[Feat] diffusion: Native Gemma-4-Unified text encoder for LTX-2.5

python/sglang/multimodal_gen/configs/models/encoders/gemma_4_unified.py

@@ -17,27 +17,11 @@
 class Gemma4UnifiedArchConfig(TextEncoderArchConfig):
     """Gemma-4-Unified text encoder used by LTX-2.5.
 
-    Like `Gemma3ArchConfig`, the actual module is instantiated by transformers
-    from the repo's `text_encoder/config.json`
-    (`Gemma4UnifiedForConditionalGeneration`); this config carries tokenization
-    and sharding metadata.
-
-    LTX-2.5 consumes all 48 hidden layers plus the embedding output, which is why
-    the connector's `text_proj_in_factor` is 49 and `caption_channels` is 3840.
-
-    No `param_names_mapping` here: this encoder currently loads through
-    `TextEncoderLoader.load_native`, i.e. `transformers.from_pretrained`, which
-    never consults SGLang's mapping. If a customized (FSDP/TP) implementation is
-    added the way LTX-2/2.3 have `FSDPGemma3ForConditionalGeneration`, it will
-    need one, because 10 keys drift between the checkpoint and the installed
-    transformers:
-
-        model.vision_embedder.*                 -> model.embed_vision.*
-        model.embed_vision.embedding_projection.* ->
-            model.embed_vision.multimodal_embedder.embedding_projection.*
-
-    plus a tied `lm_head.weight`. All of it is on the vision path, which
-    text-to-video never runs; `from_pretrained` tolerates the drift today.
+    The repo's `text_encoder/config.json` (`Gemma4UnifiedForConditionalGeneration`)
+    is copied onto this config at load time; the native encoder reads its
+    `text_config`. LTX-2.5 consumes all 48 hidden layers plus the embedding
+    output, which is why the connector's `text_proj_in_factor` is 49 and
+    `caption_channels` is 3840.
     """
 
     hidden_size: int = 3840
@@ -48,14 +32,14 @@ class Gemma4UnifiedArchConfig(TextEncoderArchConfig):
     hidden_state_skip_layer: int = 2
     text_len: int = 1024
 
-    stacked_params_mapping: list[tuple[str, str, str]] = field(
+    stacked_params_mapping: list[tuple[str, str, str | int]] = field(
         default_factory=lambda: [
             # (param_name, shard_name, shard_id)
             (".qkv_proj", ".q_proj", "q"),
             (".qkv_proj", ".k_proj", "k"),
             (".qkv_proj", ".v_proj", "v"),
-            (".gate_up_proj", ".gate_proj", "0"),  # type: ignore
-            (".gate_up_proj", ".up_proj", "1"),  # type: ignore
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
         ]
     )
     _fsdp_shard_conditions: list = field(