diff --git a/diffsynth/configs/model_config.py b/diffsynth/configs/model_config.py
index c2e050d..b60c200 100644
--- a/diffsynth/configs/model_config.py
+++ b/diffsynth/configs/model_config.py
@@ -107,7 +107,7 @@ model_loader_configs = [
     (None, "023f054d918a84ccf503481fd1e3379e", ["flux_dit"], [FluxDiT], "civitai"),
     (None, "d02f41c13549fa5093d3521f62a5570a", ["flux_dit"], [FluxDiT], "civitai"),
     (None, "605c56eab23e9e2af863ad8f0813a25d", ["flux_dit"], [FluxDiT], "diffusers"),
-    (None, "3ede90c44b2c161240b659f3b8393c9d", ["flux_value_controller"], [SingleValueEncoder], "civitai"),
+    (None, "0629116fce1472503a66992f96f3eb1a", ["flux_value_controller"], [SingleValueEncoder], "civitai"),
     (None, "280189ee084bca10f70907bf6ce1649d", ["cog_vae_encoder", "cog_vae_decoder"], [CogVAEEncoder, CogVAEDecoder], "diffusers"),
     (None, "9b9313d104ac4df27991352fec013fd4", ["rife"], [IFNet], "civitai"),
     (None, "6b7116078c4170bfbeaedc8fe71f6649", ["esrgan"], [RRDBNet], "civitai"),
diff --git a/diffsynth/models/flux_value_control.py b/diffsynth/models/flux_value_control.py
index 0ff68d3..6981344 100644
--- a/diffsynth/models/flux_value_control.py
+++ b/diffsynth/models/flux_value_control.py
@@ -18,7 +18,7 @@ class MultiValueEncoder(torch.nn.Module):
 
 
 class SingleValueEncoder(torch.nn.Module):
-    def __init__(self, dim_in=256, dim_out=3072, prefer_len=32, computation_device=None):
+    def __init__(self, dim_in=256, dim_out=4096, prefer_len=32, computation_device=None):
         super().__init__()
         self.prefer_len = prefer_len
         self.prefer_proj = TemporalTimesteps(num_channels=dim_in, flip_sin_to_cos=True, downscale_freq_shift=0, computation_device=computation_device)
diff --git a/diffsynth/pipelines/flux_image_new.py b/diffsynth/pipelines/flux_image_new.py
index 811b119..330667c 100644
--- a/diffsynth/pipelines/flux_image_new.py
+++ b/diffsynth/pipelines/flux_image_new.py
@@ -855,18 +855,28 @@ class FluxImageUnit_InfiniteYou(PipelineUnit):
 class FluxImageUnit_ValueControl(PipelineUnit):
     def __init__(self):
         super().__init__(
+            seperate_cfg=True,
+            input_params_posi={"prompt_emb": "prompt_emb", "text_ids": "text_ids"},
+            input_params_nega={"prompt_emb": "prompt_emb", "text_ids": "text_ids"},
             input_params=("value_controller_inputs",),
             onload_model_names=("value_controller",)
         )
+        
+    def add_to_text_embedding(self, prompt_emb, text_ids, value_emb):
+        prompt_emb = torch.concat([prompt_emb, value_emb], dim=1)
+        extra_text_ids = torch.zeros((value_emb.shape[0], value_emb.shape[1], 3), device=value_emb.device, dtype=value_emb.dtype)
+        text_ids = torch.concat([text_ids, extra_text_ids], dim=1)
+        return prompt_emb, text_ids
 
-    def process(self, pipe: FluxImagePipeline, value_controller_inputs):
+    def process(self, pipe: FluxImagePipeline, prompt_emb, text_ids, value_controller_inputs):
         if value_controller_inputs is None:
             return {}
         value_controller_inputs = torch.tensor(value_controller_inputs).to(dtype=pipe.torch_dtype, device=pipe.device)
         pipe.load_models_to_device(["value_controller"])
         value_emb = pipe.value_controller(value_controller_inputs, pipe.torch_dtype)
         value_emb = value_emb.unsqueeze(0)
-        return {"value_emb": value_emb}
+        prompt_emb, text_ids = self.add_to_text_embedding(prompt_emb, text_ids, value_emb)
+        return {"prompt_emb": prompt_emb, "text_ids": text_ids}
 
 
 
@@ -1049,7 +1059,6 @@ def model_fn_flux_image(
     flex_condition=None,
     flex_uncondition=None,
     flex_control_stop_timestep=None,
-    value_emb=None,
     step1x_llm_embedding=None,
     step1x_mask=None,
     step1x_reference_latents=None,
@@ -1155,12 +1164,6 @@ def model_fn_flux_image(
         prompt_emb, image_rotary_emb, attention_mask = dit.process_entity_masks(hidden_states, prompt_emb, entity_prompt_emb, entity_masks, text_ids, image_ids)
     else:
         prompt_emb = dit.context_embedder(prompt_emb)
-        # Value Control
-        if value_emb is not None:
-            prompt_emb = torch.concat([prompt_emb, value_emb], dim=1)
-            value_text_ids = torch.zeros((value_emb.shape[0], value_emb.shape[1], 3), device=value_emb.device, dtype=value_emb.dtype)
-            text_ids = torch.concat([text_ids, value_text_ids], dim=1)
-        # Original FLUX inference
         image_rotary_emb = dit.pos_embedder(torch.cat((text_ids, image_ids), dim=1))
         attention_mask = None
 
diff --git a/examples/flux/model_inference/FLUX.1-dev-ValueControl.py b/examples/flux/model_inference/FLUX.1-dev-AttriCtrl.py
similarity index 60%
rename from examples/flux/model_inference/FLUX.1-dev-ValueControl.py
rename to examples/flux/model_inference/FLUX.1-dev-AttriCtrl.py
index 0bb3ed0..7dd4574 100644
--- a/examples/flux/model_inference/FLUX.1-dev-ValueControl.py
+++ b/examples/flux/model_inference/FLUX.1-dev-AttriCtrl.py
@@ -10,11 +10,10 @@ pipe = FluxImagePipeline.from_pretrained(
         ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
         ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
         ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
-        ModelConfig(model_id="DiffSynth-Studio/FLUX.1-dev-ValueController", origin_file_pattern="single/prefer_embed/value.ckpt")
+        ModelConfig(model_id="DiffSynth-Studio/AttriCtrl-FLUX.1-Dev", origin_file_pattern="models/brightness.safetensors")
     ],
 )
-pipe.load_lora(pipe.dit, ModelConfig(model_id="DiffSynth-Studio/FLUX.1-dev-ValueController", origin_file_pattern="single/dit_lora/dit_value.ckpt"))
 
-for i in range(10):
-    image = pipe(prompt="a cat", seed=0, value_controller_inputs=[i/10])
-    image.save(f"value_control_{i}.jpg")
\ No newline at end of file
+for i in [0.1, 0.3, 0.5, 0.7, 0.9]:
+    image = pipe(prompt="A woman.", seed=602, value_controller_inputs=[i], rand_device="cuda")
+    image.save(f"value_control_{i}.jpg")