DarthAffe/stable-diffusion.cpp
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feat: support more LoRA models (Kroma-v0.1 support) (#1842)

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stduhpf 2026-08-02 10:08:47 +02:00 committed by GitHub
parent 10378f42db
commit 8457624101
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2 changed files with 22 additions and 7 deletions
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27
src/model/diffusion/krea2.hpp
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@ -180,9 +180,12 @@ namespace Krea2 {
ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override { ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
ggml_tensor* scale = params["scale"]; ggml_tensor* scale = params["scale"];
scale = ggml_add(ctx->ggml_ctx, scale, ggml_ext_ones(ctx->ggml_ctx, scale->ne[0], 1, 1, 1)); if (ctx->weight_adapter) {
x = ggml_rms_norm(ctx->ggml_ctx, x, eps); scale = ctx->weight_adapter->patch_weight(ctx->ggml_ctx, ctx->backend, scale, prefix + "scale.weight");
x = ggml_mul_inplace(ctx->ggml_ctx, x, scale); }
scale = ggml_add(ctx->ggml_ctx, scale, ggml_ext_ones(ctx->ggml_ctx, scale->ne[0], 1, 1, 1));
x = ggml_rms_norm(ctx->ggml_ctx, x, eps);
x = ggml_mul_inplace(ctx->ggml_ctx, x, scale);
return x; return x;
} }
}; };
@ -295,10 +298,11 @@ namespace Krea2 {
class KreaDoubleSharedModulation : public GGMLBlock { class KreaDoubleSharedModulation : public GGMLBlock {
protected: protected:
int64_t dim; int64_t dim;
std::string prefix;
void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override { void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
GGML_UNUSED(tensor_storage_map); GGML_UNUSED(tensor_storage_map);
GGML_UNUSED(prefix); this->prefix = prefix;
params["lin"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, dim * 6); params["lin"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, dim * 6);
} }
@ -307,7 +311,11 @@ namespace Krea2 {
: dim(dim) {} : dim(dim) {}
std::vector<ggml_tensor*> forward(GGMLRunnerContext* ctx, ggml_tensor* vec) { std::vector<ggml_tensor*> forward(GGMLRunnerContext* ctx, ggml_tensor* vec) {
auto lin = ggml_repeat(ctx->ggml_ctx, params["lin"], vec); auto lin = params["lin"];
if (ctx->weight_adapter) {
lin = ctx->weight_adapter->patch_weight(ctx->ggml_ctx, ctx->backend, lin, prefix + "lin.weight");
}
lin = ggml_repeat(ctx->ggml_ctx, lin, vec);
auto out = ggml_add(ctx->ggml_ctx, vec, lin); auto out = ggml_add(ctx->ggml_ctx, vec, lin);
return ggml_ext_chunk(ctx->ggml_ctx, out, 6, 0); return ggml_ext_chunk(ctx->ggml_ctx, out, 6, 0);
} }
@ -316,10 +324,11 @@ namespace Krea2 {
class KreaFinalModulation : public GGMLBlock { class KreaFinalModulation : public GGMLBlock {
protected: protected:
int64_t dim; int64_t dim;
std::string prefix;
void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override { void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
GGML_UNUSED(tensor_storage_map); GGML_UNUSED(tensor_storage_map);
GGML_UNUSED(prefix); this->prefix = prefix;
params["lin"] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, dim, 2); params["lin"] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, dim, 2);
} }
@ -328,7 +337,11 @@ namespace Krea2 {
: dim(dim) {} : dim(dim) {}
std::vector<ggml_tensor*> forward(GGMLRunnerContext* ctx, ggml_tensor* vec) { std::vector<ggml_tensor*> forward(GGMLRunnerContext* ctx, ggml_tensor* vec) {
auto out = ggml_add(ctx->ggml_ctx, params["lin"], vec); auto lin = params["lin"];
if (ctx->weight_adapter) {
lin = ctx->weight_adapter->patch_weight(ctx->ggml_ctx, ctx->backend, lin, prefix + "lin.weight");
}
auto out = ggml_add(ctx->ggml_ctx, lin, vec);
return ggml_ext_chunk(ctx->ggml_ctx, out, 2, 1); return ggml_ext_chunk(ctx->ggml_ctx, out, 2, 1);
} }
}; };

2
src/name_conversion.cpp
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@ -1384,6 +1384,8 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
{".lora_B.weight", ".weight.lora_up"}, {".lora_B.weight", ".weight.lora_up"},
{".lora_A.default.weight", ".weight.lora_down"}, {".lora_A.default.weight", ".weight.lora_down"},
{".lora_B.default.weight", ".weight.lora_up"}, {".lora_B.default.weight", ".weight.lora_up"},
{".lora_A", ".weight.lora_down"},
{".lora_B", ".weight.lora_up"},
{".lora_linear", ".weight.alpha"}, {".lora_linear", ".weight.alpha"},
{".alpha", ".weight.alpha"}, {".alpha", ".weight.alpha"},
{".scale", ".weight.scale"}, {".scale", ".weight.scale"},