alphard/stable-diffusion.cpp
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perf: free unused params immediately to reduce memory usage

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This commit is contained in:
leejet 2023-08-17 00:55:36 +08:00
parent cbee3c9a4f
commit 8f34dd7cc7
4 changed files with 232 additions and 73 deletions
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5
README.md
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@ -12,6 +12,7 @@ Inference of [Stable Diffusion](https://github.com/CompVis/stable-diffusion) in
- 16-bit, 32-bit float support - 16-bit, 32-bit float support
- 4-bit, 5-bit and 8-bit integer quantization support - 4-bit, 5-bit and 8-bit integer quantization support
- Accelerated memory-efficient CPU inference - Accelerated memory-efficient CPU inference
- Only requires ~2.3GB when using txt2img with fp16 precision to generate a 512x512 image
- AVX, AVX2 and AVX512 support for x86 architectures - AVX, AVX2 and AVX512 support for x86 architectures
- Original `txt2img` and `img2img` mode - Original `txt2img` and `img2img` mode
- Negative prompt - Negative prompt
@ -152,8 +153,8 @@ Using formats of different precisions will yield results of varying quality.
| precision | f32 | f16 |q8_0 |q5_0 |q5_1 |q4_0 |q4_1 | | precision | f32 | f16 |q8_0 |q5_0 |q5_1 |q4_0 |q4_1 |
| ---- | ---- |---- |---- |---- |---- |---- |---- | | ---- | ---- |---- |---- |---- |---- |---- |---- |
| **Disk** | 2.8G | 2.0G | 1.7G | 1.6G | 1.6G | 1.5G | 1.5G | | **Disk** | 2.7G | 2.0G | 1.7G | 1.6G | 1.6G | 1.5G | 1.5G |
| **Memory**(txt2img - 512 x 512) | ~4.9G | ~4.1G | ~3.8G | ~3.7G | ~3.7G | ~3.6G | ~3.6G | | **Memory**(txt2img - 512 x 512) | ~2.8G | ~2.3G | ~2.1G | ~2.0G | ~2.0G | ~2.0G | ~2.0G |
## References ## References

3
main.cpp
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@ -322,7 +322,8 @@ int main(int argc, const char* argv[]) {
} }
init_img.assign(img_data, img_data + (opt.w * opt.h * c)); init_img.assign(img_data, img_data + (opt.w * opt.h * c));
} }
StableDiffusion sd(opt.n_threads, vae_decode_only);
StableDiffusion sd(opt.n_threads, vae_decode_only, true);
if (!sd.load_from_file(opt.model_path)) { if (!sd.load_from_file(opt.model_path)) {
return 1; return 1;
} }

294
stable-diffusion.cpp
View File

@ -1186,7 +1186,9 @@ struct DownSample {
static void asymmetric_pad(struct ggml_tensor* dst, static void asymmetric_pad(struct ggml_tensor* dst,
const struct ggml_tensor* a, const struct ggml_tensor* a,
const struct ggml_tensor* b, const struct ggml_tensor* b,
int ith, int nth, void * userdata) { int ith,
int nth,
void* userdata) {
assert(sizeof(dst->nb[0]) == sizeof(float)); assert(sizeof(dst->nb[0]) == sizeof(float));
assert(sizeof(a->nb[0]) == sizeof(float)); assert(sizeof(a->nb[0]) == sizeof(float));
assert(sizeof(b->nb[0]) == sizeof(float)); assert(sizeof(b->nb[0]) == sizeof(float));
@ -1450,6 +1452,8 @@ struct UNetModel {
mem_size += out_channels * model_channels * 3 * 3 * ggml_type_sizef(GGML_TYPE_F16); // out_2_w mem_size += out_channels * model_channels * 3 * 3 * ggml_type_sizef(GGML_TYPE_F16); // out_2_w
mem_size += out_channels * ggml_type_sizef(GGML_TYPE_F32); // out_2_b mem_size += out_channels * ggml_type_sizef(GGML_TYPE_F32); // out_2_b
mem_size += 4 * ggml_tensor_overhead();
return static_cast<size_t>(mem_size); return static_cast<size_t>(mem_size);
} }
@ -2470,13 +2474,20 @@ struct CompVisDenoiser {
class StableDiffusionGGML { class StableDiffusionGGML {
public: public:
ggml_context* params_ctx = NULL; ggml_context* clip_params_ctx = NULL;
ggml_context* unet_params_ctx = NULL;
ggml_context* vae_params_ctx = NULL;
bool dynamic = true; bool dynamic = true;
bool vae_decode_only = true; bool vae_decode_only = false;
bool free_params_immediately = false;
int32_t ftype = 1; int32_t ftype = 1;
int n_threads = -1; int n_threads = -1;
float scale_factor = 0.18215f; float scale_factor = 0.18215f;
size_t max_rt_size = 0; size_t max_mem_size = 0;
size_t curr_params_mem_size = 0;
size_t max_params_mem_size = 0;
size_t max_rt_mem_size = 0;
FrozenCLIPEmbedder cond_stage_model; FrozenCLIPEmbedder cond_stage_model;
UNetModel diffusion_model; UNetModel diffusion_model;
@ -2484,19 +2495,29 @@ class StableDiffusionGGML {
CompVisDenoiser denoiser; CompVisDenoiser denoiser;
std::map<std::string, struct ggml_tensor*> tensors;
StableDiffusionGGML() = default; StableDiffusionGGML() = default;
StableDiffusionGGML(int n_threads, bool vae_decode_only) StableDiffusionGGML(int n_threads,
: n_threads(n_threads), vae_decode_only(vae_decode_only) { bool vae_decode_only,
bool free_params_immediately)
: n_threads(n_threads),
vae_decode_only(vae_decode_only),
free_params_immediately(free_params_immediately) {
first_stage_model.decode_only = vae_decode_only; first_stage_model.decode_only = vae_decode_only;
} }
~StableDiffusionGGML() { ~StableDiffusionGGML() {
if (params_ctx != NULL) { if (clip_params_ctx != NULL) {
ggml_free(params_ctx); ggml_free(clip_params_ctx);
params_ctx = NULL; clip_params_ctx = NULL;
}
if (unet_params_ctx != NULL) {
ggml_free(unet_params_ctx);
unet_params_ctx = NULL;
}
if (vae_params_ctx != NULL) {
ggml_free(vae_params_ctx);
vae_params_ctx = NULL;
} }
} }
@ -2559,50 +2580,89 @@ class StableDiffusionGGML {
} }
} }
double ctx_size = 0; // create the ggml context for network params
LOG_DEBUG("ggml tensor size = %d bytes", (int)sizeof(ggml_tensor));
{ {
// cond_stage_model(FrozenCLIPEmbedder) // cond_stage_model(FrozenCLIPEmbedder)
double ctx_size = 1 * 1024 * 1024; // 1 MB, for padding
ctx_size += cond_stage_model.text_model.compute_params_mem_size(wtype); ctx_size += cond_stage_model.text_model.compute_params_mem_size(wtype);
LOG_DEBUG("clip params ctx size = % 6.2f MB", ctx_size / (1024.0 * 1024.0));
// diffusion_model(UNetModel)
ctx_size += diffusion_model.compute_params_mem_size(wtype);
// first_stage_model(AutoEncoderKL)
ctx_size += first_stage_model.compute_params_mem_size(wtype);
LOG_DEBUG("ggml tensor size = %d bytes", (int)sizeof(ggml_tensor));
LOG_INFO("params ctx size = % 6.2f MB", ctx_size / (1024.0 * 1024.0));
}
// create the ggml context for network params
{
struct ggml_init_params params; struct ggml_init_params params;
params.mem_size = static_cast<size_t>(ctx_size); params.mem_size = static_cast<size_t>(ctx_size);
params.mem_buffer = NULL; params.mem_buffer = NULL;
params.no_alloc = false; params.no_alloc = false;
params.dynamic = false; params.dynamic = false;
params_ctx = ggml_init(params); clip_params_ctx = ggml_init(params);
if (!params_ctx) { if (!clip_params_ctx) {
LOG_ERROR("ggml_init() failed"); LOG_ERROR("ggml_init() failed");
return false; return false;
} }
} }
{
// diffusion_model(UNetModel)
double ctx_size = 1 * 1024 * 1024; // 1 MB, for padding
ctx_size += diffusion_model.compute_params_mem_size(wtype);
LOG_DEBUG("unet params ctx size = % 6.2f MB", ctx_size / (1024.0 * 1024.0));
struct ggml_init_params params;
params.mem_size = static_cast<size_t>(ctx_size);
params.mem_buffer = NULL;
params.no_alloc = false;
params.dynamic = false;
unet_params_ctx = ggml_init(params);
if (!unet_params_ctx) {
LOG_ERROR("ggml_init() failed");
ggml_free(clip_params_ctx);
clip_params_ctx = NULL;
return false;
}
}
{
// first_stage_model(AutoEncoderKL)
double ctx_size = 1 * 1024 * 1024; // 1 MB, for padding
ctx_size += first_stage_model.compute_params_mem_size(wtype);
LOG_DEBUG("vae params ctx size = % 6.2f MB", ctx_size / (1024.0 * 1024.0));
struct ggml_init_params params;
params.mem_size = static_cast<size_t>(ctx_size);
params.mem_buffer = NULL;
params.no_alloc = false;
params.dynamic = false;
vae_params_ctx = ggml_init(params);
if (!vae_params_ctx) {
LOG_ERROR("ggml_init() failed");
ggml_free(clip_params_ctx);
clip_params_ctx = NULL;
ggml_free(unet_params_ctx);
unet_params_ctx = NULL;
return false;
}
}
std::map<std::string, struct ggml_tensor*> tensors;
LOG_DEBUG("preparing memory for the weights"); LOG_DEBUG("preparing memory for the weights");
// prepare memory for the weights // prepare memory for the weights
{ {
// cond_stage_model(FrozenCLIPEmbedder) // cond_stage_model(FrozenCLIPEmbedder)
cond_stage_model.text_model.init_params(params_ctx, wtype); cond_stage_model.text_model.init_params(clip_params_ctx, wtype);
cond_stage_model.text_model.map_by_name(tensors, "cond_stage_model.transformer.text_model."); cond_stage_model.text_model.map_by_name(tensors, "cond_stage_model.transformer.text_model.");
// diffusion_model(UNetModel) // diffusion_model(UNetModel)
diffusion_model.init_params(params_ctx, wtype); diffusion_model.init_params(unet_params_ctx, wtype);
diffusion_model.map_by_name(tensors, "model.diffusion_model."); diffusion_model.map_by_name(tensors, "model.diffusion_model.");
// firest_stage_model(AutoEncoderKL) // firest_stage_model(AutoEncoderKL)
first_stage_model.init_params(params_ctx, wtype); first_stage_model.init_params(vae_params_ctx, wtype);
first_stage_model.map_by_name(tensors, "first_stage_model."); first_stage_model.map_by_name(tensors, "first_stage_model.");
} }
LOG_DEBUG("loading weights"); LOG_DEBUG("loading weights");
std::set<std::string> tensor_names_in_file; std::set<std::string> tensor_names_in_file;
int64_t t0 = ggml_time_ms(); int64_t t0 = ggml_time_ms();
@ -2707,8 +2767,16 @@ class StableDiffusionGGML {
file.close(); file.close();
return false; return false;
} }
LOG_DEBUG("model size = %8.2fMB", total_size / 1024.0 / 1024.0); LOG_DEBUG("model size = %.2fMB", total_size / 1024.0 / 1024.0);
} }
max_params_mem_size = ggml_used_mem(clip_params_ctx) + ggml_used_mem(unet_params_ctx) + ggml_used_mem(vae_params_ctx);
max_mem_size = max_params_mem_size;
curr_params_mem_size = max_params_mem_size;
LOG_INFO("total params size = %.2fMB (clip %.2fMB, unet %.2fMB, vae %.2fMB)",
max_params_mem_size / 1024.0 / 1024.0,
ggml_used_mem(clip_params_ctx) / 1024.0 / 1024.0,
ggml_used_mem(unet_params_ctx) / 1024.0 / 1024.0,
ggml_used_mem(vae_params_ctx) / 1024.0 / 1024.0);
int64_t t1 = ggml_time_ms(); int64_t t1 = ggml_time_ms();
LOG_INFO("loading model from '%s' completed, taking %.2fs", file_path.c_str(), (t1 - t0) * 1.0f / 1000); LOG_INFO("loading model from '%s' completed, taking %.2fs", file_path.c_str(), (t1 - t0) * 1.0f / 1000);
file.close(); file.close();
@ -2784,14 +2852,26 @@ class StableDiffusionGGML {
copy_ggml_tensor(result, hidden_states); copy_ggml_tensor(result, hidden_states);
// print_ggml_tensor(result); // print_ggml_tensor(result);
size_t rt_size = ctx_size + ggml_curr_max_dynamic_size(); size_t rt_mem_size = ctx_size + ggml_curr_max_dynamic_size();
if (rt_size > max_rt_size) { if (rt_mem_size > max_rt_mem_size) {
max_rt_size = rt_size; max_rt_mem_size = rt_mem_size;
} }
LOG_INFO("condition graph use %.2fMB of memory: static %.2fMB, dynamic = %.2fMB", size_t graph_mem_size = ggml_used_mem(clip_params_ctx) + rt_mem_size;
rt_size * 1.0f / 1024 / 1024,
ctx_size * 1.0f / 1024 / 1024, size_t curr_mem_size = curr_params_mem_size + rt_mem_size;
ggml_curr_max_dynamic_size() * 1.0f / 1024 / 1024); if (curr_mem_size > max_mem_size) {
max_mem_size = curr_mem_size;
}
LOG_INFO(
"condition graph use %.2fMB of memory: params %.2fMB, "
"runtime %.2fMB (static %.2fMB, dynamic %.2fMB)",
graph_mem_size * 1.0f / 1024 / 1024,
ggml_used_mem(clip_params_ctx) * 1.0f / 1024 / 1024,
rt_mem_size * 1.0f / 1024 / 1024,
ctx_size * 1.0f / 1024 / 1024,
ggml_curr_max_dynamic_size() * 1.0f / 1024 / 1024);
LOG_DEBUG("%zu bytes of dynamic memory has not been released yet", ggml_dynamic_size()); LOG_DEBUG("%zu bytes of dynamic memory has not been released yet", ggml_dynamic_size());
ggml_free(ctx); ggml_free(ctx);
@ -2985,21 +3065,33 @@ class StableDiffusionGGML {
} }
int64_t t1 = ggml_time_ms(); int64_t t1 = ggml_time_ms();
LOG_INFO("step %d sampling completed, taking %.2fs", i + 1, (t1 - t0) * 1.0f / 1000); LOG_INFO("step %d sampling completed, taking %.2fs", i + 1, (t1 - t0) * 1.0f / 1000);
LOG_DEBUG("diffusion graph use %.2fMB of memory: static %.2fMB, dynamic = %.2fMB", LOG_DEBUG("diffusion graph use %.2fMB runtime memory: static %.2fMB, dynamic %.2fMB",
(ctx_size + ggml_curr_max_dynamic_size()) * 1.0f / 1024 / 1024, (ctx_size + ggml_curr_max_dynamic_size()) * 1.0f / 1024 / 1024,
ctx_size * 1.0f / 1024 / 1024, ctx_size * 1.0f / 1024 / 1024,
ggml_curr_max_dynamic_size() * 1.0f / 1024 / 1024); ggml_curr_max_dynamic_size() * 1.0f / 1024 / 1024);
LOG_DEBUG("%zu bytes of dynamic memory has not been released yet", ggml_dynamic_size()); LOG_DEBUG("%zu bytes of dynamic memory has not been released yet", ggml_dynamic_size());
} }
} }
size_t rt_size = ctx_size + ggml_curr_max_dynamic_size();
if (rt_size > max_rt_size) { size_t rt_mem_size = ctx_size + ggml_curr_max_dynamic_size();
max_rt_size = rt_size; if (rt_mem_size > max_rt_mem_size) {
max_rt_mem_size = rt_mem_size;
} }
LOG_INFO("diffusion graph use %.2fMB of memory: static %.2fMB, dynamic = %.2fMB", size_t graph_mem_size = ggml_used_mem(unet_params_ctx) + rt_mem_size;
rt_size * 1.0f / 1024 / 1024,
ctx_size * 1.0f / 1024 / 1024, size_t curr_mem_size = curr_params_mem_size + rt_mem_size;
ggml_curr_max_dynamic_size() * 1.0f / 1024 / 1024); if (curr_mem_size > max_mem_size) {
max_mem_size = curr_mem_size;
}
LOG_INFO(
"diffusion graph use %.2fMB of memory: params %.2fMB, "
"runtime %.2fMB (static %.2fMB, dynamic %.2fMB)",
graph_mem_size * 1.0f / 1024 / 1024,
ggml_used_mem(unet_params_ctx) * 1.0f / 1024 / 1024,
rt_mem_size * 1.0f / 1024 / 1024,
ctx_size * 1.0f / 1024 / 1024,
ggml_curr_max_dynamic_size() * 1.0f / 1024 / 1024);
LOG_DEBUG("%zu bytes of dynamic memory has not been released yet", ggml_dynamic_size()); LOG_DEBUG("%zu bytes of dynamic memory has not been released yet", ggml_dynamic_size());
ggml_free(ctx); ggml_free(ctx);
@ -3065,14 +3157,25 @@ class StableDiffusionGGML {
result = ggml_dup_tensor(res_ctx, moments); result = ggml_dup_tensor(res_ctx, moments);
copy_ggml_tensor(result, moments); copy_ggml_tensor(result, moments);
size_t rt_size = ctx_size + ggml_curr_max_dynamic_size(); size_t rt_mem_size = ctx_size + ggml_curr_max_dynamic_size();
if (rt_size > max_rt_size) { if (rt_mem_size > max_rt_mem_size) {
max_rt_size = rt_size; max_rt_mem_size = rt_mem_size;
} }
LOG_INFO("vae graph use %.2fMB of memory: static %.2fMB, dynamic = %.2fMB", size_t graph_mem_size = ggml_used_mem(vae_params_ctx) + rt_mem_size;
rt_size * 1.0f / 1024 / 1024,
ctx_size * 1.0f / 1024 / 1024, size_t curr_mem_size = curr_params_mem_size + rt_mem_size;
ggml_curr_max_dynamic_size() * 1.0f / 1024 / 1024); if (curr_mem_size > max_mem_size) {
max_mem_size = curr_mem_size;
}
LOG_INFO(
"vae graph use %.2fMB of memory: params %.2fMB, "
"runtime %.2fMB (static %.2fMB, dynamic %.2fMB)",
graph_mem_size * 1.0f / 1024 / 1024,
ggml_used_mem(vae_params_ctx) * 1.0f / 1024 / 1024,
rt_mem_size * 1.0f / 1024 / 1024,
ctx_size * 1.0f / 1024 / 1024,
ggml_curr_max_dynamic_size() * 1.0f / 1024 / 1024);
LOG_DEBUG("%zu bytes of dynamic memory has not been released yet", ggml_dynamic_size()); LOG_DEBUG("%zu bytes of dynamic memory has not been released yet", ggml_dynamic_size());
ggml_free(ctx); ggml_free(ctx);
@ -3179,14 +3282,25 @@ class StableDiffusionGGML {
result_img = ggml_dup_tensor(res_ctx, img); result_img = ggml_dup_tensor(res_ctx, img);
copy_ggml_tensor(result_img, img); copy_ggml_tensor(result_img, img);
size_t rt_size = ctx_size + ggml_curr_max_dynamic_size(); size_t rt_mem_size = ctx_size + ggml_curr_max_dynamic_size();
if (rt_size > max_rt_size) { if (rt_mem_size > max_rt_mem_size) {
max_rt_size = rt_size; max_rt_mem_size = rt_mem_size;
} }
LOG_INFO("vae graph use %.2fMB of memory: static %.2fMB, dynamic = %.2fMB", size_t graph_mem_size = ggml_used_mem(vae_params_ctx) + rt_mem_size;
rt_size * 1.0f / 1024 / 1024,
ctx_size * 1.0f / 1024 / 1024, size_t curr_mem_size = curr_params_mem_size + rt_mem_size;
ggml_curr_max_dynamic_size() * 1.0f / 1024 / 1024); if (curr_mem_size > max_mem_size) {
max_mem_size = curr_mem_size;
}
LOG_INFO(
"vae graph use %.2fMB of memory: params %.2fMB, "
"runtime %.2fMB (static %.2fMB, dynamic %.2fMB)",
graph_mem_size * 1.0f / 1024 / 1024,
ggml_used_mem(vae_params_ctx) * 1.0f / 1024 / 1024,
rt_mem_size * 1.0f / 1024 / 1024,
ctx_size * 1.0f / 1024 / 1024,
ggml_curr_max_dynamic_size() * 1.0f / 1024 / 1024);
LOG_DEBUG("%zu bytes of dynamic memory has not been released yet", ggml_dynamic_size()); LOG_DEBUG("%zu bytes of dynamic memory has not been released yet", ggml_dynamic_size());
ggml_free(ctx); ggml_free(ctx);
@ -3198,8 +3312,12 @@ class StableDiffusionGGML {
/*================================================= StableDiffusion ==================================================*/ /*================================================= StableDiffusion ==================================================*/
StableDiffusion::StableDiffusion(int n_threads, bool vae_decode_only) { StableDiffusion::StableDiffusion(int n_threads,
sd = std::make_shared<StableDiffusionGGML>(n_threads, vae_decode_only); bool vae_decode_only,
bool free_params_immediately) {
sd = std::make_shared<StableDiffusionGGML>(n_threads,
vae_decode_only,
free_params_immediately);
} }
bool StableDiffusion::load_from_file(const std::string& file_path) { bool StableDiffusion::load_from_file(const std::string& file_path) {
@ -3240,6 +3358,12 @@ std::vector<uint8_t> StableDiffusion::txt2img(const std::string& prompt,
int64_t t1 = ggml_time_ms(); int64_t t1 = ggml_time_ms();
LOG_INFO("get_learned_condition completed, taking %.2fs", (t1 - t0) * 1.0f / 1000); LOG_INFO("get_learned_condition completed, taking %.2fs", (t1 - t0) * 1.0f / 1000);
if (sd->free_params_immediately) {
sd->curr_params_mem_size -= ggml_used_mem(sd->clip_params_ctx);
ggml_free(sd->clip_params_ctx);
sd->clip_params_ctx = NULL;
}
int C = 4; int C = 4;
int W = width / 8; int W = width / 8;
int H = height / 8; int H = height / 8;
@ -3255,18 +3379,32 @@ std::vector<uint8_t> StableDiffusion::txt2img(const std::string& prompt,
int64_t t2 = ggml_time_ms(); int64_t t2 = ggml_time_ms();
LOG_INFO("sampling completed, taking %.2fs", (t2 - t1) * 1.0f / 1000); LOG_INFO("sampling completed, taking %.2fs", (t2 - t1) * 1.0f / 1000);
if (sd->free_params_immediately) {
sd->curr_params_mem_size -= ggml_used_mem(sd->unet_params_ctx);
ggml_free(sd->unet_params_ctx);
sd->unet_params_ctx = NULL;
}
struct ggml_tensor* img = sd->decode_first_stage(ctx, x_0); struct ggml_tensor* img = sd->decode_first_stage(ctx, x_0);
if (img != NULL) { if (img != NULL) {
result = ggml_to_image_vec(img); result = ggml_to_image_vec(img);
} }
int64_t t3 = ggml_time_ms(); int64_t t3 = ggml_time_ms();
LOG_INFO("decode_first_stage completed, taking %.2fs", (t3 - t2) * 1.0f / 1000); LOG_INFO("decode_first_stage completed, taking %.2fs", (t3 - t2) * 1.0f / 1000);
if (sd->free_params_immediately) {
sd->curr_params_mem_size -= ggml_used_mem(sd->vae_params_ctx);
ggml_free(sd->vae_params_ctx);
sd->vae_params_ctx = NULL;
}
LOG_INFO( LOG_INFO(
"txt2img completed in %.2fs, " "txt2img completed in %.2fs, use %.2fMB of memory: peak params memory %.2fMB, "
"with a runtime memory usage of %.2fMB and parameter memory usage of %.2fMB", "peak runtime memory %.2fMB",
(t3 - t0) * 1.0f / 1000, (t3 - t0) * 1.0f / 1000,
sd->max_rt_size * 1.0f / 1024 / 1024, sd->max_mem_size * 1.0f / 1024 / 1024,
ggml_used_mem(sd->params_ctx) * 1.0f / 1024 / 1024); sd->max_params_mem_size * 1.0f / 1024 / 1024,
sd->max_rt_mem_size * 1.0f / 1024 / 1024);
ggml_free(ctx); ggml_free(ctx);
return result; return result;
@ -3330,6 +3468,11 @@ std::vector<uint8_t> StableDiffusion::img2img(const std::vector<uint8_t>& init_i
} }
int64_t t2 = ggml_time_ms(); int64_t t2 = ggml_time_ms();
LOG_INFO("get_learned_condition completed, taking %.2fs", (t2 - t1) * 1.0f / 1000); LOG_INFO("get_learned_condition completed, taking %.2fs", (t2 - t1) * 1.0f / 1000);
if (sd->free_params_immediately) {
sd->curr_params_mem_size -= ggml_used_mem(sd->clip_params_ctx);
ggml_free(sd->clip_params_ctx);
sd->clip_params_ctx = NULL;
}
LOG_INFO("start sampling"); LOG_INFO("start sampling");
struct ggml_tensor* x_0 = sd->sample(ctx, init_latent, c, uc, cfg_scale, sample_method, sigma_sched); struct ggml_tensor* x_0 = sd->sample(ctx, init_latent, c, uc, cfg_scale, sample_method, sigma_sched);
@ -3337,6 +3480,11 @@ std::vector<uint8_t> StableDiffusion::img2img(const std::vector<uint8_t>& init_i
// print_ggml_tensor(x_0); // print_ggml_tensor(x_0);
int64_t t3 = ggml_time_ms(); int64_t t3 = ggml_time_ms();
LOG_INFO("sampling completed, taking %.2fs", (t3 - t2) * 1.0f / 1000); LOG_INFO("sampling completed, taking %.2fs", (t3 - t2) * 1.0f / 1000);
if (sd->free_params_immediately) {
sd->curr_params_mem_size -= ggml_used_mem(sd->unet_params_ctx);
ggml_free(sd->unet_params_ctx);
sd->unet_params_ctx = NULL;
}
struct ggml_tensor* img = sd->decode_first_stage(ctx, x_0); struct ggml_tensor* img = sd->decode_first_stage(ctx, x_0);
if (img != NULL) { if (img != NULL) {
@ -3344,12 +3492,20 @@ std::vector<uint8_t> StableDiffusion::img2img(const std::vector<uint8_t>& init_i
} }
int64_t t4 = ggml_time_ms(); int64_t t4 = ggml_time_ms();
LOG_INFO("decode_first_stage completed, taking %.2fs", (t4 - t3) * 1.0f / 1000); LOG_INFO("decode_first_stage completed, taking %.2fs", (t4 - t3) * 1.0f / 1000);
if (sd->free_params_immediately) {
sd->curr_params_mem_size -= ggml_used_mem(sd->vae_params_ctx);
ggml_free(sd->vae_params_ctx);
sd->vae_params_ctx = NULL;
}
LOG_INFO( LOG_INFO(
"img2img completed in %.2fs, " "img2img completed in %.2fs, use %.2fMB of memory: peak params memory %.2fMB, "
"with a runtime memory usage of %.2fMB and parameter memory usage of %.2fMB", "peak runtime memory %.2fMB",
(t4 - t0) * 1.0f / 1000, (t4 - t0) * 1.0f / 1000,
sd->max_rt_size * 1.0f / 1024 / 1024, sd->max_mem_size * 1.0f / 1024 / 1024,
ggml_used_mem(sd->params_ctx) * 1.0f / 1024 / 1024); sd->max_params_mem_size * 1.0f / 1024 / 1024,
sd->max_rt_mem_size * 1.0f / 1024 / 1024);
ggml_free(ctx); ggml_free(ctx);

3
stable-diffusion.h
View File

@ -23,7 +23,8 @@ class StableDiffusion {
public: public:
StableDiffusion(int n_threads = -1, StableDiffusion(int n_threads = -1,
bool vae_decode_only = false); bool vae_decode_only = false,
bool free_params_immediately = false);
bool load_from_file(const std::string& file_path); bool load_from_file(const std::string& file_path);
std::vector<uint8_t> txt2img( std::vector<uint8_t> txt2img(
const std::string& prompt, const std::string& prompt,