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John Doe
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feat: last
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‎chroma.hpp

Lines changed: 1 addition & 237 deletions
Original file line numberDiff line numberDiff line change
@@ -935,240 +935,4 @@ struct ChromaT5Embedder : public Conditioner {
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GGML_ASSERT(0 && "Not implemented yet!");
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return "";
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}
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};
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-> #ifndef __DIFFUSION_MODEL_H__
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#define __DIFFUSION_MODEL_H__
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#include "flux.hpp"
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#include "mmdit.hpp"
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#include "unet.hpp"
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#include "chroma.hpp"
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#include "ggml_extend.hpp" // Required for set_timestep_embedding
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949-
struct DiffusionModel {
950-
virtual void compute(int n_threads,
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struct ggml_tensor* x,
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struct ggml_tensor* timesteps,
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struct ggml_tensor* context,
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struct ggml_tensor* c_concat,
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struct ggml_tensor* y,
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struct ggml_tensor* guidance,
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int num_video_frames = -1,
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std::vector<struct ggml_tensor*> controls = {},
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float control_strength = 0.f,
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struct ggml_tensor** output = NULL,
961-
struct ggml_context* output_ctx = NULL,
962-
std::vector<int> skip_layers = std::vector<int>()) = 0;
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virtual void alloc_params_buffer() = 0;
964-
virtual void free_params_buffer() = 0;
965-
virtual void free_compute_buffer() = 0;
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virtual void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) = 0;
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virtual size_t get_params_buffer_size() = 0;
968-
virtual int64_t get_adm_in_channels() = 0;
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};
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struct UNetModel : public DiffusionModel {
972-
UNetModelRunner unet;
973-
974-
UNetModel(ggml_backend_t backend,
975-
std::map<std::string, enum ggml_type>& tensor_types,
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SDVersion version = VERSION_SD1,
977-
bool flash_attn = false)
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: unet(backend, tensor_types, "model.diffusion_model", version, flash_attn) {
979-
}
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981-
void alloc_params_buffer() {
982-
unet.alloc_params_buffer();
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}
984-
985-
void free_params_buffer() {
986-
unet.free_params_buffer();
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}
988-
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void free_compute_buffer() {
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unet.free_compute_buffer();
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}
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void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
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unet.get_param_tensors(tensors, "model.diffusion_model");
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}
996-
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size_t get_params_buffer_size() {
998-
return unet.get_params_buffer_size();
999-
}
1000-
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int64_t get_adm_in_channels() {
1002-
return unet.unet.adm_in_channels;
1003-
}
1004-
1005-
void compute(int n_threads,
1006-
struct ggml_tensor* x,
1007-
struct ggml_tensor* timesteps,
1008-
struct ggml_tensor* context,
1009-
struct ggml_tensor* c_concat,
1010-
struct ggml_tensor* y,
1011-
struct ggml_tensor* guidance,
1012-
int num_video_frames = -1,
1013-
std::vector<struct ggml_tensor*> controls = {},
1014-
float control_strength = 0.f,
1015-
struct ggml_tensor** output = NULL,
1016-
struct ggml_context* output_ctx = NULL,
1017-
std::vector<int> skip_layers = std::vector<int>()) {
1018-
(void)skip_layers; // SLG doesn't work with UNet models
1019-
return unet.compute(n_threads, x, timesteps, context, c_concat, y, num_video_frames, controls, control_strength, output, output_ctx);
1020-
}
1021-
};
1022-
1023-
struct MMDiTModel : public DiffusionModel {
1024-
MMDiTRunner mmdit;
1025-
1026-
MMDiTModel(ggml_backend_t backend,
1027-
std::map<std::string, enum ggml_type>& tensor_types)
1028-
: mmdit(backend, tensor_types, "model.diffusion_model") {
1029-
}
1030-
1031-
void alloc_params_buffer() {
1032-
mmdit.alloc_params_buffer();
1033-
}
1034-
1035-
void free_params_buffer() {
1036-
mmdit.free_params_buffer();
1037-
}
1038-
1039-
void free_compute_buffer() {
1040-
mmdit.free_compute_buffer();
1041-
}
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void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
1044-
mmdit.get_param_tensors(tensors, "model.diffusion_model");
1045-
}
1046-
1047-
size_t get_params_buffer_size() {
1048-
return mmdit.get_params_buffer_size();
1049-
}
1050-
1051-
int64_t get_adm_in_channels() {
1052-
return 768 + 1280;
1053-
}
1054-
1055-
void compute(int n_threads,
1056-
struct ggml_tensor* x,
1057-
struct ggml_tensor* timesteps,
1058-
struct ggml_tensor* context,
1059-
struct ggml_tensor* c_concat,
1060-
struct ggml_tensor* y,
1061-
struct ggml_tensor* guidance,
1062-
int num_video_frames = -1,
1063-
std::vector<struct ggml_tensor*> controls = {},
1064-
float control_strength = 0.f,
1065-
struct ggml_tensor** output = NULL,
1066-
struct ggml_context* output_ctx = NULL,
1067-
std::vector<int> skip_layers = std::vector<int>()) {
1068-
return mmdit.compute(n_threads, x, timesteps, context, y, output, output_ctx, skip_layers);
1069-
}
1070-
};
1071-
1072-
struct FluxModel : public DiffusionModel {
1073-
Flux::FluxRunner flux;
1074-
1075-
FluxModel(ggml_backend_t backend,
1076-
std::map<std::string, enum ggml_type>& tensor_types,
1077-
SDVersion version = VERSION_FLUX,
1078-
bool flash_attn = false)
1079-
: flux(backend, tensor_types, "model.diffusion_model", version, flash_attn) {
1080-
}
1081-
1082-
void alloc_params_buffer() {
1083-
flux.alloc_params_buffer();
1084-
}
1085-
1086-
void free_params_buffer() {
1087-
flux.free_params_buffer();
1088-
}
1089-
1090-
void free_compute_buffer() {
1091-
flux.free_compute_buffer();
1092-
}
1093-
1094-
void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
1095-
flux.get_param_tensors(tensors, "model.diffusion_model");
1096-
}
1097-
1098-
size_t get_params_buffer_size() {
1099-
return flux.get_params_buffer_size();
1100-
}
1101-
1102-
int64_t get_adm_in_channels() {
1103-
return 768;
1104-
}
1105-
1106-
void compute(int n_threads,
1107-
struct ggml_tensor* x,
1108-
struct ggml_tensor* timesteps,
1109-
struct ggml_tensor* context,
1110-
struct ggml_tensor* c_concat,
1111-
struct ggml_tensor* y,
1112-
struct ggml_tensor* guidance,
1113-
int num_video_frames = -1,
1114-
std::vector<struct ggml_tensor*> controls = {},
1115-
float control_strength = 0.f,
1116-
struct ggml_tensor** output = NULL,
1117-
struct ggml_context* output_ctx = NULL,
1118-
std::vector<int> skip_layers = std::vector<int>()) {
1119-
return flux.compute(n_threads, x, timesteps, context, c_concat, y, guidance, output, output_ctx, skip_layers);
1120-
}
1121-
};
1122-
1123-
struct ChromaModel : public DiffusionModel {
1124-
Chroma::ChromaRunner chroma;
1125-
1126-
ChromaModel(ggml_backend_t backend,
1127-
std::map<std::string, enum ggml_type>& tensor_types,
1128-
SDVersion version = VERSION_CHROMA,
1129-
bool flash_attn = false)
1130-
: chroma(backend, tensor_types, "model.diffusion_model",flash_attn) {
1131-
}
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1133-
void alloc_params_buffer() {
1134-
chroma.alloc_params_buffer();
1135-
}
1136-
1137-
void free_params_buffer() {
1138-
chroma.free_params_buffer();
1139-
}
1140-
1141-
void free_compute_buffer() {
1142-
chroma.free_compute_buffer();
1143-
}
1144-
1145-
void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
1146-
chroma.get_param_tensors(tensors, "model.diffusion_model");
1147-
}
1148-
1149-
size_t get_params_buffer_size() {
1150-
return chroma.get_params_buffer_size();
1151-
}
1152-
int64_t get_adm_in_channels() {
1153-
return 768;
1154-
}
1155-
1156-
1157-
void compute(int n_threads,
1158-
struct ggml_tensor* x,
1159-
struct ggml_tensor* timesteps,
1160-
struct ggml_tensor* context,
1161-
struct ggml_tensor* c_concat,
1162-
struct ggml_tensor* y,
1163-
struct ggml_tensor* guidance,
1164-
int num_video_frames = -1,
1165-
std::vector<struct ggml_tensor*> controls = {},
1166-
float control_strength = 0.f,
1167-
struct ggml_tensor** output = NULL,
1168-
struct ggml_context* output_ctx = NULL,
1169-
std::vector<int> skip_layers = std::vector<int>()) {
1170-
return chroma.compute(n_threads, x, timesteps, context, c_concat, y, guidance, output, output_ctx, skip_layers);
1171-
}
1172-
};
1173-
1174-
#endif
938+
};

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