value control

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
+
 
 model_loader_configs = [
     # These configs are provided for detecting model type automatically.
@@ -102,6 +104,7 @@ 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 +1,58 @@
+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=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)
+        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_in) 
+        )
+        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):
+        emb = self.prefer_proj(value).to(dtype)
+        emb = emb.expand(self.prefer_len, -1)
+        emb = emb + self.positional_embedding
+        emb = self.prefer_value_embedder(emb)
+        return emb
+
+    @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
@@ -94,6 +95,7 @@ class FluxImagePipeline(BasePipeline):
         self.unit_runner = PipelineUnitRunner()
         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.in_iteration_models = ("dit", "step1x_connector", "controlnet")
@@ -112,6 +114,7 @@ class FluxImagePipeline(BasePipeline):
             FluxImageUnit_TeaCache(),
             FluxImageUnit_Flex(),
             FluxImageUnit_Step1x(),
+            FluxImageUnit_ValueControl(),
         ]
         self.model_fn = model_fn_flux_image
         
@@ -295,7 +298,16 @@ 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
     
@@ -347,6 +359,8 @@ 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
@@ -380,6 +394,7 @@ 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,
@@ -720,6 +735,27 @@ class FluxImageUnit_InfiniteYou(PipelineUnit):
 
 
 
+class FluxImageUnit_ValueControl(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            take_over=True,
+            onload_model_names=("value_controller",)
+        )
+
+    def process(self, pipe: FluxImagePipeline, inputs_shared, inputs_posi, inputs_nega):
+        if inputs_shared.get("value_controller_inputs", None) is None:
+            return inputs_shared, inputs_posi, inputs_nega
+        value_controller_inputs = torch.tensor(inputs_shared["value_controller_inputs"]).to(dtype=pipe.torch_dtype, device=pipe.device)
+        pipe.load_models_to_device(["value_controller_inputs"])
+        value_emb = pipe.value_controller(value_controller_inputs, pipe.torch_dtype)
+        value_emb = value_emb.unsqueeze(0)
+        value_text_ids = torch.zeros((value_emb.shape[0], value_emb.shape[1], 3), device=value_emb.device, dtype=value_emb.dtype)
+        inputs_posi["prompt_emb"] = torch.concat([inputs_posi["prompt_emb"], value_emb], dim=1)
+        inputs_posi["text_ids"] = torch.concat([inputs_posi["text_ids"], value_text_ids], dim=1)
+        return inputs_shared, inputs_posi, inputs_nega
+
+
+
 class InfinitYou:
     def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
         from facexlib.recognition import init_recognition_model

examples/flux/model_inference/FLUX.1-dev-ValueControl.py

@@ -0,0 +1,20 @@
+import torch
+from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
+
+
+pipe = FluxImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
+        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"),
+    ],
+)
+
+from diffsynth.models.flux_value_control import SingleValueEncoder, MultiValueEncoder
+pipe.value_controller = MultiValueEncoder(encoders=[SingleValueEncoder(), SingleValueEncoder(), SingleValueEncoder(), SingleValueEncoder()]).to(dtype=torch.bfloat16, device="cuda")
+
+image = pipe(prompt="a cat", seed=0, value_controller_inputs=[0.5, 0.5, 1, 0])
+image.save("flux.jpg")

examples/flux/model_training/train_value_controller.py

@@ -0,0 +1,120 @@
+import torch, os, json
+from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
+from diffsynth.trainers.utils import DiffusionTrainingModule, ImageDataset, ModelLogger, launch_training_task, flux_parser
+from diffsynth.models.lora import FluxLoRAConverter
+from diffsynth.models.flux_value_control import SingleValueEncoder, MultiValueEncoder
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+
+
+class FluxTrainingModule(DiffusionTrainingModule):
+    def __init__(
+        self,
+        model_paths=None, model_id_with_origin_paths=None,
+        trainable_models=None,
+        lora_base_model=None, lora_target_modules="a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp", lora_rank=32,
+        use_gradient_checkpointing=True,
+        use_gradient_checkpointing_offload=False,
+        extra_inputs=None,
+    ):
+        super().__init__()
+        # Load models
+        model_configs = []
+        if model_paths is not None:
+            model_paths = json.loads(model_paths)
+            model_configs += [ModelConfig(path=path) for path in model_paths]
+        if model_id_with_origin_paths is not None:
+            model_id_with_origin_paths = model_id_with_origin_paths.split(",")
+            model_configs += [ModelConfig(model_id=i.split(":")[0], origin_file_pattern=i.split(":")[1]) for i in model_id_with_origin_paths]
+        self.pipe = FluxImagePipeline.from_pretrained(torch_dtype=torch.bfloat16, device="cpu", model_configs=model_configs)
+        
+        self.pipe.value_controller = MultiValueEncoder(encoders=[SingleValueEncoder(), SingleValueEncoder(), SingleValueEncoder(), SingleValueEncoder()]).to(dtype=torch.bfloat16)
+        
+        # Reset training scheduler
+        self.pipe.scheduler.set_timesteps(1000, training=True)
+        
+        # Freeze untrainable models
+        self.pipe.freeze_except([] if trainable_models is None else trainable_models.split(","))
+        
+        # Add LoRA to the base models
+        if lora_base_model is not None:
+            model = self.add_lora_to_model(
+                getattr(self.pipe, lora_base_model),
+                target_modules=lora_target_modules.split(","),
+                lora_rank=lora_rank
+            )
+            setattr(self.pipe, lora_base_model, model)
+            
+        # Store other configs
+        self.use_gradient_checkpointing = use_gradient_checkpointing
+        self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload
+        self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else []
+        
+    
+    def forward_preprocess(self, data):
+        # CFG-sensitive parameters
+        inputs_posi = {"prompt": data["prompt"]}
+        inputs_nega = {}
+        
+        # CFG-unsensitive parameters
+        inputs_shared = {
+            # Assume you are using this pipeline for inference,
+            # please fill in the input parameters.
+            "input_image": data["image"],
+            "height": data["image"].size[1],
+            "width": data["image"].size[0],
+            # Please do not modify the following parameters
+            # unless you clearly know what this will cause.
+            "cfg_scale": 1,
+            "embedded_guidance": 1,
+            "t5_sequence_length": 512,
+            "tiled": False,
+            "rand_device": self.pipe.device,
+            "use_gradient_checkpointing": self.use_gradient_checkpointing,
+            "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload,
+        }
+        
+        # Extra inputs
+        for extra_input in self.extra_inputs:
+            inputs_shared[extra_input] = data[extra_input]
+        
+        # Pipeline units will automatically process the input parameters.
+        for unit in self.pipe.units:
+            inputs_shared, inputs_posi, inputs_nega = self.pipe.unit_runner(unit, self.pipe, inputs_shared, inputs_posi, inputs_nega)
+        return {**inputs_shared, **inputs_posi}
+    
+    
+    def forward(self, data, inputs=None):
+        if inputs is None: inputs = self.forward_preprocess(data)
+        models = {name: getattr(self.pipe, name) for name in self.pipe.in_iteration_models}
+        loss = self.pipe.training_loss(**models, **inputs)
+        return loss
+
+
+
+if __name__ == "__main__":
+    parser = flux_parser()
+    args = parser.parse_args()
+    dataset = ImageDataset(args=args)
+    model = FluxTrainingModule(
+        model_paths=args.model_paths,
+        model_id_with_origin_paths=args.model_id_with_origin_paths,
+        trainable_models=args.trainable_models,
+        lora_base_model=args.lora_base_model,
+        lora_target_modules=args.lora_target_modules,
+        lora_rank=args.lora_rank,
+        use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload,
+        extra_inputs=args.extra_inputs,
+    )
+    model_logger = ModelLogger(
+        args.output_path,
+        remove_prefix_in_ckpt=args.remove_prefix_in_ckpt,
+        state_dict_converter=FluxLoRAConverter.align_to_opensource_format if args.align_to_opensource_format else lambda x:x,
+    )
+    optimizer = torch.optim.AdamW(model.trainable_modules(), lr=args.learning_rate)
+    scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer)
+    launch_training_task(
+        dataset, model, model_logger, optimizer, scheduler,
+        num_epochs=args.num_epochs,
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+    )