update code

diffsynth/configs/model_configs.py

@@ -255,12 +255,36 @@ wan_series = [
         "state_dict_converter": "diffsynth.utils.state_dict_converters.wan_video_vae.WanVideoVAEStateDictConverter",
     },
     {
-        # ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/model.safetensors")
+        # Example: ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/model.safetensors")
         "model_hash": "06be60f3a4526586d8431cd038a71486",
         "model_name": "wans2v_audio_encoder",
         "model_class": "diffsynth.models.wav2vec.WanS2VAudioEncoder",
         "state_dict_converter": "diffsynth.utils.state_dict_converters.wans2v_audio_encoder.WanS2VAudioEncoderStateDictConverter",
-    }
+    },
 ]
 
-MODEL_CONFIGS = qwen_image_series + wan_series
+flux_series = [
+    {
+        # Example: ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors")
+        "model_hash": "a29710fea6dddb0314663ee823598e50",
+        "model_name": "flux_dit",
+        "model_class": "diffsynth.models.flux_dit.FluxDiT",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_dit.FluxDiTStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors")
+        "model_hash": "94eefa3dac9cec93cb1ebaf1747d7b78",
+        "model_name": "flux_text_encoder_clip",
+        "model_class": "diffsynth.models.flux_text_encoder_clip.FluxTextEncoderClip",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_text_encoder_clip.FluxTextEncoderClipStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/*.safetensors")
+        "model_hash": "22540b49eaedbc2f2784b2091a234c7c",
+        "model_name": "flux_text_encoder_t5",
+        "model_class": "diffsynth.models.flux_text_encoder_t5.FluxTextEncoderT5",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_text_encoder_t5.FluxTextEncoderT5StateDictConverter",
+    },
+]
+
+MODEL_CONFIGS = qwen_image_series + wan_series + flux_series

diffsynth/models/flux_controlnet.py

@@ -0,0 +1,331 @@
+import torch
+from einops import rearrange, repeat
+from .flux_dit import RoPEEmbedding, TimestepEmbeddings, FluxJointTransformerBlock, FluxSingleTransformerBlock, RMSNorm
+from .utils import hash_state_dict_keys, init_weights_on_device
+
+
+
+class FluxControlNet(torch.nn.Module):
+    def __init__(self, disable_guidance_embedder=False, num_joint_blocks=5, num_single_blocks=10, num_mode=0, mode_dict={}, additional_input_dim=0):
+        super().__init__()
+        self.pos_embedder = RoPEEmbedding(3072, 10000, [16, 56, 56])
+        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.context_embedder = torch.nn.Linear(4096, 3072)
+        self.x_embedder = torch.nn.Linear(64, 3072)
+
+        self.blocks = torch.nn.ModuleList([FluxJointTransformerBlock(3072, 24) for _ in range(num_joint_blocks)])
+        self.single_blocks = torch.nn.ModuleList([FluxSingleTransformerBlock(3072, 24) for _ in range(num_single_blocks)])
+
+        self.controlnet_blocks = torch.nn.ModuleList([torch.nn.Linear(3072, 3072) for _ in range(num_joint_blocks)])
+        self.controlnet_single_blocks = torch.nn.ModuleList([torch.nn.Linear(3072, 3072) for _ in range(num_single_blocks)])
+        
+        self.mode_dict = mode_dict
+        self.controlnet_mode_embedder = torch.nn.Embedding(num_mode, 3072) if len(mode_dict) > 0 else None
+        self.controlnet_x_embedder = torch.nn.Linear(64 + additional_input_dim, 3072)
+
+
+    def prepare_image_ids(self, latents):
+        batch_size, _, height, width = latents.shape
+        latent_image_ids = torch.zeros(height // 2, width // 2, 3)
+        latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height // 2)[:, None]
+        latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width // 2)[None, :]
+
+        latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
+
+        latent_image_ids = latent_image_ids[None, :].repeat(batch_size, 1, 1, 1)
+        latent_image_ids = latent_image_ids.reshape(
+            batch_size, latent_image_id_height * latent_image_id_width, latent_image_id_channels
+        )
+        latent_image_ids = latent_image_ids.to(device=latents.device, dtype=latents.dtype)
+
+        return latent_image_ids
+    
+
+    def patchify(self, hidden_states):
+        hidden_states = rearrange(hidden_states, "B C (H P) (W Q) -> B (H W) (C P Q)", P=2, Q=2)
+        return hidden_states
+    
+
+    def align_res_stack_to_original_blocks(self, res_stack, num_blocks, hidden_states):
+        if len(res_stack) == 0:
+            return [torch.zeros_like(hidden_states)] * num_blocks
+        interval = (num_blocks + len(res_stack) - 1) // len(res_stack)
+        aligned_res_stack = [res_stack[block_id // interval] for block_id in range(num_blocks)]
+        return aligned_res_stack
+
+
+    def forward(
+        self,
+        hidden_states,
+        controlnet_conditioning,
+        timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None,
+        processor_id=None,
+        tiled=False, tile_size=128, tile_stride=64,
+        **kwargs
+    ):
+        if image_ids is None:
+            image_ids = self.prepare_image_ids(hidden_states)
+
+        conditioning = self.time_embedder(timestep, hidden_states.dtype) + self.pooled_text_embedder(pooled_prompt_emb)
+        if self.guidance_embedder is not None:
+            guidance = guidance * 1000
+            conditioning = conditioning + self.guidance_embedder(guidance, hidden_states.dtype)
+        prompt_emb = self.context_embedder(prompt_emb)
+        if self.controlnet_mode_embedder is not None: # Different from FluxDiT
+            processor_id = torch.tensor([self.mode_dict[processor_id]], dtype=torch.int)
+            processor_id = repeat(processor_id, "D -> B D", B=1).to(text_ids.device)
+            prompt_emb = torch.concat([self.controlnet_mode_embedder(processor_id), prompt_emb], dim=1)
+            text_ids = torch.cat([text_ids[:, :1], text_ids], dim=1)
+        image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1))
+
+        hidden_states = self.patchify(hidden_states)
+        hidden_states = self.x_embedder(hidden_states)
+        controlnet_conditioning = self.patchify(controlnet_conditioning) # Different from FluxDiT
+        hidden_states = hidden_states + self.controlnet_x_embedder(controlnet_conditioning) # Different from FluxDiT
+
+        controlnet_res_stack = []
+        for block, controlnet_block in zip(self.blocks, self.controlnet_blocks):
+            hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb)
+            controlnet_res_stack.append(controlnet_block(hidden_states))
+
+        controlnet_single_res_stack = []
+        hidden_states = torch.cat([prompt_emb, hidden_states], dim=1)
+        for block, controlnet_block in zip(self.single_blocks, self.controlnet_single_blocks):
+            hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb)
+            controlnet_single_res_stack.append(controlnet_block(hidden_states[:, prompt_emb.shape[1]:]))
+
+        controlnet_res_stack = self.align_res_stack_to_original_blocks(controlnet_res_stack, 19, hidden_states[:, prompt_emb.shape[1]:])
+        controlnet_single_res_stack = self.align_res_stack_to_original_blocks(controlnet_single_res_stack, 38, hidden_states[:, prompt_emb.shape[1]:])
+
+        return controlnet_res_stack, controlnet_single_res_stack
+
+
+    @staticmethod
+    def state_dict_converter():
+        return FluxControlNetStateDictConverter()
+    
+    def quantize(self):
+        def cast_to(weight, dtype=None, device=None, copy=False):
+            if device is None or weight.device == device:
+                if not copy:
+                    if dtype is None or weight.dtype == dtype:
+                        return weight
+                return weight.to(dtype=dtype, copy=copy)
+
+            r = torch.empty_like(weight, dtype=dtype, device=device)
+            r.copy_(weight)
+            return r
+
+        def cast_weight(s, input=None, dtype=None, device=None):
+            if input is not None:
+                if dtype is None:
+                    dtype = input.dtype
+                if device is None:
+                    device = input.device
+            weight = cast_to(s.weight, dtype, device)
+            return weight
+
+        def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
+            if input is not None:
+                if dtype is None:
+                    dtype = input.dtype
+                if bias_dtype is None:
+                    bias_dtype = dtype
+                if device is None:
+                    device = input.device
+            bias = None
+            weight = cast_to(s.weight, dtype, device)
+            bias = cast_to(s.bias, bias_dtype, device)
+            return weight, bias
+
+        class quantized_layer:
+            class QLinear(torch.nn.Linear):
+                def __init__(self, *args, **kwargs):
+                    super().__init__(*args, **kwargs)
+                    
+                def forward(self,input,**kwargs):
+                    weight,bias= cast_bias_weight(self,input)
+                    return torch.nn.functional.linear(input,weight,bias)
+            
+            class QRMSNorm(torch.nn.Module):
+                def __init__(self, module):
+                    super().__init__()
+                    self.module = module
+                    
+                def forward(self,hidden_states,**kwargs):
+                    weight= cast_weight(self.module,hidden_states)
+                    input_dtype = hidden_states.dtype
+                    variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True)
+                    hidden_states = hidden_states * torch.rsqrt(variance + self.module.eps)
+                    hidden_states = hidden_states.to(input_dtype) * weight
+                    return hidden_states
+            
+            class QEmbedding(torch.nn.Embedding):
+                def __init__(self, *args, **kwargs):
+                    super().__init__(*args, **kwargs)
+                    
+                def forward(self,input,**kwargs):
+                    weight= cast_weight(self,input)
+                    return torch.nn.functional.embedding(
+                        input, weight, self.padding_idx, self.max_norm,
+                        self.norm_type, self.scale_grad_by_freq, self.sparse)
+            
+        def replace_layer(model):
+            for name, module in model.named_children():
+                if isinstance(module,quantized_layer.QRMSNorm):
+                    continue
+                if isinstance(module, torch.nn.Linear):
+                    with init_weights_on_device():
+                        new_layer = quantized_layer.QLinear(module.in_features,module.out_features)
+                    new_layer.weight = module.weight
+                    if module.bias is not None:
+                        new_layer.bias = module.bias
+                    setattr(model, name, new_layer)
+                elif isinstance(module, RMSNorm):
+                    if hasattr(module,"quantized"):
+                        continue
+                    module.quantized= True
+                    new_layer = quantized_layer.QRMSNorm(module)
+                    setattr(model, name, new_layer)
+                elif isinstance(module,torch.nn.Embedding):
+                    rows, cols = module.weight.shape
+                    new_layer = quantized_layer.QEmbedding(
+                        num_embeddings=rows,
+                        embedding_dim=cols,
+                        _weight=module.weight,
+                        # _freeze=module.freeze,
+                        padding_idx=module.padding_idx,
+                        max_norm=module.max_norm,
+                        norm_type=module.norm_type,
+                        scale_grad_by_freq=module.scale_grad_by_freq,
+                        sparse=module.sparse)
+                    setattr(model, name, new_layer)
+                else:
+                    replace_layer(module)
+
+        replace_layer(self)
+    
+
+
+class FluxControlNetStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        hash_value = hash_state_dict_keys(state_dict)
+        global_rename_dict = {
+            "context_embedder": "context_embedder",
+            "x_embedder": "x_embedder",
+            "time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0",
+            "time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2",
+            "time_text_embed.guidance_embedder.linear_1": "guidance_embedder.timestep_embedder.0",
+            "time_text_embed.guidance_embedder.linear_2": "guidance_embedder.timestep_embedder.2",
+            "time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0",
+            "time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2",
+            "norm_out.linear": "final_norm_out.linear",
+            "proj_out": "final_proj_out",
+        }
+        rename_dict = {
+            "proj_out": "proj_out",
+            "norm1.linear": "norm1_a.linear",
+            "norm1_context.linear": "norm1_b.linear",
+            "attn.to_q": "attn.a_to_q",
+            "attn.to_k": "attn.a_to_k",
+            "attn.to_v": "attn.a_to_v",
+            "attn.to_out.0": "attn.a_to_out",
+            "attn.add_q_proj": "attn.b_to_q",
+            "attn.add_k_proj": "attn.b_to_k",
+            "attn.add_v_proj": "attn.b_to_v",
+            "attn.to_add_out": "attn.b_to_out",
+            "ff.net.0.proj": "ff_a.0",
+            "ff.net.2": "ff_a.2",
+            "ff_context.net.0.proj": "ff_b.0",
+            "ff_context.net.2": "ff_b.2",
+            "attn.norm_q": "attn.norm_q_a",
+            "attn.norm_k": "attn.norm_k_a",
+            "attn.norm_added_q": "attn.norm_q_b",
+            "attn.norm_added_k": "attn.norm_k_b",
+        }
+        rename_dict_single = {
+            "attn.to_q": "a_to_q",
+            "attn.to_k": "a_to_k",
+            "attn.to_v": "a_to_v",
+            "attn.norm_q": "norm_q_a",
+            "attn.norm_k": "norm_k_a",
+            "norm.linear": "norm.linear",
+            "proj_mlp": "proj_in_besides_attn",
+            "proj_out": "proj_out",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name.endswith(".weight") or name.endswith(".bias"):
+                suffix = ".weight" if name.endswith(".weight") else ".bias"
+                prefix = name[:-len(suffix)]
+                if prefix in global_rename_dict:
+                    state_dict_[global_rename_dict[prefix] + suffix] = param
+                elif prefix.startswith("transformer_blocks."):
+                    names = prefix.split(".")
+                    names[0] = "blocks"
+                    middle = ".".join(names[2:])
+                    if middle in rename_dict:
+                        name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]])
+                        state_dict_[name_] = param
+                elif prefix.startswith("single_transformer_blocks."):
+                    names = prefix.split(".")
+                    names[0] = "single_blocks"
+                    middle = ".".join(names[2:])
+                    if middle in rename_dict_single:
+                        name_ = ".".join(names[:2] + [rename_dict_single[middle]] + [suffix[1:]])
+                        state_dict_[name_] = param
+                    else:
+                        state_dict_[name] = param
+                else:
+                    state_dict_[name] = param
+        for name in list(state_dict_.keys()):
+            if ".proj_in_besides_attn." in name:
+                name_ = name.replace(".proj_in_besides_attn.", ".to_qkv_mlp.")
+                param = torch.concat([
+                    state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_q.")],
+                    state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_k.")],
+                    state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_v.")],
+                    state_dict_[name],
+                ], dim=0)
+                state_dict_[name_] = param
+                state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_q."))
+                state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_k."))
+                state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_v."))
+                state_dict_.pop(name)
+        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)
+                    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."))
+                    state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_v."))
+        if hash_value == "78d18b9101345ff695f312e7e62538c0":
+            extra_kwargs = {"num_mode": 10, "mode_dict": {"canny": 0, "tile": 1, "depth": 2, "blur": 3, "pose": 4, "gray": 5, "lq": 6}}
+        elif hash_value == "b001c89139b5f053c715fe772362dd2a":
+            extra_kwargs = {"num_single_blocks": 0}
+        elif hash_value == "52357cb26250681367488a8954c271e8":
+            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}
+        elif hash_value == "43ad5aaa27dd4ee01b832ed16773fa52":
+            extra_kwargs = {"num_joint_blocks": 6, "num_single_blocks": 0}
+        else:
+            extra_kwargs = {}
+        return state_dict_, extra_kwargs
+    
+
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)

diffsynth/models/flux_dit.py

@@ -0,0 +1,395 @@
+import torch
+from .general_modules import TimestepEmbeddings, AdaLayerNorm, RMSNorm
+from einops import rearrange
+
+
+def interact_with_ipadapter(hidden_states, q, ip_k, ip_v, scale=1.0):
+    batch_size, num_tokens = hidden_states.shape[0:2]
+    ip_hidden_states = torch.nn.functional.scaled_dot_product_attention(q, ip_k, ip_v)
+    ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, num_tokens, -1)
+    hidden_states = hidden_states + scale * ip_hidden_states
+    return hidden_states
+
+
+class RoPEEmbedding(torch.nn.Module):
+    def __init__(self, dim, theta, axes_dim):
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        self.axes_dim = axes_dim
+
+
+    def rope(self, pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor:
+        assert dim % 2 == 0, "The dimension must be even."
+
+        scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
+        omega = 1.0 / (theta**scale)
+
+        batch_size, seq_length = pos.shape
+        out = torch.einsum("...n,d->...nd", pos, omega)
+        cos_out = torch.cos(out)
+        sin_out = torch.sin(out)
+
+        stacked_out = torch.stack([cos_out, -sin_out, sin_out, cos_out], dim=-1)
+        out = stacked_out.view(batch_size, -1, dim // 2, 2, 2)
+        return out.float()
+
+
+    def forward(self, ids):
+        n_axes = ids.shape[-1]
+        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 FluxJointAttention(torch.nn.Module):
+    def __init__(self, dim_a, dim_b, num_heads, head_dim, only_out_a=False):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+        self.only_out_a = only_out_a
+
+        self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3)
+        self.b_to_qkv = torch.nn.Linear(dim_b, dim_b * 3)
+
+        self.norm_q_a = RMSNorm(head_dim, eps=1e-6)
+        self.norm_k_a = RMSNorm(head_dim, eps=1e-6)
+        self.norm_q_b = RMSNorm(head_dim, eps=1e-6)
+        self.norm_k_b = RMSNorm(head_dim, eps=1e-6)
+
+        self.a_to_out = torch.nn.Linear(dim_a, dim_a)
+        if not only_out_a:
+            self.b_to_out = torch.nn.Linear(dim_b, dim_b)
+
+
+    def apply_rope(self, xq, xk, freqs_cis):
+        xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+        xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+        xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+        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):
+        batch_size = hidden_states_a.shape[0]
+
+        # Part A
+        qkv_a = self.a_to_qkv(hidden_states_a)
+        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 = 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)
+
+        q = torch.concat([q_b, q_a], dim=2)
+        k = torch.concat([k_b, k_a], dim=2)
+        v = torch.concat([v_b, v_a], dim=2)
+
+        q, k = self.apply_rope(q, k, image_rotary_emb)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+        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)
+        if self.only_out_a:
+            return hidden_states_a
+        else:
+            hidden_states_b = self.b_to_out(hidden_states_b)
+            return hidden_states_a, hidden_states_b
+
+
+
+class FluxJointTransformerBlock(torch.nn.Module):
+    def __init__(self, dim, num_attention_heads):
+        super().__init__()
+        self.norm1_a = AdaLayerNorm(dim)
+        self.norm1_b = AdaLayerNorm(dim)
+
+        self.attn = FluxJointAttention(dim, dim, num_attention_heads, dim // num_attention_heads)
+
+        self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_a = torch.nn.Sequential(
+            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(
+            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)
+
+        # 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)
+
+        # Part A
+        hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a
+        norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a
+        hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a)
+
+        # Part B
+        hidden_states_b = hidden_states_b + gate_msa_b * attn_output_b
+        norm_hidden_states_b = self.norm2_b(hidden_states_b) * (1 + scale_mlp_b) + shift_mlp_b
+        hidden_states_b = hidden_states_b + gate_mlp_b * self.ff_b(norm_hidden_states_b)
+
+        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__()
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+
+        self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3)
+
+        self.norm_q_a = RMSNorm(head_dim, eps=1e-6)
+        self.norm_k_a = RMSNorm(head_dim, eps=1e-6)
+
+
+    def apply_rope(self, xq, xk, freqs_cis):
+        xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+        xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+        xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+        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, image_rotary_emb):
+        batch_size = hidden_states.shape[0]
+
+        qkv_a = self.a_to_qkv(hidden_states)
+        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)
+
+        q, k = self.apply_rope(q_a, k_a, image_rotary_emb)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+        return hidden_states
+
+
+
+class AdaLayerNormSingle(torch.nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.silu = torch.nn.SiLU()
+        self.linear = torch.nn.Linear(dim, 3 * dim, bias=True)
+        self.norm = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+
+    def forward(self, x, emb):
+        emb = self.linear(self.silu(emb))
+        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
+
+
+
+class FluxSingleTransformerBlock(torch.nn.Module):
+    def __init__(self, dim, num_attention_heads):
+        super().__init__()
+        self.num_heads = num_attention_heads
+        self.head_dim = dim // num_attention_heads
+        self.dim = dim
+
+        self.norm = AdaLayerNormSingle(dim)
+        self.to_qkv_mlp = torch.nn.Linear(dim, dim * (3 + 4))
+        self.norm_q_a = RMSNorm(self.head_dim, eps=1e-6)
+        self.norm_k_a = RMSNorm(self.head_dim, eps=1e-6)
+
+        self.proj_out = torch.nn.Linear(dim * 5, dim)
+
+
+    def apply_rope(self, xq, xk, freqs_cis):
+        xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+        xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+        xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+        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 process_attention(self, hidden_states, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
+        batch_size = hidden_states.shape[0]
+
+        qkv = hidden_states.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        q, k, v = qkv.chunk(3, dim=1)
+        q, k = self.norm_q_a(q), self.norm_k_a(k)
+
+        q, k = self.apply_rope(q, k, image_rotary_emb)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+        if ipadapter_kwargs_list is not None:
+            hidden_states = interact_with_ipadapter(hidden_states, q, **ipadapter_kwargs_list)
+        return hidden_states
+
+
+    def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
+        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)
+        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)
+        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 = residual + hidden_states_a
+
+        return hidden_states_a, hidden_states_b
+
+
+
+class AdaLayerNormContinuous(torch.nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.silu = torch.nn.SiLU()
+        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))
+        shift, scale = 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, input_dim=64, num_blocks=19):
+        super().__init__()
+        self.pos_embedder = RoPEEmbedding(3072, 10000, [16, 56, 56])
+        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.context_embedder = torch.nn.Linear(4096, 3072)
+        self.x_embedder = torch.nn.Linear(input_dim, 3072)
+
+        self.blocks = torch.nn.ModuleList([FluxJointTransformerBlock(3072, 24) for _ in range(num_blocks)])
+        self.single_blocks = torch.nn.ModuleList([FluxSingleTransformerBlock(3072, 24) for _ in range(38)])
+
+        self.final_norm_out = AdaLayerNormContinuous(3072)
+        self.final_proj_out = torch.nn.Linear(3072, 64)
+        
+        self.input_dim = input_dim
+
+
+    def patchify(self, hidden_states):
+        hidden_states = rearrange(hidden_states, "B C (H P) (W Q) -> B (H W) (C P Q)", P=2, Q=2)
+        return hidden_states
+
+
+    def unpatchify(self, hidden_states, height, width):
+        hidden_states = rearrange(hidden_states, "B (H W) (C P Q) -> B C (H P) (W Q)", P=2, Q=2, H=height//2, W=width//2)
+        return hidden_states
+
+
+    def prepare_image_ids(self, latents):
+        batch_size, _, height, width = latents.shape
+        latent_image_ids = torch.zeros(height // 2, width // 2, 3)
+        latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height // 2)[:, None]
+        latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width // 2)[None, :]
+
+        latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
+
+        latent_image_ids = latent_image_ids[None, :].repeat(batch_size, 1, 1, 1)
+        latent_image_ids = latent_image_ids.reshape(
+            batch_size, latent_image_id_height * latent_image_id_width, latent_image_id_channels
+        )
+        latent_image_ids = latent_image_ids.to(device=latents.device, dtype=latents.dtype)
+
+        return latent_image_ids
+
+
+    def construct_mask(self, entity_masks, prompt_seq_len, image_seq_len):
+        N = len(entity_masks)
+        batch_size = entity_masks[0].shape[0]
+        total_seq_len = N * prompt_seq_len + image_seq_len
+        patched_masks = [self.patchify(entity_masks[i]) for i in range(N)]
+        attention_mask = torch.ones((batch_size, total_seq_len, total_seq_len), dtype=torch.bool).to(device=entity_masks[0].device)
+
+        image_start = N * prompt_seq_len
+        image_end = N * prompt_seq_len + image_seq_len
+        # prompt-image mask
+        for i in range(N):
+            prompt_start = i * prompt_seq_len
+            prompt_end = (i + 1) * prompt_seq_len
+            image_mask = torch.sum(patched_masks[i], dim=-1) > 0
+            image_mask = image_mask.unsqueeze(1).repeat(1, prompt_seq_len, 1)
+            # prompt update with image
+            attention_mask[:, prompt_start:prompt_end, image_start:image_end] = image_mask
+            # image update with prompt
+            attention_mask[:, image_start:image_end, prompt_start:prompt_end] = image_mask.transpose(1, 2)
+        # prompt-prompt mask
+        for i in range(N):
+            for j in range(N):
+                if i != j:
+                    prompt_start_i = i * prompt_seq_len
+                    prompt_end_i = (i + 1) * prompt_seq_len
+                    prompt_start_j = j * prompt_seq_len
+                    prompt_end_j = (j + 1) * prompt_seq_len
+                    attention_mask[:, prompt_start_i:prompt_end_i, prompt_start_j:prompt_end_j] = False
+
+        attention_mask = attention_mask.float()
+        attention_mask[attention_mask == 0] = float('-inf')
+        attention_mask[attention_mask == 1] = 0
+        return attention_mask
+
+
+    def process_entity_masks(self, hidden_states, prompt_emb, entity_prompt_emb, entity_masks, text_ids, image_ids, repeat_dim):
+        max_masks = 0
+        attention_mask = None
+        prompt_embs = [prompt_emb]
+        if entity_masks is not None:
+            # entity_masks
+            batch_size, max_masks = entity_masks.shape[0], entity_masks.shape[1]
+            entity_masks = entity_masks.repeat(1, 1, repeat_dim, 1, 1)
+            entity_masks = [entity_masks[:, i, None].squeeze(1) for i in range(max_masks)]
+            # global mask
+            global_mask = torch.ones_like(entity_masks[0]).to(device=hidden_states.device, dtype=hidden_states.dtype)
+            entity_masks = entity_masks + [global_mask] # append global to last
+            # attention mask
+            attention_mask = self.construct_mask(entity_masks, prompt_emb.shape[1], hidden_states.shape[1])
+            attention_mask = attention_mask.to(device=hidden_states.device, dtype=hidden_states.dtype)
+            attention_mask = attention_mask.unsqueeze(1)
+            # embds: n_masks * b * seq * d
+            local_embs = [entity_prompt_emb[:, i, None].squeeze(1) for i in range(max_masks)]
+            prompt_embs = local_embs + prompt_embs # append global to last
+        prompt_embs = [self.context_embedder(prompt_emb) for prompt_emb in prompt_embs]
+        prompt_emb = torch.cat(prompt_embs, dim=1)
+
+        # positional embedding
+        text_ids = torch.cat([text_ids] * (max_masks + 1), dim=1)
+        image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1))
+        return prompt_emb, image_rotary_emb, attention_mask
+
+
+    def forward(
+        self,
+        hidden_states,
+        timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None,
+        tiled=False, tile_size=128, tile_stride=64, entity_prompt_emb=None, entity_masks=None,
+        use_gradient_checkpointing=False,
+        **kwargs
+    ):
+        # (Deprecated) The real forward is in `pipelines.flux_image`.
+        return None

diffsynth/models/flux_infiniteyou.py

@@ -0,0 +1,129 @@
+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)
+        latents = latents.to(dtype=x.dtype, device=x.device)
+
+        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/flux_ipadapter.py

@@ -0,0 +1,94 @@
+from .svd_image_encoder import SVDImageEncoder
+from .sd3_dit import RMSNorm
+from transformers import CLIPImageProcessor
+import torch
+
+
+class MLPProjModel(torch.nn.Module):
+    def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num_tokens=4):
+        super().__init__()
+        
+        self.cross_attention_dim = cross_attention_dim
+        self.num_tokens = num_tokens
+        
+        self.proj = torch.nn.Sequential(
+            torch.nn.Linear(id_embeddings_dim, id_embeddings_dim*2),
+            torch.nn.GELU(),
+            torch.nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens),
+        )
+        self.norm = torch.nn.LayerNorm(cross_attention_dim)
+        
+    def forward(self, id_embeds):
+        x = self.proj(id_embeds)
+        x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
+        x = self.norm(x)
+        return x
+
+class IpAdapterModule(torch.nn.Module):
+    def __init__(self, num_attention_heads, attention_head_dim, input_dim):
+        super().__init__()
+        self.num_heads = num_attention_heads
+        self.head_dim = attention_head_dim
+        output_dim = num_attention_heads * attention_head_dim
+        self.to_k_ip = torch.nn.Linear(input_dim, output_dim, bias=False)
+        self.to_v_ip = torch.nn.Linear(input_dim, output_dim, bias=False)
+        self.norm_added_k = RMSNorm(attention_head_dim, eps=1e-5, elementwise_affine=False)
+        
+
+    def forward(self, hidden_states):
+        batch_size = hidden_states.shape[0]
+        # ip_k
+        ip_k = self.to_k_ip(hidden_states)
+        ip_k = ip_k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        ip_k = self.norm_added_k(ip_k)
+        # ip_v
+        ip_v = self.to_v_ip(hidden_states)
+        ip_v = ip_v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        return ip_k, ip_v
+
+
+class FluxIpAdapter(torch.nn.Module):
+    def __init__(self, num_attention_heads=24, attention_head_dim=128, cross_attention_dim=4096, num_tokens=128, num_blocks=57):
+        super().__init__()
+        self.ipadapter_modules = torch.nn.ModuleList([IpAdapterModule(num_attention_heads, attention_head_dim, cross_attention_dim) for _ in range(num_blocks)])
+        self.image_proj = MLPProjModel(cross_attention_dim=cross_attention_dim, id_embeddings_dim=1152, num_tokens=num_tokens)
+        self.set_adapter()
+
+    def set_adapter(self):
+        self.call_block_id = {i:i for i in range(len(self.ipadapter_modules))}
+
+    def forward(self, hidden_states, scale=1.0):
+        hidden_states = self.image_proj(hidden_states)
+        hidden_states = hidden_states.view(1, -1, hidden_states.shape[-1])
+        ip_kv_dict = {}
+        for block_id in self.call_block_id:
+            ipadapter_id = self.call_block_id[block_id]
+            ip_k, ip_v = self.ipadapter_modules[ipadapter_id](hidden_states)
+            ip_kv_dict[block_id] = {
+                "ip_k": ip_k,
+                "ip_v": ip_v,
+                "scale": scale
+            }
+        return ip_kv_dict
+
+    @staticmethod
+    def state_dict_converter():
+        return FluxIpAdapterStateDictConverter()
+
+
+class FluxIpAdapterStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = {}
+        for name in state_dict["ip_adapter"]:
+            name_ = 'ipadapter_modules.' + name
+            state_dict_[name_] = state_dict["ip_adapter"][name]
+        for name in state_dict["image_proj"]:
+            name_ = "image_proj." + name
+            state_dict_[name_] = state_dict["image_proj"][name]
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)

diffsynth/models/flux_lora_encoder.py

@@ -0,0 +1,111 @@
+import torch
+from .sd_text_encoder import CLIPEncoderLayer
+
+
+class LoRALayerBlock(torch.nn.Module):
+    def __init__(self, L, dim_in, dim_out):
+        super().__init__()
+        self.x = torch.nn.Parameter(torch.randn(1, L, dim_in))
+        self.layer_norm = torch.nn.LayerNorm(dim_out)
+
+    def forward(self, lora_A, lora_B):
+        x = self.x @ lora_A.T @ lora_B.T
+        x = self.layer_norm(x)
+        return x
+    
+
+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"]
+            model_dict[name.replace(".", "___")] = LoRALayerBlock(L, dim[0], dim[1])
+        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"]
+            if layer_type not in proj_dict:
+                proj_dict[layer_type.replace(".", "___")] = torch.nn.Linear(dim[1], 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 FluxLoRAEncoder(torch.nn.Module):
+    def __init__(self, embed_dim=4096, encoder_intermediate_size=8192, num_encoder_layers=1, num_embeds_per_lora=16, num_special_embeds=1):
+        super().__init__()
+        self.num_embeds_per_lora = num_embeds_per_lora
+        # embedder
+        self.embedder = LoRAEmbedder(L=num_embeds_per_lora, out_dim=embed_dim)
+        
+        # encoders
+        self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size, num_heads=32, head_dim=128) for _ in range(num_encoder_layers)])
+
+        # special embedding
+        self.special_embeds = torch.nn.Parameter(torch.randn(1, num_special_embeds, embed_dim))
+        self.num_special_embeds = num_special_embeds
+        
+        # final layer
+        self.final_layer_norm = torch.nn.LayerNorm(embed_dim)
+        self.final_linear = torch.nn.Linear(embed_dim, embed_dim)
+
+    def forward(self, lora):
+        lora_embeds = self.embedder(lora)
+        special_embeds = self.special_embeds.to(dtype=lora_embeds.dtype, device=lora_embeds.device)
+        embeds = torch.concat([special_embeds, lora_embeds], dim=1)
+        for encoder_id, encoder in enumerate(self.encoders):
+            embeds = encoder(embeds)
+        embeds = embeds[:, :self.num_special_embeds]
+        embeds = self.final_layer_norm(embeds)
+        embeds = self.final_linear(embeds)
+        return embeds
+    
+    @staticmethod
+    def state_dict_converter():
+        return FluxLoRAEncoderStateDictConverter()
+
+
+class FluxLoRAEncoderStateDictConverter:
+    def from_civitai(self, state_dict):
+        return state_dict

diffsynth/models/flux_text_encoder_clip.py

@@ -0,0 +1,112 @@
+import torch
+
+
+class Attention(torch.nn.Module):
+
+    def __init__(self, q_dim, num_heads, head_dim, kv_dim=None, bias_q=False, bias_kv=False, bias_out=False):
+        super().__init__()
+        dim_inner = head_dim * num_heads
+        kv_dim = kv_dim if kv_dim is not None else q_dim
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+
+        self.to_q = torch.nn.Linear(q_dim, dim_inner, bias=bias_q)
+        self.to_k = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv)
+        self.to_v = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv)
+        self.to_out = torch.nn.Linear(dim_inner, q_dim, bias=bias_out)
+
+    def forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None):
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+
+        batch_size = encoder_hidden_states.shape[0]
+
+        q = self.to_q(hidden_states)
+        k = self.to_k(encoder_hidden_states)
+        v = self.to_v(encoder_hidden_states)
+
+        q = q.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        k = k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        v = v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+
+        hidden_states = self.to_out(hidden_states)
+
+        return hidden_states
+
+
+class CLIPEncoderLayer(torch.nn.Module):
+    def __init__(self, embed_dim, intermediate_size, num_heads=12, head_dim=64, use_quick_gelu=True):
+        super().__init__()
+        self.attn = Attention(q_dim=embed_dim, num_heads=num_heads, head_dim=head_dim, bias_q=True, bias_kv=True, bias_out=True)
+        self.layer_norm1 = torch.nn.LayerNorm(embed_dim)
+        self.layer_norm2 = torch.nn.LayerNorm(embed_dim)
+        self.fc1 = torch.nn.Linear(embed_dim, intermediate_size)
+        self.fc2 = torch.nn.Linear(intermediate_size, embed_dim)
+
+        self.use_quick_gelu = use_quick_gelu
+
+    def quickGELU(self, x):
+        return x * torch.sigmoid(1.702 * x)
+    
+    def forward(self, hidden_states, attn_mask=None):
+        residual = hidden_states
+
+        hidden_states = self.layer_norm1(hidden_states)
+        hidden_states = self.attn(hidden_states, attn_mask=attn_mask)
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.layer_norm2(hidden_states)
+        hidden_states = self.fc1(hidden_states)
+        if self.use_quick_gelu:
+            hidden_states = self.quickGELU(hidden_states)
+        else:
+            hidden_states = torch.nn.functional.gelu(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+    
+
+class FluxTextEncoderClip(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=2, extra_mask=None):
+        embeds = self.token_embedding(input_ids)
+        embeds = embeds + self.position_embeds.to(dtype=embeds.dtype, device=input_ids.device)
+        attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype)
+        if extra_mask is not None:
+            attn_mask[:, extra_mask[0]==0] = float("-inf")
+        for encoder_id, encoder in enumerate(self.encoders):
+            embeds = encoder(embeds, attn_mask=attn_mask)
+            if encoder_id + clip_skip == len(self.encoders):
+                hidden_states = embeds
+        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, hidden_states

diffsynth/models/flux_text_encoder_t5.py

@@ -0,0 +1,43 @@
+import torch
+from transformers import T5EncoderModel, T5Config
+
+
+class FluxTextEncoderT5(T5EncoderModel):
+    def __init__(self):
+        config = T5Config(**{
+            "architectures": [
+                "T5EncoderModel"
+            ],
+            "classifier_dropout": 0.0,
+            "d_ff": 10240,
+            "d_kv": 64,
+            "d_model": 4096,
+            "decoder_start_token_id": 0,
+            "dense_act_fn": "gelu_new",
+            "dropout_rate": 0.1,
+            "dtype": "bfloat16",
+            "eos_token_id": 1,
+            "feed_forward_proj": "gated-gelu",
+            "initializer_factor": 1.0,
+            "is_encoder_decoder": True,
+            "is_gated_act": True,
+            "layer_norm_epsilon": 1e-06,
+            "model_type": "t5",
+            "num_decoder_layers": 24,
+            "num_heads": 64,
+            "num_layers": 24,
+            "output_past": True,
+            "pad_token_id": 0,
+            "relative_attention_max_distance": 128,
+            "relative_attention_num_buckets": 32,
+            "tie_word_embeddings": False,
+            "transformers_version": "4.57.1",
+            "use_cache": True,
+            "vocab_size": 32128
+        })
+        super().__init__(config)
+
+    def forward(self, input_ids):
+        outputs = super().forward(input_ids=input_ids)
+        prompt_emb = outputs.last_hidden_state
+        return prompt_emb

diffsynth/models/flux_vae.py

@@ -0,0 +1,394 @@
+import torch
+from einops import rearrange, repeat
+
+
+class TileWorker:
+    def __init__(self):
+        pass
+
+
+    def mask(self, height, width, border_width):
+        # Create a mask with shape (height, width).
+        # The centre area is filled with 1, and the border line is filled with values in range (0, 1].
+        x = torch.arange(height).repeat(width, 1).T
+        y = torch.arange(width).repeat(height, 1)
+        mask = torch.stack([x + 1, height - x, y + 1, width - y]).min(dim=0).values
+        mask = (mask / border_width).clip(0, 1)
+        return mask
+
+
+    def tile(self, model_input, tile_size, tile_stride, tile_device, tile_dtype):
+        # Convert a tensor (b, c, h, w) to (b, c, tile_size, tile_size, tile_num)
+        batch_size, channel, _, _ = model_input.shape
+        model_input = model_input.to(device=tile_device, dtype=tile_dtype)
+        unfold_operator = torch.nn.Unfold(
+            kernel_size=(tile_size, tile_size),
+            stride=(tile_stride, tile_stride)
+        )
+        model_input = unfold_operator(model_input)
+        model_input = model_input.view((batch_size, channel, tile_size, tile_size, -1))
+
+        return model_input
+
+
+    def tiled_inference(self, forward_fn, model_input, tile_batch_size, inference_device, inference_dtype, tile_device, tile_dtype):
+        # Call y=forward_fn(x) for each tile
+        tile_num = model_input.shape[-1]
+        model_output_stack = []
+
+        for tile_id in range(0, tile_num, tile_batch_size):
+
+            # process input
+            tile_id_ = min(tile_id + tile_batch_size, tile_num)
+            x = model_input[:, :, :, :, tile_id: tile_id_]
+            x = x.to(device=inference_device, dtype=inference_dtype)
+            x = rearrange(x, "b c h w n -> (n b) c h w")
+
+            # process output
+            y = forward_fn(x)
+            y = rearrange(y, "(n b) c h w -> b c h w n", n=tile_id_-tile_id)
+            y = y.to(device=tile_device, dtype=tile_dtype)
+            model_output_stack.append(y)
+
+        model_output = torch.concat(model_output_stack, dim=-1)
+        return model_output
+
+
+    def io_scale(self, model_output, tile_size):
+        # Determine the size modification happened in forward_fn
+        # We only consider the same scale on height and width.
+        io_scale = model_output.shape[2] / tile_size
+        return io_scale
+    
+
+    def untile(self, model_output, height, width, tile_size, tile_stride, border_width, tile_device, tile_dtype):
+        # The reversed function of tile
+        mask = self.mask(tile_size, tile_size, border_width)
+        mask = mask.to(device=tile_device, dtype=tile_dtype)
+        mask = rearrange(mask, "h w -> 1 1 h w 1")
+        model_output = model_output * mask
+
+        fold_operator = torch.nn.Fold(
+            output_size=(height, width),
+            kernel_size=(tile_size, tile_size),
+            stride=(tile_stride, tile_stride)
+        )
+        mask = repeat(mask[0, 0, :, :, 0], "h w -> 1 (h w) n", n=model_output.shape[-1])
+        model_output = rearrange(model_output, "b c h w n -> b (c h w) n")
+        model_output = fold_operator(model_output) / fold_operator(mask)
+
+        return model_output
+
+
+    def tiled_forward(self, forward_fn, model_input, tile_size, tile_stride, tile_batch_size=1, tile_device="cpu", tile_dtype=torch.float32, border_width=None):
+        # Prepare
+        inference_device, inference_dtype = model_input.device, model_input.dtype
+        height, width = model_input.shape[2], model_input.shape[3]
+        border_width = int(tile_stride*0.5) if border_width is None else border_width
+
+        # tile
+        model_input = self.tile(model_input, tile_size, tile_stride, tile_device, tile_dtype)
+
+        # inference
+        model_output = self.tiled_inference(forward_fn, model_input, tile_batch_size, inference_device, inference_dtype, tile_device, tile_dtype)
+
+        # resize
+        io_scale = self.io_scale(model_output, tile_size)
+        height, width = int(height*io_scale), int(width*io_scale)
+        tile_size, tile_stride = int(tile_size*io_scale), int(tile_stride*io_scale)
+        border_width = int(border_width*io_scale)
+
+        # untile
+        model_output = self.untile(model_output, height, width, tile_size, tile_stride, border_width, tile_device, tile_dtype)
+        
+        # Done!
+        model_output = model_output.to(device=inference_device, dtype=inference_dtype)
+        return model_output
+
+
+class Attention(torch.nn.Module):
+
+    def __init__(self, q_dim, num_heads, head_dim, kv_dim=None, bias_q=False, bias_kv=False, bias_out=False):
+        super().__init__()
+        dim_inner = head_dim * num_heads
+        kv_dim = kv_dim if kv_dim is not None else q_dim
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+
+        self.to_q = torch.nn.Linear(q_dim, dim_inner, bias=bias_q)
+        self.to_k = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv)
+        self.to_v = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv)
+        self.to_out = torch.nn.Linear(dim_inner, q_dim, bias=bias_out)
+
+    def forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None):
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+
+        batch_size = encoder_hidden_states.shape[0]
+
+        q = self.to_q(hidden_states)
+        k = self.to_k(encoder_hidden_states)
+        v = self.to_v(encoder_hidden_states)
+
+        q = q.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        k = k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        v = v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+
+        hidden_states = self.to_out(hidden_states)
+
+        return hidden_states
+
+
+class VAEAttentionBlock(torch.nn.Module):
+
+    def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5):
+        super().__init__()
+        inner_dim = num_attention_heads * attention_head_dim
+
+        self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True)
+
+        self.transformer_blocks = torch.nn.ModuleList([
+            Attention(
+                inner_dim,
+                num_attention_heads,
+                attention_head_dim,
+                bias_q=True,
+                bias_kv=True,
+                bias_out=True
+            )
+            for d in range(num_layers)
+        ])
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack):
+        batch, _, height, width = hidden_states.shape
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+        inner_dim = hidden_states.shape[1]
+        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
+
+        for block in self.transformer_blocks:
+            hidden_states = block(hidden_states)
+
+        hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
+        hidden_states = hidden_states + residual
+
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class ResnetBlock(torch.nn.Module):
+    def __init__(self, in_channels, out_channels, temb_channels=None, groups=32, eps=1e-5):
+        super().__init__()
+        self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
+        self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        if temb_channels is not None:
+            self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels)
+        self.norm2 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True)
+        self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        self.nonlinearity = torch.nn.SiLU()
+        self.conv_shortcut = None
+        if in_channels != out_channels:
+            self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=True)
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs):
+        x = hidden_states
+        x = self.norm1(x)
+        x = self.nonlinearity(x)
+        x = self.conv1(x)
+        if time_emb is not None:
+            emb = self.nonlinearity(time_emb)
+            emb = self.time_emb_proj(emb)[:, :, None, None]
+            x = x + emb
+        x = self.norm2(x)
+        x = self.nonlinearity(x)
+        x = self.conv2(x)
+        if self.conv_shortcut is not None:
+            hidden_states = self.conv_shortcut(hidden_states)
+        hidden_states = hidden_states + x
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class UpSampler(torch.nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.conv = torch.nn.Conv2d(channels, channels, 3, padding=1)
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs):
+        hidden_states = torch.nn.functional.interpolate(hidden_states, scale_factor=2.0, mode="nearest")
+        hidden_states = self.conv(hidden_states)
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class DownSampler(torch.nn.Module):
+    def __init__(self, channels, padding=1, extra_padding=False):
+        super().__init__()
+        self.conv = torch.nn.Conv2d(channels, channels, 3, stride=2, padding=padding)
+        self.extra_padding = extra_padding
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs):
+        if self.extra_padding:
+            hidden_states = torch.nn.functional.pad(hidden_states, (0, 1, 0, 1), mode="constant", value=0)
+        hidden_states = self.conv(hidden_states)
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class SD3VAEDecoder(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.3611
+        self.shift_factor = 0.1159
+        self.conv_in = torch.nn.Conv2d(16, 512, kernel_size=3, padding=1) # Different from SD 1.x
+
+        self.blocks = torch.nn.ModuleList([
+            # UNetMidBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            VAEAttentionBlock(1, 512, 512, 1, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            # UpDecoderBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            UpSampler(512),
+            # UpDecoderBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            UpSampler(512),
+            # UpDecoderBlock2D
+            ResnetBlock(512, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            UpSampler(256),
+            # UpDecoderBlock2D
+            ResnetBlock(256, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+        ])
+
+        self.conv_norm_out = torch.nn.GroupNorm(num_channels=128, num_groups=32, eps=1e-6)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = torch.nn.Conv2d(128, 3, kernel_size=3, padding=1)
+    
+    def tiled_forward(self, sample, tile_size=64, tile_stride=32):
+        hidden_states = TileWorker().tiled_forward(
+            lambda x: self.forward(x),
+            sample,
+            tile_size,
+            tile_stride,
+            tile_device=sample.device,
+            tile_dtype=sample.dtype
+        )
+        return hidden_states
+
+    def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs):
+        # For VAE Decoder, we do not need to apply the tiler on each layer.
+        if tiled:
+            return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride)
+
+        # 1. pre-process
+        hidden_states = sample / self.scaling_factor + self.shift_factor
+        hidden_states = self.conv_in(hidden_states)
+        time_emb = None
+        text_emb = None
+        res_stack = None
+
+        # 2. blocks
+        for i, block in enumerate(self.blocks):
+            hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
+        
+        # 3. output
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+
+        return hidden_states
+
+
+class SD3VAEEncoder(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.3611
+        self.shift_factor = 0.1159
+        self.conv_in = torch.nn.Conv2d(3, 128, kernel_size=3, padding=1)
+
+        self.blocks = torch.nn.ModuleList([
+            # DownEncoderBlock2D
+            ResnetBlock(128, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+            DownSampler(128, padding=0, extra_padding=True),
+            # DownEncoderBlock2D
+            ResnetBlock(128, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            DownSampler(256, padding=0, extra_padding=True),
+            # DownEncoderBlock2D
+            ResnetBlock(256, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            DownSampler(512, padding=0, extra_padding=True),
+            # DownEncoderBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            # UNetMidBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            VAEAttentionBlock(1, 512, 512, 1, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+        ])
+
+        self.conv_norm_out = torch.nn.GroupNorm(num_channels=512, num_groups=32, eps=1e-6)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = torch.nn.Conv2d(512, 32, kernel_size=3, padding=1)
+
+    def tiled_forward(self, sample, tile_size=64, tile_stride=32):
+        hidden_states = TileWorker().tiled_forward(
+            lambda x: self.forward(x),
+            sample,
+            tile_size,
+            tile_stride,
+            tile_device=sample.device,
+            tile_dtype=sample.dtype
+        )
+        return hidden_states
+
+    def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs):
+        # For VAE Decoder, we do not need to apply the tiler on each layer.
+        if tiled:
+            return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride)
+        
+        # 1. pre-process
+        hidden_states = self.conv_in(sample)
+        time_emb = None
+        text_emb = None
+        res_stack = None
+
+        # 2. blocks
+        for i, block in enumerate(self.blocks):
+            hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
+        
+        # 3. output
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+        hidden_states = hidden_states[:, :16]
+        hidden_states = (hidden_states - self.shift_factor) * self.scaling_factor
+
+        return hidden_states
+    
+    def encode_video(self, sample, batch_size=8):
+        B = sample.shape[0]
+        hidden_states = []
+
+        for i in range(0, sample.shape[2], batch_size):
+
+            j = min(i + batch_size, sample.shape[2])
+            sample_batch = rearrange(sample[:,:,i:j], "B C T H W -> (B T) C H W")
+
+            hidden_states_batch = self(sample_batch)
+            hidden_states_batch = rearrange(hidden_states_batch, "(B T) C H W -> B C T H W", B=B)
+
+            hidden_states.append(hidden_states_batch)
+        
+        hidden_states = torch.concat(hidden_states, dim=2)
+        return hidden_states

diffsynth/models/flux_value_control.py

@@ -0,0 +1,60 @@
+import torch
+from diffsynth.models.svd_unet import TemporalTimesteps
+
+
+class MultiValueEncoder(torch.nn.Module):
+    def __init__(self, encoders=()):
+        super().__init__()
+        self.encoders = torch.nn.ModuleList(encoders)
+
+    def __call__(self, values, dtype):
+        emb = []
+        for encoder, value in zip(self.encoders, values):
+            if value is not None:
+                value = value.unsqueeze(0)
+                emb.append(encoder(value, dtype))
+        emb = torch.concat(emb, dim=0)
+        return emb
+
+
+class SingleValueEncoder(torch.nn.Module):
+    def __init__(self, dim_in=256, dim_out=4096, prefer_len=32, computation_device=None):
+        super().__init__()
+        self.prefer_len = prefer_len
+        self.prefer_proj = TemporalTimesteps(num_channels=dim_in, flip_sin_to_cos=True, downscale_freq_shift=0, computation_device=computation_device)
+        self.prefer_value_embedder = torch.nn.Sequential(
+            torch.nn.Linear(dim_in, dim_out), torch.nn.SiLU(), torch.nn.Linear(dim_out, dim_out)
+        )
+        self.positional_embedding = torch.nn.Parameter(
+            torch.randn(self.prefer_len, dim_out) 
+        )
+        self._initialize_weights()
+
+    def _initialize_weights(self):
+        last_linear = self.prefer_value_embedder[-1]
+        torch.nn.init.zeros_(last_linear.weight)
+        torch.nn.init.zeros_(last_linear.bias)
+
+    def forward(self, value, dtype):
+        value = value * 1000
+        emb = self.prefer_proj(value).to(dtype)
+        emb = self.prefer_value_embedder(emb).squeeze(0)
+        base_embeddings = emb.expand(self.prefer_len, -1)
+        positional_embedding = self.positional_embedding.to(dtype=base_embeddings.dtype, device=base_embeddings.device)
+        learned_embeddings = base_embeddings + positional_embedding
+        return learned_embeddings
+
+    @staticmethod
+    def state_dict_converter():
+        return SingleValueEncoderStateDictConverter()
+
+
+class SingleValueEncoderStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return state_dict
+
+    def from_civitai(self, state_dict):
+        return state_dict

diffsynth/models/model_loader.py

@@ -77,7 +77,7 @@ class ModelPool:
                 print(f"Loaded model: {json.dumps(model_info, indent=4)}")
                 loaded = True
         if not loaded:
-            raise ValueError(f"Cannot detect the model type. File: {path}")
+            raise ValueError(f"Cannot detect the model type. File: {path}. Model hash: {model_hash}.")
     
     def fetch_model(self, model_name, index=None):
         fetched_models = []

diffsynth/utils/state_dict_converters/flux_dit.py

@@ -0,0 +1,77 @@
+def FluxDiTStateDictConverter(state_dict):
+    rename_dict = {
+        "time_in.in_layer.bias": "time_embedder.timestep_embedder.0.bias",
+        "time_in.in_layer.weight": "time_embedder.timestep_embedder.0.weight",
+        "time_in.out_layer.bias": "time_embedder.timestep_embedder.2.bias",
+        "time_in.out_layer.weight": "time_embedder.timestep_embedder.2.weight",
+        "txt_in.bias": "context_embedder.bias",
+        "txt_in.weight": "context_embedder.weight",
+        "vector_in.in_layer.bias": "pooled_text_embedder.0.bias",
+        "vector_in.in_layer.weight": "pooled_text_embedder.0.weight",
+        "vector_in.out_layer.bias": "pooled_text_embedder.2.bias",
+        "vector_in.out_layer.weight": "pooled_text_embedder.2.weight",
+        "final_layer.linear.bias": "final_proj_out.bias",
+        "final_layer.linear.weight": "final_proj_out.weight",
+        "guidance_in.in_layer.bias": "guidance_embedder.timestep_embedder.0.bias",
+        "guidance_in.in_layer.weight": "guidance_embedder.timestep_embedder.0.weight",
+        "guidance_in.out_layer.bias": "guidance_embedder.timestep_embedder.2.bias",
+        "guidance_in.out_layer.weight": "guidance_embedder.timestep_embedder.2.weight",
+        "img_in.bias": "x_embedder.bias",
+        "img_in.weight": "x_embedder.weight",
+        "final_layer.adaLN_modulation.1.weight": "final_norm_out.linear.weight",
+        "final_layer.adaLN_modulation.1.bias": "final_norm_out.linear.bias",
+    }
+    suffix_rename_dict = {
+        "img_attn.norm.key_norm.scale": "attn.norm_k_a.weight",
+        "img_attn.norm.query_norm.scale": "attn.norm_q_a.weight",
+        "img_attn.proj.bias": "attn.a_to_out.bias",
+        "img_attn.proj.weight": "attn.a_to_out.weight",
+        "img_attn.qkv.bias": "attn.a_to_qkv.bias",
+        "img_attn.qkv.weight": "attn.a_to_qkv.weight",
+        "img_mlp.0.bias": "ff_a.0.bias",
+        "img_mlp.0.weight": "ff_a.0.weight",
+        "img_mlp.2.bias": "ff_a.2.bias",
+        "img_mlp.2.weight": "ff_a.2.weight",
+        "img_mod.lin.bias": "norm1_a.linear.bias",
+        "img_mod.lin.weight": "norm1_a.linear.weight",
+        "txt_attn.norm.key_norm.scale": "attn.norm_k_b.weight",
+        "txt_attn.norm.query_norm.scale": "attn.norm_q_b.weight",
+        "txt_attn.proj.bias": "attn.b_to_out.bias",
+        "txt_attn.proj.weight": "attn.b_to_out.weight",
+        "txt_attn.qkv.bias": "attn.b_to_qkv.bias",
+        "txt_attn.qkv.weight": "attn.b_to_qkv.weight",
+        "txt_mlp.0.bias": "ff_b.0.bias",
+        "txt_mlp.0.weight": "ff_b.0.weight",
+        "txt_mlp.2.bias": "ff_b.2.bias",
+        "txt_mlp.2.weight": "ff_b.2.weight",
+        "txt_mod.lin.bias": "norm1_b.linear.bias",
+        "txt_mod.lin.weight": "norm1_b.linear.weight",
+
+        "linear1.bias": "to_qkv_mlp.bias",
+        "linear1.weight": "to_qkv_mlp.weight",
+        "linear2.bias": "proj_out.bias",
+        "linear2.weight": "proj_out.weight",
+        "modulation.lin.bias": "norm.linear.bias",
+        "modulation.lin.weight": "norm.linear.weight",
+        "norm.key_norm.scale": "norm_k_a.weight",
+        "norm.query_norm.scale": "norm_q_a.weight",
+    }
+    state_dict_ = {}
+    for name in state_dict:
+        original_name = 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]
+            state_dict_[rename] = state_dict[original_name]
+        elif names[0] == "double_blocks":
+            rename = f"blocks.{names[1]}." + suffix_rename_dict[".".join(names[2:])]
+            state_dict_[rename] = state_dict[original_name]
+        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] = state_dict[original_name]
+        else:
+            pass
+    return state_dict_

diffsynth/utils/state_dict_converters/flux_text_encoder_clip.py

@@ -0,0 +1,31 @@
+def FluxTextEncoderClipStateDictConverter(state_dict):
+    rename_dict = {
+        "text_model.embeddings.token_embedding.weight": "token_embedding.weight",
+        "text_model.embeddings.position_embedding.weight": "position_embeds",
+        "text_model.final_layer_norm.weight": "final_layer_norm.weight",
+        "text_model.final_layer_norm.bias": "final_layer_norm.bias",
+    }
+    attn_rename_dict = {
+        "self_attn.q_proj": "attn.to_q",
+        "self_attn.k_proj": "attn.to_k",
+        "self_attn.v_proj": "attn.to_v",
+        "self_attn.out_proj": "attn.to_out",
+        "layer_norm1": "layer_norm1",
+        "layer_norm2": "layer_norm2",
+        "mlp.fc1": "fc1",
+        "mlp.fc2": "fc2",
+    }
+    state_dict_ = {}
+    for name in state_dict:
+        if name in rename_dict:
+            param = state_dict[name]
+            if name == "text_model.embeddings.position_embedding.weight":
+                param = param.reshape((1, param.shape[0], param.shape[1]))
+            state_dict_[rename_dict[name]] = param
+        elif name.startswith("text_model.encoder.layers."):
+            param = state_dict[name]
+            names = name.split(".")
+            layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1]
+            name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail])
+            state_dict_[name_] = param
+    return state_dict_

diffsynth/utils/state_dict_converters/flux_text_encoder_t5.py

@@ -0,0 +1,4 @@
+def FluxTextEncoderT5StateDictConverter(state_dict):
+    state_dict_ = {i: state_dict[i] for i in state_dict}
+    state_dict_["encoder.embed_tokens.weight"] = state_dict["shared.weight"]
+    return state_dict_

examples/test/run.py

@@ -71,13 +71,13 @@ if __name__ == "__main__":
     # run_inference("examples/qwen_image/model_inference_low_vram")
     # run_inference("examples/qwen_image/model_training/validate_full")
     # run_inference("examples/qwen_image/model_training/validate_lora")
-    run_train_single_GPU("examples/wanvideo/model_inference_low_vram")
-    move_files("video_", "data/output/model_inference_low_vram")
-    run_train_single_GPU("examples/wanvideo/model_inference")
-    move_files("video_", "data/output/model_inference")
-    run_train_single_GPU("examples/wanvideo/model_training/lora")
-    run_train_single_GPU("examples/wanvideo/model_training/validate_lora")
-    move_files("video_", "data/output/validate_lora")
-    run_train_multi_GPU("examples/wanvideo/model_training/full")
-    run_train_single_GPU("examples/wanvideo/model_training/validate_full")
+    # run_train_single_GPU("examples/wanvideo/model_inference_low_vram")
+    # move_files("video_", "data/output/model_inference_low_vram")
+    # run_train_single_GPU("examples/wanvideo/model_inference")
+    # move_files("video_", "data/output/model_inference")
+    # run_train_single_GPU("examples/wanvideo/model_training/lora")
+    # run_train_single_GPU("examples/wanvideo/model_training/validate_lora")
+    # move_files("video_", "data/output/validate_lora")
+    # run_train_multi_GPU("examples/wanvideo/model_training/full")
+    run_train_multi_GPU("examples/wanvideo/model_training/validate_full")
     move_files("video_", "data/output/validate_full")

examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh

@@ -9,7 +9,7 @@ accelerate launch --config_file examples/wanvideo/model_training/full/accelerate
   --learning_rate 1e-4 \
   --num_epochs 5 \
   --remove_prefix_in_ckpt "pipe.dit." \
-  --output_path "./models/train/Wan2.2-Fun-A14B-Control-Camera_high_niose_full" \
+  --output_path "./models/train/Wan2.2-Fun-A14B-Control-Camera_high_noise_full" \
   --trainable_models "dit" \
   --extra_inputs "input_image,camera_control_direction,camera_control_speed" \
   --max_timestep_boundary 0.358 \

examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh

@@ -9,7 +9,7 @@ accelerate launch --config_file examples/wanvideo/model_training/full/accelerate
   --learning_rate 1e-4 \
   --num_epochs 5 \
   --remove_prefix_in_ckpt "pipe.dit." \
-  --output_path "./models/train/Wan2.2-Fun-A14B-Control_high_niose_full" \
+  --output_path "./models/train/Wan2.2-Fun-A14B-Control_high_noise_full" \
   --trainable_models "dit" \
   --extra_inputs "control_video,reference_image" \
   --max_timestep_boundary 0.358 \

examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh

@@ -8,7 +8,7 @@ accelerate launch --config_file examples/wanvideo/model_training/full/accelerate
   --learning_rate 1e-4 \
   --num_epochs 5 \
   --remove_prefix_in_ckpt "pipe.dit." \
-  --output_path "./models/train/Wan2.2-Fun-A14B-InP_high_niose_full" \
+  --output_path "./models/train/Wan2.2-Fun-A14B-InP_high_noise_full" \
   --trainable_models "dit" \
   --extra_inputs "input_image,end_image" \
   --max_timestep_boundary 0.358 \

examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh

@@ -9,7 +9,7 @@ accelerate launch examples/wanvideo/model_training/train.py \
   --learning_rate 1e-4 \
   --num_epochs 5 \
   --remove_prefix_in_ckpt "pipe.dit." \
-  --output_path "./models/train/Wan2.2-Fun-A14B-Control-Camera_high_niose_lora" \
+  --output_path "./models/train/Wan2.2-Fun-A14B-Control-Camera_high_noise_lora" \
   --lora_base_model "dit" \
   --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
   --lora_rank 32 \

examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh

@@ -9,7 +9,7 @@ accelerate launch examples/wanvideo/model_training/train.py \
   --learning_rate 1e-4 \
   --num_epochs 5 \
   --remove_prefix_in_ckpt "pipe.dit." \
-  --output_path "./models/train/Wan2.2-Fun-A14B-Control_high_niose_lora" \
+  --output_path "./models/train/Wan2.2-Fun-A14B-Control_high_noise_lora" \
   --lora_base_model "dit" \
   --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
   --lora_rank 32 \

examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh

@@ -8,7 +8,7 @@ accelerate launch examples/wanvideo/model_training/train.py \
   --learning_rate 1e-4 \
   --num_epochs 5 \
   --remove_prefix_in_ckpt "pipe.dit." \
-  --output_path "./models/train/Wan2.2-Fun-A14B-InP_high_niose_lora" \
+  --output_path "./models/train/Wan2.2-Fun-A14B-InP_high_noise_lora" \
   --lora_base_model "dit" \
   --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
   --lora_rank 32 \

examples/wanvideo/model_training/validate_full/Wan2.2-S2V-14B.py

@@ -1,7 +1,8 @@
 import torch
 from PIL import Image
 import librosa
-from diffsynth import VideoData, save_video_with_audio, load_state_dict
+from diffsynth.utils.data import VideoData, save_video_with_audio
+from diffsynth.core import load_state_dict
 from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig
 
 

examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py

@@ -16,7 +16,7 @@ pipe = WanVideoPipeline.from_pretrained(
         ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"),
     ],
 )
-pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-V1.1-1.3B-Control-Camera_lora/epoch-4.safetensors", alpha=1)
+pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-V1.1-14B-Control-Camera_lora/epoch-4.safetensors", alpha=1)
 
 video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)
 

examples/wanvideo/model_training/validate_lora/Wan2.2-S2V-14B.py

@@ -1,7 +1,7 @@
 import torch
 from PIL import Image
 import librosa
-from diffsynth import VideoData, save_video_with_audio
+from diffsynth.utils.data import VideoData, save_video_with_audio
 from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig
 
 pipe = WanVideoPipeline.from_pretrained(