feat: add support for HunYuanDit ControlNet (#4245)

* add support for HunYuanDit ControlNet

* fix hunyuandit controlnet

* fix typo in hunyuandit controlnet

* fix typo in hunyuandit controlnet

* fix code format style

* add control_weight support for HunyuanDit Controlnet

* use control_weights in HunyuanDit Controlnet

* fix typo

comfy/controlnet.py

@@ -13,7 +13,7 @@ import comfy.cldm.cldm
 import comfy.t2i_adapter.adapter
 import comfy.ldm.cascade.controlnet
 import comfy.cldm.mmdit
-
+import comfy.ldm.hydit.controlnet
 
 def broadcast_image_to(tensor, target_batch_size, batched_number):
     current_batch_size = tensor.shape[0]
@@ -382,9 +382,116 @@ def load_controlnet_mmdit(sd):
     control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
     return control
 
+class ControlNetWarperHunyuanDiT(ControlNet): 
+    def get_control(self, x_noisy, t, cond, batched_number):
+        control_prev = None
+        if self.previous_controlnet is not None:
+            control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
+
+        if self.timestep_range is not None:
+            if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
+                if control_prev is not None:
+                    return control_prev
+                else:
+                    return None
+
+        dtype = self.control_model.dtype
+        if self.manual_cast_dtype is not None:
+            dtype = self.manual_cast_dtype
+
+        output_dtype = x_noisy.dtype
+        if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]:
+            if self.cond_hint is not None:
+                del self.cond_hint
+            self.cond_hint = None
+            compression_ratio = self.compression_ratio
+            if self.vae is not None:
+                compression_ratio *= self.vae.downscale_ratio
+            self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, self.upscale_algorithm, "center")
+            if self.vae is not None:
+                loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
+                self.cond_hint = self.vae.encode(self.cond_hint.movedim(1, -1))
+                comfy.model_management.load_models_gpu(loaded_models)
+            if self.latent_format is not None:
+                self.cond_hint = self.latent_format.process_in(self.cond_hint)
+            self.cond_hint = self.cond_hint.to(device=self.device, dtype=dtype)
+        if x_noisy.shape[0] != self.cond_hint.shape[0]:
+            self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
+
+        def get_tensor(name):
+            if name in cond:
+                if isinstance(cond[name], torch.Tensor):
+                    return cond[name].to(dtype)
+                else:
+                    return cond[name]
+            else:
+                return None
+            
+        encoder_hidden_states = get_tensor('c_crossattn')
+        text_embedding_mask = get_tensor('text_embedding_mask')
+        encoder_hidden_states_t5 = get_tensor('encoder_hidden_states_t5')
+        text_embedding_mask_t5 = get_tensor('text_embedding_mask_t5')
+        image_meta_size = get_tensor('image_meta_size')
+        style = get_tensor('style')
+        cos_cis_img = get_tensor('cos_cis_img')
+        sin_cis_img = get_tensor('sin_cis_img')
+        
+        timestep = self.model_sampling_current.timestep(t)
+        x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
+
+        control = self.control_model(
+                x=x_noisy.to(dtype), 
+                t=timestep.float(),  
+                condition=self.cond_hint, 
+                encoder_hidden_states=encoder_hidden_states,
+                text_embedding_mask=text_embedding_mask,
+                encoder_hidden_states_t5=encoder_hidden_states_t5,
+                text_embedding_mask_t5=text_embedding_mask_t5,
+                image_meta_size=image_meta_size,
+                style=style,
+                cos_cis_img=cos_cis_img,
+                sin_cis_img=sin_cis_img,
+                **self.extra_args
+            )
+        return self.control_merge(control, control_prev, output_dtype)
+
+    def copy(self):
+        c = ControlNetWarperHunyuanDiT(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)
+        c.control_model = self.control_model
+        c.control_model_wrapped = self.control_model_wrapped
+        self.copy_to(c)
+        return c
+
+def load_controlnet_hunyuandit(controlnet_data):       
+
+    supported_inference_dtypes = [torch.float16, torch.float32]
+
+    unet_dtype = comfy.model_management.unet_dtype(supported_dtypes=supported_inference_dtypes)
+    load_device = comfy.model_management.get_torch_device()
+    manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
+    if manual_cast_dtype is not None:
+        operations = comfy.ops.manual_cast
+    else:
+        operations = comfy.ops.disable_weight_init
+    
+    control_model = comfy.ldm.hydit.controlnet.HunYuanControlNet(operations=operations, device=load_device, dtype=unet_dtype)
+    missing, unexpected = control_model.load_state_dict(controlnet_data)
+
+    if len(missing) > 0:
+        logging.warning("missing controlnet keys: {}".format(missing))
+
+    if len(unexpected) > 0:
+        logging.debug("unexpected controlnet keys: {}".format(unexpected))
+
+    latent_format = comfy.latent_formats.SDXL()
+    control = ControlNetWarperHunyuanDiT(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
+    return control
 
 def load_controlnet(ckpt_path, model=None):
     controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
+    if 'after_proj_list.18.bias' in controlnet_data.keys(): #Hunyuan DiT
+        return load_controlnet_hunyuandit(controlnet_data)
+    
     if "lora_controlnet" in controlnet_data:
         return ControlLora(controlnet_data)
 

comfy/ldm/hydit/controlnet.py

@@ -0,0 +1,348 @@
+from typing import Any, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from torch.utils import checkpoint
+
+from comfy.ldm.modules.diffusionmodules.mmdit import (
+    Mlp,
+    TimestepEmbedder,
+    PatchEmbed,
+    RMSNorm,
+)
+from comfy.ldm.modules.diffusionmodules.util import timestep_embedding
+from .poolers import AttentionPool
+
+import comfy.latent_formats
+from .models import HunYuanDiTBlock
+
+from .posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop
+
+
+def zero_module(module):
+    for p in module.parameters():
+        nn.init.zeros_(p)
+    return module
+
+
+def calc_rope(x, patch_size, head_size):
+    th = (x.shape[2] + (patch_size // 2)) // patch_size
+    tw = (x.shape[3] + (patch_size // 2)) // patch_size
+    base_size = 512 // 8 // patch_size
+    start, stop = get_fill_resize_and_crop((th, tw), base_size)
+    sub_args = [start, stop, (th, tw)]
+    # head_size = HUNYUAN_DIT_CONFIG['DiT-g/2']['hidden_size'] // HUNYUAN_DIT_CONFIG['DiT-g/2']['num_heads']
+    rope = get_2d_rotary_pos_embed(head_size, *sub_args)
+    return rope
+
+
+class HunYuanControlNet(nn.Module):
+    """
+    HunYuanDiT: Diffusion model with a Transformer backbone.
+
+    Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.
+
+    Inherit PeftAdapterMixin to be compatible with the PEFT training pipeline.
+
+    Parameters
+    ----------
+    args: argparse.Namespace
+        The arguments parsed by argparse.
+    input_size: tuple
+        The size of the input image.
+    patch_size: int
+        The size of the patch.
+    in_channels: int
+        The number of input channels.
+    hidden_size: int
+        The hidden size of the transformer backbone.
+    depth: int
+        The number of transformer blocks.
+    num_heads: int
+        The number of attention heads.
+    mlp_ratio: float
+        The ratio of the hidden size of the MLP in the transformer block.
+    log_fn: callable
+        The logging function.
+    """
+
+    def __init__(
+        self,
+        input_size: tuple = 128,
+        patch_size: int = 2,
+        in_channels: int = 4,
+        hidden_size: int = 1408,
+        depth: int = 40,
+        num_heads: int = 16,
+        mlp_ratio: float = 4.3637,
+        text_states_dim=1024,
+        text_states_dim_t5=2048,
+        text_len=77,
+        text_len_t5=256,
+        qk_norm=True,  # See http://arxiv.org/abs/2302.05442 for details.
+        size_cond=False,
+        use_style_cond=False,
+        learn_sigma=True,
+        norm="layer",
+        log_fn: callable = print,
+        attn_precision=None,
+        dtype=None,
+        device=None,
+        operations=None,
+        **kwargs,
+    ):
+        super().__init__()
+        self.log_fn = log_fn
+        self.depth = depth
+        self.learn_sigma = learn_sigma
+        self.in_channels = in_channels
+        self.out_channels = in_channels * 2 if learn_sigma else in_channels
+        self.patch_size = patch_size
+        self.num_heads = num_heads
+        self.hidden_size = hidden_size
+        self.text_states_dim = text_states_dim
+        self.text_states_dim_t5 = text_states_dim_t5
+        self.text_len = text_len
+        self.text_len_t5 = text_len_t5
+        self.size_cond = size_cond
+        self.use_style_cond = use_style_cond
+        self.norm = norm
+        self.dtype = dtype
+        self.latent_format = comfy.latent_formats.SDXL
+
+        self.mlp_t5 = nn.Sequential(
+            nn.Linear(
+                self.text_states_dim_t5,
+                self.text_states_dim_t5 * 4,
+                bias=True,
+                dtype=dtype,
+                device=device,
+            ),
+            nn.SiLU(),
+            nn.Linear(
+                self.text_states_dim_t5 * 4,
+                self.text_states_dim,
+                bias=True,
+                dtype=dtype,
+                device=device,
+            ),
+        )
+        # learnable replace
+        self.text_embedding_padding = nn.Parameter(
+            torch.randn(
+                self.text_len + self.text_len_t5,
+                self.text_states_dim,
+                dtype=dtype,
+                device=device,
+            )
+        )
+
+        # Attention pooling
+        pooler_out_dim = 1024
+        self.pooler = AttentionPool(
+            self.text_len_t5,
+            self.text_states_dim_t5,
+            num_heads=8,
+            output_dim=pooler_out_dim,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+        # Dimension of the extra input vectors
+        self.extra_in_dim = pooler_out_dim
+
+        if self.size_cond:
+            # Image size and crop size conditions
+            self.extra_in_dim += 6 * 256
+
+        if self.use_style_cond:
+            # Here we use a default learned embedder layer for future extension.
+            self.style_embedder = nn.Embedding(
+                1, hidden_size, dtype=dtype, device=device
+            )
+            self.extra_in_dim += hidden_size
+
+        # Text embedding for `add`
+        self.x_embedder = PatchEmbed(
+            input_size,
+            patch_size,
+            in_channels,
+            hidden_size,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+        self.t_embedder = TimestepEmbedder(
+            hidden_size, dtype=dtype, device=device, operations=operations
+        )
+        self.extra_embedder = nn.Sequential(
+            operations.Linear(
+                self.extra_in_dim, hidden_size * 4, dtype=dtype, device=device
+            ),
+            nn.SiLU(),
+            operations.Linear(
+                hidden_size * 4, hidden_size, bias=True, dtype=dtype, device=device
+            ),
+        )
+
+        # Image embedding
+        num_patches = self.x_embedder.num_patches
+
+        # HUnYuanDiT Blocks
+        self.blocks = nn.ModuleList(
+            [
+                HunYuanDiTBlock(
+                    hidden_size=hidden_size,
+                    c_emb_size=hidden_size,
+                    num_heads=num_heads,
+                    mlp_ratio=mlp_ratio,
+                    text_states_dim=self.text_states_dim,
+                    qk_norm=qk_norm,
+                    norm_type=self.norm,
+                    skip=False,
+                    attn_precision=attn_precision,
+                    dtype=dtype,
+                    device=device,
+                    operations=operations,
+                )
+                for _ in range(19)
+            ]
+        )
+
+        # Input zero linear for the first block
+        self.before_proj = zero_module(
+            nn.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)
+        )
+
+        # Output zero linear for the every block
+        self.after_proj_list = nn.ModuleList(
+            [
+                zero_module(
+                    nn.Linear(
+                        self.hidden_size, self.hidden_size, dtype=dtype, device=device
+                    )
+                )
+                for _ in range(len(self.blocks))
+            ]
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        t: torch.Tensor = None,
+        condition=None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        text_embedding_mask=None,
+        encoder_hidden_states_t5=None,
+        text_embedding_mask_t5=None,
+        image_meta_size=None,
+        style=None,
+        control_weight=1.0,
+        transformer_options=None,
+        **kwarg,
+    ):
+        """
+        Forward pass of the encoder.
+
+        Parameters
+        ----------
+        x: torch.Tensor
+            (B, D, H, W)
+        t: torch.Tensor
+            (B)
+        encoder_hidden_states: torch.Tensor
+            CLIP text embedding, (B, L_clip, D)
+        text_embedding_mask: torch.Tensor
+            CLIP text embedding mask, (B, L_clip)
+        encoder_hidden_states_t5: torch.Tensor
+            T5 text embedding, (B, L_t5, D)
+        text_embedding_mask_t5: torch.Tensor
+            T5 text embedding mask, (B, L_t5)
+        image_meta_size: torch.Tensor
+            (B, 6)
+        style: torch.Tensor
+            (B)
+        cos_cis_img: torch.Tensor
+        sin_cis_img: torch.Tensor
+        return_dict: bool
+            Whether to return a dictionary.
+        """
+        if condition.shape[0] == 1:
+            condition = torch.repeat_interleave(condition, x.shape[0], dim=0)
+
+        text_states = encoder_hidden_states  # 2,77,1024
+        text_states_t5 = encoder_hidden_states_t5  # 2,256,2048
+        text_states_mask = text_embedding_mask.bool()  # 2,77
+        text_states_t5_mask = text_embedding_mask_t5.bool()  # 2,256
+        b_t5, l_t5, c_t5 = text_states_t5.shape
+        text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5)).view(b_t5, l_t5, -1)
+
+        padding = comfy.ops.cast_to_input(self.text_embedding_padding, text_states)
+
+        text_states[:, -self.text_len :] = torch.where(
+            text_states_mask[:, -self.text_len :].unsqueeze(2),
+            text_states[:, -self.text_len :],
+            padding[: self.text_len],
+        )
+        text_states_t5[:, -self.text_len_t5 :] = torch.where(
+            text_states_t5_mask[:, -self.text_len_t5 :].unsqueeze(2),
+            text_states_t5[:, -self.text_len_t5 :],
+            padding[self.text_len :],
+        )
+
+        text_states = torch.cat([text_states, text_states_t5], dim=1)  # 2,205，1024
+
+        # _, _, oh, ow = x.shape
+        # th, tw = oh // self.patch_size, ow // self.patch_size
+
+        # Get image RoPE embedding according to `reso`lution.
+        freqs_cis_img = calc_rope(
+            x, self.patch_size, self.hidden_size // self.num_heads
+        )  # (cos_cis_img, sin_cis_img)
+
+        # ========================= Build time and image embedding =========================
+        t = self.t_embedder(t, dtype=self.dtype)
+        x = self.x_embedder(x)
+
+        # ========================= Concatenate all extra vectors =========================
+        # Build text tokens with pooling
+        extra_vec = self.pooler(encoder_hidden_states_t5)
+
+        # Build image meta size tokens if applicable
+        # if image_meta_size is not None:
+        #     image_meta_size = timestep_embedding(image_meta_size.view(-1), 256)   # [B * 6, 256]
+        #     if image_meta_size.dtype != self.dtype:
+        #         image_meta_size = image_meta_size.half()
+        #     image_meta_size = image_meta_size.view(-1, 6 * 256)
+        #     extra_vec = torch.cat([extra_vec, image_meta_size], dim=1)  # [B, D + 6 * 256]
+
+        # Build style tokens
+        if style is not None:
+            style_embedding = self.style_embedder(style)
+            extra_vec = torch.cat([extra_vec, style_embedding], dim=1)
+
+        # Concatenate all extra vectors
+        c = t + self.extra_embedder(extra_vec)  # [B, D]
+
+        # ========================= Deal with Condition =========================
+        condition = self.x_embedder(condition)
+
+        # ========================= Forward pass through HunYuanDiT blocks =========================
+        controls = []
+        x = x + self.before_proj(condition)  # add condition
+        for layer, block in enumerate(self.blocks):
+            x = block(x, c, text_states, freqs_cis_img)
+            controls.append(self.after_proj_list[layer](x))  # zero linear for output
+
+        control_weights = [1.0 * (control_weight ** float(19 - i)) for i in range(19)]
+        assert len(control_weights) == len(
+            controls
+        ), "control_weights and controls should have the same length"
+        controls = [
+            control * weight for control, weight in zip(controls, control_weights)
+        ]
+
+        return {"output": controls}

comfy/ldm/hydit/models.py

@@ -91,6 +91,8 @@ class HunYuanDiTBlock(nn.Module):
         # Long Skip Connection
         if self.skip_linear is not None:
             cat = torch.cat([x, skip], dim=-1)
+            if cat.dtype != x.dtype:
+                cat = cat.to(x.dtype)
             cat = self.skip_norm(cat)
             x = self.skip_linear(cat)
 
@@ -362,6 +364,8 @@ class HunYuanDiT(nn.Module):
         c = t + self.extra_embedder(extra_vec)  # [B, D]
 
         controls = None
+        if control:
+            controls = control.get("output", None)
         # ========================= Forward pass through HunYuanDiT blocks =========================
         skips = []
         for layer, block in enumerate(self.blocks):

comfy_extras/nodes_hunyuan.py

@@ -19,6 +19,58 @@ class CLIPTextEncodeHunyuanDiT:
         cond = output.pop("cond")
         return ([[cond, output]], )
 
+    
+class ControlNetApplyAdvancedHunYuan:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "control_net": ("CONTROL_NET", ),
+                             "image": ("IMAGE", ),
+                             "vae": ("VAE", ),
+                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                             "control_weight": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.001}),
+                             "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                             "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
+                             }}
+
+    RETURN_TYPES = ("CONDITIONING","CONDITIONING")
+    RETURN_NAMES = ("positive", "negative")
+    FUNCTION = "apply_controlnet"
+
+    CATEGORY = "conditioning/controlnet"
+
+    def apply_controlnet(self, positive, negative, control_net, image, strength, control_weight, start_percent, end_percent, vae=None):
+        if strength == 0:
+            return (positive, negative)
+
+        control_hint = image.movedim(-1,1)
+        cnets = {}
+
+        out = []
+        for conditioning in [positive, negative]:
+            c = []
+            for t in conditioning:
+                d = t[1].copy()
+
+                prev_cnet = d.get('control', None)
+                if prev_cnet in cnets:
+                    c_net = cnets[prev_cnet]
+                else:
+                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae)
+                    c_net.set_extra_arg('control_weight', control_weight)
+                    
+                    c_net.set_previous_controlnet(prev_cnet)
+                    cnets[prev_cnet] = c_net
+
+                d['control'] = c_net
+                d['control_apply_to_uncond'] = False
+                n = [t[0], d]
+                c.append(n)
+            out.append(c)
+        return (out[0], out[1])
+    
 NODE_CLASS_MAPPINGS = {
     "CLIPTextEncodeHunyuanDiT": CLIPTextEncodeHunyuanDiT,
+    "ControlNetApplyAdvancedHunYuan": ControlNetApplyAdvancedHunYuan,
 }