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import torch
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from typing import Dict, Optional
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import comfy.ldm.modules.diffusionmodules.mmdit
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class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):
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def __init__(
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self,
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num_blocks = None,
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dtype = None,
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device = None,
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operations = None,
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**kwargs,
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):
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super().__init__(dtype=dtype, device=device, operations=operations, final_layer=False, num_blocks=num_blocks, **kwargs)
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self.controlnet_blocks = torch.nn.ModuleList([])
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for _ in range(len(self.joint_blocks)):
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self.controlnet_blocks.append(operations.Linear(self.hidden_size, self.hidden_size, device=device, dtype=dtype))
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self.pos_embed_input = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(
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None,
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self.patch_size,
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self.in_channels,
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self.hidden_size,
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bias=True,
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strict_img_size=False,
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dtype=dtype,
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device=device,
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operations=operations
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)
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def forward(
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self,
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x: torch.Tensor,
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timesteps: torch.Tensor,
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y: Optional[torch.Tensor] = None,
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context: Optional[torch.Tensor] = None,
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hint = None,
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) -> torch.Tensor:
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y = torch.zeros_like(y)
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if self.context_processor is not None:
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context = self.context_processor(context)
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hw = x.shape[-2:]
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x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype, device=x.device)
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x += self.pos_embed_input(hint)
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c = self.t_embedder(timesteps, dtype=x.dtype)
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if y is not None and self.y_embedder is not None:
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y = self.y_embedder(y)
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c = c + y
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if context is not None:
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context = self.context_embedder(context)
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output = []
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blocks = len(self.joint_blocks)
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for i in range(blocks):
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context, x = self.joint_blocks[i](
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context,
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x,
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c=c,
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use_checkpoint=self.use_checkpoint,
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)
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out = self.controlnet_blocks[i](x)
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count = self.depth // blocks
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if i == blocks - 1:
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count -= 1
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for j in range(count):
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output.append(out)
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return {"output": output}
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