161 lines
6.0 KiB
Python
161 lines
6.0 KiB
Python
#Original code can be found on: https://github.com/black-forest-labs/flux
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from dataclasses import dataclass
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import torch
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from torch import Tensor, nn
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from .layers import (
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DoubleStreamBlock,
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EmbedND,
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LastLayer,
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MLPEmbedder,
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SingleStreamBlock,
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timestep_embedding,
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)
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from einops import rearrange, repeat
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import comfy.ldm.common_dit
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@dataclass
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class FluxParams:
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in_channels: int
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vec_in_dim: int
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context_in_dim: int
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hidden_size: int
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mlp_ratio: float
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num_heads: int
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depth: int
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depth_single_blocks: int
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axes_dim: list
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theta: int
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qkv_bias: bool
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guidance_embed: bool
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class Flux(nn.Module):
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"""
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Transformer model for flow matching on sequences.
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"""
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def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs):
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super().__init__()
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self.dtype = dtype
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params = FluxParams(**kwargs)
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self.params = params
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self.in_channels = params.in_channels * 2 * 2
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self.out_channels = self.in_channels
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if params.hidden_size % params.num_heads != 0:
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raise ValueError(
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f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
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)
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pe_dim = params.hidden_size // params.num_heads
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if sum(params.axes_dim) != pe_dim:
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raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
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self.hidden_size = params.hidden_size
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self.num_heads = params.num_heads
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self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
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self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
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self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations)
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self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations)
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self.guidance_in = (
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MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity()
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)
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self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device)
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self.double_blocks = nn.ModuleList(
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[
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DoubleStreamBlock(
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self.hidden_size,
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self.num_heads,
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mlp_ratio=params.mlp_ratio,
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qkv_bias=params.qkv_bias,
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dtype=dtype, device=device, operations=operations
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)
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for _ in range(params.depth)
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]
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)
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self.single_blocks = nn.ModuleList(
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[
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SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations)
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for _ in range(params.depth_single_blocks)
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]
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)
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if final_layer:
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self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations)
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def forward_orig(
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self,
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img: Tensor,
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img_ids: Tensor,
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txt: Tensor,
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txt_ids: Tensor,
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timesteps: Tensor,
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y: Tensor,
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guidance: Tensor = None,
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control=None,
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) -> Tensor:
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if img.ndim != 3 or txt.ndim != 3:
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raise ValueError("Input img and txt tensors must have 3 dimensions.")
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# running on sequences img
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img = self.img_in(img)
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vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype))
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if self.params.guidance_embed:
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if guidance is None:
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raise ValueError("Didn't get guidance strength for guidance distilled model.")
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vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
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vec = vec + self.vector_in(y)
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txt = self.txt_in(txt)
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ids = torch.cat((txt_ids, img_ids), dim=1)
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pe = self.pe_embedder(ids)
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for i, block in enumerate(self.double_blocks):
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img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
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if control is not None: # Controlnet
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control_i = control.get("input")
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if i < len(control_i):
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add = control_i[i]
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if add is not None:
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img += add
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img = torch.cat((txt, img), 1)
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for i, block in enumerate(self.single_blocks):
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img = block(img, vec=vec, pe=pe)
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if control is not None: # Controlnet
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control_o = control.get("output")
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if i < len(control_o):
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add = control_o[i]
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if add is not None:
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img[:, txt.shape[1] :, ...] += add
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img = img[:, txt.shape[1] :, ...]
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img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels)
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return img
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def forward(self, x, timestep, context, y, guidance, control=None, **kwargs):
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bs, c, h, w = x.shape
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patch_size = 2
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x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
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img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
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h_len = ((h + (patch_size // 2)) // patch_size)
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w_len = ((w + (patch_size // 2)) // patch_size)
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img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
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img_ids[..., 1] = img_ids[..., 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype)[:, None]
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img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :]
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img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
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txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
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out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control)
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return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w]
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