DarthAffe/stable-diffusion.cpp
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feat: handle ggml compute failures without crashing the program (#1003)

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* Feat: handle compute failures more gracefully

* fix Unreachable code after return

Co-authored-by: idostyle <idostyl3@googlemail.com>

* adjust z_image.hpp

---------

Co-authored-by: idostyle <idostyl3@googlemail.com>
Co-authored-by: leejet <leejet714@gmail.com>
This commit is contained in:
stduhpf 2025-12-04 15:04:27 +01:00 committed by GitHub
parent 5865b5e703
commit bcc9c0d0b3
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GPG Key ID: B5690EEEBB952194
20 changed files with 163 additions and 79 deletions
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4
clip.hpp
View File

@ -963,7 +963,7 @@ struct CLIPTextModelRunner : public GGMLRunner {
return gf;
}
void compute(const int n_threads,
bool compute(const int n_threads,
struct ggml_tensor* input_ids,
int num_custom_embeddings,
void* custom_embeddings_data,
@ -975,7 +975,7 @@ struct CLIPTextModelRunner : public GGMLRunner {
auto get_graph = [&]() -> struct ggml_cgraph* {
return build_graph(input_ids, num_custom_embeddings, custom_embeddings_data, max_token_idx, return_pooled, clip_skip);
};
GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
return GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
}
};

4
conditioner.hpp
View File

@ -703,7 +703,7 @@ struct FrozenCLIPVisionEmbedder : public GGMLRunner {
return gf;
}
void compute(const int n_threads,
bool compute(const int n_threads,
ggml_tensor* pixel_values,
bool return_pooled,
int clip_skip,
@ -712,7 +712,7 @@ struct FrozenCLIPVisionEmbedder : public GGMLRunner {
auto get_graph = [&]() -> struct ggml_cgraph* {
return build_graph(pixel_values, return_pooled, clip_skip);
};
GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
return GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
}
};

10
control.hpp
View File

@ -414,7 +414,7 @@ struct ControlNet : public GGMLRunner {
return gf;
}
void compute(int n_threads,
bool compute(int n_threads,
struct ggml_tensor* x,
struct ggml_tensor* hint,
struct ggml_tensor* timesteps,
@ -430,8 +430,12 @@ struct ControlNet : public GGMLRunner {
return build_graph(x, hint, timesteps, context, y);
};
GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
guided_hint_cached = true;
bool res = GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
if (res) {
// cache guided_hint
guided_hint_cached = true;
}
return res;
}
bool load_from_file(const std::string& file_path, int n_threads) {

47
denoiser.hpp
View File

@ -666,7 +666,7 @@ struct Flux2FlowDenoiser : public FluxFlowDenoiser {
typedef std::function<ggml_tensor*(ggml_tensor*, float, int)> denoise_cb_t;
// k diffusion reverse ODE: dx = (x - D(x;\sigma)) / \sigma dt; \sigma(t) = t
static void sample_k_diffusion(sample_method_t method,
static bool sample_k_diffusion(sample_method_t method,
denoise_cb_t model,
ggml_context* work_ctx,
ggml_tensor* x,
@ -685,6 +685,9 @@ static void sample_k_diffusion(sample_method_t method,
// denoise
ggml_tensor* denoised = model(x, sigma, i + 1);
if (denoised == nullptr) {
return false;
}
// d = (x - denoised) / sigma
{
@ -738,6 +741,9 @@ static void sample_k_diffusion(sample_method_t method,
// denoise
ggml_tensor* denoised = model(x, sigma, i + 1);
if (denoised == nullptr) {
return false;
}
// d = (x - denoised) / sigma
{
@ -769,6 +775,9 @@ static void sample_k_diffusion(sample_method_t method,
for (int i = 0; i < steps; i++) {
// denoise
ggml_tensor* denoised = model(x, sigmas[i], -(i + 1));
if (denoised == nullptr) {
return false;
}
// d = (x - denoised) / sigma
{
@ -803,7 +812,10 @@ static void sample_k_diffusion(sample_method_t method,
}
ggml_tensor* denoised = model(x2, sigmas[i + 1], i + 1);
float* vec_denoised = (float*)denoised->data;
if (denoised == nullptr) {
return false;
}
float* vec_denoised = (float*)denoised->data;
for (int j = 0; j < ggml_nelements(x); j++) {
float d2 = (vec_x2[j] - vec_denoised[j]) / sigmas[i + 1];
vec_d[j] = (vec_d[j] + d2) / 2;
@ -819,6 +831,9 @@ static void sample_k_diffusion(sample_method_t method,
for (int i = 0; i < steps; i++) {
// denoise
ggml_tensor* denoised = model(x, sigmas[i], i + 1);
if (denoised == nullptr) {
return false;
}
// d = (x - denoised) / sigma
{
@ -855,7 +870,10 @@ static void sample_k_diffusion(sample_method_t method,
}
ggml_tensor* denoised = model(x2, sigma_mid, i + 1);
float* vec_denoised = (float*)denoised->data;
if (denoised == nullptr) {
return false;
}
float* vec_denoised = (float*)denoised->data;
for (int j = 0; j < ggml_nelements(x); j++) {
float d2 = (vec_x2[j] - vec_denoised[j]) / sigma_mid;
vec_x[j] = vec_x[j] + d2 * dt_2;
@ -871,6 +889,9 @@ static void sample_k_diffusion(sample_method_t method,
for (int i = 0; i < steps; i++) {
// denoise
ggml_tensor* denoised = model(x, sigmas[i], i + 1);
if (denoised == nullptr) {
return false;
}
// get_ancestral_step
float sigma_up = std::min(sigmas[i + 1],
@ -907,6 +928,9 @@ static void sample_k_diffusion(sample_method_t method,
}
ggml_tensor* denoised = model(x2, sigmas[i + 1], i + 1);
if (denoised == nullptr) {
return false;
}
// Second half-step
for (int j = 0; j < ggml_nelements(x); j++) {
@ -937,6 +961,9 @@ static void sample_k_diffusion(sample_method_t method,
for (int i = 0; i < steps; i++) {
// denoise
ggml_tensor* denoised = model(x, sigmas[i], i + 1);
if (denoised == nullptr) {
return false;
}
float t = t_fn(sigmas[i]);
float t_next = t_fn(sigmas[i + 1]);
@ -976,6 +1003,9 @@ static void sample_k_diffusion(sample_method_t method,
for (int i = 0; i < steps; i++) {
// denoise
ggml_tensor* denoised = model(x, sigmas[i], i + 1);
if (denoised == nullptr) {
return false;
}
float t = t_fn(sigmas[i]);
float t_next = t_fn(sigmas[i + 1]);
@ -1026,7 +1056,10 @@ static void sample_k_diffusion(sample_method_t method,
// Denoising step
ggml_tensor* denoised = model(x_cur, sigma, i + 1);
float* vec_denoised = (float*)denoised->data;
if (denoised == nullptr) {
return false;
}
float* vec_denoised = (float*)denoised->data;
// d_cur = (x_cur - denoised) / sigma
struct ggml_tensor* d_cur = ggml_dup_tensor(work_ctx, x_cur);
float* vec_d_cur = (float*)d_cur->data;
@ -1169,6 +1202,9 @@ static void sample_k_diffusion(sample_method_t method,
// denoise
ggml_tensor* denoised = model(x, sigma, i + 1);
if (denoised == nullptr) {
return false;
}
// x = denoised
{
@ -1561,8 +1597,9 @@ static void sample_k_diffusion(sample_method_t method,
default:
LOG_ERROR("Attempting to sample with nonexisting sample method %i", method);
abort();
return false;
}
return true;
}
#endif // __DENOISER_HPP__

14
diffusion_model.hpp
View File

@ -27,7 +27,7 @@ struct DiffusionParams {
struct DiffusionModel {
virtual std::string get_desc() = 0;
virtual void compute(int n_threads,
virtual bool compute(int n_threads,
DiffusionParams diffusion_params,
struct ggml_tensor** output = nullptr,
struct ggml_context* output_ctx = nullptr) = 0;
@ -87,7 +87,7 @@ struct UNetModel : public DiffusionModel {
unet.set_flash_attention_enabled(enabled);
}
void compute(int n_threads,
bool compute(int n_threads,
DiffusionParams diffusion_params,
struct ggml_tensor** output = nullptr,
struct ggml_context* output_ctx = nullptr) override {
@ -148,7 +148,7 @@ struct MMDiTModel : public DiffusionModel {
mmdit.set_flash_attention_enabled(enabled);
}
void compute(int n_threads,
bool compute(int n_threads,
DiffusionParams diffusion_params,
struct ggml_tensor** output = nullptr,
struct ggml_context* output_ctx = nullptr) override {
@ -210,7 +210,7 @@ struct FluxModel : public DiffusionModel {
flux.set_flash_attention_enabled(enabled);
}
void compute(int n_threads,
bool compute(int n_threads,
DiffusionParams diffusion_params,
struct ggml_tensor** output = nullptr,
struct ggml_context* output_ctx = nullptr) override {
@ -277,7 +277,7 @@ struct WanModel : public DiffusionModel {
wan.set_flash_attention_enabled(enabled);
}
void compute(int n_threads,
bool compute(int n_threads,
DiffusionParams diffusion_params,
struct ggml_tensor** output = nullptr,
struct ggml_context* output_ctx = nullptr) override {
@ -343,7 +343,7 @@ struct QwenImageModel : public DiffusionModel {
qwen_image.set_flash_attention_enabled(enabled);
}
void compute(int n_threads,
bool compute(int n_threads,
DiffusionParams diffusion_params,
struct ggml_tensor** output = nullptr,
struct ggml_context* output_ctx = nullptr) override {
@ -406,7 +406,7 @@ struct ZImageModel : public DiffusionModel {
z_image.set_flash_attention_enabled(enabled);
}
void compute(int n_threads,
bool compute(int n_threads,
DiffusionParams diffusion_params,
struct ggml_tensor** output = nullptr,
struct ggml_context* output_ctx = nullptr) override {

4
esrgan.hpp
View File

@ -353,14 +353,14 @@ struct ESRGAN : public GGMLRunner {
return gf;
}
void compute(const int n_threads,
bool compute(const int n_threads,
struct ggml_tensor* x,
ggml_tensor** output,
ggml_context* output_ctx = nullptr) {
auto get_graph = [&]() -> struct ggml_cgraph* {
return build_graph(x);
};
GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
}
};

4
flux.hpp
View File

@ -1413,7 +1413,7 @@ namespace Flux {
return gf;
}
void compute(int n_threads,
bool compute(int n_threads,
struct ggml_tensor* x,
struct ggml_tensor* timesteps,
struct ggml_tensor* context,
@ -1434,7 +1434,7 @@ namespace Flux {
return build_graph(x, timesteps, context, c_concat, y, guidance, ref_latents, increase_ref_index, skip_layers);
};
GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
}
void test() {

21
ggml_extend.hpp
View File

@ -1938,25 +1938,35 @@ public:
return ggml_get_tensor(cache_ctx, name.c_str());
}
void compute(get_graph_cb_t get_graph,
bool compute(get_graph_cb_t get_graph,
int n_threads,
bool free_compute_buffer_immediately = true,
struct ggml_tensor** output = nullptr,
struct ggml_context* output_ctx = nullptr) {
if (!offload_params_to_runtime_backend()) {
LOG_ERROR("%s offload params to runtime backend failed", get_desc().c_str());
return;
return false;
}
if (!alloc_compute_buffer(get_graph)) {
LOG_ERROR("%s alloc compute buffer failed", get_desc().c_str());
return false;
}
alloc_compute_buffer(get_graph);
reset_compute_ctx();
struct ggml_cgraph* gf = get_compute_graph(get_graph);
GGML_ASSERT(ggml_gallocr_alloc_graph(compute_allocr, gf));
if (!ggml_gallocr_alloc_graph(compute_allocr, gf)) {
LOG_ERROR("%s alloc compute graph failed", get_desc().c_str());
return false;
}
copy_data_to_backend_tensor();
if (ggml_backend_is_cpu(runtime_backend)) {
ggml_backend_cpu_set_n_threads(runtime_backend, n_threads);
}
ggml_backend_graph_compute(runtime_backend, gf);
ggml_status status = ggml_backend_graph_compute(runtime_backend, gf);
if (status != GGML_STATUS_SUCCESS) {
LOG_ERROR("%s compute failed: %s", get_desc().c_str(), ggml_status_to_string(status));
return false;
}
#ifdef GGML_PERF
ggml_graph_print(gf);
#endif
@ -1974,6 +1984,7 @@ public:
if (free_compute_buffer_immediately) {
free_compute_buffer();
}
return true;
}
void set_flash_attention_enabled(bool enabled) {

4
llm.hpp
View File

@ -1191,7 +1191,7 @@ namespace LLM {
return gf;
}
void compute(const int n_threads,
bool compute(const int n_threads,
struct ggml_tensor* input_ids,
std::vector<std::pair<int, ggml_tensor*>> image_embeds,
std::set<int> out_layers,
@ -1200,7 +1200,7 @@ namespace LLM {
auto get_graph = [&]() -> struct ggml_cgraph* {
return build_graph(input_ids, image_embeds, out_layers);
};
GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
return GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
}
int64_t get_num_image_tokens(int64_t t, int64_t h, int64_t w) {

4
mmdit.hpp
View File

@ -894,7 +894,7 @@ struct MMDiTRunner : public GGMLRunner {
return gf;
}
void compute(int n_threads,
bool compute(int n_threads,
struct ggml_tensor* x,
struct ggml_tensor* timesteps,
struct ggml_tensor* context,
@ -910,7 +910,7 @@ struct MMDiTRunner : public GGMLRunner {
return build_graph(x, timesteps, context, y, skip_layers);
};
GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
}
void test() {

4
model.cpp
View File

@ -104,8 +104,8 @@ const char* unused_tensors[] = {
"embedding_manager",
"denoiser.sigmas",
"text_encoders.t5xxl.transformer.encoder.embed_tokens.weight", // only used during training
"ztsnr", // Found in some SDXL vpred models
"edm_vpred.sigma_min", // Found in CosXL
"ztsnr", // Found in some SDXL vpred models
"edm_vpred.sigma_min", // Found in CosXL
// TODO: find another way to avoid the "unknown tensor" for these two
// "edm_vpred.sigma_max", // Used to detect CosXL
// "v_pred", // Used to detect SDXL vpred models

4
pmid.hpp
View File

@ -548,7 +548,7 @@ public:
return gf;
}
void compute(const int n_threads,
bool compute(const int n_threads,
struct ggml_tensor* id_pixel_values,
struct ggml_tensor* prompt_embeds,
struct ggml_tensor* id_embeds,
@ -561,7 +561,7 @@ public:
};
// GGMLRunner::compute(get_graph, n_threads, updated_prompt_embeds);
GGMLRunner::compute(get_graph, n_threads, true, updated_prompt_embeds, output_ctx);
return GGMLRunner::compute(get_graph, n_threads, true, updated_prompt_embeds, output_ctx);
}
};

4
qwen_image.hpp
View File

@ -588,7 +588,7 @@ namespace Qwen {
return gf;
}
void compute(int n_threads,
bool compute(int n_threads,
struct ggml_tensor* x,
struct ggml_tensor* timesteps,
struct ggml_tensor* context,
@ -603,7 +603,7 @@ namespace Qwen {
return build_graph(x, timesteps, context, ref_latents, increase_ref_index);
};
GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
}
void test() {

72
stable-diffusion.cpp
View File

@ -1683,8 +1683,11 @@ public:
std::vector<struct ggml_tensor*> controls;
if (control_hint != nullptr && control_net != nullptr) {
control_net->compute(n_threads, noised_input, control_hint, timesteps, cond.c_crossattn, cond.c_vector);
controls = control_net->controls;
if (control_net->compute(n_threads, noised_input, control_hint, timesteps, cond.c_crossattn, cond.c_vector)) {
controls = control_net->controls;
} else {
LOG_ERROR("controlnet compute failed");
}
// print_ggml_tensor(controls[12]);
// GGML_ASSERT(0);
}
@ -1716,9 +1719,12 @@ public:
bool skip_model = easycache_before_condition(active_condition, *active_output);
if (!skip_model) {
work_diffusion_model->compute(n_threads,
diffusion_params,
active_output);
if (!work_diffusion_model->compute(n_threads,
diffusion_params,
active_output)) {
LOG_ERROR("diffusion model compute failed");
return nullptr;
}
easycache_after_condition(active_condition, *active_output);
}
@ -1728,8 +1734,11 @@ public:
if (has_unconditioned) {
// uncond
if (!current_step_skipped && control_hint != nullptr && control_net != nullptr) {
control_net->compute(n_threads, noised_input, control_hint, timesteps, uncond.c_crossattn, uncond.c_vector);
controls = control_net->controls;
if (control_net->compute(n_threads, noised_input, control_hint, timesteps, uncond.c_crossattn, uncond.c_vector)) {
controls = control_net->controls;
} else {
LOG_ERROR("controlnet compute failed");
}
}
current_step_skipped = easycache_step_is_skipped();
diffusion_params.controls = controls;
@ -1738,9 +1747,12 @@ public:
diffusion_params.y = uncond.c_vector;
bool skip_uncond = easycache_before_condition(&uncond, out_uncond);
if (!skip_uncond) {
work_diffusion_model->compute(n_threads,
diffusion_params,
&out_uncond);
if (!work_diffusion_model->compute(n_threads,
diffusion_params,
&out_uncond)) {
LOG_ERROR("diffusion model compute failed");
return nullptr;
}
easycache_after_condition(&uncond, out_uncond);
}
negative_data = (float*)out_uncond->data;
@ -1753,9 +1765,12 @@ public:
diffusion_params.y = img_cond.c_vector;
bool skip_img_cond = easycache_before_condition(&img_cond, out_img_cond);
if (!skip_img_cond) {
work_diffusion_model->compute(n_threads,
diffusion_params,
&out_img_cond);
if (!work_diffusion_model->compute(n_threads,
diffusion_params,
&out_img_cond)) {
LOG_ERROR("diffusion model compute failed");
return nullptr;
}
easycache_after_condition(&img_cond, out_img_cond);
}
img_cond_data = (float*)out_img_cond->data;
@ -1772,9 +1787,12 @@ public:
diffusion_params.c_concat = cond.c_concat;
diffusion_params.y = cond.c_vector;
diffusion_params.skip_layers = skip_layers;
work_diffusion_model->compute(n_threads,
diffusion_params,
&out_skip);
if (!work_diffusion_model->compute(n_threads,
diffusion_params,
&out_skip)) {
LOG_ERROR("diffusion model compute failed");
return nullptr;
}
}
skip_layer_data = (float*)out_skip->data;
}
@ -1837,7 +1855,15 @@ public:
return denoised;
};
sample_k_diffusion(method, denoise, work_ctx, x, sigmas, sampler_rng, eta);
if (!sample_k_diffusion(method, denoise, work_ctx, x, sigmas, sampler_rng, eta)) {
LOG_ERROR("Diffusion model sampling failed");
if (control_net) {
control_net->free_control_ctx();
control_net->free_compute_buffer();
}
diffusion_model->free_compute_buffer();
return NULL;
}
if (easycache_enabled) {
size_t total_steps = sigmas.size() > 0 ? sigmas.size() - 1 : 0;
@ -3064,10 +3090,14 @@ sd_image_t* generate_image_internal(sd_ctx_t* sd_ctx,
nullptr,
1.0f,
easycache_params);
// print_ggml_tensor(x_0);
int64_t sampling_end = ggml_time_ms();
LOG_INFO("sampling completed, taking %.2fs", (sampling_end - sampling_start) * 1.0f / 1000);
final_latents.push_back(x_0);
int64_t sampling_end = ggml_time_ms();
if (x_0 != nullptr) {
// print_ggml_tensor(x_0);
LOG_INFO("sampling completed, taking %.2fs", (sampling_end - sampling_start) * 1.0f / 1000);
final_latents.push_back(x_0);
} else {
LOG_ERROR("sampling for image %d/%d failed after %.2fs", b + 1, batch_count, (sampling_end - sampling_start) * 1.0f / 1000);
}
}
if (sd_ctx->sd->free_params_immediately) {

4
t5.hpp
View File

@ -820,7 +820,7 @@ struct T5Runner : public GGMLRunner {
return gf;
}
void compute(const int n_threads,
bool compute(const int n_threads,
struct ggml_tensor* input_ids,
struct ggml_tensor* attention_mask,
ggml_tensor** output,
@ -828,7 +828,7 @@ struct T5Runner : public GGMLRunner {
auto get_graph = [&]() -> struct ggml_cgraph* {
return build_graph(input_ids, attention_mask);
};
GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
return GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
}
static std::vector<int> _relative_position_bucket(const std::vector<int>& relative_position,

4
tae.hpp
View File

@ -247,7 +247,7 @@ struct TinyAutoEncoder : public GGMLRunner {
return gf;
}
void compute(const int n_threads,
bool compute(const int n_threads,
struct ggml_tensor* z,
bool decode_graph,
struct ggml_tensor** output,
@ -256,7 +256,7 @@ struct TinyAutoEncoder : public GGMLRunner {
return build_graph(z, decode_graph);
};
GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
}
};

4
unet.hpp
View File

@ -645,7 +645,7 @@ struct UNetModelRunner : public GGMLRunner {
return gf;
}
void compute(int n_threads,
bool compute(int n_threads,
struct ggml_tensor* x,
struct ggml_tensor* timesteps,
struct ggml_tensor* context,
@ -665,7 +665,7 @@ struct UNetModelRunner : public GGMLRunner {
return build_graph(x, timesteps, context, c_concat, y, num_video_frames, controls, control_strength);
};
GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
}
void test() {

9
vae.hpp
View File

@ -617,7 +617,7 @@ public:
struct VAE : public GGMLRunner {
VAE(ggml_backend_t backend, bool offload_params_to_cpu)
: GGMLRunner(backend, offload_params_to_cpu) {}
virtual void compute(const int n_threads,
virtual bool compute(const int n_threads,
struct ggml_tensor* z,
bool decode_graph,
struct ggml_tensor** output,
@ -629,7 +629,7 @@ struct VAE : public GGMLRunner {
struct FakeVAE : public VAE {
FakeVAE(ggml_backend_t backend, bool offload_params_to_cpu)
: VAE(backend, offload_params_to_cpu) {}
void compute(const int n_threads,
bool compute(const int n_threads,
struct ggml_tensor* z,
bool decode_graph,
struct ggml_tensor** output,
@ -641,6 +641,7 @@ struct FakeVAE : public VAE {
float value = ggml_ext_tensor_get_f32(z, i0, i1, i2, i3);
ggml_ext_tensor_set_f32(*output, value, i0, i1, i2, i3);
});
return true;
}
void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) override {}
@ -711,7 +712,7 @@ struct AutoEncoderKL : public VAE {
return gf;
}
void compute(const int n_threads,
bool compute(const int n_threads,
struct ggml_tensor* z,
bool decode_graph,
struct ggml_tensor** output,
@ -722,7 +723,7 @@ struct AutoEncoderKL : public VAE {
};
// ggml_set_f32(z, 0.5f);
// print_ggml_tensor(z);
GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
}
void test() {

15
wan.hpp
View File

@ -1175,7 +1175,7 @@ namespace WAN {
return gf;
}
void compute(const int n_threads,
bool compute(const int n_threads,
struct ggml_tensor* z,
bool decode_graph,
struct ggml_tensor** output,
@ -1184,7 +1184,7 @@ namespace WAN {
auto get_graph = [&]() -> struct ggml_cgraph* {
return build_graph(z, decode_graph);
};
GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
return GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
} else { // chunk 1 result is weird
ae.clear_cache();
int64_t t = z->ne[2];
@ -1193,11 +1193,11 @@ namespace WAN {
return build_graph_partial(z, decode_graph, i);
};
struct ggml_tensor* out = nullptr;
GGMLRunner::compute(get_graph, n_threads, true, &out, output_ctx);
bool res = GGMLRunner::compute(get_graph, n_threads, true, &out, output_ctx);
ae.clear_cache();
if (t == 1) {
*output = out;
return;
return res;
}
*output = ggml_new_tensor_4d(output_ctx, GGML_TYPE_F32, out->ne[0], out->ne[1], (t - 1) * 4 + 1, out->ne[3]);
@ -1221,11 +1221,12 @@ namespace WAN {
out = ggml_new_tensor_4d(output_ctx, GGML_TYPE_F32, out->ne[0], out->ne[1], 4, out->ne[3]);
for (i = 1; i < t; i++) {
GGMLRunner::compute(get_graph, n_threads, true, &out);
res = res || GGMLRunner::compute(get_graph, n_threads, true, &out);
ae.clear_cache();
copy_to_output();
}
free_cache_ctx_and_buffer();
return res;
}
}
@ -2194,7 +2195,7 @@ namespace WAN {
return gf;
}
void compute(int n_threads,
bool compute(int n_threads,
struct ggml_tensor* x,
struct ggml_tensor* timesteps,
struct ggml_tensor* context,
@ -2209,7 +2210,7 @@ namespace WAN {
return build_graph(x, timesteps, context, clip_fea, c_concat, time_dim_concat, vace_context, vace_strength);
};
GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
}
void test() {

6
z_image.hpp
View File

@ -30,7 +30,7 @@ namespace ZImage {
JointAttention(int64_t hidden_size, int64_t head_dim, int64_t num_heads, int64_t num_kv_heads, bool qk_norm)
: head_dim(head_dim), num_heads(num_heads), num_kv_heads(num_kv_heads), qk_norm(qk_norm) {
blocks["qkv"] = std::make_shared<Linear>(hidden_size, (num_heads + num_kv_heads * 2) * head_dim, false);
float scale = 1.f;
float scale = 1.f;
#if GGML_USE_HIP
// Prevent NaN issues with certain ROCm setups
scale = 1.f / 16.f;
@ -574,7 +574,7 @@ namespace ZImage {
return gf;
}
void compute(int n_threads,
bool compute(int n_threads,
struct ggml_tensor* x,
struct ggml_tensor* timesteps,
struct ggml_tensor* context,
@ -589,7 +589,7 @@ namespace ZImage {
return build_graph(x, timesteps, context, ref_latents, increase_ref_index);
};
GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
}
void test() {