alphard/stable-diffusion.cpp
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feat: add sd3.5 medium and skip layer guidance support (#451)

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* mmdit-x

* add support for sd3.5 medium

* add skip layer guidance support (mmdit only)

* ignore slg if slg_scale is zero (optimization)

* init out_skip once

* slg support for flux (expermiental)

* warn if version doesn't support slg

* refactor slg cli args

* set default slg_scale to 0 (oops)

* format code

---------

Co-authored-by: leejet <leejet714@gmail.com>
This commit is contained in:
stduhpf 2024-11-23 04:15:31 +01:00 committed by GitHub
parent ac54e00760
commit 65fa646684
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GPG Key ID: B5690EEEBB952194
9 changed files with 414 additions and 79 deletions
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17
diffusion_model.hpp
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@ -17,7 +17,8 @@ struct DiffusionModel {
std::vector<struct ggml_tensor*> controls = {}, std::vector<struct ggml_tensor*> controls = {},
float control_strength = 0.f, float control_strength = 0.f,
struct ggml_tensor** output = NULL, struct ggml_tensor** output = NULL,
struct ggml_context* output_ctx = NULL) = 0; struct ggml_context* output_ctx = NULL,
std::vector<int> skip_layers = std::vector<int>()) = 0;
virtual void alloc_params_buffer() = 0; virtual void alloc_params_buffer() = 0;
virtual void free_params_buffer() = 0; virtual void free_params_buffer() = 0;
virtual void free_compute_buffer() = 0; virtual void free_compute_buffer() = 0;
@ -70,7 +71,9 @@ struct UNetModel : public DiffusionModel {
std::vector<struct ggml_tensor*> controls = {}, std::vector<struct ggml_tensor*> controls = {},
float control_strength = 0.f, float control_strength = 0.f,
struct ggml_tensor** output = NULL, struct ggml_tensor** output = NULL,
struct ggml_context* output_ctx = NULL) { struct ggml_context* output_ctx = NULL,
std::vector<int> skip_layers = std::vector<int>()) {
(void)skip_layers; // SLG doesn't work with UNet models
return unet.compute(n_threads, x, timesteps, context, c_concat, y, num_video_frames, controls, control_strength, output, output_ctx); return unet.compute(n_threads, x, timesteps, context, c_concat, y, num_video_frames, controls, control_strength, output, output_ctx);
} }
}; };
@ -119,8 +122,9 @@ struct MMDiTModel : public DiffusionModel {
std::vector<struct ggml_tensor*> controls = {}, std::vector<struct ggml_tensor*> controls = {},
float control_strength = 0.f, float control_strength = 0.f,
struct ggml_tensor** output = NULL, struct ggml_tensor** output = NULL,
struct ggml_context* output_ctx = NULL) { struct ggml_context* output_ctx = NULL,
return mmdit.compute(n_threads, x, timesteps, context, y, output, output_ctx); std::vector<int> skip_layers = std::vector<int>()) {
return mmdit.compute(n_threads, x, timesteps, context, y, output, output_ctx, skip_layers);
} }
}; };
@ -168,8 +172,9 @@ struct FluxModel : public DiffusionModel {
std::vector<struct ggml_tensor*> controls = {}, std::vector<struct ggml_tensor*> controls = {},
float control_strength = 0.f, float control_strength = 0.f,
struct ggml_tensor** output = NULL, struct ggml_tensor** output = NULL,
struct ggml_context* output_ctx = NULL) { struct ggml_context* output_ctx = NULL,
return flux.compute(n_threads, x, timesteps, context, y, guidance, output, output_ctx); std::vector<int> skip_layers = std::vector<int>()) {
return flux.compute(n_threads, x, timesteps, context, y, guidance, output, output_ctx, skip_layers);
} }
}; };

83
examples/cli/main.cpp
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@ -119,6 +119,11 @@ struct SDParams {
bool canny_preprocess = false; bool canny_preprocess = false;
bool color = false; bool color = false;
int upscale_repeats = 1; int upscale_repeats = 1;
std::vector<int> skip_layers = {7, 8, 9};
float slg_scale = 0.;
float skip_layer_start = 0.01;
float skip_layer_end = 0.2;
}; };
void print_params(SDParams params) { void print_params(SDParams params) {
@ -151,6 +156,7 @@ void print_params(SDParams params) {
printf(" negative_prompt: %s\n", params.negative_prompt.c_str()); printf(" negative_prompt: %s\n", params.negative_prompt.c_str());
printf(" min_cfg: %.2f\n", params.min_cfg); printf(" min_cfg: %.2f\n", params.min_cfg);
printf(" cfg_scale: %.2f\n", params.cfg_scale); printf(" cfg_scale: %.2f\n", params.cfg_scale);
printf(" slg_scale: %.2f\n", params.slg_scale);
printf(" guidance: %.2f\n", params.guidance); printf(" guidance: %.2f\n", params.guidance);
printf(" clip_skip: %d\n", params.clip_skip); printf(" clip_skip: %d\n", params.clip_skip);
printf(" width: %d\n", params.width); printf(" width: %d\n", params.width);
@ -197,6 +203,12 @@ void print_usage(int argc, const char* argv[]) {
printf(" -p, --prompt [PROMPT] the prompt to render\n"); printf(" -p, --prompt [PROMPT] the prompt to render\n");
printf(" -n, --negative-prompt PROMPT the negative prompt (default: \"\")\n"); printf(" -n, --negative-prompt PROMPT the negative prompt (default: \"\")\n");
printf(" --cfg-scale SCALE unconditional guidance scale: (default: 7.0)\n"); printf(" --cfg-scale SCALE unconditional guidance scale: (default: 7.0)\n");
printf(" --slg-scale SCALE skip layer guidance (SLG) scale, only for DiT models: (default: 0)\n");
printf(" 0 means disabled, a value of 2.5 is nice for sd3.5 medium\n");
printf(" --skip_layers LAYERS Layers to skip for SLG steps: (default: [7,8,9])\n");
printf(" --skip_layer_start START SLG enabling point: (default: 0.01)\n");
printf(" --skip_layer_end END SLG disabling point: (default: 0.2)\n");
printf(" SLG will be enabled at step int([STEPS]*[START]) and disabled at int([STEPS]*[END])\n");
printf(" --strength STRENGTH strength for noising/unnoising (default: 0.75)\n"); printf(" --strength STRENGTH strength for noising/unnoising (default: 0.75)\n");
printf(" --style-ratio STYLE-RATIO strength for keeping input identity (default: 20%%)\n"); printf(" --style-ratio STYLE-RATIO strength for keeping input identity (default: 20%%)\n");
printf(" --control-strength STRENGTH strength to apply Control Net (default: 0.9)\n"); printf(" --control-strength STRENGTH strength to apply Control Net (default: 0.9)\n");
@ -534,6 +546,61 @@ void parse_args(int argc, const char** argv, SDParams& params) {
params.verbose = true; params.verbose = true;
} else if (arg == "--color") { } else if (arg == "--color") {
params.color = true; params.color = true;
} else if (arg == "--slg-scale") {
if (++i >= argc) {
invalid_arg = true;
break;
}
params.slg_scale = std::stof(argv[i]);
} else if (arg == "--skip-layers") {
if (++i >= argc) {
invalid_arg = true;
break;
}
if (argv[i][0] != '[') {
invalid_arg = true;
break;
}
std::string layers_str = argv[i];
while (layers_str.back() != ']') {
if (++i >= argc) {
invalid_arg = true;
break;
}
layers_str += " " + std::string(argv[i]);
}
layers_str = layers_str.substr(1, layers_str.size() - 2);
std::regex regex("[, ]+");
std::sregex_token_iterator iter(layers_str.begin(), layers_str.end(), regex, -1);
std::sregex_token_iterator end;
std::vector<std::string> tokens(iter, end);
std::vector<int> layers;
for (const auto& token : tokens) {
try {
layers.push_back(std::stoi(token));
} catch (const std::invalid_argument& e) {
invalid_arg = true;
break;
}
}
params.skip_layers = layers;
if (invalid_arg) {
break;
}
} else if (arg == "--skip-layer-start") {
if (++i >= argc) {
invalid_arg = true;
break;
}
params.skip_layer_start = std::stof(argv[i]);
} else if (arg == "--skip-layer-end") {
if (++i >= argc) {
invalid_arg = true;
break;
}
params.skip_layer_end = std::stof(argv[i]);
} else { } else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
print_usage(argc, argv); print_usage(argc, argv);
@ -624,6 +691,16 @@ std::string get_image_params(SDParams params, int64_t seed) {
} }
parameter_string += "Steps: " + std::to_string(params.sample_steps) + ", "; parameter_string += "Steps: " + std::to_string(params.sample_steps) + ", ";
parameter_string += "CFG scale: " + std::to_string(params.cfg_scale) + ", "; parameter_string += "CFG scale: " + std::to_string(params.cfg_scale) + ", ";
if (params.slg_scale != 0 && params.skip_layers.size() != 0) {
parameter_string += "SLG scale: " + std::to_string(params.cfg_scale) + ", ";
parameter_string += "Skip layers: [";
for (const auto& layer : params.skip_layers) {
parameter_string += std::to_string(layer) + ", ";
}
parameter_string += "], ";
parameter_string += "Skip layer start: " + std::to_string(params.skip_layer_start) + ", ";
parameter_string += "Skip layer end: " + std::to_string(params.skip_layer_end) + ", ";
}
parameter_string += "Guidance: " + std::to_string(params.guidance) + ", "; parameter_string += "Guidance: " + std::to_string(params.guidance) + ", ";
parameter_string += "Seed: " + std::to_string(seed) + ", "; parameter_string += "Seed: " + std::to_string(seed) + ", ";
parameter_string += "Size: " + std::to_string(params.width) + "x" + std::to_string(params.height) + ", "; parameter_string += "Size: " + std::to_string(params.width) + "x" + std::to_string(params.height) + ", ";
@ -840,7 +917,11 @@ int main(int argc, const char* argv[]) {
params.control_strength, params.control_strength,
params.style_ratio, params.style_ratio,
params.normalize_input, params.normalize_input,
params.input_id_images_path.c_str()); params.input_id_images_path.c_str(),
params.skip_layers,
params.slg_scale,
params.skip_layer_start,
params.skip_layer_end);
} else { } else {
sd_image_t input_image = {(uint32_t)params.width, sd_image_t input_image = {(uint32_t)params.width,
(uint32_t)params.height, (uint32_t)params.height,

26
flux.hpp
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@ -711,7 +711,8 @@ namespace Flux {
struct ggml_tensor* timesteps, struct ggml_tensor* timesteps,
struct ggml_tensor* y, struct ggml_tensor* y,
struct ggml_tensor* guidance, struct ggml_tensor* guidance,
struct ggml_tensor* pe) { struct ggml_tensor* pe,
std::vector<int> skip_layers = std::vector<int>()) {
auto img_in = std::dynamic_pointer_cast<Linear>(blocks["img_in"]); auto img_in = std::dynamic_pointer_cast<Linear>(blocks["img_in"]);
auto time_in = std::dynamic_pointer_cast<MLPEmbedder>(blocks["time_in"]); auto time_in = std::dynamic_pointer_cast<MLPEmbedder>(blocks["time_in"]);
auto vector_in = std::dynamic_pointer_cast<MLPEmbedder>(blocks["vector_in"]); auto vector_in = std::dynamic_pointer_cast<MLPEmbedder>(blocks["vector_in"]);
@ -733,6 +734,10 @@ namespace Flux {
txt = txt_in->forward(ctx, txt); txt = txt_in->forward(ctx, txt);
for (int i = 0; i < params.depth; i++) { for (int i = 0; i < params.depth; i++) {
if (skip_layers.size() > 0 && std::find(skip_layers.begin(), skip_layers.end(), i) != skip_layers.end()) {
continue;
}
auto block = std::dynamic_pointer_cast<DoubleStreamBlock>(blocks["double_blocks." + std::to_string(i)]); auto block = std::dynamic_pointer_cast<DoubleStreamBlock>(blocks["double_blocks." + std::to_string(i)]);
auto img_txt = block->forward(ctx, img, txt, vec, pe); auto img_txt = block->forward(ctx, img, txt, vec, pe);
@ -742,6 +747,9 @@ namespace Flux {
auto txt_img = ggml_concat(ctx, txt, img, 1); // [N, n_txt_token + n_img_token, hidden_size] auto txt_img = ggml_concat(ctx, txt, img, 1); // [N, n_txt_token + n_img_token, hidden_size]
for (int i = 0; i < params.depth_single_blocks; i++) { for (int i = 0; i < params.depth_single_blocks; i++) {
if (skip_layers.size() > 0 && std::find(skip_layers.begin(), skip_layers.end(), i + params.depth) != skip_layers.end()) {
continue;
}
auto block = std::dynamic_pointer_cast<SingleStreamBlock>(blocks["single_blocks." + std::to_string(i)]); auto block = std::dynamic_pointer_cast<SingleStreamBlock>(blocks["single_blocks." + std::to_string(i)]);
txt_img = block->forward(ctx, txt_img, vec, pe); txt_img = block->forward(ctx, txt_img, vec, pe);
@ -769,7 +777,8 @@ namespace Flux {
struct ggml_tensor* context, struct ggml_tensor* context,
struct ggml_tensor* y, struct ggml_tensor* y,
struct ggml_tensor* guidance, struct ggml_tensor* guidance,
struct ggml_tensor* pe) { struct ggml_tensor* pe,
std::vector<int> skip_layers = std::vector<int>()) {
// Forward pass of DiT. // Forward pass of DiT.
// x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) // x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
// timestep: (N,) tensor of diffusion timesteps // timestep: (N,) tensor of diffusion timesteps
@ -791,7 +800,7 @@ namespace Flux {
// img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) // img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
auto img = patchify(ctx, x, patch_size); // [N, h*w, C * patch_size * patch_size] auto img = patchify(ctx, x, patch_size); // [N, h*w, C * patch_size * patch_size]
auto out = forward_orig(ctx, img, context, timestep, y, guidance, pe); // [N, h*w, C * patch_size * patch_size] auto out = forward_orig(ctx, img, context, timestep, y, guidance, pe, skip_layers); // [N, h*w, C * patch_size * patch_size]
// rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2) // rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)
out = unpatchify(ctx, out, (H + pad_h) / patch_size, (W + pad_w) / patch_size, patch_size); // [N, C, H + pad_h, W + pad_w] out = unpatchify(ctx, out, (H + pad_h) / patch_size, (W + pad_w) / patch_size, patch_size); // [N, C, H + pad_h, W + pad_w]
@ -829,7 +838,8 @@ namespace Flux {
struct ggml_tensor* timesteps, struct ggml_tensor* timesteps,
struct ggml_tensor* context, struct ggml_tensor* context,
struct ggml_tensor* y, struct ggml_tensor* y,
struct ggml_tensor* guidance) { struct ggml_tensor* guidance,
std::vector<int> skip_layers = std::vector<int>()) {
GGML_ASSERT(x->ne[3] == 1); GGML_ASSERT(x->ne[3] == 1);
struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, FLUX_GRAPH_SIZE, false); struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, FLUX_GRAPH_SIZE, false);
@ -856,7 +866,8 @@ namespace Flux {
context, context,
y, y,
guidance, guidance,
pe); pe,
skip_layers);
ggml_build_forward_expand(gf, out); ggml_build_forward_expand(gf, out);
@ -870,14 +881,15 @@ namespace Flux {
struct ggml_tensor* y, struct ggml_tensor* y,
struct ggml_tensor* guidance, struct ggml_tensor* guidance,
struct ggml_tensor** output = NULL, struct ggml_tensor** output = NULL,
struct ggml_context* output_ctx = NULL) { struct ggml_context* output_ctx = NULL,
std::vector<int> skip_layers = std::vector<int>()) {
// x: [N, in_channels, h, w] // x: [N, in_channels, h, w]
// timesteps: [N, ] // timesteps: [N, ]
// context: [N, max_position, hidden_size] // context: [N, max_position, hidden_size]
// y: [N, adm_in_channels] or [1, adm_in_channels] // y: [N, adm_in_channels] or [1, adm_in_channels]
// guidance: [N, ] // guidance: [N, ]
auto get_graph = [&]() -> struct ggml_cgraph* { auto get_graph = [&]() -> struct ggml_cgraph* {
return build_graph(x, timesteps, context, y, guidance); return build_graph(x, timesteps, context, y, guidance, skip_layers);
}; };
GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx); GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);

194
mmdit.hpp
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@ -252,6 +252,7 @@ struct DismantledBlock : public GGMLBlock {
public: public:
int64_t num_heads; int64_t num_heads;
bool pre_only; bool pre_only;
bool self_attn;
public: public:
DismantledBlock(int64_t hidden_size, DismantledBlock(int64_t hidden_size,
@ -259,14 +260,19 @@ public:
float mlp_ratio = 4.0, float mlp_ratio = 4.0,
std::string qk_norm = "", std::string qk_norm = "",
bool qkv_bias = false, bool qkv_bias = false,
bool pre_only = false) bool pre_only = false,
: num_heads(num_heads), pre_only(pre_only) { bool self_attn = false)
: num_heads(num_heads), pre_only(pre_only), self_attn(self_attn) {
// rmsnorm is always Flase // rmsnorm is always Flase
// scale_mod_only is always Flase // scale_mod_only is always Flase
// swiglu is always Flase // swiglu is always Flase
blocks["norm1"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-06f, false)); blocks["norm1"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-06f, false));
blocks["attn"] = std::shared_ptr<GGMLBlock>(new SelfAttention(hidden_size, num_heads, qk_norm, qkv_bias, pre_only)); blocks["attn"] = std::shared_ptr<GGMLBlock>(new SelfAttention(hidden_size, num_heads, qk_norm, qkv_bias, pre_only));
if (self_attn) {
blocks["attn2"] = std::shared_ptr<GGMLBlock>(new SelfAttention(hidden_size, num_heads, qk_norm, qkv_bias, false));
}
if (!pre_only) { if (!pre_only) {
blocks["norm2"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-06f, false)); blocks["norm2"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-06f, false));
int64_t mlp_hidden_dim = (int64_t)(hidden_size * mlp_ratio); int64_t mlp_hidden_dim = (int64_t)(hidden_size * mlp_ratio);
@ -277,9 +283,52 @@ public:
if (pre_only) { if (pre_only) {
n_mods = 2; n_mods = 2;
} }
if (self_attn) {
n_mods = 9;
}
blocks["adaLN_modulation.1"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, n_mods * hidden_size)); blocks["adaLN_modulation.1"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, n_mods * hidden_size));
} }
std::tuple<std::vector<struct ggml_tensor*>, std::vector<struct ggml_tensor*>, std::vector<struct ggml_tensor*>> pre_attention_x(struct ggml_context* ctx,
struct ggml_tensor* x,
struct ggml_tensor* c) {
GGML_ASSERT(self_attn);
// x: [N, n_token, hidden_size]
// c: [N, hidden_size]
auto norm1 = std::dynamic_pointer_cast<LayerNorm>(blocks["norm1"]);
auto attn = std::dynamic_pointer_cast<SelfAttention>(blocks["attn"]);
auto attn2 = std::dynamic_pointer_cast<SelfAttention>(blocks["attn2"]);
auto adaLN_modulation_1 = std::dynamic_pointer_cast<Linear>(blocks["adaLN_modulation.1"]);
int64_t n_mods = 9;
auto m = adaLN_modulation_1->forward(ctx, ggml_silu(ctx, c)); // [N, n_mods * hidden_size]
m = ggml_reshape_3d(ctx, m, c->ne[0], n_mods, c->ne[1]); // [N, n_mods, hidden_size]
m = ggml_cont(ctx, ggml_permute(ctx, m, 0, 2, 1, 3)); // [n_mods, N, hidden_size]
int64_t offset = m->nb[1] * m->ne[1];
auto shift_msa = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 0); // [N, hidden_size]
auto scale_msa = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 1); // [N, hidden_size]
auto gate_msa = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 2); // [N, hidden_size]
auto shift_mlp = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 3); // [N, hidden_size]
auto scale_mlp = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 4); // [N, hidden_size]
auto gate_mlp = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 5); // [N, hidden_size]
auto shift_msa2 = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 6); // [N, hidden_size]
auto scale_msa2 = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 7); // [N, hidden_size]
auto gate_msa2 = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 8); // [N, hidden_size]
auto x_norm = norm1->forward(ctx, x);
auto attn_in = modulate(ctx, x_norm, shift_msa, scale_msa);
auto qkv = attn->pre_attention(ctx, attn_in);
auto attn2_in = modulate(ctx, x_norm, shift_msa2, scale_msa2);
auto qkv2 = attn2->pre_attention(ctx, attn2_in);
return {qkv, qkv2, {x, gate_msa, shift_mlp, scale_mlp, gate_mlp, gate_msa2}};
}
std::pair<std::vector<struct ggml_tensor*>, std::vector<struct ggml_tensor*>> pre_attention(struct ggml_context* ctx, std::pair<std::vector<struct ggml_tensor*>, std::vector<struct ggml_tensor*>> pre_attention(struct ggml_context* ctx,
struct ggml_tensor* x, struct ggml_tensor* x,
struct ggml_tensor* c) { struct ggml_tensor* c) {
@ -319,6 +368,44 @@ public:
} }
} }
struct ggml_tensor* post_attention_x(struct ggml_context* ctx,
struct ggml_tensor* attn_out,
struct ggml_tensor* attn2_out,
struct ggml_tensor* x,
struct ggml_tensor* gate_msa,
struct ggml_tensor* shift_mlp,
struct ggml_tensor* scale_mlp,
struct ggml_tensor* gate_mlp,
struct ggml_tensor* gate_msa2) {
// attn_out: [N, n_token, hidden_size]
// x: [N, n_token, hidden_size]
// gate_msa: [N, hidden_size]
// shift_mlp: [N, hidden_size]
// scale_mlp: [N, hidden_size]
// gate_mlp: [N, hidden_size]
// return: [N, n_token, hidden_size]
GGML_ASSERT(!pre_only);
auto attn = std::dynamic_pointer_cast<SelfAttention>(blocks["attn"]);
auto attn2 = std::dynamic_pointer_cast<SelfAttention>(blocks["attn2"]);
auto norm2 = std::dynamic_pointer_cast<LayerNorm>(blocks["norm2"]);
auto mlp = std::dynamic_pointer_cast<Mlp>(blocks["mlp"]);
gate_msa = ggml_reshape_3d(ctx, gate_msa, gate_msa->ne[0], 1, gate_msa->ne[1]); // [N, 1, hidden_size]
gate_mlp = ggml_reshape_3d(ctx, gate_mlp, gate_mlp->ne[0], 1, gate_mlp->ne[1]); // [N, 1, hidden_size]
gate_msa2 = ggml_reshape_3d(ctx, gate_msa2, gate_msa2->ne[0], 1, gate_msa2->ne[1]); // [N, 1, hidden_size]
attn_out = attn->post_attention(ctx, attn_out);
attn2_out = attn2->post_attention(ctx, attn2_out);
x = ggml_add(ctx, x, ggml_mul(ctx, attn_out, gate_msa));
x = ggml_add(ctx, x, ggml_mul(ctx, attn2_out, gate_msa2));
auto mlp_out = mlp->forward(ctx, modulate(ctx, norm2->forward(ctx, x), shift_mlp, scale_mlp));
x = ggml_add(ctx, x, ggml_mul(ctx, mlp_out, gate_mlp));
return x;
}
struct ggml_tensor* post_attention(struct ggml_context* ctx, struct ggml_tensor* post_attention(struct ggml_context* ctx,
struct ggml_tensor* attn_out, struct ggml_tensor* attn_out,
struct ggml_tensor* x, struct ggml_tensor* x,
@ -357,7 +444,28 @@ public:
// return: [N, n_token, hidden_size] // return: [N, n_token, hidden_size]
auto attn = std::dynamic_pointer_cast<SelfAttention>(blocks["attn"]); auto attn = std::dynamic_pointer_cast<SelfAttention>(blocks["attn"]);
if (self_attn) {
auto qkv_intermediates = pre_attention_x(ctx, x, c);
// auto qkv = qkv_intermediates.first;
// auto intermediates = qkv_intermediates.second;
// no longer a pair, but a tuple
auto qkv = std::get<0>(qkv_intermediates);
auto qkv2 = std::get<1>(qkv_intermediates);
auto intermediates = std::get<2>(qkv_intermediates);
auto attn_out = ggml_nn_attention_ext(ctx, qkv[0], qkv[1], qkv[2], num_heads); // [N, n_token, dim]
auto attn2_out = ggml_nn_attention_ext(ctx, qkv2[0], qkv2[1], qkv2[2], num_heads); // [N, n_token, dim]
x = post_attention_x(ctx,
attn_out,
attn2_out,
intermediates[0],
intermediates[1],
intermediates[2],
intermediates[3],
intermediates[4],
intermediates[5]);
return x; // [N, n_token, dim]
} else {
auto qkv_intermediates = pre_attention(ctx, x, c); auto qkv_intermediates = pre_attention(ctx, x, c);
auto qkv = qkv_intermediates.first; auto qkv = qkv_intermediates.first;
auto intermediates = qkv_intermediates.second; auto intermediates = qkv_intermediates.second;
@ -372,9 +480,11 @@ public:
intermediates[4]); intermediates[4]);
return x; // [N, n_token, dim] return x; // [N, n_token, dim]
} }
}
}; };
__STATIC_INLINE__ std::pair<struct ggml_tensor*, struct ggml_tensor*> block_mixing(struct ggml_context* ctx, __STATIC_INLINE__ std::pair<struct ggml_tensor*, struct ggml_tensor*>
block_mixing(struct ggml_context* ctx,
struct ggml_tensor* context, struct ggml_tensor* context,
struct ggml_tensor* x, struct ggml_tensor* x,
struct ggml_tensor* c, struct ggml_tensor* c,
@ -387,10 +497,18 @@ __STATIC_INLINE__ std::pair<struct ggml_tensor*, struct ggml_tensor*> block_mixi
auto context_qkv = context_qkv_intermediates.first; auto context_qkv = context_qkv_intermediates.first;
auto context_intermediates = context_qkv_intermediates.second; auto context_intermediates = context_qkv_intermediates.second;
auto x_qkv_intermediates = x_block->pre_attention(ctx, x, c); std::vector<ggml_tensor*> x_qkv, x_qkv2, x_intermediates;
auto x_qkv = x_qkv_intermediates.first;
auto x_intermediates = x_qkv_intermediates.second;
if (x_block->self_attn) {
auto x_qkv_intermediates = x_block->pre_attention_x(ctx, x, c);
x_qkv = std::get<0>(x_qkv_intermediates);
x_qkv2 = std::get<1>(x_qkv_intermediates);
x_intermediates = std::get<2>(x_qkv_intermediates);
} else {
auto x_qkv_intermediates = x_block->pre_attention(ctx, x, c);
x_qkv = x_qkv_intermediates.first;
x_intermediates = x_qkv_intermediates.second;
}
std::vector<struct ggml_tensor*> qkv; std::vector<struct ggml_tensor*> qkv;
for (int i = 0; i < 3; i++) { for (int i = 0; i < 3; i++) {
qkv.push_back(ggml_concat(ctx, context_qkv[i], x_qkv[i], 1)); qkv.push_back(ggml_concat(ctx, context_qkv[i], x_qkv[i], 1));
@ -429,6 +547,19 @@ __STATIC_INLINE__ std::pair<struct ggml_tensor*, struct ggml_tensor*> block_mixi
context = NULL; context = NULL;
} }
if (x_block->self_attn) {
auto attn2 = ggml_nn_attention_ext(ctx, x_qkv2[0], x_qkv2[1], x_qkv2[2], x_block->num_heads); // [N, n_token, hidden_size]
x = x_block->post_attention_x(ctx,
x_attn,
attn2,
x_intermediates[0],
x_intermediates[1],
x_intermediates[2],
x_intermediates[3],
x_intermediates[4],
x_intermediates[5]);
} else {
x = x_block->post_attention(ctx, x = x_block->post_attention(ctx,
x_attn, x_attn,
x_intermediates[0], x_intermediates[0],
@ -436,6 +567,7 @@ __STATIC_INLINE__ std::pair<struct ggml_tensor*, struct ggml_tensor*> block_mixi
x_intermediates[2], x_intermediates[2],
x_intermediates[3], x_intermediates[3],
x_intermediates[4]); x_intermediates[4]);
}
return {context, x}; return {context, x};
} }
@ -447,9 +579,10 @@ public:
float mlp_ratio = 4.0, float mlp_ratio = 4.0,
std::string qk_norm = "", std::string qk_norm = "",
bool qkv_bias = false, bool qkv_bias = false,
bool pre_only = false) { bool pre_only = false,
bool self_attn_x = false) {
blocks["context_block"] = std::shared_ptr<GGMLBlock>(new DismantledBlock(hidden_size, num_heads, mlp_ratio, qk_norm, qkv_bias, pre_only)); blocks["context_block"] = std::shared_ptr<GGMLBlock>(new DismantledBlock(hidden_size, num_heads, mlp_ratio, qk_norm, qkv_bias, pre_only));
blocks["x_block"] = std::shared_ptr<GGMLBlock>(new DismantledBlock(hidden_size, num_heads, mlp_ratio, qk_norm, qkv_bias, false)); blocks["x_block"] = std::shared_ptr<GGMLBlock>(new DismantledBlock(hidden_size, num_heads, mlp_ratio, qk_norm, qkv_bias, false, self_attn_x));
} }
std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(struct ggml_context* ctx, std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(struct ggml_context* ctx,
@ -507,6 +640,7 @@ protected:
int64_t input_size = -1; int64_t input_size = -1;
int64_t patch_size = 2; int64_t patch_size = 2;
int64_t in_channels = 16; int64_t in_channels = 16;
int64_t d_self = -1; // >=0 for MMdiT-X
int64_t depth = 24; int64_t depth = 24;
float mlp_ratio = 4.0f; float mlp_ratio = 4.0f;
int64_t adm_in_channels = 2048; int64_t adm_in_channels = 2048;
@ -561,6 +695,20 @@ public:
context_size = 4096; context_size = 4096;
context_embedder_out_dim = 2432; context_embedder_out_dim = 2432;
qk_norm = "rms"; qk_norm = "rms";
} else if (version == VERSION_SD3_5_2B) {
input_size = -1;
patch_size = 2;
in_channels = 16;
depth = 24;
d_self = 12;
mlp_ratio = 4.0f;
adm_in_channels = 2048;
out_channels = 16;
pos_embed_max_size = 384;
num_patchs = 147456;
context_size = 4096;
context_embedder_out_dim = 1536;
qk_norm = "rms";
} }
int64_t default_out_channels = in_channels; int64_t default_out_channels = in_channels;
hidden_size = 64 * depth; hidden_size = 64 * depth;
@ -581,13 +729,15 @@ public:
mlp_ratio, mlp_ratio,
qk_norm, qk_norm,
true, true,
i == depth - 1)); i == depth - 1,
i <= d_self));
} }
blocks["final_layer"] = std::shared_ptr<GGMLBlock>(new FinalLayer(hidden_size, patch_size, out_channels)); blocks["final_layer"] = std::shared_ptr<GGMLBlock>(new FinalLayer(hidden_size, patch_size, out_channels));
} }
struct ggml_tensor* cropped_pos_embed(struct ggml_context* ctx, struct ggml_tensor*
cropped_pos_embed(struct ggml_context* ctx,
int64_t h, int64_t h,
int64_t w) { int64_t w) {
auto pos_embed = params["pos_embed"]; auto pos_embed = params["pos_embed"];
@ -651,7 +801,8 @@ public:
struct ggml_tensor* forward_core_with_concat(struct ggml_context* ctx, struct ggml_tensor* forward_core_with_concat(struct ggml_context* ctx,
struct ggml_tensor* x, struct ggml_tensor* x,
struct ggml_tensor* c_mod, struct ggml_tensor* c_mod,
struct ggml_tensor* context) { struct ggml_tensor* context,
std::vector<int> skip_layers = std::vector<int>()) {
// x: [N, H*W, hidden_size] // x: [N, H*W, hidden_size]
// context: [N, n_context, d_context] // context: [N, n_context, d_context]
// c: [N, hidden_size] // c: [N, hidden_size]
@ -659,6 +810,11 @@ public:
auto final_layer = std::dynamic_pointer_cast<FinalLayer>(blocks["final_layer"]); auto final_layer = std::dynamic_pointer_cast<FinalLayer>(blocks["final_layer"]);
for (int i = 0; i < depth; i++) { for (int i = 0; i < depth; i++) {
// skip iteration if i is in skip_layers
if (skip_layers.size() > 0 && std::find(skip_layers.begin(), skip_layers.end(), i) != skip_layers.end()) {
continue;
}
auto block = std::dynamic_pointer_cast<JointBlock>(blocks["joint_blocks." + std::to_string(i)]); auto block = std::dynamic_pointer_cast<JointBlock>(blocks["joint_blocks." + std::to_string(i)]);
auto context_x = block->forward(ctx, context, x, c_mod); auto context_x = block->forward(ctx, context, x, c_mod);
@ -675,7 +831,8 @@ public:
struct ggml_tensor* x, struct ggml_tensor* x,
struct ggml_tensor* t, struct ggml_tensor* t,
struct ggml_tensor* y = NULL, struct ggml_tensor* y = NULL,
struct ggml_tensor* context = NULL) { struct ggml_tensor* context = NULL,
std::vector<int> skip_layers = std::vector<int>()) {
// Forward pass of DiT. // Forward pass of DiT.
// x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) // x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
// t: (N,) tensor of diffusion timesteps // t: (N,) tensor of diffusion timesteps
@ -706,7 +863,7 @@ public:
context = context_embedder->forward(ctx, context); // [N, L, D] aka [N, L, 1536] context = context_embedder->forward(ctx, context); // [N, L, D] aka [N, L, 1536]
} }
x = forward_core_with_concat(ctx, x, c, context); // (N, H*W, patch_size ** 2 * out_channels) x = forward_core_with_concat(ctx, x, c, context, skip_layers); // (N, H*W, patch_size ** 2 * out_channels)
x = unpatchify(ctx, x, h, w); // [N, C, H, W] x = unpatchify(ctx, x, h, w); // [N, C, H, W]
@ -735,7 +892,8 @@ struct MMDiTRunner : public GGMLRunner {
struct ggml_cgraph* build_graph(struct ggml_tensor* x, struct ggml_cgraph* build_graph(struct ggml_tensor* x,
struct ggml_tensor* timesteps, struct ggml_tensor* timesteps,
struct ggml_tensor* context, struct ggml_tensor* context,
struct ggml_tensor* y) { struct ggml_tensor* y,
std::vector<int> skip_layers = std::vector<int>()) {
struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, MMDIT_GRAPH_SIZE, false); struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, MMDIT_GRAPH_SIZE, false);
x = to_backend(x); x = to_backend(x);
@ -747,7 +905,8 @@ struct MMDiTRunner : public GGMLRunner {
x, x,
timesteps, timesteps,
y, y,
context); context,
skip_layers);
ggml_build_forward_expand(gf, out); ggml_build_forward_expand(gf, out);
@ -760,13 +919,14 @@ struct MMDiTRunner : public GGMLRunner {
struct ggml_tensor* context, struct ggml_tensor* context,
struct ggml_tensor* y, struct ggml_tensor* y,
struct ggml_tensor** output = NULL, struct ggml_tensor** output = NULL,
struct ggml_context* output_ctx = NULL) { struct ggml_context* output_ctx = NULL,
std::vector<int> skip_layers = std::vector<int>()) {
// x: [N, in_channels, h, w] // x: [N, in_channels, h, w]
// timesteps: [N, ] // timesteps: [N, ]
// context: [N, max_position, hidden_size]([N, 154, 4096]) or [1, max_position, hidden_size] // context: [N, max_position, hidden_size]([N, 154, 4096]) or [1, max_position, hidden_size]
// y: [N, adm_in_channels] or [1, adm_in_channels] // y: [N, adm_in_channels] or [1, adm_in_channels]
auto get_graph = [&]() -> struct ggml_cgraph* { auto get_graph = [&]() -> struct ggml_cgraph* {
return build_graph(x, timesteps, context, y); return build_graph(x, timesteps, context, y, skip_layers);
}; };
GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx); GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);

3
model.cpp
View File

@ -1373,6 +1373,9 @@ SDVersion ModelLoader::get_sd_version() {
if (tensor_storage.name.find("model.diffusion_model.double_blocks.") != std::string::npos) { if (tensor_storage.name.find("model.diffusion_model.double_blocks.") != std::string::npos) {
is_flux = true; is_flux = true;
} }
if (tensor_storage.name.find("joint_blocks.0.x_block.attn2.ln_q.weight") != std::string::npos) {
return VERSION_SD3_5_2B;
}
if (tensor_storage.name.find("joint_blocks.37.x_block.attn.ln_q.weight") != std::string::npos) { if (tensor_storage.name.find("joint_blocks.37.x_block.attn.ln_q.weight") != std::string::npos) {
return VERSION_SD3_5_8B; return VERSION_SD3_5_8B;
} }

1
model.h
View File

@ -26,6 +26,7 @@ enum SDVersion {
VERSION_FLUX_DEV, VERSION_FLUX_DEV,
VERSION_FLUX_SCHNELL, VERSION_FLUX_SCHNELL,
VERSION_SD3_5_8B, VERSION_SD3_5_8B,
VERSION_SD3_5_2B,
VERSION_COUNT, VERSION_COUNT,
}; };

101
stable-diffusion.cpp
View File

@ -32,7 +32,8 @@ const char* model_version_to_str[] = {
"SD3 2B", "SD3 2B",
"Flux Dev", "Flux Dev",
"Flux Schnell", "Flux Schnell",
"SD3.5 8B"}; "SD3.5 8B",
"SD3.5 2B"};
const char* sampling_methods_str[] = { const char* sampling_methods_str[] = {
"Euler A", "Euler A",
@ -288,7 +289,7 @@ public:
"try specifying SDXL VAE FP16 Fix with the --vae parameter. " "try specifying SDXL VAE FP16 Fix with the --vae parameter. "
"You can find it here: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/blob/main/sdxl_vae.safetensors"); "You can find it here: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/blob/main/sdxl_vae.safetensors");
} }
} else if (version == VERSION_SD3_2B || version == VERSION_SD3_5_8B) { } else if (version == VERSION_SD3_2B || version == VERSION_SD3_5_8B || version == VERSION_SD3_5_2B) {
scale_factor = 1.5305f; scale_factor = 1.5305f;
} else if (version == VERSION_FLUX_DEV || version == VERSION_FLUX_SCHNELL) { } else if (version == VERSION_FLUX_DEV || version == VERSION_FLUX_SCHNELL) {
scale_factor = 0.3611; scale_factor = 0.3611;
@ -311,7 +312,7 @@ public:
} else { } else {
clip_backend = backend; clip_backend = backend;
bool use_t5xxl = false; bool use_t5xxl = false;
if (version == VERSION_SD3_2B || version == VERSION_SD3_5_8B || version == VERSION_FLUX_DEV || version == VERSION_FLUX_SCHNELL) { if (version == VERSION_SD3_2B || version == VERSION_SD3_5_8B || version == VERSION_SD3_5_2B || version == VERSION_FLUX_DEV || version == VERSION_FLUX_SCHNELL) {
use_t5xxl = true; use_t5xxl = true;
} }
if (!ggml_backend_is_cpu(backend) && use_t5xxl && conditioner_wtype != GGML_TYPE_F32) { if (!ggml_backend_is_cpu(backend) && use_t5xxl && conditioner_wtype != GGML_TYPE_F32) {
@ -322,7 +323,7 @@ public:
LOG_INFO("CLIP: Using CPU backend"); LOG_INFO("CLIP: Using CPU backend");
clip_backend = ggml_backend_cpu_init(); clip_backend = ggml_backend_cpu_init();
} }
if (version == VERSION_SD3_2B || version == VERSION_SD3_5_8B) { if (version == VERSION_SD3_2B || version == VERSION_SD3_5_8B || version == VERSION_SD3_5_2B) {
cond_stage_model = std::make_shared<SD3CLIPEmbedder>(clip_backend, conditioner_wtype); cond_stage_model = std::make_shared<SD3CLIPEmbedder>(clip_backend, conditioner_wtype);
diffusion_model = std::make_shared<MMDiTModel>(backend, diffusion_model_wtype, version); diffusion_model = std::make_shared<MMDiTModel>(backend, diffusion_model_wtype, version);
} else if (version == VERSION_FLUX_DEV || version == VERSION_FLUX_SCHNELL) { } else if (version == VERSION_FLUX_DEV || version == VERSION_FLUX_SCHNELL) {
@ -520,7 +521,7 @@ public:
is_using_v_parameterization = true; is_using_v_parameterization = true;
} }
if (version == VERSION_SD3_2B || version == VERSION_SD3_5_8B) { if (version == VERSION_SD3_2B || version == VERSION_SD3_5_8B || version == VERSION_SD3_5_2B) {
LOG_INFO("running in FLOW mode"); LOG_INFO("running in FLOW mode");
denoiser = std::make_shared<DiscreteFlowDenoiser>(); denoiser = std::make_shared<DiscreteFlowDenoiser>();
} else if (version == VERSION_FLUX_DEV || version == VERSION_FLUX_SCHNELL) { } else if (version == VERSION_FLUX_DEV || version == VERSION_FLUX_SCHNELL) {
@ -771,7 +772,11 @@ public:
sample_method_t method, sample_method_t method,
const std::vector<float>& sigmas, const std::vector<float>& sigmas,
int start_merge_step, int start_merge_step,
SDCondition id_cond) { SDCondition id_cond,
std::vector<int> skip_layers = {},
float slg_scale = 2.5,
float skip_layer_start = 0.01,
float skip_layer_end = 0.2) {
size_t steps = sigmas.size() - 1; size_t steps = sigmas.size() - 1;
// noise = load_tensor_from_file(work_ctx, "./rand0.bin"); // noise = load_tensor_from_file(work_ctx, "./rand0.bin");
// print_ggml_tensor(noise); // print_ggml_tensor(noise);
@ -782,13 +787,24 @@ public:
struct ggml_tensor* noised_input = ggml_dup_tensor(work_ctx, noise); struct ggml_tensor* noised_input = ggml_dup_tensor(work_ctx, noise);
bool has_unconditioned = cfg_scale != 1.0 && uncond.c_crossattn != NULL; bool has_unconditioned = cfg_scale != 1.0 && uncond.c_crossattn != NULL;
bool has_skiplayer = slg_scale != 0.0 && skip_layers.size() > 0;
// denoise wrapper // denoise wrapper
struct ggml_tensor* out_cond = ggml_dup_tensor(work_ctx, x); struct ggml_tensor* out_cond = ggml_dup_tensor(work_ctx, x);
struct ggml_tensor* out_uncond = NULL; struct ggml_tensor* out_uncond = NULL;
struct ggml_tensor* out_skip = NULL;
if (has_unconditioned) { if (has_unconditioned) {
out_uncond = ggml_dup_tensor(work_ctx, x); out_uncond = ggml_dup_tensor(work_ctx, x);
} }
if (has_skiplayer) {
if (version == VERSION_SD3_2B || version == VERSION_SD3_5_2B || version == VERSION_SD3_5_8B || version == VERSION_FLUX_DEV || version == VERSION_FLUX_SCHNELL) {
out_skip = ggml_dup_tensor(work_ctx, x);
} else {
has_skiplayer = false;
LOG_WARN("SLG is incompatible with %s models", model_version_to_str[version]);
}
}
struct ggml_tensor* denoised = ggml_dup_tensor(work_ctx, x); struct ggml_tensor* denoised = ggml_dup_tensor(work_ctx, x);
auto denoise = [&](ggml_tensor* input, float sigma, int step) -> ggml_tensor* { auto denoise = [&](ggml_tensor* input, float sigma, int step) -> ggml_tensor* {
@ -869,6 +885,28 @@ public:
&out_uncond); &out_uncond);
negative_data = (float*)out_uncond->data; negative_data = (float*)out_uncond->data;
} }
int step_count = sigmas.size();
bool is_skiplayer_step = has_skiplayer && step > (int)(skip_layer_start * step_count) && step < (int)(skip_layer_end * step_count);
float* skip_layer_data = NULL;
if (is_skiplayer_step) {
LOG_DEBUG("Skipping layers at step %d\n", step);
// skip layer (same as conditionned)
diffusion_model->compute(n_threads,
noised_input,
timesteps,
cond.c_crossattn,
cond.c_concat,
cond.c_vector,
guidance_tensor,
-1,
controls,
control_strength,
&out_skip,
NULL,
skip_layers);
skip_layer_data = (float*)out_skip->data;
}
float* vec_denoised = (float*)denoised->data; float* vec_denoised = (float*)denoised->data;
float* vec_input = (float*)input->data; float* vec_input = (float*)input->data;
float* positive_data = (float*)out_cond->data; float* positive_data = (float*)out_cond->data;
@ -885,6 +923,9 @@ public:
latent_result = negative_data[i] + cfg_scale * (positive_data[i] - negative_data[i]); latent_result = negative_data[i] + cfg_scale * (positive_data[i] - negative_data[i]);
} }
} }
if (is_skiplayer_step) {
latent_result = latent_result + (positive_data[i] - skip_layer_data[i]) * slg_scale;
}
// v = latent_result, eps = latent_result // v = latent_result, eps = latent_result
// denoised = (v * c_out + input * c_skip) or (input + eps * c_out) // denoised = (v * c_out + input * c_skip) or (input + eps * c_out)
vec_denoised[i] = latent_result * c_out + vec_input[i] * c_skip; vec_denoised[i] = latent_result * c_out + vec_input[i] * c_skip;
@ -948,7 +989,7 @@ public:
if (use_tiny_autoencoder) { if (use_tiny_autoencoder) {
C = 4; C = 4;
} else { } else {
if (version == VERSION_SD3_2B || version == VERSION_SD3_5_8B) { if (version == VERSION_SD3_2B || version == VERSION_SD3_5_8B || version == VERSION_SD3_5_2B) {
C = 32; C = 32;
} else if (version == VERSION_FLUX_DEV || version == VERSION_FLUX_SCHNELL) { } else if (version == VERSION_FLUX_DEV || version == VERSION_FLUX_SCHNELL) {
C = 32; C = 32;
@ -1111,7 +1152,11 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx,
float control_strength, float control_strength,
float style_ratio, float style_ratio,
bool normalize_input, bool normalize_input,
std::string input_id_images_path) { std::string input_id_images_path,
std::vector<int> skip_layers = {},
float slg_scale = 2.5,
float skip_layer_start = 0.01,
float skip_layer_end = 0.2) {
if (seed < 0) { if (seed < 0) {
// Generally, when using the provided command line, the seed is always >0. // Generally, when using the provided command line, the seed is always >0.
// However, to prevent potential issues if 'stable-diffusion.cpp' is invoked as a library // However, to prevent potential issues if 'stable-diffusion.cpp' is invoked as a library
@ -1281,7 +1326,7 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx,
// Sample // Sample
std::vector<struct ggml_tensor*> final_latents; // collect latents to decode std::vector<struct ggml_tensor*> final_latents; // collect latents to decode
int C = 4; int C = 4;
if (sd_ctx->sd->version == VERSION_SD3_2B || sd_ctx->sd->version == VERSION_SD3_5_8B) { if (sd_ctx->sd->version == VERSION_SD3_2B || sd_ctx->sd->version == VERSION_SD3_5_8B || sd_ctx->sd->version == VERSION_SD3_5_2B) {
C = 16; C = 16;
} else if (sd_ctx->sd->version == VERSION_FLUX_DEV || sd_ctx->sd->version == VERSION_FLUX_SCHNELL) { } else if (sd_ctx->sd->version == VERSION_FLUX_DEV || sd_ctx->sd->version == VERSION_FLUX_SCHNELL) {
C = 16; C = 16;
@ -1320,7 +1365,11 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx,
sample_method, sample_method,
sigmas, sigmas,
start_merge_step, start_merge_step,
id_cond); id_cond,
skip_layers,
slg_scale,
skip_layer_start,
skip_layer_end);
// struct ggml_tensor* x_0 = load_tensor_from_file(ctx, "samples_ddim.bin"); // struct ggml_tensor* x_0 = load_tensor_from_file(ctx, "samples_ddim.bin");
// print_ggml_tensor(x_0); // print_ggml_tensor(x_0);
int64_t sampling_end = ggml_time_ms(); int64_t sampling_end = ggml_time_ms();
@ -1386,7 +1435,11 @@ sd_image_t* txt2img(sd_ctx_t* sd_ctx,
float control_strength, float control_strength,
float style_ratio, float style_ratio,
bool normalize_input, bool normalize_input,
const char* input_id_images_path_c_str) { const char* input_id_images_path_c_str,
std::vector<int> skip_layers,
float slg_scale,
float skip_layer_start,
float skip_layer_end) {
LOG_DEBUG("txt2img %dx%d", width, height); LOG_DEBUG("txt2img %dx%d", width, height);
if (sd_ctx == NULL) { if (sd_ctx == NULL) {
return NULL; return NULL;
@ -1394,7 +1447,7 @@ sd_image_t* txt2img(sd_ctx_t* sd_ctx,
struct ggml_init_params params; struct ggml_init_params params;
params.mem_size = static_cast<size_t>(10 * 1024 * 1024); // 10 MB params.mem_size = static_cast<size_t>(10 * 1024 * 1024); // 10 MB
if (sd_ctx->sd->version == VERSION_SD3_2B || sd_ctx->sd->version == VERSION_SD3_5_8B) { if (sd_ctx->sd->version == VERSION_SD3_2B || sd_ctx->sd->version == VERSION_SD3_5_8B || sd_ctx->sd->version == VERSION_SD3_5_2B) {
params.mem_size *= 3; params.mem_size *= 3;
} }
if (sd_ctx->sd->version == VERSION_FLUX_DEV || sd_ctx->sd->version == VERSION_FLUX_SCHNELL) { if (sd_ctx->sd->version == VERSION_FLUX_DEV || sd_ctx->sd->version == VERSION_FLUX_SCHNELL) {
@ -1420,7 +1473,7 @@ sd_image_t* txt2img(sd_ctx_t* sd_ctx,
std::vector<float> sigmas = sd_ctx->sd->denoiser->get_sigmas(sample_steps); std::vector<float> sigmas = sd_ctx->sd->denoiser->get_sigmas(sample_steps);
int C = 4; int C = 4;
if (sd_ctx->sd->version == VERSION_SD3_2B || sd_ctx->sd->version == VERSION_SD3_5_8B) { if (sd_ctx->sd->version == VERSION_SD3_2B || sd_ctx->sd->version == VERSION_SD3_5_8B || sd_ctx->sd->version == VERSION_SD3_5_2B) {
C = 16; C = 16;
} else if (sd_ctx->sd->version == VERSION_FLUX_DEV || sd_ctx->sd->version == VERSION_FLUX_SCHNELL) { } else if (sd_ctx->sd->version == VERSION_FLUX_DEV || sd_ctx->sd->version == VERSION_FLUX_SCHNELL) {
C = 16; C = 16;
@ -1428,7 +1481,7 @@ sd_image_t* txt2img(sd_ctx_t* sd_ctx,
int W = width / 8; int W = width / 8;
int H = height / 8; int H = height / 8;
ggml_tensor* init_latent = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, W, H, C, 1); ggml_tensor* init_latent = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, W, H, C, 1);
if (sd_ctx->sd->version == VERSION_SD3_2B || sd_ctx->sd->version == VERSION_SD3_5_8B) { if (sd_ctx->sd->version == VERSION_SD3_2B || sd_ctx->sd->version == VERSION_SD3_5_8B || sd_ctx->sd->version == VERSION_SD3_5_2B) {
ggml_set_f32(init_latent, 0.0609f); ggml_set_f32(init_latent, 0.0609f);
} else if (sd_ctx->sd->version == VERSION_FLUX_DEV || sd_ctx->sd->version == VERSION_FLUX_SCHNELL) { } else if (sd_ctx->sd->version == VERSION_FLUX_DEV || sd_ctx->sd->version == VERSION_FLUX_SCHNELL) {
ggml_set_f32(init_latent, 0.1159f); ggml_set_f32(init_latent, 0.1159f);
@ -1454,7 +1507,11 @@ sd_image_t* txt2img(sd_ctx_t* sd_ctx,
control_strength, control_strength,
style_ratio, style_ratio,
normalize_input, normalize_input,
input_id_images_path_c_str); input_id_images_path_c_str,
skip_layers,
slg_scale,
skip_layer_start,
skip_layer_end);
size_t t1 = ggml_time_ms(); size_t t1 = ggml_time_ms();
@ -1481,7 +1538,11 @@ sd_image_t* img2img(sd_ctx_t* sd_ctx,
float control_strength, float control_strength,
float style_ratio, float style_ratio,
bool normalize_input, bool normalize_input,
const char* input_id_images_path_c_str) { const char* input_id_images_path_c_str,
std::vector<int> skip_layers,
float slg_scale,
float skip_layer_start,
float skip_layer_end) {
LOG_DEBUG("img2img %dx%d", width, height); LOG_DEBUG("img2img %dx%d", width, height);
if (sd_ctx == NULL) { if (sd_ctx == NULL) {
return NULL; return NULL;
@ -1489,7 +1550,7 @@ sd_image_t* img2img(sd_ctx_t* sd_ctx,
struct ggml_init_params params; struct ggml_init_params params;
params.mem_size = static_cast<size_t>(10 * 1024 * 1024); // 10 MB params.mem_size = static_cast<size_t>(10 * 1024 * 1024); // 10 MB
if (sd_ctx->sd->version == VERSION_SD3_2B || sd_ctx->sd->version == VERSION_SD3_5_8B) { if (sd_ctx->sd->version == VERSION_SD3_2B || sd_ctx->sd->version == VERSION_SD3_5_8B || sd_ctx->sd->version == VERSION_SD3_5_2B) {
params.mem_size *= 2; params.mem_size *= 2;
} }
if (sd_ctx->sd->version == VERSION_FLUX_DEV || sd_ctx->sd->version == VERSION_FLUX_SCHNELL) { if (sd_ctx->sd->version == VERSION_FLUX_DEV || sd_ctx->sd->version == VERSION_FLUX_SCHNELL) {
@ -1555,7 +1616,11 @@ sd_image_t* img2img(sd_ctx_t* sd_ctx,
control_strength, control_strength,
style_ratio, style_ratio,
normalize_input, normalize_input,
input_id_images_path_c_str); input_id_images_path_c_str,
skip_layers,
slg_scale,
skip_layer_start,
skip_layer_end);
size_t t2 = ggml_time_ms(); size_t t2 = ggml_time_ms();

12
stable-diffusion.h
View File

@ -162,7 +162,11 @@ SD_API sd_image_t* txt2img(sd_ctx_t* sd_ctx,
float control_strength, float control_strength,
float style_strength, float style_strength,
bool normalize_input, bool normalize_input,
const char* input_id_images_path); const char* input_id_images_path,
std::vector<int> skip_layers = {},
float slg_scale = 2.5,
float skip_layer_start = 0.01,
float skip_layer_end = 0.2);
SD_API sd_image_t* img2img(sd_ctx_t* sd_ctx, SD_API sd_image_t* img2img(sd_ctx_t* sd_ctx,
sd_image_t init_image, sd_image_t init_image,
@ -182,7 +186,11 @@ SD_API sd_image_t* img2img(sd_ctx_t* sd_ctx,
float control_strength, float control_strength,
float style_strength, float style_strength,
bool normalize_input, bool normalize_input,
const char* input_id_images_path); const char* input_id_images_path,
std::vector<int> skip_layers = {},
float slg_scale = 2.5,
float skip_layer_start = 0.01,
float skip_layer_end = 0.2);
SD_API sd_image_t* img2vid(sd_ctx_t* sd_ctx, SD_API sd_image_t* img2vid(sd_ctx_t* sd_ctx,
sd_image_t init_image, sd_image_t init_image,

2
vae.hpp
View File

@ -457,7 +457,7 @@ public:
bool use_video_decoder = false, bool use_video_decoder = false,
SDVersion version = VERSION_SD1) SDVersion version = VERSION_SD1)
: decode_only(decode_only), use_video_decoder(use_video_decoder) { : decode_only(decode_only), use_video_decoder(use_video_decoder) {
if (version == VERSION_SD3_2B || version == VERSION_SD3_5_8B || version == VERSION_FLUX_DEV || version == VERSION_FLUX_SCHNELL) { if (version == VERSION_SD3_2B || version == VERSION_SD3_5_8B || version == VERSION_SD3_5_2B || version == VERSION_FLUX_DEV || version == VERSION_FLUX_SCHNELL) {
dd_config.z_channels = 16; dd_config.z_channels = 16;
use_quant = false; use_quant = false;
} }