support infiniteyou

mi804 · mi804 · commit e88328d152fb · 2025-03-31T14:29:15.000+08:00
diff --git a/README.md b/README.md
@@ -42,6 +42,8 @@ Until now, DiffSynth-Studio has supported the following models:
 * [Stable Diffusion](https://huggingface.co/runwayml/stable-diffusion-v1-5)
 
 ## News
+- **March 31, 2025** We support InfiniteYou, an identity preserving method for FLUX. Please refer to [./examples/InfiniteYou/](./examples/InfiniteYou/) for more details.
+
 - **March 25, 2025** 🔥🔥🔥 Our new open-source project, [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine), is now open-sourced! Focused on stable model deployment. Geared towards industry. Offers better engineering support, higher computational performance, and more stable functionality.
 
 - **March 13, 2025** We support HunyuanVideo-I2V, the image-to-video generation version of HunyuanVideo open-sourced by Tencent. Please refer to [./examples/HunyuanVideo/](./examples/HunyuanVideo/) for more details.
diff --git a/diffsynth/configs/model_config.py b/diffsynth/configs/model_config.py
@@ -37,6 +37,7 @@
 from ..models.flux_vae import FluxVAEEncoder, FluxVAEDecoder
 from ..models.flux_controlnet import FluxControlNet
 from ..models.flux_ipadapter import FluxIpAdapter
+from ..models.flux_infiniteyou import InfiniteYouImageProjector
 
 from ..models.cog_vae import CogVAEEncoder, CogVAEDecoder
 from ..models.cog_dit import CogDiT
@@ -104,6 +105,8 @@
     (None, "b001c89139b5f053c715fe772362dd2a", ["flux_controlnet"], [FluxControlNet], "diffusers"),
     (None, "52357cb26250681367488a8954c271e8", ["flux_controlnet"], [FluxControlNet], "diffusers"),
     (None, "0cfd1740758423a2a854d67c136d1e8c", ["flux_controlnet"], [FluxControlNet], "diffusers"),
+    (None, "7f9583eb8ba86642abb9a21a4b2c9e16", ["flux_controlnet"], [FluxControlNet], "diffusers"),
+    (None, "c07c0f04f5ff55e86b4e937c7a40d481", ["infiniteyou_image_projector"], [InfiniteYouImageProjector], "diffusers"),
     (None, "4daaa66cc656a8fe369908693dad0a35", ["flux_ipadapter"], [FluxIpAdapter], "diffusers"),
     (None, "51aed3d27d482fceb5e0739b03060e8f", ["sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"),
     (None, "98cc34ccc5b54ae0e56bdea8688dcd5a", ["sd3_text_encoder_2"], [SD3TextEncoder2], "civitai"),
@@ -598,6 +601,25 @@
             "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder",
         ],
     },
+    "InfiniteYou":{
+        "file_list":[
+            ("ByteDance/InfiniteYou", "infu_flux_v1.0/aes_stage2/InfuseNetModel/diffusion_pytorch_model-00001-of-00002.safetensors", "models/InfiniteYou/InfuseNetModel"),
+            ("ByteDance/InfiniteYou", "infu_flux_v1.0/aes_stage2/InfuseNetModel/diffusion_pytorch_model-00002-of-00002.safetensors", "models/InfiniteYou/InfuseNetModel"),
+            ("ByteDance/InfiniteYou", "infu_flux_v1.0/aes_stage2/image_proj_model.bin", "models/InfiniteYou"),
+            ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/1k3d68.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
+            ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/2d106det.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
+            ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/genderage.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
+            ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/glintr100.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
+            ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/scrfd_10g_bnkps.onnx", "models/InfiniteYou/insightface/models/antelopev2"),            
+        ],
+        "load_path":[
+            [
+                "models/InfiniteYou/InfuseNetModel/diffusion_pytorch_model-00001-of-00002.safetensors",
+                "models/InfiniteYou/InfuseNetModel/diffusion_pytorch_model-00002-of-00002.safetensors"
+            ],
+            "models/InfiniteYou/image_proj_model.bin",
+            ],
+    },
     # ESRGAN
     "ESRGAN_x4": [
         ("AI-ModelScope/Real-ESRGAN", "RealESRGAN_x4.pth", "models/ESRGAN"),
@@ -757,6 +779,7 @@
     "Shakker-Labs/FLUX.1-dev-ControlNet-Depth",
     "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro",
     "InstantX/FLUX.1-dev-IP-Adapter",
+    "InfiniteYou",
     "SDXL_lora_zyd232_ChineseInkStyle_SDXL_v1_0",
     "QwenPrompt",
     "OmostPrompt",
diff --git a/diffsynth/models/flux_controlnet.py b/diffsynth/models/flux_controlnet.py
@@ -318,6 +318,8 @@ def from_diffusers(self, state_dict):
             extra_kwargs = {"num_joint_blocks": 6, "num_single_blocks": 0, "additional_input_dim": 4}
         elif hash_value == "0cfd1740758423a2a854d67c136d1e8c":
             extra_kwargs = {"num_joint_blocks": 4, "num_single_blocks": 1}
+        elif hash_value == "7f9583eb8ba86642abb9a21a4b2c9e16":
+            extra_kwargs = {"num_joint_blocks": 4, "num_single_blocks": 10}
         else:
             extra_kwargs = {}
         return state_dict_, extra_kwargs
diff --git a/diffsynth/models/flux_infiniteyou.py b/diffsynth/models/flux_infiniteyou.py
@@ -0,0 +1,128 @@
+import math
+import torch
+import torch.nn as nn
+
+
+# FFN
+def FeedForward(dim, mult=4):
+    inner_dim = int(dim * mult)
+    return nn.Sequential(
+        nn.LayerNorm(dim),
+        nn.Linear(dim, inner_dim, bias=False),
+        nn.GELU(),
+        nn.Linear(inner_dim, dim, bias=False),
+    )
+
+
+def reshape_tensor(x, heads):
+    bs, length, width = x.shape
+    #(bs, length, width) --> (bs, length, n_heads, dim_per_head)
+    x = x.view(bs, length, heads, -1)
+    # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
+    x = x.transpose(1, 2)
+    # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
+    x = x.reshape(bs, heads, length, -1)
+    return x
+
+
+class PerceiverAttention(nn.Module):
+
+    def __init__(self, *, dim, dim_head=64, heads=8):
+        super().__init__()
+        self.scale = dim_head**-0.5
+        self.dim_head = dim_head
+        self.heads = heads
+        inner_dim = dim_head * heads
+
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+    def forward(self, x, latents):
+        """
+        Args:
+            x (torch.Tensor): image features
+                shape (b, n1, D)
+            latent (torch.Tensor): latent features
+                shape (b, n2, D)
+        """
+        x = self.norm1(x)
+        latents = self.norm2(latents)
+
+        b, l, _ = latents.shape
+
+        q = self.to_q(latents)
+        kv_input = torch.cat((x, latents), dim=-2)
+        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+
+        q = reshape_tensor(q, self.heads)
+        k = reshape_tensor(k, self.heads)
+        v = reshape_tensor(v, self.heads)
+
+        # attention
+        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
+        weight = (q * scale) @ (k * scale).transpose(-2, -1)  # More stable with f16 than dividing afterwards
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        out = weight @ v
+
+        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
+
+        return self.to_out(out)
+
+
+class InfiniteYouImageProjector(nn.Module):
+
+    def __init__(
+        self,
+        dim=1280,
+        depth=4,
+        dim_head=64,
+        heads=20,
+        num_queries=8,
+        embedding_dim=512,
+        output_dim=4096,
+        ff_mult=4,
+    ):
+        super().__init__()
+        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
+        self.proj_in = nn.Linear(embedding_dim, dim)
+
+        self.proj_out = nn.Linear(dim, output_dim)
+        self.norm_out = nn.LayerNorm(output_dim)
+
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                nn.ModuleList([
+                    PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
+                    FeedForward(dim=dim, mult=ff_mult),
+                ]))
+
+    def forward(self, x):
+
+        latents = self.latents.repeat(x.size(0), 1, 1)
+
+        x = self.proj_in(x)
+
+        for attn, ff in self.layers:
+            latents = attn(x, latents) + latents
+            latents = ff(latents) + latents
+
+        latents = self.proj_out(latents)
+        return self.norm_out(latents)
+
+    @staticmethod
+    def state_dict_converter():
+        return FluxInfiniteYouImageProjectorStateDictConverter()
+
+
+class FluxInfiniteYouImageProjectorStateDictConverter:
+
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return state_dict['image_proj']
diff --git a/diffsynth/pipelines/flux_image.py b/diffsynth/pipelines/flux_image.py
@@ -4,10 +4,12 @@
 from ..schedulers import FlowMatchScheduler
 from .base import BasePipeline
 from typing import List
+import math
 import torch
 from tqdm import tqdm
 import numpy as np
 from PIL import Image
+import cv2
 from ..models.tiler import FastTileWorker
 from transformers import SiglipVisionModel
 from copy import deepcopy
@@ -162,6 +164,20 @@ def fetch_models(self, model_manager: ModelManager, controlnet_config_units: Lis
         self.ipadapter = model_manager.fetch_model("flux_ipadapter")
         self.ipadapter_image_encoder = model_manager.fetch_model("siglip_vision_model")
 
+        # InfiniteYou
+        self.image_proj_model = model_manager.fetch_model("infiniteyou_image_projector")
+        if self.image_proj_model is not None:
+            from facexlib.recognition import init_recognition_model
+            from insightface.app import FaceAnalysis
+            insightface_root_path = 'models/InfiniteYou/insightface'
+            self.app_640 = FaceAnalysis(name='antelopev2', root=insightface_root_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+            self.app_640.prepare(ctx_id=0, det_size=(640, 640))
+            self.app_320 = FaceAnalysis(name='antelopev2', root=insightface_root_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+            self.app_320.prepare(ctx_id=0, det_size=(320, 320))
+            self.app_160 = FaceAnalysis(name='antelopev2', root=insightface_root_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+            self.app_160.prepare(ctx_id=0, det_size=(160, 160))
+            self.arcface_model = init_recognition_model('arcface', device=self.device)
+
 
     @staticmethod
     def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[], prompt_extender_classes=[], device=None, torch_dtype=None):
@@ -337,6 +353,66 @@ def prepare_eligen(self, prompt_emb_nega, eligen_entity_prompts, eligen_entity_m
         return eligen_kwargs_posi, eligen_kwargs_nega, fg_mask, bg_mask
 
 
+    def draw_kps(image_pil, kps, color_list=[(255,0,0), (0,255,0), (0,0,255), (255,255,0), (255,0,255)]):
+        stickwidth = 4
+        limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]])
+        kps = np.array(kps)
+        w, h = image_pil.size
+        out_img = np.zeros([h, w, 3])
+        for i in range(len(limbSeq)):
+            index = limbSeq[i]
+            color = color_list[index[0]]
+            x = kps[index][:, 0]
+            y = kps[index][:, 1]
+            length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5
+            angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1]))
+            polygon = cv2.ellipse2Poly((int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
+            out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color)
+        out_img = (out_img * 0.6).astype(np.uint8)
+        for idx_kp, kp in enumerate(kps):
+            color = color_list[idx_kp]
+            out_img = cv2.circle(out_img.copy(), (int(kp[0]), int(kp[1])), 10, color, -1)
+        out_img_pil = Image.fromarray(out_img.astype(np.uint8))
+        return out_img_pil
+
+
+    def extract_arcface_bgr_embedding(self, in_image, landmark):
+        from insightface.utils import face_align
+        arc_face_image = face_align.norm_crop(in_image, landmark=np.array(landmark), image_size=112)
+        arc_face_image = torch.from_numpy(arc_face_image).unsqueeze(0).permute(0, 3, 1, 2) / 255.
+        arc_face_image = 2 * arc_face_image - 1
+        arc_face_image = arc_face_image.contiguous().to(self.device)
+        face_emb = self.arcface_model(arc_face_image)[0] # [512], normalized
+        return face_emb
+
+
+    def _detect_face(self, id_image_cv2):
+        face_info = self.app_640.get(id_image_cv2)
+        if len(face_info) > 0:
+            return face_info
+        face_info = self.app_320.get(id_image_cv2)
+        if len(face_info) > 0:
+            return face_info
+        face_info = self.app_160.get(id_image_cv2)
+        return face_info
+
+
+    def prepare_infinite_you(self, id_image, controlnet_image, controlnet_guidance, height, width):
+        if id_image is None:
+            return {'id_emb': None}, controlnet_image
+        id_image_cv2 = cv2.cvtColor(np.array(id_image), cv2.COLOR_RGB2BGR)
+        face_info = self._detect_face(id_image_cv2)
+        if len(face_info) == 0:
+            raise ValueError('No face detected in the input ID image')
+        landmark = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*(x['bbox'][3]-x['bbox'][1]))[-1]['kps'] # only use the maximum face
+        id_emb = self.extract_arcface_bgr_embedding(id_image_cv2, landmark)
+        id_emb = self.image_proj_model(id_emb.unsqueeze(0).reshape([1, -1, 512]).to(dtype=self.torch_dtype))
+        if controlnet_image is None:
+            controlnet_image = Image.fromarray(np.zeros([height, width, 3]).astype(np.uint8))
+        controlnet_guidance = torch.Tensor([controlnet_guidance]).to(device=self.device, dtype=self.torch_dtype)
+        return {'id_emb': id_emb, 'controlnet_guidance': controlnet_guidance}, controlnet_image
+
+
     def prepare_prompts(self, prompt, local_prompts, masks, mask_scales, t5_sequence_length, negative_prompt, cfg_scale):
         # Extend prompt
         self.load_models_to_device(['text_encoder_1', 'text_encoder_2'])
@@ -374,6 +450,7 @@ def __call__(
         controlnet_image=None,
         controlnet_inpaint_mask=None,
         enable_controlnet_on_negative=False,
+        controlnet_guidance=1.0,
         # IP-Adapter
         ipadapter_images=None,
         ipadapter_scale=1.0,
@@ -382,6 +459,8 @@ def __call__(
         eligen_entity_masks=None,
         enable_eligen_on_negative=False,
         enable_eligen_inpaint=False,
+        # InfiniteYou
+        id_image=None,
         # TeaCache
         tea_cache_l1_thresh=None,
         # Tile
@@ -409,6 +488,9 @@ def __call__(
         # Extra input
         extra_input = self.prepare_extra_input(latents, guidance=embedded_guidance)
 
+        # InfiniteYou
+        infiniteyou_kwargs, controlnet_image = self.prepare_infinite_you(id_image, controlnet_image, controlnet_guidance, height, width)
+        
         # Entity control
         eligen_kwargs_posi, eligen_kwargs_nega, fg_mask, bg_mask = self.prepare_eligen(prompt_emb_nega, eligen_entity_prompts, eligen_entity_masks, width, height, t5_sequence_length, enable_eligen_inpaint, enable_eligen_on_negative, cfg_scale)
 
@@ -430,7 +512,7 @@ def __call__(
             inference_callback = lambda prompt_emb_posi, controlnet_kwargs: lets_dance_flux(
                 dit=self.dit, controlnet=self.controlnet,
                 hidden_states=latents, timestep=timestep,
-                **prompt_emb_posi, **tiler_kwargs, **extra_input, **controlnet_kwargs, **ipadapter_kwargs_list_posi, **eligen_kwargs_posi, **tea_cache_kwargs,
+                **prompt_emb_posi, **tiler_kwargs, **extra_input, **controlnet_kwargs, **ipadapter_kwargs_list_posi, **eligen_kwargs_posi, **tea_cache_kwargs, **infiniteyou_kwargs
             )
             noise_pred_posi = self.control_noise_via_local_prompts(
                 prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback,
@@ -529,6 +611,8 @@ def lets_dance_flux(
     entity_prompt_emb=None,
     entity_masks=None,
     ipadapter_kwargs_list={},
+    id_emb=None,
+    controlnet_guidance=None,
     tea_cache: TeaCache = None,
     **kwargs
 ):
@@ -573,6 +657,9 @@ def flux_forward_fn(hl, hr, wl, wr):
             "tile_size": tile_size,
             "tile_stride": tile_stride,
         }
+        if id_emb is not None:
+            controlnet_text_ids = torch.zeros(id_emb.shape[0], id_emb.shape[1], 3).to(device=hidden_states.device, dtype=hidden_states.dtype)
+            controlnet_extra_kwargs.update({"prompt_emb": id_emb, 'text_ids': controlnet_text_ids, 'guidance': controlnet_guidance})
         controlnet_res_stack, controlnet_single_res_stack = controlnet(
             controlnet_frames, **controlnet_extra_kwargs
         )
diff --git a/examples/InfiniteYou/README.md b/examples/InfiniteYou/README.md
@@ -0,0 +1,7 @@
+# InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity
+We support the identity preserving feature of InfiniteYou. See [./infiniteyou.py](./infiniteyou.py) for example. The visualization of the result is shown below.
+
+|Identity Image|Generated Image|
+|-|-|
+|![man_id](https://github.com/user-attachments/assets/bbc38a91-966e-49e8-a0d7-c5467582ad1f)|![man](https://github.com/user-attachments/assets/0decd5e1-5f65-437c-98fa-90991b6f23c1)|
+|![woman_id](https://github.com/user-attachments/assets/b2894695-690e-465b-929c-61e5dc57feeb)|![woman](https://github.com/user-attachments/assets/67cc7496-c4d3-4de1-a8f1-9eb4991d95e8)|
diff --git a/examples/InfiniteYou/infiniteyou.py b/examples/InfiniteYou/infiniteyou.py
