| 270 | config.dim, |
| 271 | config.codebook_dim, |
| 272 | true); |
| 273 | weights.vq_codebooks.reserve(static_cast<size_t>(config.num_quantizers)); |
| 274 | for (int64_t quantizer = 0; quantizer < config.num_quantizers; ++quantizer) { |
| 275 | const std::string name = "flow_matching.vq_embed.layers." + std::to_string(quantizer) + "._codebook.embed"; |
| 276 | weights.vq_codebooks.push_back(store.load_tensor_as_shape( |
| 277 | source, |
| 278 | name, |
| 279 | storage_type, |
| 280 | {1, config.codebook_size, config.codebook_dim}, |
| 281 | core::TensorShape::from_dims({config.codebook_size, config.codebook_dim}))); |
| 282 | } |
| 283 | weights.cond_feature_emb = binding::linear_from_source( |
| 284 | store, |
| 285 | source, |
| 286 | "flow_matching.cond_feature_emb", |
| 287 | storage_type, |
| 288 | config.dim, |
| 289 | config.dim, |
| 290 | true); |
| 291 | weights.zero_cond_embedding = store.load_f32_tensor( |
| 292 | source, |
| 293 | "flow_matching.zero_cond_embedding1", |
| 294 | {config.dim}); |
| 295 | weights.zero_cond_embedding_host = source.require_f32_tensor("flow_matching.zero_cond_embedding1", {config.dim}); |
no test coverage detected