flux-refactor

diffsynth/pipelines/flux_image_new.py

@@ -18,10 +18,13 @@ 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_infiniteyou import InfiniteYouImageProjector
 from ..models.tiler import FastTileWorker
 from .wan_video_new import BasePipeline, ModelConfig, PipelineUnitRunner, PipelineUnit
 from ..lora.flux_lora import FluxLoRALoader
 
+from ..vram_management import gradient_checkpoint_forward
+
 
 
 @dataclass
@@ -89,6 +92,8 @@ class FluxImagePipeline(BasePipeline):
         self.unit_runner = PipelineUnitRunner()
         self.qwenvl = None
         self.step1x_connector: Qwen2Connector = None
+        self.infinityou_processor: InfinitYou = None
+        self.image_proj_model: InfiniteYouImageProjector = None
         self.in_iteration_models = ("dit", "step1x_connector", "controlnet")
         self.units = [
             FluxImageUnit_ShapeChecker(),
@@ -209,7 +214,7 @@ class FluxImagePipeline(BasePipeline):
         # ControlNet
         controlnet_inputs: list[ControlNetInput] = None,
         # IP-Adapter
-        ipadapter_images: list[Image.Image] = None,
+        ipadapter_images: Union[list[Image.Image], Image.Image] = None,
         ipadapter_scale: float = 1.0,
         # EliGen
         eligen_entity_prompts: list[str] = None,
@@ -426,6 +431,8 @@ class FluxImageUnit_IPAdapter(PipelineUnit):
         ipadapter_images, ipadapter_scale = inputs_shared.get("ipadapter_images", None), inputs_shared.get("ipadapter_scale", 1.0)
         if ipadapter_images is None:
             return inputs_shared, inputs_posi, inputs_nega
+        if not isinstance(ipadapter_images, list):
+            ipadapter_images = [ipadapter_images]
 
         pipe.load_models_to_device(self.onload_model_names)
         images = [image.convert("RGB").resize((384, 384), resample=3) for image in ipadapter_images]
@@ -700,6 +707,8 @@ def model_fn_flux_image(
     tea_cache: TeaCache = None,
     progress_id=0,
     num_inference_steps=1,
+    use_gradient_checkpointing=False,
+    use_gradient_checkpointing_offload=False,
     **kwargs
 ):
     if tiled:
@@ -805,13 +814,16 @@ def model_fn_flux_image(
     else:
         # Joint Blocks
         for block_id, block in enumerate(dit.blocks):
-            hidden_states, prompt_emb = block(
+            hidden_states, prompt_emb = gradient_checkpoint_forward(
+                block,
+                use_gradient_checkpointing,
+                use_gradient_checkpointing_offload,
                 hidden_states,
                 prompt_emb,
                 conditioning,
                 image_rotary_emb,
                 attention_mask,
-                ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, None)
+                ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, None),
             )
             # ControlNet
             if controlnet is not None and controlnet_conditionings is not None and controlnet_res_stack is not None:
@@ -821,13 +833,16 @@ def model_fn_flux_image(
         hidden_states = torch.cat([prompt_emb, hidden_states], dim=1)
         num_joint_blocks = len(dit.blocks)
         for block_id, block in enumerate(dit.single_blocks):
-            hidden_states, prompt_emb = block(
+            hidden_states, prompt_emb = gradient_checkpoint_forward(
+                block,
+                use_gradient_checkpointing,
+                use_gradient_checkpointing_offload,
                 hidden_states,
                 prompt_emb,
                 conditioning,
                 image_rotary_emb,
                 attention_mask,
-                ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id + num_joint_blocks, None)
+                ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id + num_joint_blocks, None),
             )
             # ControlNet
             if controlnet is not None and controlnet_conditionings is not None and controlnet_single_res_stack is not None:

diffsynth/pipelines/wan_video_new.py

@@ -178,7 +178,7 @@ class ModelConfig:
                 skip_download = dist.get_rank() != 0
                 
             # Check whether the origin path is a folder
-            if self.origin_file_pattern is None:
+            if self.origin_file_pattern is None or self.origin_file_pattern == "":
                 self.origin_file_pattern = ""
                 allow_file_pattern = None
                 is_folder = True

diffsynth/trainers/utils.py

@@ -7,6 +7,127 @@ from accelerate import Accelerator
 
 
 
+class ImageDataset(torch.utils.data.Dataset):
+    def __init__(
+        self,
+        base_path=None, metadata_path=None,
+        max_pixels=1920*1080, height=None, width=None,
+        height_division_factor=16, width_division_factor=16,
+        data_file_keys=("image",),
+        image_file_extension=("jpg", "jpeg", "png", "webp"),
+        repeat=1,
+        args=None,
+    ):
+        if args is not None:
+            base_path = args.dataset_base_path
+            metadata_path = args.dataset_metadata_path
+            height = args.height
+            width = args.width
+            max_pixels = args.max_pixels
+            data_file_keys = args.data_file_keys.split(",")
+            repeat = args.dataset_repeat
+            
+        self.base_path = base_path
+        self.max_pixels = max_pixels
+        self.height = height
+        self.width = width
+        self.height_division_factor = height_division_factor
+        self.width_division_factor = width_division_factor
+        self.data_file_keys = data_file_keys
+        self.image_file_extension = image_file_extension
+        self.repeat = repeat
+
+        if height is not None and width is not None:
+            print("Height and width are fixed. Setting `dynamic_resolution` to False.")
+            self.dynamic_resolution = False
+        elif height is None and width is None:
+            print("Height and width are none. Setting `dynamic_resolution` to True.")
+            self.dynamic_resolution = True
+            
+        if metadata_path is None:
+            print("No metadata. Trying to generate it.")
+            metadata = self.generate_metadata(base_path)
+            print(f"{len(metadata)} lines in metadata.")
+        else:
+            metadata = pd.read_csv(metadata_path)
+        self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))]
+
+
+    def generate_metadata(self, folder):
+        image_list, prompt_list = [], []
+        file_set = set(os.listdir(folder))
+        for file_name in file_set:
+            if "." not in file_name:
+                continue
+            file_ext_name = file_name.split(".")[-1].lower()
+            file_base_name = file_name[:-len(file_ext_name)-1]
+            if file_ext_name not in self.image_file_extension:
+                continue
+            prompt_file_name = file_base_name + ".txt"
+            if prompt_file_name not in file_set:
+                continue
+            with open(os.path.join(folder, prompt_file_name), "r", encoding="utf-8") as f:
+                prompt = f.read().strip()
+            image_list.append(file_name)
+            prompt_list.append(prompt)
+        metadata = pd.DataFrame()
+        metadata["image"] = image_list
+        metadata["prompt"] = prompt_list
+        return metadata
+    
+    
+    def crop_and_resize(self, image, target_height, target_width):
+        width, height = image.size
+        scale = max(target_width / width, target_height / height)
+        image = torchvision.transforms.functional.resize(
+            image,
+            (round(height*scale), round(width*scale)),
+            interpolation=torchvision.transforms.InterpolationMode.BILINEAR
+        )
+        image = torchvision.transforms.functional.center_crop(image, (target_height, target_width))
+        return image
+    
+    
+    def get_height_width(self, image):
+        if self.dynamic_resolution:
+            width, height = image.size
+            if width * height > self.max_pixels:
+                scale = (width * height / self.max_pixels) ** 0.5
+                height, width = int(height / scale), int(width / scale)
+            height = height // self.height_division_factor * self.height_division_factor
+            width = width // self.width_division_factor * self.width_division_factor
+        else:
+            height, width = self.height, self.width
+        return height, width
+    
+    
+    def load_image(self, file_path):
+        image = Image.open(file_path).convert("RGB")
+        image = self.crop_and_resize(image, *self.get_height_width(image))
+        return image
+    
+    
+    def load_data(self, file_path):
+        return self.load_image(file_path)
+
+
+    def __getitem__(self, data_id):
+        data = self.data[data_id % len(self.data)].copy()
+        for key in self.data_file_keys:
+            if key in data:
+                path = os.path.join(self.base_path, data[key])
+                data[key] = self.load_data(path)
+                if data[key] is None:
+                    warnings.warn(f"cannot load file {data[key]}.")
+                    return None
+        return data
+    
+
+    def __len__(self):
+        return len(self.data) * self.repeat
+
+
+
 class VideoDataset(torch.utils.data.Dataset):
     def __init__(
         self,
@@ -218,9 +339,10 @@ class DiffusionTrainingModule(torch.nn.Module):
 
 
 class ModelLogger:
-    def __init__(self, output_path, remove_prefix_in_ckpt=None):
+    def __init__(self, output_path, remove_prefix_in_ckpt=None, state_dict_converter=lambda x:x):
         self.output_path = output_path
         self.remove_prefix_in_ckpt = remove_prefix_in_ckpt
+        self.state_dict_converter = state_dict_converter
         
     
     def on_step_end(self, loss):
@@ -232,6 +354,7 @@ class ModelLogger:
         if accelerator.is_main_process:
             state_dict = accelerator.get_state_dict(model)
             state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=self.remove_prefix_in_ckpt)
+            state_dict = self.state_dict_converter(state_dict)
             os.makedirs(self.output_path, exist_ok=True)
             path = os.path.join(self.output_path, f"epoch-{epoch_id}.safetensors")
             accelerator.save(state_dict, path, safe_serialization=True)
@@ -302,3 +425,30 @@ def wan_parser():
     parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.")
     return parser
 
+
+
+def flux_parser():
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument("--dataset_base_path", type=str, default="", required=True, help="Base path of the dataset.")
+    parser.add_argument("--dataset_metadata_path", type=str, default=None, help="Path to the metadata file of the dataset.")
+    parser.add_argument("--max_pixels", type=int, default=1024*1024, help="Maximum number of pixels per frame, used for dynamic resolution..")
+    parser.add_argument("--height", type=int, default=None, help="Height of images. Leave `height` and `width` empty to enable dynamic resolution.")
+    parser.add_argument("--width", type=int, default=None, help="Width of images. Leave `height` and `width` empty to enable dynamic resolution.")
+    parser.add_argument("--data_file_keys", type=str, default="image", help="Data file keys in the metadata. Comma-separated.")
+    parser.add_argument("--dataset_repeat", type=int, default=1, help="Number of times to repeat the dataset per epoch.")
+    parser.add_argument("--model_paths", type=str, default=None, help="Paths to load models. In JSON format.")
+    parser.add_argument("--model_id_with_origin_paths", type=str, default=None, help="Model ID with origin paths, e.g., Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors. Comma-separated.")
+    parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate.")
+    parser.add_argument("--num_epochs", type=int, default=1, help="Number of epochs.")
+    parser.add_argument("--output_path", type=str, default="./models", help="Output save path.")
+    parser.add_argument("--remove_prefix_in_ckpt", type=str, default="pipe.dit.", help="Remove prefix in ckpt.")
+    parser.add_argument("--trainable_models", type=str, default=None, help="Models to train, e.g., dit, vae, text_encoder.")
+    parser.add_argument("--lora_base_model", type=str, default=None, help="Which model LoRA is added to.")
+    parser.add_argument("--lora_target_modules", type=str, default="q,k,v,o,ffn.0,ffn.2", help="Which layers LoRA is added to.")
+    parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.")
+    parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.")
+    parser.add_argument("--align_to_opensource_format", default=False, action="store_true", help="Whether to align the lora format to opensource format. Only for DiT's LoRA.")
+    parser.add_argument("--use_gradient_checkpointing", default=False, action="store_true", help="Whether to use gradient checkpointing.")
+    parser.add_argument("--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to offload gradient checkpointing to CPU memory.")
+    parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.")
+    return parser

diffsynth/vram_management/__init__.py

@@ -1 +1,2 @@
 from .layers import *
+from .gradient_checkpointing import *

diffsynth/vram_management/gradient_checkpointing.py

@@ -0,0 +1,34 @@
+import torch
+
+
+def create_custom_forward(module):
+    def custom_forward(*inputs, **kwargs):
+        return module(*inputs, **kwargs)
+    return custom_forward
+
+
+def gradient_checkpoint_forward(
+    model,
+    use_gradient_checkpointing,
+    use_gradient_checkpointing_offload,
+    *args,
+    **kwargs,
+):
+    if use_gradient_checkpointing_offload:
+        with torch.autograd.graph.save_on_cpu():
+            model_output = torch.utils.checkpoint.checkpoint(
+                create_custom_forward(model),
+                *args,
+                **kwargs,
+                use_reentrant=False,
+            )
+    elif use_gradient_checkpointing:
+        model_output = torch.utils.checkpoint.checkpoint(
+            create_custom_forward(model),
+            *args,
+            **kwargs,
+            use_reentrant=False,
+        )
+    else:
+        model_output = model(*args, **kwargs)
+    return model_output

download.py

@@ -0,0 +1,3 @@
+#模型下载
+from modelscope import snapshot_download
+model_dir = snapshot_download('black-forest-labs/FLUX.1-Kontext-dev', cache_dir="models", ignore_file_pattern="transformer/*")

examples/flux/model_training/full/FLUX.1-dev-IP-Adapter.sh

@@ -0,0 +1,14 @@
+accelerate launch examples/flux/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata_ipadapter.csv \
+  --data_file_keys "image,ipadapter_images" \
+  --max_pixels 1048576 \
+  --dataset_repeat 100 \
+  --model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,InstantX/FLUX.1-dev-IP-Adapter:ip-adapter.bin,google/siglip-so400m-patch14-384:" \
+  --learning_rate 1e-5 \
+  --num_epochs 1 \
+  --remove_prefix_in_ckpt "pipe.ipadapter." \
+  --output_path "./models/train/FLUX.1-dev-IP-Adapter_full" \
+  --trainable_models "ipadapter" \
+  --extra_inputs "ipadapter_images" \
+  --use_gradient_checkpointing

examples/flux/model_training/full/FLUX.1-dev.sh

@@ -0,0 +1,12 @@
+accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --dataset_repeat 400 \
+  --model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \
+  --learning_rate 1e-5 \
+  --num_epochs 1 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/FLUX.1-dev_full" \
+  --trainable_models "dit" \
+  --use_gradient_checkpointing

examples/flux/model_training/full/accelerate_config.yaml

@@ -0,0 +1,22 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+deepspeed_config:
+  gradient_accumulation_steps: 1
+  offload_optimizer_device: none
+  offload_param_device: none
+  zero3_init_flag: false
+  zero_stage: 2
+distributed_type: DEEPSPEED
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+machine_rank: 0
+main_training_function: main
+mixed_precision: bf16
+num_machines: 1
+num_processes: 8
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

examples/flux/model_training/lora/FLUX.1-dev.sh

@@ -0,0 +1,15 @@
+accelerate launch examples/flux/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/FLUX.1-dev_lora" \
+  --lora_base_model "dit" \
+  --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 \
+  --align_to_opensource_format \
+  --use_gradient_checkpointing

examples/flux/model_training/train.py

@@ -0,0 +1,117 @@
+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
+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)
+        
+        # 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,
+    )

examples/flux/model_training/validate_full/FLUX.1-dev-IP-Adapter.py

@@ -0,0 +1,28 @@
+import torch
+from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
+from diffsynth import load_state_dict
+from PIL import Image
+
+
+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"),
+        ModelConfig(model_id="InstantX/FLUX.1-dev-IP-Adapter", origin_file_pattern="ip-adapter.bin"),
+        ModelConfig(model_id="google/siglip-so400m-patch14-384"),
+    ],
+)
+state_dict = load_state_dict("models/train/FLUX.1-dev-IP-Adapter_full/epoch-0.safetensors")
+pipe.ipadapter.load_state_dict(state_dict)
+
+image = pipe(
+    prompt="a dog",
+    ipadapter_images=Image.open("data/example_image_dataset/1.jpg"),
+    height=768, width=768,
+    seed=0
+)
+image.save("image_FLUX.1-dev-IP-Adapter_full.jpg")

examples/flux/model_training/validate_full/FLUX.1-dev.py

@@ -0,0 +1,20 @@
+import torch
+from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
+from diffsynth import load_state_dict
+
+
+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"),
+    ],
+)
+state_dict = load_state_dict("models/train/FLUX.1-dev_full/epoch-0.safetensors")
+pipe.dit.load_state_dict(state_dict)
+
+image = pipe(prompt="a dog", seed=0)
+image.save("image_FLUX.1-dev_full.jpg")

examples/flux/model_training/validate_lora/FLUX.1-dev.py

@@ -0,0 +1,18 @@
+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"),
+    ],
+)
+pipe.load_lora(pipe.dit, "models/train/FLUX.1-dev_lora/epoch-4.safetensors", alpha=1)
+
+image = pipe(prompt="a dog", seed=0)
+image.save("image_FLUX.1-dev_lora.jpg")