Merge pull request #684 from modelscope/value_controller

support flux value controller

diffsynth/configs/model_config.py

@@ -64,6 +64,8 @@ from ..models.wan_video_vace import VaceWanModel
 
 from ..models.step1x_connector import Qwen2Connector
 
+from ..models.flux_value_control import SingleValueEncoder
+
 from ..lora.flux_lora import FluxLoraPatcher
 
 
@@ -104,6 +106,7 @@ 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, "280189ee084bca10f70907bf6ce1649d", ["cog_vae_encoder", "cog_vae_decoder"], [CogVAEEncoder, CogVAEDecoder], "diffusers"),
     (None, "9b9313d104ac4df27991352fec013fd4", ["rife"], [IFNet], "civitai"),
     (None, "6b7116078c4170bfbeaedc8fe71f6649", ["esrgan"], [RRDBNet], "civitai"),

diffsynth/models/flux_value_control.py

@@ -0,0 +1,59 @@
+import torch
+from diffsynth.models.svd_unet import TemporalTimesteps
+
+
+class MultiValueEncoder(torch.nn.Module):
+    def __init__(self, encoders=()):
+        super().__init__()
+        self.encoders = torch.nn.ModuleList(encoders)
+
+    def __call__(self, values, dtype):
+        emb = []
+        for encoder, value in zip(self.encoders, values):
+            if value is not None:
+                value = value.unsqueeze(0)
+                emb.append(encoder(value, dtype))
+        emb = torch.concat(emb, dim=0)
+        return emb
+
+
+class SingleValueEncoder(torch.nn.Module):
+    def __init__(self, dim_in=256, dim_out=3072, 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)
+        self.prefer_value_embedder = torch.nn.Sequential(
+            torch.nn.Linear(dim_in, dim_out), torch.nn.SiLU(), torch.nn.Linear(dim_out, dim_out)
+        )
+        self.positional_embedding = torch.nn.Parameter(
+            torch.randn(self.prefer_len, dim_out) 
+        )
+        self._initialize_weights()
+
+    def _initialize_weights(self):
+        last_linear = self.prefer_value_embedder[-1]
+        torch.nn.init.zeros_(last_linear.weight)
+        torch.nn.init.zeros_(last_linear.bias)
+
+    def forward(self, value, dtype):
+        value = value * 1000
+        emb = self.prefer_proj(value).to(dtype)
+        emb = self.prefer_value_embedder(emb).squeeze(0)
+        base_embeddings = emb.expand(self.prefer_len, -1)
+        learned_embeddings = base_embeddings + self.positional_embedding
+        return learned_embeddings
+
+    @staticmethod
+    def state_dict_converter():
+        return SingleValueEncoderStateDictConverter()
+
+
+class SingleValueEncoderStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return state_dict
+
+    def from_civitai(self, state_dict):
+        return state_dict

diffsynth/pipelines/flux_image_new.py

@@ -18,6 +18,7 @@ from ..models import ModelManager, load_state_dict, SD3TextEncoder1, FluxTextEnc
 from ..models.step1x_connector import Qwen2Connector
 from ..models.flux_controlnet import FluxControlNet
 from ..models.flux_ipadapter import FluxIpAdapter
+from ..models.flux_value_control import MultiValueEncoder
 from ..models.flux_infiniteyou import InfiniteYouImageProjector
 from ..models.tiler import FastTileWorker
 from .wan_video_new import BasePipeline, ModelConfig, PipelineUnitRunner, PipelineUnit
@@ -93,6 +94,7 @@ class FluxImagePipeline(BasePipeline):
         self.ipadapter_image_encoder = None
         self.qwenvl = None
         self.step1x_connector: Qwen2Connector = None
+        self.value_controller: MultiValueEncoder = None
         self.infinityou_processor: InfinitYou = None
         self.image_proj_model: InfiniteYouImageProjector = None
         self.lora_patcher: FluxLoraPatcher = None
@@ -113,6 +115,7 @@ class FluxImagePipeline(BasePipeline):
             FluxImageUnit_TeaCache(),
             FluxImageUnit_Flex(),
             FluxImageUnit_Step1x(),
+            FluxImageUnit_ValueControl(),
         ]
         self.model_fn = model_fn_flux_image
         
@@ -341,7 +344,16 @@ class FluxImagePipeline(BasePipeline):
         for model_name, model in zip(model_manager.model_name, model_manager.model):
             if model_name == "flux_controlnet":
                 controlnets.append(model)
-        pipe.controlnet = MultiControlNet(controlnets)
+        if len(controlnets) > 0:
+            pipe.controlnet = MultiControlNet(controlnets)
+
+        # Value Controller
+        value_controllers = []
+        for model_name, model in zip(model_manager.model_name, model_manager.model):
+            if model_name == "flux_value_controller":
+                value_controllers.append(model)
+        if len(value_controllers) > 0:
+            pipe.value_controller = MultiValueEncoder(value_controllers)
 
         return pipe
     
@@ -393,6 +405,8 @@ class FluxImagePipeline(BasePipeline):
         flex_control_image: Image.Image = None,
         flex_control_strength: float = 0.5,
         flex_control_stop: float = 0.5,
+        # Value Controller
+        value_controller_inputs: list[float] = None,
         # Step1x
         step1x_reference_image: Image.Image = None,
         # TeaCache
@@ -426,6 +440,7 @@ class FluxImagePipeline(BasePipeline):
             "eligen_entity_prompts": eligen_entity_prompts, "eligen_entity_masks": eligen_entity_masks, "eligen_enable_on_negative": eligen_enable_on_negative, "eligen_enable_inpaint": eligen_enable_inpaint,
             "infinityou_id_image": infinityou_id_image, "infinityou_guidance": infinityou_guidance,
             "flex_inpaint_image": flex_inpaint_image, "flex_inpaint_mask": flex_inpaint_mask, "flex_control_image": flex_control_image, "flex_control_strength": flex_control_strength, "flex_control_stop": flex_control_stop,
+            "value_controller_inputs": value_controller_inputs,
             "step1x_reference_image": step1x_reference_image,
             "tea_cache_l1_thresh": tea_cache_l1_thresh,
             "tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride,
@@ -724,7 +739,7 @@ class FluxImageUnit_Flex(PipelineUnit):
         super().__init__(
             input_params=("latents", "flex_inpaint_image", "flex_inpaint_mask", "flex_control_image", "flex_control_strength", "flex_control_stop", "tiled", "tile_size", "tile_stride"),
             onload_model_names=("vae_encoder",)
-            )
+        )
 
     def process(self, pipe: FluxImagePipeline, latents, flex_inpaint_image, flex_inpaint_mask, flex_control_image, flex_control_strength, flex_control_stop, tiled, tile_size, tile_stride):
         if pipe.dit.input_dim == 196:
@@ -769,6 +784,24 @@ class FluxImageUnit_InfiniteYou(PipelineUnit):
 
 
 
+class FluxImageUnit_ValueControl(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("value_controller_inputs",),
+            onload_model_names=("value_controller",)
+        )
+
+    def process(self, pipe: FluxImagePipeline, 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}
+
+
+
 class InfinitYou(torch.nn.Module):
     def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
         super().__init__()
@@ -888,6 +921,7 @@ 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,
@@ -988,10 +1022,17 @@ def model_fn_flux_image(
         
     hidden_states = dit.x_embedder(hidden_states)
 
+    # EliGen
     if entity_prompt_emb is not None and entity_masks is not None:
         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