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