lora retrieval

README.md

@@ -13,15 +13,9 @@ Document: https://diffsynth-studio.readthedocs.io/zh-cn/latest/index.html
 
 ## Introduction
 
-Welcome to the magic world of Diffusion models!
+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!
 
-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:
+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)
@@ -42,11 +36,7 @@ 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.
+- **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.
 
 - **February 25, 2025** We support Wan-Video, a collection of SOTA video synthesis models open-sourced by Alibaba. See [./examples/wanvideo/](./examples/wanvideo/).
 
@@ -83,7 +73,7 @@ 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

@@ -37,7 +37,6 @@ 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
@@ -105,8 +104,6 @@ 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"),
@@ -601,25 +598,6 @@ 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"),
@@ -779,7 +757,6 @@ 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

@@ -1,129 +0,0 @@
-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

@@ -318,8 +318,6 @@ 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_dit.py

@@ -41,6 +41,30 @@ class RoPEEmbedding(torch.nn.Module):
         emb = torch.cat([self.rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], dim=-3)
         return emb.unsqueeze(1)
 
+class AdaLayerNorm(torch.nn.Module):
+    def __init__(self, dim, single=False, dual=False):
+        super().__init__()
+        self.single = single
+        self.dual = dual
+        self.linear = torch.nn.Linear(dim, dim * [[6, 2][single], 9][dual])
+        self.norm = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+    def forward(self, x, emb, **kwargs):
+        emb = self.linear(torch.nn.functional.silu(emb),**kwargs)
+        if self.single:
+            scale, shift = emb.unsqueeze(1).chunk(2, dim=2)
+            x = self.norm(x) * (1 + scale) + shift
+            return x
+        elif self.dual:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp, shift_msa2, scale_msa2, gate_msa2 = emb.unsqueeze(1).chunk(9, dim=2)
+            norm_x = self.norm(x)
+            x = norm_x * (1 + scale_msa) + shift_msa
+            norm_x2 = norm_x * (1 + scale_msa2) + shift_msa2
+            return x, gate_msa, shift_mlp, scale_mlp, gate_mlp, norm_x2, gate_msa2
+        else:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.unsqueeze(1).chunk(6, dim=2)
+            x = self.norm(x) * (1 + scale_msa) + shift_msa
+            return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
 
 
 class FluxJointAttention(torch.nn.Module):
@@ -70,17 +94,17 @@ class FluxJointAttention(torch.nn.Module):
         xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
         return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
 
-    def forward(self, hidden_states_a, hidden_states_b, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
+    def forward(self, hidden_states_a, hidden_states_b, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None, **kwargs):
         batch_size = hidden_states_a.shape[0]
 
         # Part A
-        qkv_a = self.a_to_qkv(hidden_states_a)
+        qkv_a = self.a_to_qkv(hidden_states_a,**kwargs)
         qkv_a = qkv_a.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
         q_a, k_a, v_a = qkv_a.chunk(3, dim=1)
         q_a, k_a = self.norm_q_a(q_a), self.norm_k_a(k_a)
 
         # Part B
-        qkv_b = self.b_to_qkv(hidden_states_b)
+        qkv_b = self.b_to_qkv(hidden_states_b,**kwargs)
         qkv_b = qkv_b.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
         q_b, k_b, v_b = qkv_b.chunk(3, dim=1)
         q_b, k_b = self.norm_q_b(q_b), self.norm_k_b(k_b)
@@ -97,13 +121,25 @@ class FluxJointAttention(torch.nn.Module):
         hidden_states_b, hidden_states_a = hidden_states[:, :hidden_states_b.shape[1]], hidden_states[:, hidden_states_b.shape[1]:]
         if ipadapter_kwargs_list is not None:
             hidden_states_a = interact_with_ipadapter(hidden_states_a, q_a, **ipadapter_kwargs_list)
-        hidden_states_a = self.a_to_out(hidden_states_a)
+        hidden_states_a = self.a_to_out(hidden_states_a,**kwargs)
         if self.only_out_a:
             return hidden_states_a
         else:
-            hidden_states_b = self.b_to_out(hidden_states_b)
+            hidden_states_b = self.b_to_out(hidden_states_b,**kwargs)
             return hidden_states_a, hidden_states_b
 
+class AutoSequential(torch.nn.Sequential):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+    def forward(self, input, **kwargs):
+        for module in self:
+            
+            if isinstance(module, torch.nn.Linear):
+                # print("##"*10)
+                input = module(input, **kwargs)
+            else:
+                input = module(input)
+        return input
 
 
 class FluxJointTransformerBlock(torch.nn.Module):
@@ -120,6 +156,11 @@ class FluxJointTransformerBlock(torch.nn.Module):
             torch.nn.GELU(approximate="tanh"),
             torch.nn.Linear(dim*4, dim)
         )
+        # self.ff_a = AutoSequential(
+        #     torch.nn.Linear(dim, dim*4),
+        #     torch.nn.GELU(approximate="tanh"),
+        #     torch.nn.Linear(dim*4, dim)
+        # )
 
         self.norm2_b = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
         self.ff_b = torch.nn.Sequential(
@@ -127,14 +168,18 @@ class FluxJointTransformerBlock(torch.nn.Module):
             torch.nn.GELU(approximate="tanh"),
             torch.nn.Linear(dim*4, dim)
         )
+        # self.ff_b = AutoSequential(
+        #     torch.nn.Linear(dim, dim*4),
+        #     torch.nn.GELU(approximate="tanh"),
+        #     torch.nn.Linear(dim*4, dim)
+        # )
 
-
-    def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
-        norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb)
-        norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb)
+    def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None, **kwargs):
+        norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb, **kwargs)
+        norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb, **kwargs)
 
         # Attention
-        attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b, image_rotary_emb, attn_mask, ipadapter_kwargs_list)
+        attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b, image_rotary_emb, attn_mask, ipadapter_kwargs_list, **kwargs)
 
         # Part A
         hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a
@@ -149,7 +194,6 @@ class FluxJointTransformerBlock(torch.nn.Module):
         return hidden_states_a, hidden_states_b
 
 
-
 class FluxSingleAttention(torch.nn.Module):
     def __init__(self, dim_a, dim_b, num_heads, head_dim):
         super().__init__()
@@ -170,10 +214,10 @@ class FluxSingleAttention(torch.nn.Module):
         return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
 
 
-    def forward(self, hidden_states, image_rotary_emb):
+    def forward(self, hidden_states, image_rotary_emb, **kwargs):
         batch_size = hidden_states.shape[0]
 
-        qkv_a = self.a_to_qkv(hidden_states)
+        qkv_a = self.a_to_qkv(hidden_states,**kwargs)
         qkv_a = qkv_a.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
         q_a, k_a, v = qkv_a.chunk(3, dim=1)
         q_a, k_a = self.norm_q_a(q_a), self.norm_k_a(k_a)
@@ -195,8 +239,8 @@ class AdaLayerNormSingle(torch.nn.Module):
         self.norm = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
 
 
-    def forward(self, x, emb):
-        emb = self.linear(self.silu(emb))
+    def forward(self, x, emb, **kwargs):
+        emb = self.linear(self.silu(emb),**kwargs)
         shift_msa, scale_msa, gate_msa = emb.chunk(3, dim=1)
         x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
         return x, gate_msa
@@ -226,7 +270,7 @@ class FluxSingleTransformerBlock(torch.nn.Module):
         return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
 
 
-    def process_attention(self, hidden_states, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
+    def process_attention(self, hidden_states, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None, **kwargs):
         batch_size = hidden_states.shape[0]
 
         qkv = hidden_states.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
@@ -243,17 +287,17 @@ class FluxSingleTransformerBlock(torch.nn.Module):
         return hidden_states
 
 
-    def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
+    def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None, **kwargs):
         residual = hidden_states_a
-        norm_hidden_states, gate = self.norm(hidden_states_a, emb=temb)
-        hidden_states_a = self.to_qkv_mlp(norm_hidden_states)
+        norm_hidden_states, gate = self.norm(hidden_states_a, emb=temb, **kwargs)
+        hidden_states_a = self.to_qkv_mlp(norm_hidden_states, **kwargs)
         attn_output, mlp_hidden_states = hidden_states_a[:, :, :self.dim * 3], hidden_states_a[:, :, self.dim * 3:]
 
-        attn_output = self.process_attention(attn_output, image_rotary_emb, attn_mask, ipadapter_kwargs_list)
+        attn_output = self.process_attention(attn_output, image_rotary_emb, attn_mask, ipadapter_kwargs_list, **kwargs)
         mlp_hidden_states = torch.nn.functional.gelu(mlp_hidden_states, approximate="tanh")
 
         hidden_states_a = torch.cat([attn_output, mlp_hidden_states], dim=2)
-        hidden_states_a = gate.unsqueeze(1) * self.proj_out(hidden_states_a)
+        hidden_states_a = gate.unsqueeze(1) * self.proj_out(hidden_states_a, **kwargs)
         hidden_states_a = residual + hidden_states_a
 
         return hidden_states_a, hidden_states_b
@@ -267,14 +311,13 @@ class AdaLayerNormContinuous(torch.nn.Module):
         self.linear = torch.nn.Linear(dim, dim * 2, bias=True)
         self.norm = torch.nn.LayerNorm(dim, eps=1e-6, elementwise_affine=False)
 
-    def forward(self, x, conditioning):
-        emb = self.linear(self.silu(conditioning))
+    def forward(self, x, conditioning, **kwargs):
+        emb = self.linear(self.silu(conditioning),**kwargs)
         scale, shift = torch.chunk(emb, 2, dim=1)
         x = self.norm(x) * (1 + scale)[:, None] + shift[:, None]
         return x
 
 
-
 class FluxDiT(torch.nn.Module):
     def __init__(self, disable_guidance_embedder=False):
         super().__init__()
@@ -282,6 +325,8 @@ class FluxDiT(torch.nn.Module):
         self.time_embedder = TimestepEmbeddings(256, 3072)
         self.guidance_embedder = None if disable_guidance_embedder else TimestepEmbeddings(256, 3072)
         self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(768, 3072), torch.nn.SiLU(), torch.nn.Linear(3072, 3072))
+        
+        # self.pooled_text_embedder = AutoSequential(torch.nn.Linear(768, 3072), torch.nn.SiLU(), torch.nn.Linear(3072, 3072))
         self.context_embedder = torch.nn.Linear(4096, 3072)
         self.x_embedder = torch.nn.Linear(64, 3072)
 
@@ -428,12 +473,12 @@ class FluxDiT(torch.nn.Module):
 
         height, width = hidden_states.shape[-2:]
         hidden_states = self.patchify(hidden_states)
-        hidden_states = self.x_embedder(hidden_states)
+        hidden_states = self.x_embedder(hidden_states,**kwargs)
 
         if entity_prompt_emb is not None and entity_masks is not None:
             prompt_emb, image_rotary_emb, attention_mask = self.process_entity_masks(hidden_states, prompt_emb, entity_prompt_emb, entity_masks, text_ids, image_ids)
         else:
-            prompt_emb = self.context_embedder(prompt_emb)
+            prompt_emb = self.context_embedder(prompt_emb, **kwargs)
             image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1))
             attention_mask = None
 
@@ -446,26 +491,26 @@ class FluxDiT(torch.nn.Module):
             if self.training and use_gradient_checkpointing:
                 hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint(
                     create_custom_forward(block),
-                    hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask,
+                    hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask, **kwargs,
                     use_reentrant=False,
                 )
             else:
-                hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask)
+                hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask, **kwargs)
 
         hidden_states = torch.cat([prompt_emb, hidden_states], dim=1)
         for block in self.single_blocks:
             if self.training and use_gradient_checkpointing:
                 hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint(
                     create_custom_forward(block),
-                    hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask,
+                    hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask, **kwargs,
                     use_reentrant=False,
                 )
             else:
-                hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask)
+                hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask, **kwargs)
         hidden_states = hidden_states[:, prompt_emb.shape[1]:]
 
-        hidden_states = self.final_norm_out(hidden_states, conditioning)
-        hidden_states = self.final_proj_out(hidden_states)
+        hidden_states = self.final_norm_out(hidden_states, conditioning, **kwargs)
+        hidden_states = self.final_proj_out(hidden_states, **kwargs)
         hidden_states = self.unpatchify(hidden_states, height, width)
 
         return hidden_states
@@ -606,6 +651,10 @@ class FluxDiTStateDictConverter:
         for name, param in state_dict.items():
             if name.endswith(".weight") or name.endswith(".bias"):
                 suffix = ".weight" if name.endswith(".weight") else ".bias"
+                if "lora_B" in name:
+                    suffix = ".lora_B" + suffix
+                if "lora_A" in name:
+                    suffix = ".lora_A" + suffix
                 prefix = name[:-len(suffix)]
                 if prefix in global_rename_dict:
                     state_dict_[global_rename_dict[prefix] + suffix] = param
@@ -630,29 +679,73 @@ class FluxDiTStateDictConverter:
         for name in list(state_dict_.keys()):
             if "single_blocks." in name and ".a_to_q." in name:
                 mlp = state_dict_.get(name.replace(".a_to_q.", ".proj_in_besides_attn."), None)
+                
                 if mlp is None:
-                    mlp = torch.zeros(4 * state_dict_[name].shape[0],
+                    dim = 4
+                    if 'lora_A' in name:
+                        dim = 1
+                    mlp = torch.zeros(dim * state_dict_[name].shape[0],
                                       *state_dict_[name].shape[1:],
                                       dtype=state_dict_[name].dtype)
                 else:
+                    # print('$$'*10)
+                    # mlp_name = name.replace(".a_to_q.", ".proj_in_besides_attn.")
+                    # print(f'mlp name: {mlp_name}')
+                    # print(f'mlp shape: {mlp.shape}')
                     state_dict_.pop(name.replace(".a_to_q.", ".proj_in_besides_attn."))
-                param = torch.concat([
-                    state_dict_.pop(name),
-                    state_dict_.pop(name.replace(".a_to_q.", ".a_to_k.")),
-                    state_dict_.pop(name.replace(".a_to_q.", ".a_to_v.")),
-                    mlp,
-                ], dim=0)
+                # print(f'mlp shape: {mlp.shape}')
+                if 'lora_A' in name:
+
+                    param = torch.concat([
+                        state_dict_.pop(name),
+                        state_dict_.pop(name.replace(".a_to_q.", ".a_to_k.")),
+                        state_dict_.pop(name.replace(".a_to_q.", ".a_to_v.")),
+                        mlp,
+                    ], dim=0)
+                elif 'lora_B' in name:
+                    # create zreo matrix
+                    d, r = state_dict_[name].shape
+                    # print('--'*10)
+                    # print(d, r)
+                    param = torch.zeros((3*d+mlp.shape[0], 3*r+mlp.shape[1]), dtype=state_dict_[name].dtype, device=state_dict_[name].device)
+                    param[:d, :r] = state_dict_.pop(name)
+                    param[d:2*d, r:2*r] = state_dict_.pop(name.replace(".a_to_q.", ".a_to_k."))
+                    param[2*d:3*d, 2*r:3*r] = state_dict_.pop(name.replace(".a_to_q.", ".a_to_v."))
+                    param[3*d:, 3*r:] = mlp
+                else:
+                    param = torch.concat([
+                        state_dict_.pop(name),
+                        state_dict_.pop(name.replace(".a_to_q.", ".a_to_k.")),
+                        state_dict_.pop(name.replace(".a_to_q.", ".a_to_v.")),
+                        mlp,
+                    ], dim=0)
                 name_ = name.replace(".a_to_q.", ".to_qkv_mlp.")
                 state_dict_[name_] = param
         for name in list(state_dict_.keys()):
             for component in ["a", "b"]:
                 if f".{component}_to_q." in name:
                     name_ = name.replace(f".{component}_to_q.", f".{component}_to_qkv.")
-                    param = torch.concat([
-                        state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")],
-                        state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")],
-                        state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")],
-                    ], dim=0)
+                    concat_dim = 0
+                    if 'lora_A' in name:
+                        param = torch.concat([
+                            state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")],
+                            state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")],
+                            state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")],
+                        ], dim=0)
+                    elif 'lora_B' in name:
+                        origin = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")]
+                        d, r = origin.shape
+                        # print(d, r)
+                        param = torch.zeros((3*d, 3*r), dtype=origin.dtype, device=origin.device)
+                        param[:d, :r] = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")]
+                        param[d:2*d, r:2*r] = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")]
+                        param[2*d:3*d, 2*r:3*r] = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")]
+                    else:
+                        param = torch.concat([
+                            state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")],
+                            state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")],
+                            state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")],
+                        ], dim=0)
                     state_dict_[name_] = param
                     state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_q."))
                     state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_k."))
@@ -718,22 +811,48 @@ class FluxDiTStateDictConverter:
             "norm.query_norm.scale": "norm_q_a.weight",
         }
         state_dict_ = {}
+
+
         for name, param in state_dict.items():
+            # match lora load
+            l_name = ''
+            if 'lora_A' in name :
+                l_name = 'lora_A'
+            if 'lora_B' in name :
+                l_name = 'lora_B'
+            if l_name != '':
+                name = name.replace(l_name+'.', '')
+            
+
             if name.startswith("model.diffusion_model."):
                 name = name[len("model.diffusion_model."):]
             names = name.split(".")
             if name in rename_dict:
                 rename = rename_dict[name]
                 if name.startswith("final_layer.adaLN_modulation.1."):
-                    param = torch.concat([param[3072:], param[:3072]], dim=0)
-                state_dict_[rename] = param
+                    if l_name == 'lora_A':
+                        param = torch.concat([param[:,3072:], param[:,:3072]], dim=1)
+                    else:
+                        param = torch.concat([param[3072:], param[:3072]], dim=0)
+                if l_name != '':
+                    state_dict_[rename.replace('weight',l_name+'.weight')] = param
+                else:
+                    state_dict_[rename] = param
+               
             elif names[0] == "double_blocks":
                 rename = f"blocks.{names[1]}." + suffix_rename_dict[".".join(names[2:])]
-                state_dict_[rename] = param
+                if l_name != '':
+                    state_dict_[rename.replace('weight',l_name+'.weight')] = param
+                else:
+                    state_dict_[rename] = param
+
             elif names[0] == "single_blocks":
                 if ".".join(names[2:]) in suffix_rename_dict:
                     rename = f"single_blocks.{names[1]}." + suffix_rename_dict[".".join(names[2:])]
-                    state_dict_[rename] = param
+                    if l_name != '':
+                        state_dict_[rename.replace('weight',l_name+'.weight')] = param
+                    else:
+                        state_dict_[rename] = param
             else:
                 pass
         if "guidance_embedder.timestep_embedder.0.weight" not in state_dict_:

diffsynth/models/flux_infiniteyou.py

@@ -1,128 +0,0 @@
-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/lora.py

@@ -26,6 +26,12 @@ class LoRAFromCivitai:
                 return self.convert_state_dict_up_down(state_dict, lora_prefix, alpha)
         return self.convert_state_dict_AB(state_dict, lora_prefix, alpha)
 
+    def convert_state_name(self, state_dict, lora_prefix="lora_unet_", alpha=1.0):
+        for key in state_dict:
+            if ".lora_up" in key:
+                return self.convert_state_name_up_down(state_dict, lora_prefix, alpha)
+        return self.convert_state_name_AB(state_dict, lora_prefix, alpha)
+
 
     def convert_state_dict_up_down(self, state_dict, lora_prefix="lora_unet_", alpha=1.0):
         renamed_lora_prefix = self.renamed_lora_prefix.get(lora_prefix, "")
@@ -50,13 +56,37 @@ class LoRAFromCivitai:
         return state_dict_
     
 
+    def convert_state_name_up_down(self, state_dict, lora_prefix="lora_unet_", alpha=1.0):
+        renamed_lora_prefix = self.renamed_lora_prefix.get(lora_prefix, "")
+        state_dict_ = {}
+        for key in state_dict:
+            if ".lora_up" not in key:
+                continue
+            if not key.startswith(lora_prefix):
+                continue
+            weight_up = state_dict[key].to(device="cuda", dtype=torch.float16)
+            weight_down = state_dict[key.replace(".lora_up", ".lora_down")].to(device="cuda", dtype=torch.float16)
+            if len(weight_up.shape) == 4:
+                weight_up = weight_up.squeeze(3).squeeze(2).to(torch.float32)
+                weight_down = weight_down.squeeze(3).squeeze(2).to(torch.float32)
+            target_name = key.split(".")[0].replace(lora_prefix, renamed_lora_prefix).replace("_", ".") + ".weight"
+            for special_key in self.special_keys:
+                target_name = target_name.replace(special_key, self.special_keys[special_key])
+            
+            state_dict_[target_name.replace(".weight",".lora_B.weight")] = weight_up.cpu()
+            state_dict_[target_name.replace(".weight",".lora_A.weight")] = weight_down.cpu()
+        return state_dict_
+    
+
     def convert_state_dict_AB(self, state_dict, lora_prefix="", alpha=1.0, device="cuda", torch_dtype=torch.float16):
         state_dict_ = {}
+
         for key in state_dict:
             if ".lora_B." not in key:
                 continue
             if not key.startswith(lora_prefix):
                 continue
+
             weight_up = state_dict[key].to(device=device, dtype=torch_dtype)
             weight_down = state_dict[key.replace(".lora_B.", ".lora_A.")].to(device=device, dtype=torch_dtype)
             if len(weight_up.shape) == 4:
@@ -67,11 +97,39 @@ class LoRAFromCivitai:
                 lora_weight = alpha * torch.mm(weight_up, weight_down)
             keys = key.split(".")
             keys.pop(keys.index("lora_B"))
+            
             target_name = ".".join(keys)
+           
             target_name = target_name[len(lora_prefix):]
+           
             state_dict_[target_name] = lora_weight.cpu()
         return state_dict_
     
+    def convert_state_name_AB(self, state_dict, lora_prefix="", alpha=1.0, device="cuda", torch_dtype=torch.float16):
+        state_dict_ = {}
+ 
+        for key in state_dict:
+            if ".lora_B." not in key:
+                continue
+            if not key.startswith(lora_prefix):
+                continue
+
+            weight_up = state_dict[key].to(device=device, dtype=torch_dtype)
+            weight_down = state_dict[key.replace(".lora_B.", ".lora_A.")].to(device=device, dtype=torch_dtype)
+            if len(weight_up.shape) == 4:
+                weight_up = weight_up.squeeze(3).squeeze(2)
+                weight_down = weight_down.squeeze(3).squeeze(2)
+
+            keys = key.split(".")
+            keys.pop(keys.index("lora_B"))
+
+            target_name = ".".join(keys)
+
+            target_name = target_name[len(lora_prefix):]
+
+            state_dict_[target_name.replace(".weight",".lora_B.weight")] = weight_up.cpu()
+            state_dict_[target_name.replace(".weight",".lora_A.weight")] = weight_down.cpu()
+        return state_dict_
 
     def load(self, model, state_dict_lora, lora_prefix, alpha=1.0, model_resource=None):
         state_dict_model = model.state_dict()
@@ -100,13 +158,16 @@ class LoRAFromCivitai:
         for lora_prefix, model_class in zip(self.lora_prefix, self.supported_model_classes):
             if not isinstance(model, model_class):
                 continue
+            # print(f'lora_prefix: {lora_prefix}')
             state_dict_model = model.state_dict()
             for model_resource in ["diffusers", "civitai"]:
                 try:
                     state_dict_lora_ = self.convert_state_dict(state_dict_lora, lora_prefix=lora_prefix, alpha=1.0)
+                    # print(f'after convert_state_dict lora state_dict:{state_dict_lora_.keys()}')
                     converter_fn = model.__class__.state_dict_converter().from_diffusers if model_resource == "diffusers" \
                         else model.__class__.state_dict_converter().from_civitai
                     state_dict_lora_ = converter_fn(state_dict_lora_)
+                    # print(f'after converter_fn lora state_dict:{state_dict_lora_.keys()}')
                     if isinstance(state_dict_lora_, tuple):
                         state_dict_lora_ = state_dict_lora_[0]
                     if len(state_dict_lora_) == 0:
@@ -120,7 +181,35 @@ class LoRAFromCivitai:
                     pass
         return None
 
+    def get_converted_lora_state_dict(self, model, state_dict_lora):
+        for lora_prefix, model_class in zip(self.lora_prefix, self.supported_model_classes):
+            if not isinstance(model, model_class):
+                continue
 
+            state_dict_model = model.state_dict()
+            for model_resource in ["diffusers","civitai"]:
+                try:
+                    state_dict_lora_ = self.convert_state_name(state_dict_lora, lora_prefix=lora_prefix, alpha=1.0)
+ 
+                    converter_fn = model.__class__.state_dict_converter().from_diffusers if model_resource == 'diffusers' \
+                        else model.__class__.state_dict_converter().from_civitai
+                    state_dict_lora_ = converter_fn(state_dict_lora_)
+                    
+                    if isinstance(state_dict_lora_, tuple):
+                        state_dict_lora_ = state_dict_lora_[0]
+
+                    if len(state_dict_lora_) == 0:
+                        continue
+                    # return state_dict_lora_
+                    for name in state_dict_lora_:
+                        if name.replace('.lora_B','').replace('.lora_A','') not in state_dict_model:
+                            print(f"   lora's {name} is not in model.")
+                            break
+                    else:
+                        return state_dict_lora_
+                except Exception as e:
+                    print(f"error   {str(e)}")
+        return None
 
 class SDLoRAFromCivitai(LoRAFromCivitai):
     def __init__(self):
@@ -195,73 +284,85 @@ class FluxLoRAFromCivitai(LoRAFromCivitai):
             "txt.mod": "txt_mod",
         }
 
-    
-    
+
 class GeneralLoRAFromPeft:
     def __init__(self):
         self.supported_model_classes = [SDUNet, SDXLUNet, SD3DiT, HunyuanDiT, FluxDiT, CogDiT, WanModel]
-    
-    
-    def get_name_dict(self, lora_state_dict):
-        lora_name_dict = {}
-        for key in lora_state_dict:
+
+
+    def fetch_device_dtype_from_state_dict(self, state_dict):
+        device, torch_dtype = None, None
+        for name, param in state_dict.items():
+            device, torch_dtype = param.device, param.dtype
+            break
+        return device, torch_dtype
+
+
+    def convert_state_dict(self, state_dict, alpha=1.0, target_state_dict={}):
+        device, torch_dtype = self.fetch_device_dtype_from_state_dict(target_state_dict)
+        if torch_dtype == torch.float8_e4m3fn:
+            torch_dtype = torch.float32
+        state_dict_ = {}
+        for key in state_dict:
             if ".lora_B." not in key:
                 continue
+            weight_up = state_dict[key].to(device=device, dtype=torch_dtype)
+            weight_down = state_dict[key.replace(".lora_B.", ".lora_A.")].to(device=device, dtype=torch_dtype)
+            if len(weight_up.shape) == 4:
+                weight_up = weight_up.squeeze(3).squeeze(2)
+                weight_down = weight_down.squeeze(3).squeeze(2)
+                lora_weight = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)
+            else:
+                lora_weight = alpha * torch.mm(weight_up, weight_down)
             keys = key.split(".")
             if len(keys) > keys.index("lora_B") + 2:
                 keys.pop(keys.index("lora_B") + 1)
             keys.pop(keys.index("lora_B"))
-            if keys[0] == "diffusion_model":
-                keys.pop(0)
             target_name = ".".join(keys)
-            lora_name_dict[target_name] = (key, key.replace(".lora_B.", ".lora_A."))
-        return lora_name_dict
+            if target_name.startswith("diffusion_model."):
+                target_name = target_name[len("diffusion_model."):]
+            if target_name not in target_state_dict:
+                return {}
+            state_dict_[target_name] = lora_weight.cpu()
+        return state_dict_
     
-    
-    def match(self, model: torch.nn.Module, state_dict_lora):
-        lora_name_dict = self.get_name_dict(state_dict_lora)
-        model_name_dict = {name: None for name, _ in model.named_parameters()}
-        matched_num = sum([i in model_name_dict for i in lora_name_dict])
-        if matched_num == len(lora_name_dict):
-            return "", ""
-        else:
-            return None
-    
-    
-    def fetch_device_and_dtype(self, state_dict):
-        device, dtype = None, None
-        for name, param in state_dict.items():
-            device, dtype = param.device, param.dtype
-            break
-        computation_device = device
-        computation_dtype = dtype
-        if computation_device == torch.device("cpu"):
-            if torch.cuda.is_available():
-                computation_device = torch.device("cuda")
-        if computation_dtype == torch.float8_e4m3fn:
-            computation_dtype = torch.float32
-        return device, dtype, computation_device, computation_dtype
-
 
     def load(self, model, state_dict_lora, lora_prefix="", alpha=1.0, model_resource=""):
         state_dict_model = model.state_dict()
-        device, dtype, computation_device, computation_dtype = self.fetch_device_and_dtype(state_dict_model)
-        lora_name_dict = self.get_name_dict(state_dict_lora)
-        for name in lora_name_dict:
-            weight_up = state_dict_lora[lora_name_dict[name][0]].to(device=computation_device, dtype=computation_dtype)
-            weight_down = state_dict_lora[lora_name_dict[name][1]].to(device=computation_device, dtype=computation_dtype)
-            if len(weight_up.shape) == 4:
-                weight_up = weight_up.squeeze(3).squeeze(2)
-                weight_down = weight_down.squeeze(3).squeeze(2)
-                weight_lora = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)
-            else:
-                weight_lora = alpha * torch.mm(weight_up, weight_down)
-            weight_model = state_dict_model[name].to(device=computation_device, dtype=computation_dtype)
-            weight_patched = weight_model + weight_lora
-            state_dict_model[name] = weight_patched.to(device=device, dtype=dtype)
-        print(f"    {len(lora_name_dict)} tensors are updated.")
-        model.load_state_dict(state_dict_model)
+        state_dict_lora = self.convert_state_dict(state_dict_lora, alpha=alpha, target_state_dict=state_dict_model)
+        if len(state_dict_lora) > 0:
+            print(f"    {len(state_dict_lora)} tensors are updated.")
+            for name in state_dict_lora:
+                if state_dict_model[name].dtype == torch.float8_e4m3fn:
+                    weight = state_dict_model[name].to(torch.float32)
+                    lora_weight = state_dict_lora[name].to(
+                        dtype=torch.float32,
+                        device=state_dict_model[name].device
+                    )
+                    state_dict_model[name] = (weight + lora_weight).to(
+                        dtype=state_dict_model[name].dtype,
+                        device=state_dict_model[name].device
+                    )
+                else:
+                    state_dict_model[name] += state_dict_lora[name].to(
+                        dtype=state_dict_model[name].dtype,
+                        device=state_dict_model[name].device
+                    )
+            model.load_state_dict(state_dict_model)
     
+
+    def match(self, model, state_dict_lora):
+        for model_class in self.supported_model_classes:
+            if not isinstance(model, model_class):
+                continue
+            state_dict_model = model.state_dict()
+            try:
+                state_dict_lora_ = self.convert_state_dict(state_dict_lora, alpha=1.0, target_state_dict=state_dict_model)
+                if len(state_dict_lora_) > 0:
+                    return "", ""
+            except:
+                pass
+        return None
     
 
 class HunyuanVideoLoRAFromCivitai(LoRAFromCivitai):
@@ -365,22 +466,7 @@ class FluxLoRAConverter:
             else:
                 state_dict_[name] = param
         return state_dict_
-
-
-class WanLoRAConverter:
-    def __init__(self):
-        pass
-
-    @staticmethod
-    def align_to_opensource_format(state_dict, **kwargs):
-        state_dict = {"diffusion_model." + name.replace(".default.", "."): param for name, param in state_dict.items()}
-        return state_dict
     
-    @staticmethod
-    def align_to_diffsynth_format(state_dict, **kwargs):
-        state_dict = {name.replace("diffusion_model.", "").replace(".lora_A.weight", ".lora_A.default.weight").replace(".lora_B.weight", ".lora_B.default.weight"): param for name, param in state_dict.items()}
-        return state_dict
-
 
 def get_lora_loaders():
     return [SDLoRAFromCivitai(), SDXLLoRAFromCivitai(), FluxLoRAFromCivitai(), HunyuanVideoLoRAFromCivitai(), GeneralLoRAFromPeft()]

diffsynth/models/utils.py

@@ -62,25 +62,26 @@ def load_state_dict_from_folder(file_path, torch_dtype=None):
     return state_dict
 
 
-def load_state_dict(file_path, torch_dtype=None):
+def load_state_dict(file_path, torch_dtype=None, device="cpu"):
     if file_path.endswith(".safetensors"):
-        return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype)
+        return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype, device=device)
     else:
-        return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype)
+        return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype, device=device)
 
 
-def load_state_dict_from_safetensors(file_path, torch_dtype=None):
+def load_state_dict_from_safetensors(file_path, torch_dtype=None, device="cpu"):
     state_dict = {}
     with safe_open(file_path, framework="pt", device="cpu") as f:
         for k in f.keys():
             state_dict[k] = f.get_tensor(k)
             if torch_dtype is not None:
                 state_dict[k] = state_dict[k].to(torch_dtype)
+            state_dict[k] = state_dict[k].to(device)
     return state_dict
 
 
-def load_state_dict_from_bin(file_path, torch_dtype=None):
-    state_dict = torch.load(file_path, map_location="cpu", weights_only=True)
+def load_state_dict_from_bin(file_path, torch_dtype=None, device="cpu"):
+    state_dict = torch.load(file_path, map_location=device, weights_only=True)
     if torch_dtype is not None:
         for i in state_dict:
             if isinstance(state_dict[i], torch.Tensor):

diffsynth/models/wan_video_dit.py

@@ -183,13 +183,6 @@ 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__()
@@ -206,17 +199,16 @@ 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 = self.gate(x, gate_msa, self.self_attn(input_x, freqs))
+        x = 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 = self.gate(x, gate_mlp, self.ffn(input_x))
+        x = x + gate_mlp * self.ffn(input_x)
         return x
 
 

diffsynth/pipelines/flux_image.py

@@ -13,7 +13,7 @@ from transformers import SiglipVisionModel
 from copy import deepcopy
 from transformers.models.t5.modeling_t5 import T5LayerNorm, T5DenseActDense, T5DenseGatedActDense
 from ..models.flux_dit import RMSNorm
-from ..vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear
+from ..vram_management import enable_vram_management, enable_auto_lora, AutoLoRALinear, AutoWrappedModule, AutoWrappedLinear
 
 
 class FluxImagePipeline(BasePipeline):
@@ -31,7 +31,6 @@ 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']
 
 
@@ -133,6 +132,15 @@ class FluxImagePipeline(BasePipeline):
         )
         self.enable_cpu_offload()
 
+    def enable_auto_lora(self):
+        enable_auto_lora(
+            self.dit,
+            module_map={
+                RMSNorm: AutoWrappedModule,
+                torch.nn.Linear: AutoLoRALinear,
+            },
+            name_prefix=''
+        )
 
     def denoising_model(self):
         return self.dit
@@ -163,11 +171,6 @@ 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):
@@ -353,13 +356,6 @@ 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()
@@ -395,9 +391,6 @@ 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
@@ -407,6 +400,9 @@ class FluxImagePipeline(BasePipeline):
         # Progress bar
         progress_bar_cmd=tqdm,
         progress_bar_st=None,
+        lora_state_dicts=[],
+        lora_alphas=[],
+        lora_patcher=None,
     ):
         height, width = self.check_resize_height_width(height, width)
 
@@ -425,9 +421,6 @@ 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)
 
@@ -449,7 +442,10 @@ 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, **infiniteyou_kwargs
+                lora_state_dicts=lora_state_dicts,
+                lora_alphas = lora_alphas,
+                lora_patcher=lora_patcher,
+                **prompt_emb_posi, **tiler_kwargs, **extra_input, **controlnet_kwargs, **ipadapter_kwargs_list_posi, **eligen_kwargs_posi, **tea_cache_kwargs,
             )
             noise_pred_posi = self.control_noise_via_local_prompts(
                 prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback,
@@ -466,7 +462,10 @@ 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, **infiniteyou_kwargs,
+                    lora_state_dicts=lora_state_dicts,
+                    lora_alphas = lora_alphas,
+                    lora_patcher=lora_patcher,
+                    **prompt_emb_nega, **tiler_kwargs, **extra_input, **controlnet_kwargs_nega, **ipadapter_kwargs_list_nega, **eligen_kwargs_nega,
                 )
                 noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
             else:
@@ -486,58 +485,6 @@ 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:
@@ -582,7 +529,6 @@ class TeaCache:
         hidden_states = hidden_states + self.previous_residual
         return hidden_states
 
-
 def lets_dance_flux(
     dit: FluxDiT,
     controlnet: FluxMultiControlNetManager = None,
@@ -600,11 +546,11 @@ def lets_dance_flux(
     entity_prompt_emb=None,
     entity_masks=None,
     ipadapter_kwargs_list={},
-    id_emb=None,
-    infinityou_guidance=None,
     tea_cache: TeaCache = None,
+    use_gradient_checkpointing=False,
     **kwargs
 ):
+
     if tiled:
         def flux_forward_fn(hl, hr, wl, wr):
             tiled_controlnet_frames = [f[:, :, hl: hr, wl: wr] for f in controlnet_frames] if controlnet_frames is not None else None
@@ -646,9 +592,6 @@ 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
         )
@@ -671,6 +614,11 @@ def lets_dance_flux(
         prompt_emb = dit.context_embedder(prompt_emb)
         image_rotary_emb = dit.pos_embedder(torch.cat((text_ids, image_ids), dim=1))
         attention_mask = None
+        
+    def create_custom_forward(module):
+        def custom_forward(*inputs, **kwargs):
+            return module(*inputs, **kwargs)
+        return custom_forward
 
     # TeaCache
     if tea_cache is not None:
@@ -683,14 +631,22 @@ def lets_dance_flux(
     else:
         # Joint Blocks
         for block_id, block in enumerate(dit.blocks):
-            hidden_states, prompt_emb = block(
-                hidden_states,
-                prompt_emb,
-                conditioning,
-                image_rotary_emb,
-                attention_mask,
-                ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, None)
-            )
+            if use_gradient_checkpointing:
+                hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask, ipadapter_kwargs_list.get(block_id, None), **kwargs,
+                    use_reentrant=False,
+                )
+            else:
+                hidden_states, prompt_emb = block(
+                    hidden_states,
+                    prompt_emb,
+                    conditioning,
+                    image_rotary_emb,
+                    attention_mask,
+                    ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, None),
+                    **kwargs
+                )
             # ControlNet
             if controlnet is not None and controlnet_frames is not None:
                 hidden_states = hidden_states + controlnet_res_stack[block_id]
@@ -699,14 +655,22 @@ def lets_dance_flux(
         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,
-                conditioning,
-                image_rotary_emb,
-                attention_mask,
-                ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id + num_joint_blocks, None)
-            )
+            if use_gradient_checkpointing:
+                hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask, ipadapter_kwargs_list.get(block_id + num_joint_blocks, None), **kwargs,
+                    use_reentrant=False,
+                )
+            else:
+                hidden_states, prompt_emb = block(
+                    hidden_states,
+                    prompt_emb,
+                    conditioning,
+                    image_rotary_emb,
+                    attention_mask,
+                    ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id + num_joint_blocks, None),
+                    **kwargs
+                )
             # ControlNet
             if controlnet is not None and controlnet_frames is not None:
                 hidden_states[:, prompt_emb.shape[1]:] = hidden_states[:, prompt_emb.shape[1]:] + controlnet_single_res_stack[block_id]
@@ -715,8 +679,8 @@ def lets_dance_flux(
         if tea_cache is not None:
             tea_cache.store(hidden_states)
 
-    hidden_states = dit.final_norm_out(hidden_states, conditioning)
-    hidden_states = dit.final_proj_out(hidden_states)
+    hidden_states = dit.final_norm_out(hidden_states, conditioning, **kwargs)
+    hidden_states = dit.final_proj_out(hidden_states, **kwargs)
     hidden_states = dit.unpatchify(hidden_states, height, width)
 
     return hidden_states

diffsynth/pipelines/wan_video.py

@@ -1,4 +1,3 @@
-import types
 from ..models import ModelManager
 from ..models.wan_video_dit import WanModel
 from ..models.wan_video_text_encoder import WanTextEncoder
@@ -31,10 +30,9 @@ class WanVideoPipeline(BasePipeline):
         self.image_encoder: WanImageEncoder = None
         self.dit: WanModel = None
         self.vae: WanVideoVAE = None
-        self.model_names = ['text_encoder', 'dit', 'vae', 'image_encoder']
+        self.model_names = ['text_encoder', 'dit', 'vae']
         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):
@@ -137,20 +135,11 @@ class WanVideoPipeline(BasePipeline):
 
 
     @staticmethod
-    def from_model_manager(model_manager: ModelManager, torch_dtype=None, device=None, use_usp=False):
+    def from_model_manager(model_manager: ModelManager, torch_dtype=None, device=None):
         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
     
     
@@ -159,7 +148,7 @@ class WanVideoPipeline(BasePipeline):
 
 
     def encode_prompt(self, prompt, positive=True):
-        prompt_emb = self.prompter.encode_prompt(prompt, positive=positive, device=self.device)
+        prompt_emb = self.prompter.encode_prompt(prompt, positive=positive)
         return {"context": prompt_emb}
     
     
@@ -200,10 +189,6 @@ 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()
@@ -273,9 +258,6 @@ 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"])
@@ -283,9 +265,9 @@ 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, **usp_kwargs)
+            noise_pred_posi = model_fn_wan_video(self.dit, latents, timestep=timestep, **prompt_emb_posi, **image_emb, **extra_input, **tea_cache_posi)
             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, **usp_kwargs)
+                noise_pred_nega = model_fn_wan_video(self.dit, latents, timestep=timestep, **prompt_emb_nega, **image_emb, **extra_input, **tea_cache_nega)
                 noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
             else:
                 noise_pred = noise_pred_posi
@@ -364,15 +346,8 @@ 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)
@@ -396,21 +371,15 @@ 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

diffsynth/vram_management/layers.py

@@ -70,6 +70,56 @@ class AutoWrappedLinear(torch.nn.Linear):
             bias = None if self.bias is None else cast_to(self.bias, self.computation_dtype, self.computation_device)
         return torch.nn.functional.linear(x, weight, bias)
 
+class AutoLoRALinear(torch.nn.Linear):
+    def __init__(self, name='', in_features=1, out_features=2, bias=True, device=None, dtype=None):
+        super().__init__(in_features, out_features, bias, device, dtype)
+        self.name = name
+    
+    def forward(self, x, lora_state_dicts=[], lora_alphas=[1.0,1.0], lora_patcher=None, **kwargs):
+        out = torch.nn.functional.linear(x, self.weight, self.bias)
+        lora_a_name = f'{self.name}.lora_A.default.weight'
+        lora_b_name = f'{self.name}.lora_B.default.weight'
+
+        lora_output = []
+        for i, lora_state_dict in enumerate(lora_state_dicts):
+            if lora_state_dict is None:
+                break
+            if lora_a_name in lora_state_dict and lora_b_name in lora_state_dict:
+                lora_A = lora_state_dict[lora_a_name].to(dtype=self.weight.dtype,device=self.weight.device)
+                lora_B = lora_state_dict[lora_b_name].to(dtype=self.weight.dtype,device=self.weight.device)
+                out_lora = x @ lora_A.T @ lora_B.T
+                lora_output.append(out_lora)
+        if len(lora_output) > 0:
+            lora_output = torch.stack(lora_output)
+            out = lora_patcher(out, lora_output, self.name)
+        return out
+
+def enable_auto_lora(model:torch.nn.Module, module_map: dict, name_prefix=''):
+    targets = list(module_map.keys())
+    for name, module in model.named_children():
+        if name_prefix != '':
+            full_name = name_prefix + '.' + name
+        else:
+            full_name = name
+        if isinstance(module,targets[1]):
+            # print(full_name)
+            # print(module)
+            # ToDo: replace the linear to the AutoLoRALinear 
+            new_module = AutoLoRALinear(
+                name=full_name, 
+                in_features=module.in_features, 
+                out_features=module.out_features, 
+                bias=module.bias is not None, 
+                device=module.weight.device, 
+                dtype=module.weight.dtype)
+            new_module.weight.data.copy_(module.weight.data)
+            new_module.bias.data.copy_(module.bias.data)
+            setattr(model, name, new_module)
+        elif isinstance(module, targets[0]):
+            pass
+        else:
+            enable_auto_lora(module, module_map, full_name)
+       
 
 def enable_vram_management_recursively(model: torch.nn.Module, module_map: dict, module_config: dict, max_num_param=None, overflow_module_config: dict = None, total_num_param=0):
     for name, module in model.named_children():

examples/InfiniteYou/README.md

@@ -1,7 +0,0 @@
-# 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

@@ -1,58 +0,0 @@
-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

@@ -49,20 +49,6 @@ 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

@@ -44,28 +44,11 @@ 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=(Shard(1), Replicate()),
-                    desired_input_layouts=(Shard(1), Shard(0)),
+                    input_layouts=(Replicate(), Replicate()),
+                    desired_input_layouts=(Replicate(), Shard(0)),
                 ),
                 "self_attn.q": SequenceParallel(),
                 "self_attn.k": SequenceParallel(),
@@ -76,11 +59,11 @@ class LitModel(pl.LightningModule):
                     input_layouts=(Shard(1), Shard(1), Shard(1)),
                     desired_input_layouts=(Shard(2), Shard(2), Shard(2)),
                 ),
-                "self_attn.o": RowwiseParallel(input_layouts=Shard(2), output_layouts=Replicate()),
-
+                "self_attn.o": ColwiseParallel(output_layouts=Replicate()),
+                
                 "cross_attn": PrepareModuleInput(
-                    input_layouts=(Shard(1), Replicate()),
-                    desired_input_layouts=(Shard(1), Replicate()),
+                    input_layouts=(Replicate(), Replicate()),
+                    desired_input_layouts=(Replicate(), Replicate()),
                 ),
                 "cross_attn.q": SequenceParallel(),
                 "cross_attn.k": SequenceParallel(),
@@ -91,26 +74,18 @@ class LitModel(pl.LightningModule):
                     input_layouts=(Shard(1), Shard(1), Shard(1)),
                     desired_input_layouts=(Shard(2), Shard(2), Shard(2)),
                 ),
-                "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()),
-                )
+                "cross_attn.o": ColwiseParallel(output_layouts=Replicate()),
+                
+                "ffn.0": ColwiseParallel(),
+                "ffn.2": RowwiseParallel(),
             }
             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
@@ -119,8 +94,9 @@ 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

@@ -1,58 +0,0 @@
-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)

lora/dataset.py

@@ -0,0 +1,54 @@
+import torch, os
+import pandas as pd
+from PIL import Image
+from torchvision.transforms import v2
+from diffsynth.data.video import crop_and_resize
+
+
+class LoraDataset(torch.utils.data.Dataset):
+    def __init__(self, base_path, metadata_path, steps_per_epoch=1000, loras_per_item=1):
+        self.base_path = base_path
+        data_df = pd.read_csv(metadata_path)
+        self.model_file = data_df["model_file"].tolist()
+        self.image_file = data_df["image_file"].tolist()
+        self.text = data_df["text"].tolist()
+        self.max_resolution = 1920 * 1080
+        self.steps_per_epoch = steps_per_epoch
+        self.loras_per_item = loras_per_item
+        
+        
+    def read_image(self, image_file):
+        image = Image.open(image_file).convert("RGB")
+        width, height = image.size
+        if width * height > self.max_resolution:
+            scale = (width * height / self.max_resolution) ** 0.5
+            image = image.resize((int(width / scale), int(height / scale)))
+            width, height = image.size
+        if width % 16 != 0 or height % 16 != 0:
+            image = crop_and_resize(image, height // 16 * 16, width // 16 * 16)
+        image = v2.functional.to_image(image)
+        image = v2.functional.to_dtype(image, dtype=torch.float32, scale=True)
+        image = v2.functional.normalize(image, [0.5], [0.5])
+        return image
+    
+    
+    def get_data(self, data_id):
+        data = {
+            "model_file": os.path.join(self.base_path, self.model_file[data_id]),
+            "image": self.read_image(os.path.join(self.base_path, self.image_file[data_id])),
+            "text": self.text[data_id]
+        }
+        return data
+
+
+    def __getitem__(self, index):
+        data = []
+        while len(data) < self.loras_per_item:
+            data_id = torch.randint(0, len(self.model_file), (1,))[0]
+            data_id = (data_id + index) % len(self.model_file) # For fixed seed.
+            data.append(self.get_data(data_id))
+        return data
+
+
+    def __len__(self):
+        return self.steps_per_epoch

lora/merger.py

@@ -0,0 +1,61 @@
+import torch
+
+
+class LoraMerger(torch.nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.weight_base = torch.nn.Parameter(torch.randn((dim,)))
+        self.weight_lora = torch.nn.Parameter(torch.randn((dim,)))
+        self.weight_cross = torch.nn.Parameter(torch.randn((dim,)))
+        self.weight_out = torch.nn.Parameter(torch.ones((dim,)))
+        self.bias = torch.nn.Parameter(torch.randn((dim,)))
+        self.activation = torch.nn.Sigmoid()
+        self.norm_base = torch.nn.LayerNorm(dim, eps=1e-5)
+        self.norm_lora = torch.nn.LayerNorm(dim, eps=1e-5)
+        
+    def forward(self, base_output, lora_outputs):
+        norm_base_output = self.norm_base(base_output)
+        norm_lora_outputs = self.norm_lora(lora_outputs)
+        gate = self.activation(
+            norm_base_output * self.weight_base \
+            + norm_lora_outputs * self.weight_lora \
+            + norm_base_output * norm_lora_outputs * self.weight_cross + self.bias
+        )
+        output = base_output + (self.weight_out * gate * lora_outputs).sum(dim=0)
+        return output
+
+
+class LoraPatcher(torch.nn.Module):
+    def __init__(self, lora_patterns=None):
+        super().__init__()
+        if lora_patterns is None:
+            lora_patterns = self.default_lora_patterns()
+        model_dict = {}
+        for lora_pattern in lora_patterns:
+            name, dim = lora_pattern["name"], lora_pattern["dim"]
+            model_dict[name.replace(".", "___")] = LoraMerger(dim)
+        self.model_dict = torch.nn.ModuleDict(model_dict)
+        
+    def default_lora_patterns(self):
+        lora_patterns = []
+        lora_dict = {
+            "attn.a_to_qkv": 9216, "attn.a_to_out": 3072, "ff_a.0": 12288, "ff_a.2": 3072, "norm1_a.linear": 18432,
+            "attn.b_to_qkv": 9216, "attn.b_to_out": 3072, "ff_b.0": 12288, "ff_b.2": 3072, "norm1_b.linear": 18432,
+        }
+        for i in range(19):
+            for suffix in lora_dict:
+                lora_patterns.append({
+                    "name": f"blocks.{i}.{suffix}",
+                    "dim": lora_dict[suffix]
+                })
+        lora_dict = {"to_qkv_mlp": 21504, "proj_out": 3072, "norm.linear": 9216}
+        for i in range(38):
+            for suffix in lora_dict:
+                lora_patterns.append({
+                    "name": f"single_blocks.{i}.{suffix}",
+                    "dim": lora_dict[suffix]
+                })
+        return lora_patterns
+        
+    def forward(self, base_output, lora_outputs, name):
+        return self.model_dict[name.replace(".", "___")](base_output, lora_outputs)

lora/retriever.py

@@ -0,0 +1,149 @@
+import torch
+from diffsynth import SDTextEncoder
+from diffsynth.models.sd3_text_encoder import SD3TextEncoder1StateDictConverter
+from diffsynth.models.sd_text_encoder import CLIPEncoderLayer
+
+
+class LoRALayerBlock(torch.nn.Module):
+    def __init__(self, L, dim_in):
+        super().__init__()
+        self.x = torch.nn.Parameter(torch.randn(1, L, dim_in))
+
+    def forward(self, lora_A, lora_B):
+        out = self.x @ lora_A.T @ lora_B.T
+        return out
+    
+
+class LoRAEmbedder(torch.nn.Module):
+    def __init__(self, lora_patterns=None, L=1, out_dim=2048):
+        super().__init__()
+        if lora_patterns is None:
+            lora_patterns = self.default_lora_patterns()
+            
+        model_dict = {}
+        for lora_pattern in lora_patterns:
+            name, dim = lora_pattern["name"], lora_pattern["dim"][0]
+            model_dict[name.replace(".", "___")] = LoRALayerBlock(L, dim)
+        self.model_dict = torch.nn.ModuleDict(model_dict)
+        
+        proj_dict = {}
+        for lora_pattern in lora_patterns:
+            layer_type, dim = lora_pattern["type"], lora_pattern["dim"][1]
+            if layer_type not in proj_dict:
+                proj_dict[layer_type.replace(".", "___")] = torch.nn.Linear(dim, out_dim)
+        self.proj_dict = torch.nn.ModuleDict(proj_dict)
+        
+        self.lora_patterns = lora_patterns
+        
+        
+    def default_lora_patterns(self):
+        lora_patterns = []
+        lora_dict = {
+            "attn.a_to_qkv": (3072, 9216), "attn.a_to_out": (3072, 3072), "ff_a.0": (3072, 12288), "ff_a.2": (12288, 3072), "norm1_a.linear": (3072, 18432),
+            "attn.b_to_qkv": (3072, 9216), "attn.b_to_out": (3072, 3072), "ff_b.0": (3072, 12288), "ff_b.2": (12288, 3072), "norm1_b.linear": (3072, 18432),
+        }
+        for i in range(19):
+            for suffix in lora_dict:
+                lora_patterns.append({
+                    "name": f"blocks.{i}.{suffix}",
+                    "dim": lora_dict[suffix],
+                    "type": suffix,
+                })
+        lora_dict = {"to_qkv_mlp": (3072, 21504), "proj_out": (15360, 3072), "norm.linear": (3072, 9216)}
+        for i in range(38):
+            for suffix in lora_dict:
+                lora_patterns.append({
+                    "name": f"single_blocks.{i}.{suffix}",
+                    "dim": lora_dict[suffix],
+                    "type": suffix,
+                })
+        return lora_patterns
+        
+    def forward(self, lora):
+        lora_emb = []
+        for lora_pattern in self.lora_patterns:
+            name, layer_type = lora_pattern["name"], lora_pattern["type"]
+            lora_A = lora[name + ".lora_A.default.weight"]
+            lora_B = lora[name + ".lora_B.default.weight"]
+            lora_out = self.model_dict[name.replace(".", "___")](lora_A, lora_B)
+            lora_out = self.proj_dict[layer_type.replace(".", "___")](lora_out)
+            lora_emb.append(lora_out)
+        lora_emb = torch.concat(lora_emb, dim=1)
+        return lora_emb
+
+
+class TextEncoder(torch.nn.Module):
+    def __init__(self, embed_dim=768, vocab_size=49408, max_position_embeddings=77, num_encoder_layers=12, encoder_intermediate_size=3072):
+        super().__init__()
+
+        # token_embedding
+        self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim)
+
+        # position_embeds (This is a fixed tensor)
+        self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim))
+
+        # encoders
+        self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size) for _ in range(num_encoder_layers)])
+
+        # attn_mask
+        self.attn_mask = self.attention_mask(max_position_embeddings)
+
+        # final_layer_norm
+        self.final_layer_norm = torch.nn.LayerNorm(embed_dim)
+
+    def attention_mask(self, length):
+        mask = torch.empty(length, length)
+        mask.fill_(float("-inf"))
+        mask.triu_(1)
+        return mask
+
+    def forward(self, input_ids, clip_skip=1):
+        embeds = self.token_embedding(input_ids) + self.position_embeds
+        attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype)
+        for encoder_id, encoder in enumerate(self.encoders):
+            embeds = encoder(embeds, attn_mask=attn_mask)
+            if encoder_id + clip_skip == len(self.encoders):
+                break
+        embeds = self.final_layer_norm(embeds)
+        pooled_embeds = embeds[torch.arange(embeds.shape[0]), input_ids.to(dtype=torch.int).argmax(dim=-1)]
+        return pooled_embeds
+    
+    @staticmethod
+    def state_dict_converter():
+        return SD3TextEncoder1StateDictConverter()
+    
+    
+class LoRAEncoder(torch.nn.Module):
+    def __init__(self, embed_dim=768, max_position_embeddings=304, num_encoder_layers=2, encoder_intermediate_size=3072, L=1):
+        super().__init__()
+        max_position_embeddings *= L
+        
+        # Embedder
+        self.embedder = LoRAEmbedder(L=L, out_dim=embed_dim)
+
+        # position_embeds (This is a fixed tensor)
+        self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim))
+
+        # encoders
+        self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size) for _ in range(num_encoder_layers)])
+
+        # attn_mask
+        self.attn_mask = self.attention_mask(max_position_embeddings)
+
+        # final_layer_norm
+        self.final_layer_norm = torch.nn.LayerNorm(embed_dim)
+
+    def attention_mask(self, length):
+        mask = torch.empty(length, length)
+        mask.fill_(float("-inf"))
+        mask.triu_(1)
+        return mask
+
+    def forward(self, lora):
+        embeds = self.embedder(lora) + self.position_embeds
+        attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype)
+        for encoder_id, encoder in enumerate(self.encoders):
+            embeds = encoder(embeds, attn_mask=attn_mask)
+        embeds = self.final_layer_norm(embeds)
+        embeds = embeds.mean(dim=1)
+        return embeds

lora/test_merger.py

@@ -0,0 +1,46 @@
+from diffsynth import FluxImagePipeline, ModelManager, load_state_dict
+from diffsynth.models.lora import FluxLoRAConverter
+from diffsynth.pipelines.flux_image import lets_dance_flux
+from lora.dataset import LoraDataset
+from lora.merger import LoraPatcher
+from lora.utils import load_lora
+import torch, os
+from accelerate import Accelerator, DistributedDataParallelKwargs
+from tqdm import tqdm
+
+
+model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", model_id_list=["FLUX.1-dev"])
+pipe = FluxImagePipeline.from_model_manager(model_manager)
+pipe.enable_auto_lora()
+
+lora_patcher = LoraPatcher().to(dtype=torch.bfloat16, device="cuda")
+lora_patcher.load_state_dict(load_state_dict("models/lora_merger/epoch-3.safetensors"))
+
+dataset = LoraDataset("data/lora/models", "data/lora/lora_dataset_1000.csv", steps_per_epoch=800, loras_per_item=4)
+
+for seed in range(100):
+    batch = dataset[0]
+    num_lora = torch.randint(1, len(batch), (1,))[0]
+    lora_state_dicts = [
+        FluxLoRAConverter.align_to_diffsynth_format(load_lora(batch[i]["model_file"], device="cuda")) for i in range(num_lora)
+    ]
+    image = pipe(
+        prompt=batch[0]["text"],
+        seed=seed,
+    )
+    image.save(f"data/lora/lora_outputs/image_{seed}_nolora.jpg")
+    for i in range(num_lora):
+        image = pipe(
+            prompt=batch[0]["text"],
+            lora_state_dicts=[lora_state_dicts[i]], 
+            lora_patcher=lora_patcher,
+            seed=seed,
+        )
+        image.save(f"data/lora/lora_outputs/image_{seed}_{i}.jpg")
+    image = pipe(
+        prompt=batch[0]["text"],
+        lora_state_dicts=lora_state_dicts, 
+        lora_patcher=lora_patcher,
+        seed=seed,
+    )
+    image.save(f"data/lora/lora_outputs/image_{seed}_merger.jpg")

lora/test_retriever.py

@@ -0,0 +1,148 @@
+from diffsynth import FluxImagePipeline, ModelManager, load_state_dict
+from diffsynth.models.lora import FluxLoRAConverter
+from diffsynth.pipelines.flux_image import lets_dance_flux
+from lora.dataset import LoraDataset
+from lora.retriever import TextEncoder, LoRAEncoder
+from lora.merger import LoraPatcher
+from lora.utils import load_lora
+import torch, os
+from accelerate import Accelerator, DistributedDataParallelKwargs
+from tqdm import tqdm
+from transformers import CLIPTokenizer, CLIPModel
+import pandas as pd
+
+
+
+class LoRARetrieverTrainingModel(torch.nn.Module):
+    def __init__(self, pretrained_path):
+        super().__init__()
+        
+        self.text_encoder = TextEncoder().to(torch.bfloat16)
+        state_dict = load_state_dict("models/FLUX/FLUX.1-dev/text_encoder/model.safetensors")
+        self.text_encoder.load_state_dict(TextEncoder.state_dict_converter().from_civitai(state_dict))
+        self.text_encoder.requires_grad_(False)
+        self.text_encoder.eval()
+        
+        self.lora_encoder = LoRAEncoder().to(torch.bfloat16)
+        state_dict = load_state_dict(pretrained_path)
+        self.lora_encoder.load_state_dict(state_dict)
+        
+        self.tokenizer = CLIPTokenizer.from_pretrained("diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_1")
+        
+        
+    def to(self, *args, **kwargs):
+        device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to(*args, **kwargs)
+        if device is not None:
+            self.device = device
+        if dtype is not None:
+            self.torch_dtype = dtype
+        super().to(*args, **kwargs)
+        return self
+
+
+    def forward(self, batch):
+        text = [data["text"] for data in batch]
+        input_ids = self.tokenizer(
+            text,
+            return_tensors="pt",
+            padding="max_length",
+            max_length=77,
+            truncation=True
+        ).input_ids.to(self.device)
+        text_emb = self.text_encoder(input_ids)
+        text_emb = text_emb / text_emb.norm()
+        
+        lora_emb = []
+        for data in batch:
+            lora = FluxLoRAConverter.align_to_diffsynth_format(load_lora(data["model_file"], device=self.device))
+            lora_emb.append(self.lora_encoder(lora))
+        lora_emb = torch.concat(lora_emb)
+        lora_emb = lora_emb / lora_emb.norm()
+        
+        similarity = text_emb @ lora_emb.T
+        print(similarity)
+        loss = -torch.log(torch.softmax(similarity, dim=0).diag()) - torch.log(torch.softmax(similarity, dim=1).diag())
+        loss = 10 * loss.mean()
+        return loss
+    
+    
+    def trainable_modules(self):
+        return self.lora_encoder.parameters()
+    
+    @torch.no_grad()
+    def process_lora_list(self, lora_list):
+        lora_emb = []
+        for lora in tqdm(lora_list):
+            lora = FluxLoRAConverter.align_to_diffsynth_format(load_lora(lora, device="cuda"))
+            lora_emb.append(self.lora_encoder(lora))
+        lora_emb = torch.concat(lora_emb)
+        lora_emb = lora_emb / lora_emb.norm()
+        self.lora_emb = lora_emb
+        self.lora_list = lora_list
+    
+    @torch.no_grad()
+    def retrieve(self, text, k=1):
+        input_ids = self.tokenizer(
+            text,
+            return_tensors="pt",
+            padding="max_length",
+            max_length=77,
+            truncation=True
+        ).input_ids.to(self.device)
+        text_emb = self.text_encoder(input_ids)
+        text_emb = text_emb / text_emb.norm()
+        
+        similarity = text_emb @ self.lora_emb.T
+        topk = torch.topk(similarity, k, dim=1).indices[0]
+        
+        lora_list = []
+        model_url_list = []
+        for lora_id in topk:
+            print(self.lora_list[lora_id])
+            lora = FluxLoRAConverter.align_to_diffsynth_format(load_lora(self.lora_list[lora_id], device="cuda"))
+            lora_list.append(lora)
+            model_id = self.lora_list[lora_id].split("/")[3:5]
+            model_url_list.append(f"https://www.modelscope.cn/models/{model_id[0]}/{model_id[1]}")
+        return lora_list, model_url_list
+
+
+
+model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", model_id_list=["FLUX.1-dev"])
+pipe = FluxImagePipeline.from_model_manager(model_manager)
+pipe.enable_auto_lora()
+
+lora_patcher = LoraPatcher().to(dtype=torch.bfloat16, device="cuda")
+lora_patcher.load_state_dict(load_state_dict("models/lora_merger/epoch-9.safetensors"))
+
+retriever = LoRARetrieverTrainingModel("models/lora_retriever/epoch-3.safetensors").to(dtype=torch.bfloat16, device="cuda")
+retriever.process_lora_list(list(set("data/lora/models/" + i for i in pd.read_csv("data/lora/lora_dataset_1000.csv")["model_file"])))
+
+dataset = LoraDataset("data/lora/models", "data/lora/lora_dataset_1000.csv", steps_per_epoch=800, loras_per_item=1)
+
+text_list = []
+model_url_list = []
+for seed in range(100):
+    text = dataset[0][0]["text"]
+    print(text)
+    loras, urls = retriever.retrieve(text, k=3)
+    print(urls)
+    image = pipe(
+        prompt=text,
+        seed=seed,
+    )
+    image.save(f"data/lora/lora_outputs/image_{seed}_top0.jpg")
+    for i in range(2, 3):
+        image = pipe(
+            prompt=text,
+            lora_state_dicts=loras[:i+1],
+            lora_patcher=lora_patcher,
+            seed=seed,
+        )
+        image.save(f"data/lora/lora_outputs/image_{seed}_top{i+1}.jpg")
+        
+    text_list.append(text)
+    model_url_list.append(urls)
+    df = pd.DataFrame()
+    df["text"] = text_list
+    df["models"] = [",".join(i) for i in model_url_list]
+    df.to_csv("data/lora/lora_outputs/metadata.csv", index=False, encoding="utf-8-sig")

lora/train_merger.py

@@ -0,0 +1,119 @@
+from diffsynth import FluxImagePipeline, ModelManager
+from diffsynth.models.lora import FluxLoRAConverter
+from diffsynth.pipelines.flux_image import lets_dance_flux
+from lora.dataset import LoraDataset
+from lora.merger import LoraPatcher
+from lora.utils import load_lora
+import torch, os
+from accelerate import Accelerator, DistributedDataParallelKwargs
+from tqdm import tqdm
+
+
+
+class LoRAMergerTrainingModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cpu", model_id_list=["FLUX.1-dev"])
+        self.pipe = FluxImagePipeline.from_model_manager(model_manager)
+        self.lora_patcher = LoraPatcher()
+        self.pipe.enable_auto_lora()
+        self.freeze_parameters()
+        self.switch_to_training_mode()
+        self.use_gradient_checkpointing = True
+        self.state_dict_converter = FluxLoRAConverter.align_to_diffsynth_format
+        self.device = "cuda"
+        
+        
+    def to(self, *args, **kwargs):
+        device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to(*args, **kwargs)
+        if device is not None:
+            self.device = device
+        if dtype is not None:
+            self.torch_dtype = dtype
+        super().to(*args, **kwargs)
+        return self
+        
+        
+    def switch_to_training_mode(self):
+        self.pipe.scheduler.set_timesteps(1000, training=True)
+
+
+    def freeze_parameters(self):
+        self.pipe.requires_grad_(False)
+        self.pipe.eval()
+        self.pipe.denoising_model().train()
+        self.lora_patcher.requires_grad_(True)
+
+
+    def forward(self, batch):
+        # Data
+        text, image = batch[0]["text"], batch[0]["image"].unsqueeze(0)
+        num_lora = torch.randint(1, len(batch), (1,))[0]
+        lora_state_dicts = [
+            self.state_dict_converter(load_lora(batch[i]["model_file"], device=self.device)) for i in range(num_lora)
+        ]
+        lora_alphas = None
+
+        # Prepare input parameters
+        self.pipe.device = self.device
+        prompt_emb = self.pipe.encode_prompt(text, positive=True)
+        latents = self.pipe.vae_encoder(image.to(dtype=self.pipe.torch_dtype, device=self.device))
+        noise = torch.randn_like(latents)
+        timestep_id = torch.randint(0, self.pipe.scheduler.num_train_timesteps, (1,))
+        timestep = self.pipe.scheduler.timesteps[timestep_id].to(self.device)
+        extra_input = self.pipe.prepare_extra_input(latents)
+        noisy_latents = self.pipe.scheduler.add_noise(latents, noise, timestep)
+        training_target = self.pipe.scheduler.training_target(latents, noise, timestep)
+
+        # Compute loss
+        noise_pred = lets_dance_flux(
+            self.pipe.dit,
+            hidden_states=noisy_latents, timestep=timestep, **prompt_emb, **extra_input,
+            lora_state_dicts=lora_state_dicts, lora_alphas=lora_alphas, lora_patcher=self.lora_patcher,
+            use_gradient_checkpointing=self.use_gradient_checkpointing
+        )
+        loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float())
+        loss = loss * self.pipe.scheduler.training_weight(timestep)
+        return loss
+    
+    
+    def trainable_modules(self):
+        return self.lora_patcher.parameters()
+
+
+class ModelLogger:
+    def __init__(self, output_path, remove_prefix_in_ckpt=None):
+        self.output_path = output_path
+        self.remove_prefix_in_ckpt = remove_prefix_in_ckpt
+        
+    
+    def on_step_end(self, loss):
+        pass
+    
+    
+    def on_epoch_end(self, accelerator, model, epoch_id):
+        accelerator.wait_for_everyone()
+        if accelerator.is_main_process:
+            state_dict = accelerator.unwrap_model(model).lora_patcher.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)
+
+
+if __name__ == '__main__':
+    model = LoRAMergerTrainingModel()
+    dataset = LoraDataset("data/lora/models/", "data/lora/lora_dataset_1000.csv", steps_per_epoch=800, loras_per_item=4)
+    dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, batch_size=1, num_workers=1, collate_fn=lambda x: x[0])
+    optimizer = torch.optim.AdamW(model.trainable_modules(), lr=1e-4)
+    model_logger = ModelLogger("models/lora_merger")
+    accelerator = Accelerator(kwargs_handlers=[DistributedDataParallelKwargs(find_unused_parameters=True)])
+    model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)
+    
+    for epoch_id in range(1000000):
+        for data in tqdm(dataloader):
+            with accelerator.accumulate(model):
+                optimizer.zero_grad()
+                loss = model(data)
+                accelerator.backward(loss)
+                optimizer.step()
+        model_logger.on_epoch_end(accelerator, model, epoch_id)

lora/train_retriever.py

@@ -0,0 +1,105 @@
+from diffsynth import FluxImagePipeline, ModelManager, load_state_dict
+from diffsynth.models.lora import FluxLoRAConverter
+from diffsynth.pipelines.flux_image import lets_dance_flux
+from lora.dataset import LoraDataset
+from lora.retriever import TextEncoder, LoRAEncoder
+from lora.utils import load_lora
+import torch, os
+from accelerate import Accelerator, DistributedDataParallelKwargs
+from tqdm import tqdm
+from transformers import CLIPTokenizer, CLIPModel
+
+
+
+class LoRARetrieverTrainingModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        
+        self.text_encoder = TextEncoder().to(torch.bfloat16)
+        state_dict = load_state_dict("models/FLUX/FLUX.1-dev/text_encoder/model.safetensors")
+        self.text_encoder.load_state_dict(TextEncoder.state_dict_converter().from_civitai(state_dict))
+        self.text_encoder.requires_grad_(False)
+        self.text_encoder.eval()
+        
+        self.lora_encoder = LoRAEncoder().to(torch.bfloat16)
+        
+        self.tokenizer = CLIPTokenizer.from_pretrained("diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_1")
+        
+        
+    def to(self, *args, **kwargs):
+        device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to(*args, **kwargs)
+        if device is not None:
+            self.device = device
+        if dtype is not None:
+            self.torch_dtype = dtype
+        super().to(*args, **kwargs)
+        return self
+
+
+    def forward(self, batch):
+        text = [data["text"] for data in batch]
+        input_ids = self.tokenizer(
+            text,
+            return_tensors="pt",
+            padding="max_length",
+            max_length=77,
+            truncation=True
+        ).input_ids.to(self.device)
+        text_emb = self.text_encoder(input_ids)
+        text_emb = text_emb / text_emb.norm()
+        
+        lora_emb = []
+        for data in batch:
+            lora = FluxLoRAConverter.align_to_diffsynth_format(load_lora(data["model_file"], device=self.device))
+            lora_emb.append(self.lora_encoder(lora))
+        lora_emb = torch.concat(lora_emb)
+        lora_emb = lora_emb / lora_emb.norm()
+        
+        similarity = text_emb @ lora_emb.T
+        print(similarity)
+        loss = -torch.log(torch.softmax(similarity, dim=0).diag()) - torch.log(torch.softmax(similarity, dim=1).diag())
+        loss = 10 * loss.mean()
+        return loss
+    
+    
+    def trainable_modules(self):
+        return self.lora_encoder.parameters()
+
+
+class ModelLogger:
+    def __init__(self, output_path, remove_prefix_in_ckpt=None):
+        self.output_path = output_path
+        self.remove_prefix_in_ckpt = remove_prefix_in_ckpt
+        
+    
+    def on_step_end(self, loss):
+        pass
+    
+    
+    def on_epoch_end(self, accelerator, model, epoch_id):
+        accelerator.wait_for_everyone()
+        if accelerator.is_main_process:
+            state_dict = accelerator.unwrap_model(model).lora_encoder.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)
+
+
+if __name__ == '__main__':
+    model = LoRARetrieverTrainingModel()
+    dataset = LoraDataset("data/lora/models/", "data/lora/lora_dataset_1000.csv", steps_per_epoch=100, loras_per_item=32)
+    dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, batch_size=1, num_workers=1, collate_fn=lambda x: x[0])
+    optimizer = torch.optim.AdamW(model.trainable_modules(), lr=1e-4)
+    model_logger = ModelLogger("models/lora_retriever")
+    accelerator = Accelerator(kwargs_handlers=[DistributedDataParallelKwargs(find_unused_parameters=True)])
+    model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)
+    
+    for epoch_id in range(1000000):
+        for data in tqdm(dataloader):
+            with accelerator.accumulate(model):
+                optimizer.zero_grad()
+                loss = model(data)
+                accelerator.backward(loss)
+                optimizer.step()
+                print(loss)
+        model_logger.on_epoch_end(accelerator, model, epoch_id)

lora/utils.py

@@ -0,0 +1,12 @@
+from diffsynth import load_state_dict
+import math, torch
+
+
+def load_lora(file_path, device):
+    sd = load_state_dict(file_path, torch_dtype=torch.bfloat16, device=device)
+    scale = math.sqrt(sd["lora_unet_single_blocks_9_modulation_lin.alpha"] / sd["lora_unet_single_blocks_9_modulation_lin.lora_down.weight"].shape[0])
+    if scale != 1:
+        sd = {i: sd[i] * scale for i in sd}
+    return sd
+
+

setup.py

@@ -14,7 +14,7 @@ else:
 
 setup(
     name="diffsynth",
-    version="1.1.3",
+    version="1.1.2",
     description="Enjoy the magic of Diffusion models!",
     author="Artiprocher",
     packages=find_packages(),