yindo/rocm-stable-diffusion.cpp
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feat: support independent sampler rng (#978)

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leejet
2025-11-16 17:11:02 +08:00
committed by GitHub
parent 6d6dc1b8ed
commit d5b05f70c6
4 changed files with 68 additions and 26 deletions
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38
stable-diffusion.cpp
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@@ -99,10 +99,11 @@ public:
bool vae_decode_only = false;
bool free_params_immediately = false;
std::shared_ptr<RNG> rng = std::make_shared<STDDefaultRNG>();
int n_threads = -1;
float scale_factor = 0.18215f;
float shift_factor = 0.f;
std::shared_ptr<RNG> rng = std::make_shared<PhiloxRNG>();
std::shared_ptr<RNG> sampler_rng = nullptr;
int n_threads = -1;
float scale_factor = 0.18215f;
float shift_factor = 0.f;
std::shared_ptr<Conditioner> cond_stage_model;
std::shared_ptr<FrozenCLIPVisionEmbedder> clip_vision; // for svd or wan2.1 i2v
@@ -188,6 +189,16 @@ public:
}
}
std::shared_ptr<RNG> get_rng(rng_type_t rng_type) {
if (rng_type == STD_DEFAULT_RNG) {
return std::make_shared<STDDefaultRNG>();
} else if (rng_type == CPU_RNG) {
return std::make_shared<MT19937RNG>();
} else { // default: CUDA_RNG
return std::make_shared<PhiloxRNG>();
}
}
bool init(const sd_ctx_params_t* sd_ctx_params) {
n_threads = sd_ctx_params->n_threads;
vae_decode_only = sd_ctx_params->vae_decode_only;
@@ -197,12 +208,11 @@ public:
use_tiny_autoencoder = taesd_path.size() > 0;
offload_params_to_cpu = sd_ctx_params->offload_params_to_cpu;
if (sd_ctx_params->rng_type == STD_DEFAULT_RNG) {
rng = std::make_shared<STDDefaultRNG>();
} else if (sd_ctx_params->rng_type == CUDA_RNG) {
rng = std::make_shared<PhiloxRNG>();
} else if (sd_ctx_params->rng_type == CPU_RNG) {
rng = std::make_shared<MT19937RNG>();
rng = get_rng(sd_ctx_params->rng_type);
if (sd_ctx_params->sampler_rng_type != RNG_TYPE_COUNT) {
sampler_rng = get_rng(sd_ctx_params->sampler_rng_type);
} else {
sampler_rng = rng;
}
ggml_log_set(ggml_log_callback_default, nullptr);
@@ -1736,7 +1746,7 @@ public:
return denoised;
};
sample_k_diffusion(method, denoise, work_ctx, x, sigmas, rng, eta);
sample_k_diffusion(method, denoise, work_ctx, x, sigmas, sampler_rng, eta);
if (inverse_noise_scaling) {
x = denoiser->inverse_noise_scaling(sigmas[sigmas.size() - 1], x);
@@ -2291,6 +2301,7 @@ void sd_ctx_params_init(sd_ctx_params_t* sd_ctx_params) {
sd_ctx_params->n_threads = get_num_physical_cores();
sd_ctx_params->wtype = SD_TYPE_COUNT;
sd_ctx_params->rng_type = CUDA_RNG;
sd_ctx_params->sampler_rng_type = RNG_TYPE_COUNT;
sd_ctx_params->prediction = DEFAULT_PRED;
sd_ctx_params->lora_apply_mode = LORA_APPLY_AUTO;
sd_ctx_params->offload_params_to_cpu = false;
@@ -2332,6 +2343,7 @@ char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params) {
"n_threads: %d\n"
"wtype: %s\n"
"rng_type: %s\n"
"sampler_rng_type: %s\n"
"prediction: %s\n"
"offload_params_to_cpu: %s\n"
"keep_clip_on_cpu: %s\n"
@@ -2362,6 +2374,7 @@ char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params) {
sd_ctx_params->n_threads,
sd_type_name(sd_ctx_params->wtype),
sd_rng_type_name(sd_ctx_params->rng_type),
sd_rng_type_name(sd_ctx_params->sampler_rng_type),
sd_prediction_name(sd_ctx_params->prediction),
BOOL_STR(sd_ctx_params->offload_params_to_cpu),
BOOL_STR(sd_ctx_params->keep_clip_on_cpu),
@@ -2823,6 +2836,7 @@ sd_image_t* generate_image_internal(sd_ctx_t* sd_ctx,
LOG_INFO("generating image: %i/%i - seed %" PRId64, b + 1, batch_count, cur_seed);
sd_ctx->sd->rng->manual_seed(cur_seed);
sd_ctx->sd->sampler_rng->manual_seed(cur_seed);
struct ggml_tensor* x_t = init_latent;
struct ggml_tensor* noise = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, W, H, C, 1);
ggml_ext_im_set_randn_f32(noise, sd_ctx->sd->rng);
@@ -2949,6 +2963,7 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_g
seed = rand();
}
sd_ctx->sd->rng->manual_seed(seed);
sd_ctx->sd->sampler_rng->manual_seed(seed);
int sample_steps = sd_img_gen_params->sample_params.sample_steps;
@@ -3240,6 +3255,7 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
}
sd_ctx->sd->rng->manual_seed(seed);
sd_ctx->sd->sampler_rng->manual_seed(seed);
int64_t t0 = ggml_time_ms();