, params_backend);
         if (params_buffer == nullptr) {

src/ggml_graph_cut.cpp

@@ -16,6 +16,9 @@
 
 namespace sd::ggml_graph_cut {
 
+    static constexpr double MAX_VRAM_BYTES_PER_GIB      = 1024.0 * 1024.0 * 1024.0;
+    static constexpr size_t MAX_VRAM_AUTO_RESERVE_BYTES = 1024ULL * 1024ULL * 1024ULL;
+
     static std::string graph_cut_tensor_display_name(const ggml_tensor* tensor) {
         if (tensor == nullptr) {
             return "<null>";
@@ -79,6 +82,58 @@ namespace sd::ggml_graph_cut {
                segment.output_bytes;
     }
 
+    size_t max_vram_gib_to_bytes(float max_vram) {
+        if (max_vram <= 0.f) {
+            return 0;
+        }
+        return static_cast<size_t>(static_cast<double>(max_vram) * MAX_VRAM_BYTES_PER_GIB);
+    }
+
+    static float max_vram_bytes_to_gib(size_t max_vram_bytes) {
+        return static_cast<float>(static_cast<double>(max_vram_bytes) / MAX_VRAM_BYTES_PER_GIB);
+    }
+
+    static size_t resolve_auto_max_vram_bytes(ggml_backend_t backend) {
+        if (backend == nullptr) {
+            LOG_WARN("--max-vram -1 requested, but no backend is available; disabling graph splitting");
+            return 0;
+        }
+
+        ggml_backend_dev_t dev = ggml_backend_get_device(backend);
+        if (dev == nullptr) {
+            LOG_WARN("--max-vram -1 requested, but no backend device is available; disabling graph splitting");
+            return 0;
+        }
+        if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) {
+            LOG_WARN("--max-vram -1 requested, but the main backend is CPU; disabling graph splitting");
+            return 0;
+        }
+
+        size_t free_vram  = 0;
+        size_t total_vram = 0;
+        ggml_backend_dev_memory(dev, &free_vram, &total_vram);
+
+        if (free_vram <= MAX_VRAM_AUTO_RESERVE_BYTES) {
+            LOG_WARN("--max-vram -1 requested, but free VRAM is %.2f GiB; reserving 1.00 GiB leaves no graph budget",
+                     free_vram / MAX_VRAM_BYTES_PER_GIB);
+            return 0;
+        }
+
+        const size_t max_vram_bytes = free_vram - MAX_VRAM_AUTO_RESERVE_BYTES;
+        LOG_INFO("--max-vram -1 auto-detected %.2f GiB free VRAM (%.2f GiB total), reserving 1.00 GiB; using %.2f GiB",
+                 free_vram / MAX_VRAM_BYTES_PER_GIB,
+                 total_vram / MAX_VRAM_BYTES_PER_GIB,
+                 max_vram_bytes / MAX_VRAM_BYTES_PER_GIB);
+        return max_vram_bytes;
+    }
+
+    float resolve_max_vram_gib(float max_vram, ggml_backend_t backend) {
+        if (max_vram != -1.f) {
+            return max_vram;
+        }
+        return max_vram_bytes_to_gib(resolve_auto_max_vram_bytes(backend));
+    }
+
     static Segment make_segment_seed(const Plan& plan,
                                      size_t start_segment_index,
                                      size_t end_segment_index) {

src/ggml_graph_cut.h

@@ -83,6 +83,8 @@ namespace sd::ggml_graph_cut {
                                           ggml_cgraph* gf,
                                           const Segment& segment,
                                           const char* log_desc);
+    size_t max_vram_gib_to_bytes(float max_vram);
+    float resolve_max_vram_gib(float max_vram, ggml_backend_t backend);
     Plan build_plan(ggml_backend_t backend,
                     ggml_cgraph* gf,
                     const std::unordered_set<const ggml_tensor*>& params_tensor_set,

src/stable-diffusion.cpp

@@ -1,4 +1,5 @@
 #include "ggml_extend.hpp"
+#include "ggml_graph_cut.h"
 
 #include "model.h"
 #include "rng.hpp"
@@ -209,6 +210,7 @@ public:
         ggml_log_set(ggml_log_callback_default, nullptr);
 
         init_backend();
+        max_vram = sd::ggml_graph_cut::resolve_max_vram_gib(max_vram, backend);
 
         ModelLoader model_loader;
 
@@ -426,9 +428,7 @@ public:
 
         bool clip_on_cpu = sd_ctx_params->keep_clip_on_cpu;
 
-        const size_t max_graph_vram_bytes = max_vram <= 0.f
+        const size_t max_graph_vram_bytes = sd::ggml_graph_cut::max_vram_gib_to_bytes(max_vram);
-                                                ? 0
-                                                : static_cast<size_t>(static_cast<double>(max_vram) * 1024.0 * 1024.0 * 1024.0);
 
         {
             clip_backend = backend;
@@ -3597,9 +3597,7 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
             hires_upscaler                    = std::make_unique<UpscalerGGML>(sd_ctx->sd->n_threads,
                                                             false,
                                                             request.hires.upscale_tile_size);
-            const size_t max_graph_vram_bytes = sd_ctx->sd->max_vram <= 0.f
+            const size_t max_graph_vram_bytes = sd::ggml_graph_cut::max_vram_gib_to_bytes(sd_ctx->sd->max_vram);
-                                                    ? 0
-                                                    : static_cast<size_t>(static_cast<double>(sd_ctx->sd->max_vram) * 1024.0 * 1024.0 * 1024.0);
             hires_upscaler->set_max_graph_vram_bytes(max_graph_vram_bytes);
             if (!hires_upscaler->load_from_file(request.hires.model_path,
                                                 sd_ctx->sd->offload_params_to_cpu,