xxx

Infinityou

README.md

@@ -13,9 +13,15 @@ Document: https://diffsynth-studio.readthedocs.io/zh-cn/latest/index.html
 
 ## Introduction
 
-DiffSynth Studio is a Diffusion engine. We have restructured architectures including Text Encoder, UNet, VAE, among others, maintaining compatibility with models from the open-source community while enhancing computational performance. We provide many interesting features. Enjoy the magic of Diffusion models!
+Welcome to the magic world of Diffusion models!
 
-Until now, DiffSynth Studio has supported the following models:
+DiffSynth consists of two open-source projects:
+* [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio): Focused on aggressive technological exploration. Targeted at academia. Provides more cutting-edge technical support and novel inference capabilities.
+* [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine): Focused on stable model deployment. Geared towards industry. Offers better engineering support, higher computational performance, and more stable functionality.
+
+DiffSynth-Studio is an open-source project aimed at exploring innovations in AIGC technology. We have integrated numerous open-source Diffusion models, including FLUX and Wan, among others. Through this open-source project, we hope to connect models within the open-source community and explore new technologies based on diffusion models.
+
+Until now, DiffSynth-Studio has supported the following models:
 
 * [Wan-Video](https://github.com/Wan-Video/Wan2.1)
 * [StepVideo](https://github.com/stepfun-ai/Step-Video-T2V)
@@ -36,7 +42,11 @@ Until now, DiffSynth Studio has supported the following models:
 * [Stable Diffusion](https://huggingface.co/runwayml/stable-diffusion-v1-5)
 
 ## News
-- **March 25, 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.
+- **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.
 
 - **February 25, 2025** We support Wan-Video, a collection of SOTA video synthesis models open-sourced by Alibaba. See [./examples/wanvideo/](./examples/wanvideo/).
 
@@ -73,7 +83,7 @@ Until now, DiffSynth Studio has supported the following models:
   - Enable CFG and highres-fix to improve visual quality. See [here](/examples/image_synthesis/README.md)
   - LoRA, ControlNet, and additional models will be available soon.
 
-- **June 21, 2024.** 🔥🔥🔥 We propose ExVideo, a post-tuning technique aimed at enhancing the capability of video generation models. We have extended Stable Video Diffusion to achieve the generation of long videos up to 128 frames.
+- **June 21, 2024.** We propose ExVideo, a post-tuning technique aimed at enhancing the capability of video generation models. We have extended Stable Video Diffusion to achieve the generation of long videos up to 128 frames.
   - [Project Page](https://ecnu-cilab.github.io/ExVideoProjectPage/)
   - Source code is released in this repo. See [`examples/ExVideo`](./examples/ExVideo/).
   - Models are released on [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1) and [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-SVD-128f-v1).

diffsynth/configs/model_config.py

@@ -37,6 +37,7 @@ from ..models.flux_text_encoder import FluxTextEncoder2
 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 @@ model_loader_configs = [
     (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 @@ preset_models_on_modelscope = {
             "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 @@ Preset_model_id: TypeAlias = Literal[
     "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",

diffsynth/distributed/xdit_context_parallel.py

@@ -0,0 +1,129 @@
+import torch
+from typing import Optional
+from einops import rearrange
+from xfuser.core.distributed import (get_sequence_parallel_rank,
+                                     get_sequence_parallel_world_size,
+                                     get_sp_group)
+from xfuser.core.long_ctx_attention import xFuserLongContextAttention
+
+def sinusoidal_embedding_1d(dim, position):
+    sinusoid = torch.outer(position.type(torch.float64), torch.pow(
+        10000, -torch.arange(dim//2, dtype=torch.float64, device=position.device).div(dim//2)))
+    x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
+    return x.to(position.dtype)
+
+def pad_freqs(original_tensor, target_len):
+    seq_len, s1, s2 = original_tensor.shape
+    pad_size = target_len - seq_len
+    padding_tensor = torch.ones(
+        pad_size,
+        s1,
+        s2,
+        dtype=original_tensor.dtype,
+        device=original_tensor.device)
+    padded_tensor = torch.cat([original_tensor, padding_tensor], dim=0)
+    return padded_tensor
+    
+def rope_apply(x, freqs, num_heads):
+    x = rearrange(x, "b s (n d) -> b s n d", n=num_heads)
+    s_per_rank = x.shape[1]
+
+    x_out = torch.view_as_complex(x.to(torch.float64).reshape(
+        x.shape[0], x.shape[1], x.shape[2], -1, 2))
+
+    sp_size = get_sequence_parallel_world_size()
+    sp_rank = get_sequence_parallel_rank()
+    freqs = pad_freqs(freqs, s_per_rank * sp_size)
+    freqs_rank = freqs[(sp_rank * s_per_rank):((sp_rank + 1) * s_per_rank), :, :]
+
+    x_out = torch.view_as_real(x_out * freqs_rank).flatten(2)
+    return x_out.to(x.dtype)
+
+def usp_dit_forward(self,
+            x: torch.Tensor,
+            timestep: torch.Tensor,
+            context: torch.Tensor,
+            clip_feature: Optional[torch.Tensor] = None,
+            y: Optional[torch.Tensor] = None,
+            use_gradient_checkpointing: bool = False,
+            use_gradient_checkpointing_offload: bool = False,
+            **kwargs,
+            ):
+    t = self.time_embedding(
+        sinusoidal_embedding_1d(self.freq_dim, timestep))
+    t_mod = self.time_projection(t).unflatten(1, (6, self.dim))
+    context = self.text_embedding(context)
+    
+    if self.has_image_input:
+        x = torch.cat([x, y], dim=1)  # (b, c_x + c_y, f, h, w)
+        clip_embdding = self.img_emb(clip_feature)
+        context = torch.cat([clip_embdding, context], dim=1)
+    
+    x, (f, h, w) = self.patchify(x)
+    
+    freqs = torch.cat([
+        self.freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
+        self.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
+        self.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
+    ], dim=-1).reshape(f * h * w, 1, -1).to(x.device)
+    
+    def create_custom_forward(module):
+        def custom_forward(*inputs):
+            return module(*inputs)
+        return custom_forward
+
+    # Context Parallel
+    x = torch.chunk(
+        x, get_sequence_parallel_world_size(),
+        dim=1)[get_sequence_parallel_rank()]
+
+    for block in self.blocks:
+        if self.training and use_gradient_checkpointing:
+            if use_gradient_checkpointing_offload:
+                with torch.autograd.graph.save_on_cpu():
+                    x = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(block),
+                        x, context, t_mod, freqs,
+                        use_reentrant=False,
+                    )
+            else:
+                x = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    x, context, t_mod, freqs,
+                    use_reentrant=False,
+                )
+        else:
+            x = block(x, context, t_mod, freqs)
+
+    x = self.head(x, t)
+
+    # Context Parallel
+    x = get_sp_group().all_gather(x, dim=1)
+
+    # unpatchify
+    x = self.unpatchify(x, (f, h, w))
+    return x
+
+
+def usp_attn_forward(self, x, freqs):
+    q = self.norm_q(self.q(x))
+    k = self.norm_k(self.k(x))
+    v = self.v(x)
+
+    q = rope_apply(q, freqs, self.num_heads)
+    k = rope_apply(k, freqs, self.num_heads)
+    q = rearrange(q, "b s (n d) -> b s n d", n=self.num_heads)
+    k = rearrange(k, "b s (n d) -> b s n d", n=self.num_heads)
+    v = rearrange(v, "b s (n d) -> b s n d", n=self.num_heads)
+
+    x = xFuserLongContextAttention()(
+        None,
+        query=q,
+        key=k,
+        value=v,
+    )
+    x = x.flatten(2)
+
+    del q, k, v
+    torch.cuda.empty_cache()
+    return self.o(x)

diffsynth/models/flux_controlnet.py

@@ -318,6 +318,8 @@ class FluxControlNetStateDictConverter:
             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

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']

diffsynth/models/wan_video_dit.py

@@ -183,6 +183,13 @@ class CrossAttention(nn.Module):
         return self.o(x)
 
 
+class GateModule(nn.Module):
+    def __init__(self,):
+        super().__init__()
+
+    def forward(self, x, gate, residual):
+        return x + gate * residual
+
 class DiTBlock(nn.Module):
     def __init__(self, has_image_input: bool, dim: int, num_heads: int, ffn_dim: int, eps: float = 1e-6):
         super().__init__()
@@ -199,16 +206,17 @@ class DiTBlock(nn.Module):
         self.ffn = nn.Sequential(nn.Linear(dim, ffn_dim), nn.GELU(
             approximate='tanh'), nn.Linear(ffn_dim, dim))
         self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
+        self.gate = GateModule()
 
     def forward(self, x, context, t_mod, freqs):
         # msa: multi-head self-attention  mlp: multi-layer perceptron
         shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
             self.modulation.to(dtype=t_mod.dtype, device=t_mod.device) + t_mod).chunk(6, dim=1)
         input_x = modulate(self.norm1(x), shift_msa, scale_msa)
-        x = x + gate_msa * self.self_attn(input_x, freqs)
+        x = self.gate(x, gate_msa, self.self_attn(input_x, freqs))
         x = x + self.cross_attn(self.norm3(x), context)
         input_x = modulate(self.norm2(x), shift_mlp, scale_mlp)
-        x = x + gate_mlp * self.ffn(input_x)
+        x = self.gate(x, gate_mlp, self.ffn(input_x))
         return x
 
 

diffsynth/pipelines/flux_image.py

@@ -31,6 +31,7 @@ class FluxImagePipeline(BasePipeline):
         self.controlnet: FluxMultiControlNetManager = None
         self.ipadapter: FluxIpAdapter = None
         self.ipadapter_image_encoder: SiglipVisionModel = None
+        self.infinityou_processor: InfinitYou = None
         self.model_names = ['text_encoder_1', 'text_encoder_2', 'dit', 'vae_decoder', 'vae_encoder', 'controlnet', 'ipadapter', 'ipadapter_image_encoder']
 
 
@@ -162,6 +163,11 @@ class FluxImagePipeline(BasePipeline):
         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:
+            self.infinityou_processor = InfinitYou(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):
@@ -347,6 +353,13 @@ class FluxImagePipeline(BasePipeline):
         prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False, t5_sequence_length=t5_sequence_length) if cfg_scale != 1.0 else None
         prompt_emb_locals = [self.encode_prompt(prompt_local, t5_sequence_length=t5_sequence_length) for prompt_local in local_prompts]
         return prompt_emb_posi, prompt_emb_nega, prompt_emb_locals
+    
+    
+    def prepare_infinite_you(self, id_image, controlnet_image, infinityou_guidance, height, width):
+        if self.infinityou_processor is not None and id_image is not None:
+            return self.infinityou_processor.prepare_infinite_you(self.image_proj_model, id_image, controlnet_image, infinityou_guidance, height, width)
+        else:
+            return {}, controlnet_image
 
 
     @torch.no_grad()
@@ -382,6 +395,9 @@ class FluxImagePipeline(BasePipeline):
         eligen_entity_masks=None,
         enable_eligen_on_negative=False,
         enable_eligen_inpaint=False,
+        # InfiniteYou
+        infinityou_id_image=None,
+        infinityou_guidance=1.0,
         # TeaCache
         tea_cache_l1_thresh=None,
         # Tile
@@ -409,6 +425,9 @@ class FluxImagePipeline(BasePipeline):
         # Extra input
         extra_input = self.prepare_extra_input(latents, guidance=embedded_guidance)
 
+        # InfiniteYou
+        infiniteyou_kwargs, controlnet_image = self.prepare_infinite_you(infinityou_id_image, controlnet_image, infinityou_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 +449,7 @@ class FluxImagePipeline(BasePipeline):
             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,
@@ -447,7 +466,7 @@ class FluxImagePipeline(BasePipeline):
                 noise_pred_nega = lets_dance_flux(
                     dit=self.dit, controlnet=self.controlnet,
                     hidden_states=latents, timestep=timestep,
-                    **prompt_emb_nega, **tiler_kwargs, **extra_input, **controlnet_kwargs_nega, **ipadapter_kwargs_list_nega, **eligen_kwargs_nega,
+                    **prompt_emb_nega, **tiler_kwargs, **extra_input, **controlnet_kwargs_nega, **ipadapter_kwargs_list_nega, **eligen_kwargs_nega, **infiniteyou_kwargs,
                 )
                 noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
             else:
@@ -467,6 +486,58 @@ class FluxImagePipeline(BasePipeline):
         # Offload all models
         self.load_models_to_device([])
         return image
+    
+    
+    
+class InfinitYou:
+    def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
+        from facexlib.recognition import init_recognition_model
+        from insightface.app import FaceAnalysis
+        self.device = device
+        self.torch_dtype = torch_dtype
+        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)
+        
+    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 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 prepare_infinite_you(self, model, id_image, controlnet_image, infinityou_guidance, height, width):
+        import cv2
+        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 = 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))
+        infinityou_guidance = torch.Tensor([infinityou_guidance]).to(device=self.device, dtype=self.torch_dtype)
+        return {'id_emb': id_emb, 'infinityou_guidance': infinityou_guidance}, controlnet_image
 
 
 class TeaCache:
@@ -529,6 +600,8 @@ def lets_dance_flux(
     entity_prompt_emb=None,
     entity_masks=None,
     ipadapter_kwargs_list={},
+    id_emb=None,
+    infinityou_guidance=None,
     tea_cache: TeaCache = None,
     **kwargs
 ):
@@ -573,6 +646,9 @@ def lets_dance_flux(
             "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': infinityou_guidance})
         controlnet_res_stack, controlnet_single_res_stack = controlnet(
             controlnet_frames, **controlnet_extra_kwargs
         )

diffsynth/pipelines/wan_video.py

@@ -1,3 +1,4 @@
+import types
 from ..models import ModelManager
 from ..models.wan_video_dit import WanModel
 from ..models.wan_video_text_encoder import WanTextEncoder
@@ -30,9 +31,10 @@ class WanVideoPipeline(BasePipeline):
         self.image_encoder: WanImageEncoder = None
         self.dit: WanModel = None
         self.vae: WanVideoVAE = None
-        self.model_names = ['text_encoder', 'dit', 'vae']
+        self.model_names = ['text_encoder', 'dit', 'vae', 'image_encoder']
         self.height_division_factor = 16
         self.width_division_factor = 16
+        self.use_unified_sequence_parallel = False
 
 
     def enable_vram_management(self, num_persistent_param_in_dit=None):
@@ -135,11 +137,20 @@ class WanVideoPipeline(BasePipeline):
 
 
     @staticmethod
-    def from_model_manager(model_manager: ModelManager, torch_dtype=None, device=None):
+    def from_model_manager(model_manager: ModelManager, torch_dtype=None, device=None, use_usp=False):
         if device is None: device = model_manager.device
         if torch_dtype is None: torch_dtype = model_manager.torch_dtype
         pipe = WanVideoPipeline(device=device, torch_dtype=torch_dtype)
         pipe.fetch_models(model_manager)
+        if use_usp:
+            from xfuser.core.distributed import get_sequence_parallel_world_size
+            from ..distributed.xdit_context_parallel import usp_attn_forward, usp_dit_forward
+
+            for block in pipe.dit.blocks:
+                block.self_attn.forward = types.MethodType(usp_attn_forward, block.self_attn)
+            pipe.dit.forward = types.MethodType(usp_dit_forward, pipe.dit)
+            pipe.sp_size = get_sequence_parallel_world_size()
+            pipe.use_unified_sequence_parallel = True
         return pipe
     
     
@@ -148,7 +159,7 @@ class WanVideoPipeline(BasePipeline):
 
 
     def encode_prompt(self, prompt, positive=True):
-        prompt_emb = self.prompter.encode_prompt(prompt, positive=positive)
+        prompt_emb = self.prompter.encode_prompt(prompt, positive=positive, device=self.device)
         return {"context": prompt_emb}
     
     
@@ -189,6 +200,10 @@ class WanVideoPipeline(BasePipeline):
     def decode_video(self, latents, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)):
         frames = self.vae.decode(latents, device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
         return frames
+    
+    
+    def prepare_unified_sequence_parallel(self):
+        return {"use_unified_sequence_parallel": self.use_unified_sequence_parallel}
 
 
     @torch.no_grad()
@@ -258,6 +273,9 @@ class WanVideoPipeline(BasePipeline):
         # TeaCache
         tea_cache_posi = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh, model_id=tea_cache_model_id) if tea_cache_l1_thresh is not None else None}
         tea_cache_nega = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh, model_id=tea_cache_model_id) if tea_cache_l1_thresh is not None else None}
+        
+        # Unified Sequence Parallel
+        usp_kwargs = self.prepare_unified_sequence_parallel()
 
         # Denoise
         self.load_models_to_device(["dit"])
@@ -265,9 +283,9 @@ class WanVideoPipeline(BasePipeline):
             timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)
 
             # Inference
-            noise_pred_posi = model_fn_wan_video(self.dit, latents, timestep=timestep, **prompt_emb_posi, **image_emb, **extra_input, **tea_cache_posi)
+            noise_pred_posi = model_fn_wan_video(self.dit, latents, timestep=timestep, **prompt_emb_posi, **image_emb, **extra_input, **tea_cache_posi, **usp_kwargs)
             if cfg_scale != 1.0:
-                noise_pred_nega = model_fn_wan_video(self.dit, latents, timestep=timestep, **prompt_emb_nega, **image_emb, **extra_input, **tea_cache_nega)
+                noise_pred_nega = model_fn_wan_video(self.dit, latents, timestep=timestep, **prompt_emb_nega, **image_emb, **extra_input, **tea_cache_nega, **usp_kwargs)
                 noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
             else:
                 noise_pred = noise_pred_posi
@@ -346,8 +364,15 @@ def model_fn_wan_video(
     clip_feature: Optional[torch.Tensor] = None,
     y: Optional[torch.Tensor] = None,
     tea_cache: TeaCache = None,
+    use_unified_sequence_parallel: bool = False,
     **kwargs,
 ):
+    if use_unified_sequence_parallel:
+        import torch.distributed as dist
+        from xfuser.core.distributed import (get_sequence_parallel_rank,
+                                            get_sequence_parallel_world_size,
+                                            get_sp_group)
+    
     t = dit.time_embedding(sinusoidal_embedding_1d(dit.freq_dim, timestep))
     t_mod = dit.time_projection(t).unflatten(1, (6, dit.dim))
     context = dit.text_embedding(context)
@@ -371,15 +396,21 @@ def model_fn_wan_video(
     else:
         tea_cache_update = False
     
+    # blocks
+    if use_unified_sequence_parallel:
+        if dist.is_initialized() and dist.get_world_size() > 1:
+            x = torch.chunk(x, get_sequence_parallel_world_size(), dim=1)[get_sequence_parallel_rank()]
     if tea_cache_update:
         x = tea_cache.update(x)
     else:
-        # blocks
         for block in dit.blocks:
             x = block(x, context, t_mod, freqs)
         if tea_cache is not None:
             tea_cache.store(x)
 
     x = dit.head(x, t)
+    if use_unified_sequence_parallel:
+        if dist.is_initialized() and dist.get_world_size() > 1:
+            x = get_sp_group().all_gather(x, dim=1)
     x = dit.unpatchify(x, (f, h, w))
     return x

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)|

examples/InfiniteYou/infiniteyou.py

@@ -0,0 +1,58 @@
+import importlib
+import torch
+from diffsynth import ModelManager, FluxImagePipeline, download_models, ControlNetConfigUnit
+from modelscope import dataset_snapshot_download
+from PIL import Image
+
+if importlib.util.find_spec("facexlib") is None:
+    raise ImportError("You are using InifiniteYou. It depends on facexlib, which is not installed. Please install it with `pip install facexlib`.")
+if importlib.util.find_spec("insightface") is None:
+    raise ImportError("You are using InifiniteYou. It depends on insightface, which is not installed. Please install it with `pip install insightface`.")
+
+download_models(["InfiniteYou"])
+model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", model_id_list=["FLUX.1-dev"])
+model_manager.load_models([
+    [
+        "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",
+])
+
+
+pipe = FluxImagePipeline.from_model_manager(
+    model_manager,
+    controlnet_config_units=[
+        ControlNetConfigUnit(
+            processor_id="none",
+            model_path=[
+                'models/InfiniteYou/InfuseNetModel/diffusion_pytorch_model-00001-of-00002.safetensors',
+                'models/InfiniteYou/InfuseNetModel/diffusion_pytorch_model-00002-of-00002.safetensors'
+            ],
+            scale=1.0
+        )
+    ]
+)
+dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/infiniteyou/*")
+
+prompt = "A man, portrait, cinematic"
+id_image = "data/examples/infiniteyou/man.jpg"
+id_image = Image.open(id_image).convert('RGB')
+image = pipe(
+    prompt=prompt, seed=1,
+    infinityou_id_image=id_image, infinityou_guidance=1.0,
+    num_inference_steps=50, embedded_guidance=3.5,
+    height=1024, width=1024,
+)
+image.save("man.jpg")
+
+prompt = "A woman, portrait, cinematic"
+id_image = "data/examples/infiniteyou/woman.jpg"
+id_image = Image.open(id_image).convert('RGB')
+image = pipe(
+    prompt=prompt, seed=1,
+    infinityou_id_image=id_image, infinityou_guidance=1.0,
+    num_inference_steps=50, embedded_guidance=3.5,
+    height=1024, width=1024,
+)
+image.save("woman.jpg")

examples/wanvideo/README.md

@@ -49,6 +49,20 @@ We present a detailed table here. The model is tested on a single A100.
 
 https://github.com/user-attachments/assets/3908bc64-d451-485a-8b61-28f6d32dd92f
 
+### Parallel Inference
+
+1. Unified Sequence Parallel (USP)
+
+```bash
+pip install xfuser>=0.4.3
+```
+
+```bash
+torchrun --standalone --nproc_per_node=8 examples/wanvideo/wan_14b_text_to_video_usp.py
+```
+
+2. Tensor Parallel
+
 Tensor parallel module of Wan-Video-14B-T2V is still under development. An example script is provided in [`./wan_14b_text_to_video_tensor_parallel.py`](./wan_14b_text_to_video_tensor_parallel.py).
 
 ### Wan-Video-14B-I2V

examples/wanvideo/wan_14b_text_to_video_tensor_parallel.py

@@ -44,11 +44,28 @@ class LitModel(pl.LightningModule):
 
     def configure_model(self):
         tp_mesh = self.device_mesh["tensor_parallel"]
+        plan = {
+            "text_embedding.0": ColwiseParallel(),
+            "text_embedding.2": RowwiseParallel(),
+            "time_projection.1": ColwiseParallel(output_layouts=Replicate()),
+            "text_embedding.0": ColwiseParallel(),
+            "text_embedding.2": RowwiseParallel(),
+            "blocks.0": PrepareModuleInput(
+                input_layouts=(Replicate(), None, None, None),
+                desired_input_layouts=(Replicate(), None, None, None),
+            ),
+            "head": PrepareModuleInput(
+                input_layouts=(Replicate(), None),
+                desired_input_layouts=(Replicate(), None),
+                use_local_output=True,
+            )
+        }
+        self.pipe.dit = parallelize_module(self.pipe.dit, tp_mesh, plan)
         for block_id, block in enumerate(self.pipe.dit.blocks):
             layer_tp_plan = {
                 "self_attn": PrepareModuleInput(
-                    input_layouts=(Replicate(), Replicate()),
-                    desired_input_layouts=(Replicate(), Shard(0)),
+                    input_layouts=(Shard(1), Replicate()),
+                    desired_input_layouts=(Shard(1), Shard(0)),
                 ),
                 "self_attn.q": SequenceParallel(),
                 "self_attn.k": SequenceParallel(),
@@ -59,11 +76,11 @@ class LitModel(pl.LightningModule):
                     input_layouts=(Shard(1), Shard(1), Shard(1)),
                     desired_input_layouts=(Shard(2), Shard(2), Shard(2)),
                 ),
-                "self_attn.o": ColwiseParallel(output_layouts=Replicate()),
-                
+                "self_attn.o": RowwiseParallel(input_layouts=Shard(2), output_layouts=Replicate()),
+
                 "cross_attn": PrepareModuleInput(
-                    input_layouts=(Replicate(), Replicate()),
-                    desired_input_layouts=(Replicate(), Replicate()),
+                    input_layouts=(Shard(1), Replicate()),
+                    desired_input_layouts=(Shard(1), Replicate()),
                 ),
                 "cross_attn.q": SequenceParallel(),
                 "cross_attn.k": SequenceParallel(),
@@ -74,18 +91,26 @@ class LitModel(pl.LightningModule):
                     input_layouts=(Shard(1), Shard(1), Shard(1)),
                     desired_input_layouts=(Shard(2), Shard(2), Shard(2)),
                 ),
-                "cross_attn.o": ColwiseParallel(output_layouts=Replicate()),
-                
-                "ffn.0": ColwiseParallel(),
-                "ffn.2": RowwiseParallel(),
+                "cross_attn.o": RowwiseParallel(input_layouts=Shard(2), output_layouts=Replicate(), use_local_output=False),
+
+                "ffn.0": ColwiseParallel(input_layouts=Shard(1)),
+                "ffn.2": RowwiseParallel(output_layouts=Replicate()),
+
+                "norm1": SequenceParallel(use_local_output=True),
+                "norm2": SequenceParallel(use_local_output=True),
+                "norm3": SequenceParallel(use_local_output=True),
+                "gate": PrepareModuleInput(
+                    input_layouts=(Shard(1), Replicate(), Replicate()),
+                    desired_input_layouts=(Replicate(), Replicate(), Replicate()),
+                )
             }
             parallelize_module(
                 module=block,
                 device_mesh=tp_mesh,
                 parallelize_plan=layer_tp_plan,
             )
-            
-            
+
+
     def test_step(self, batch):
         data = batch[0]
         data["progress_bar_cmd"] = tqdm if self.local_rank == 0 else lambda x: x
@@ -94,9 +119,8 @@ class LitModel(pl.LightningModule):
             video = self.pipe(**data)
         if self.local_rank == 0:
             save_video(video, output_path, fps=15, quality=5)
-        
-        
-        
+
+
 if __name__ == "__main__":
     snapshot_download("Wan-AI/Wan2.1-T2V-14B", local_dir="models/Wan-AI/Wan2.1-T2V-14B")
     dataloader = torch.utils.data.DataLoader(

examples/wanvideo/wan_14b_text_to_video_usp.py

@@ -0,0 +1,58 @@
+import torch
+from diffsynth import ModelManager, WanVideoPipeline, save_video, VideoData
+from modelscope import snapshot_download
+import torch.distributed as dist
+
+
+# Download models
+snapshot_download("Wan-AI/Wan2.1-T2V-14B", local_dir="models/Wan-AI/Wan2.1-T2V-14B")
+
+# Load models
+model_manager = ModelManager(device="cpu")
+model_manager.load_models(
+    [
+        [
+            "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00001-of-00006.safetensors",
+            "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00002-of-00006.safetensors",
+            "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00003-of-00006.safetensors",
+            "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00004-of-00006.safetensors",
+            "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00005-of-00006.safetensors",
+            "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00006-of-00006.safetensors",
+        ],
+        "models/Wan-AI/Wan2.1-T2V-14B/models_t5_umt5-xxl-enc-bf16.pth",
+        "models/Wan-AI/Wan2.1-T2V-14B/Wan2.1_VAE.pth",
+    ],
+    torch_dtype=torch.float8_e4m3fn, # You can set `torch_dtype=torch.bfloat16` to disable FP8 quantization.
+)
+
+dist.init_process_group(
+    backend="nccl",
+    init_method="env://",
+)
+from xfuser.core.distributed import (initialize_model_parallel,
+                                     init_distributed_environment)
+init_distributed_environment(
+    rank=dist.get_rank(), world_size=dist.get_world_size())
+
+initialize_model_parallel(
+    sequence_parallel_degree=dist.get_world_size(),
+    ring_degree=1,
+    ulysses_degree=dist.get_world_size(),
+)
+torch.cuda.set_device(dist.get_rank())
+
+pipe = WanVideoPipeline.from_model_manager(model_manager, 
+                                           torch_dtype=torch.bfloat16, 
+                                           device=f"cuda:{dist.get_rank()}", 
+                                           use_usp=True if dist.get_world_size() > 1 else False)
+pipe.enable_vram_management(num_persistent_param_in_dit=None) # You can set `num_persistent_param_in_dit` to a small number to reduce VRAM required.
+
+# Text-to-video
+video = pipe(
+    prompt="一名宇航员身穿太空服，面朝镜头骑着一匹机械马在火星表面驰骋。红色的荒凉地表延伸至远方，点缀着巨大的陨石坑和奇特的岩石结构。机械马的步伐稳健，扬起微弱的尘埃，展现出未来科技与原始探索的完美结合。宇航员手持操控装置，目光坚定，仿佛正在开辟人类的新疆域。背景是深邃的宇宙和蔚蓝的地球，画面既科幻又充满希望，让人不禁畅想未来的星际生活。",
+    negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+    num_inference_steps=50,
+    seed=0, tiled=True
+)
+if dist.get_rank() == 0:
+    save_video(video, "video1.mp4", fps=25, quality=5)