fix: guard GPU memory capacity and propagate encoding failures

docs/backend.md

@@ -149,6 +149,11 @@ GiB", and with no budget set each device's free memory minus a 512 MiB margin
 is used. These resolved GPU budgets, including the safety margin, also drive
 the runner's graph-cut capacity checks.
 
+Runtime capacity checks also leave 512 MiB of currently free device memory for
+backend scratch buffers and pipelines, including with explicit backend assignments.
+They cap stale free-memory reports by the device's total memory minus tracked
+resident allocations and reject reports that exceed the device's total memory.
+
 Components are considered in `diffusion`, `te`, `vae` order so that repeatedly
 used diffusion weights have priority. Each component's weights use the first
 storage location with enough remaining budget:

src/conditioning/conditioner.hpp

@@ -1224,7 +1224,10 @@ struct FluxCLIPEmbedder : public Conditioner {
                                              true,
                                              clip_skip,
                                              false);
-                    GGML_ASSERT(!pooled.empty());
+                    if (pooled.empty()) {
+                        LOG_ERROR("Flux CLIP-L encoding failed");
+                        return {};
+                    }
                 } else {
                     pooled = sd::Tensor<float>::zeros({768});
                 }
@@ -1243,7 +1246,10 @@ struct FluxCLIPEmbedder : public Conditioner {
                                                   input_ids,
                                                   sd::Tensor<float>(),
                                                   false);
-                GGML_ASSERT(!chunk_hidden_states.empty());
+                if (chunk_hidden_states.empty()) {
+                    LOG_ERROR("Flux T5 encoding failed at chunk %d/%zu", chunk_idx + 1, chunk_count);
+                    return {};
+                }
                 chunk_hidden_states = ::apply_token_weights(std::move(chunk_hidden_states), chunk_weights);
                 if (zero_out_masked) {
                     chunk_hidden_states.fill_(0.0f);

src/core/ggml_runner.cpp

@@ -1,4 +1,5 @@
 #include <algorithm>
+#include <exception>
 #include <map>
 #include <utility>
 
@@ -624,8 +625,15 @@ std::optional<sd::Tensor<float>> GGMLRunner::compute(get_graph_cb_t get_graph,
                 params_tensor_set_.insert(parameter);
         }
     }
-    auto output = execute_graph(graph, n_threads, no_return, read_outputs);
-    success     = output.has_value();
+    std::optional<sd::Tensor<float>> output;
+    try {
+        output = execute_graph(graph, n_threads, no_return, read_outputs);
+    } catch (const std::exception& error) {
+        LOG_ERROR("%s graph execution failed on %s: %s", get_desc().c_str(),
+                  ggml_backend_name(runtime_backend), error.what());
+        return std::nullopt;
+    }
+    success = output.has_value();
     if (success) {
         cache_.graph_end(true);
     }

src/model_manager.cpp

@@ -1584,18 +1584,35 @@ ModelManager::CapacityCheck ModelManager::check_capacity(
     if (request.compute_backend == nullptr || sd_backend_is_cpu(request.compute_backend)) {
         return result;
     }
-    auto add                     = [](size_t a, size_t b) { return b > SIZE_MAX - a ? SIZE_MAX : a + b; };
-    const size_t missing         = compute_backend_alloc_size(states, true);
-    result.required_device_bytes = add(request.pending_allocation_bytes, missing);
-    result.required_budget_bytes = add(request.runtime_peak_bytes(), missing);
-    auto device                  = ggml_backend_get_device(request.compute_backend);
-    if (device != nullptr) {
+    auto add             = [](size_t a, size_t b) { return b > SIZE_MAX - a ? SIZE_MAX : a + b; };
+    const size_t missing = compute_backend_alloc_size(states, true);
+    // Backend scratch buffers and pipelines are not included in graph measurements.
+    constexpr size_t safety_margin = 512ULL * 1024ULL * 1024ULL;
+    result.required_device_bytes   = add(add(request.pending_allocation_bytes, missing), safety_margin);
+    result.required_budget_bytes   = add(request.runtime_peak_bytes(), missing);
+    auto available_device_bytes    = [&](ggml_backend_t backend) {
+        auto device = ggml_backend_get_device(backend);
+        if (device == nullptr) {
+            return SIZE_MAX;
+        }
         size_t free_bytes = 0, total_bytes = 0;
         ggml_backend_dev_memory(device, &free_bytes, &total_bytes);
-        if (free_bytes != 0 || total_bytes != 0) {
-            result.available_device_bytes = free_bytes;
+        if (free_bytes == 0 && total_bytes == 0) {
+            return SIZE_MAX;
         }
-    }
+        // Vulkan's heap budget subtraction can underflow when usage exceeds the budget.
+        if (total_bytes > 0 && free_bytes > total_bytes) {
+            return size_t{0};
+        }
+        const size_t resident = add(compute_backend_resident_bytes(backend),
+                                       add(other_runtime_resident_bytes(request.owner_id, backend),
+                                           request.runtime_resident_bytes));
+        if (total_bytes > 0) {
+            free_bytes = std::min(free_bytes, resident < total_bytes ? total_bytes - resident : 0);
+        }
+        return free_bytes;
+    };
+    result.available_device_bytes = available_device_bytes(request.compute_backend);
     if (request.max_backend_bytes > 0) {
         const size_t resident         = add(compute_backend_resident_bytes(request.compute_backend),
                                             other_runtime_resident_bytes(request.owner_id, request.compute_backend));
@@ -1619,11 +1636,7 @@ ModelManager::CapacityCheck ModelManager::check_capacity(
     // GGML exposes only a split buffer's total size, not per-device allocations.
     // Charge that upper bound on every participant instead of undercounting a shard.
     for (const auto& entry : split_devices) {
-        size_t free_bytes = 0, total_bytes = 0;
-        ggml_backend_dev_memory(ggml_backend_get_device(entry.first), &free_bytes, &total_bytes);
-        if (free_bytes != 0 || total_bytes != 0) {
-            result.available_device_bytes = std::min(result.available_device_bytes, free_bytes);
-        }
+        result.available_device_bytes = std::min(result.available_device_bytes, available_device_bytes(entry.first));
         if (entry.second > 0) {
             const size_t resident         = add(compute_backend_resident_bytes(entry.first),
                                                 other_runtime_resident_bytes(request.owner_id, entry.first));
@@ -1739,12 +1752,18 @@ bool ModelManager::ensure_compute_backend_capacity(
         }
     }
 
-    const auto capacity = check_capacity(request, required_states);
-    LOG_WARN("model manager cannot make enough memory available on %s: need %.2f MB device / %.2f MB budget, available %.2f MB device / %.2f MB budget",
+    const auto capacity                = check_capacity(request, required_states);
+    const std::string available_device = capacity.available_device_bytes == SIZE_MAX
+                                             ? "unknown"
+                                             : sd_format("%.2f MB", capacity.available_device_bytes / (1024.0 * 1024.0));
+    const std::string available_budget = capacity.available_budget_bytes == SIZE_MAX
+                                             ? "unlimited"
+                                             : sd_format("%.2f MB", capacity.available_budget_bytes / (1024.0 * 1024.0));
+    LOG_WARN("model manager cannot make enough memory available on %s: need %.2f MB device / %.2f MB budget, available %s device / %s budget",
              ggml_backend_name(compute_backend),
              capacity.required_device_bytes / (1024.0 * 1024.0),
              capacity.required_budget_bytes / (1024.0 * 1024.0),
-             capacity.available_device_bytes / (1024.0 * 1024.0),
-             capacity.available_budget_bytes / (1024.0 * 1024.0));
+             available_device.c_str(),
+             available_budget.c_str());
     return false;
 }

src/pipeline/image.cpp

@@ -438,6 +438,10 @@ namespace sd::pipeline {
         condition_params.zero_out_masked = false;
         auto cond                        = sd->cond_stage_model->get_learned_condition(sd->n_threads,
                                                                                        condition_params);
+        if (cond.empty()) {
+            LOG_ERROR("failed to encode prompt");
+            return std::nullopt;
+        }
         if (cond.c_concat.empty() && ref_image_params.pass_to_dit) {
             cond.c_concat = latents->concat_latent;  // TODO: optimize
         }
@@ -466,6 +470,10 @@ namespace sd::pipeline {
                 condition_params.zero_out_masked = zero_out_masked;
                 uncond                           = sd->cond_stage_model->get_learned_condition(sd->n_threads,
                                                                                                condition_params);
+                if (uncond.empty()) {
+                    LOG_ERROR("failed to encode negative prompt");
+                    return std::nullopt;
+                }
             }
             if (uncond.c_concat.empty() && ref_image_params.pass_to_dit) {
                 uncond.c_concat = latents->concat_latent;  // TODO: optimize
@@ -491,6 +499,10 @@ namespace sd::pipeline {
                 }
                 img_uncond = sd->cond_stage_model->get_learned_condition(sd->n_threads,
                                                                          condition_params);
+                if (img_uncond.empty()) {
+                    LOG_ERROR("failed to encode image guidance prompt");
+                    return std::nullopt;
+                }
                 if (img_uncond.c_concat.empty() && ref_image_params.pass_to_dit) {
                     img_uncond.c_concat = latents->img_uncond_concat_latent;  // TODO: optimize
                 }