Added legacy paths

extensions_built_in/diffusion_models/hidream/hidream_model.py

@@ -10,6 +10,7 @@ from toolkit.config_modules import GenerateImageConfig, ModelConfig
 from PIL import Image
 from toolkit.models.base_model import BaseModel
 from toolkit.models.v2.text_encoders.t5 import T5TextEncoder
+from toolkit.models.v2.text_encoders.llama import LlamaTextEncoder
 from toolkit.models.v2.text_encoders.clip import CLIPTextEncoderWithProjection
 from toolkit.models.v2.vae.autoencoder_kl import KLVAE
 from diffusers import TorchAoConfig
@@ -96,11 +97,12 @@ class HidreamModel(BaseModel):
             use_fast=False
         )
         
-        text_encoder_4 = LlamaForCausalLM.from_pretrained(
+        text_encoder_4 = LlamaTextEncoder.load_model(
             llama_model_path,
+            dtype=torch.bfloat16,
+            subfolder="",
             output_hidden_states=True,
             output_attentions=True,
-            torch_dtype=torch.bfloat16,
         )
         text_encoder_4.to(self.device_torch, dtype=dtype)
         

extensions_built_in/diffusion_models/hidream/hidream_o1_model.py

@@ -1,4 +1,5 @@
 import os
+from toolkit.models.v2.text_encoders.qwen3_vl import Qwen3VLTextEncoder
 from typing import List, Optional
 
 import torch
@@ -189,7 +190,7 @@ class HidreamO1Model(BaseModel):
             )
 
             # transformer.load_state_dict(state_dict, assign=True)
-            transformer = Qwen3VLForConditionalGeneration.from_pretrained(
+            transformer = Qwen3VLTextEncoder.from_pretrained(
                 None,
                 config=Qwen3VLConfig(**model_config),
                 state_dict=state_dict,
@@ -197,7 +198,7 @@ class HidreamO1Model(BaseModel):
             )
             del state_dict  # free memory
         else:
-            transformer = Qwen3VLForConditionalGeneration.from_pretrained(
+            transformer = Qwen3VLTextEncoder.from_pretrained(
                 model_path,
                 torch_dtype=self.torch_dtype,
             )