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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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out_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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patch_size: 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.patch_size = params.patch_size |
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self.in_channels = params.in_channels * params.patch_size * params.patch_size |
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self.out_channels = params.out_channels * params.patch_size * params.patch_size |
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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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transformer_options={}, |
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) -> Tensor: |
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patches_replace = transformer_options.get("patches_replace", {}) |
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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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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[:,:self.params.vec_in_dim]) |
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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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blocks_replace = patches_replace.get("dit", {}) |
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for i, block in enumerate(self.double_blocks): |
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if ("double_block", i) in blocks_replace: |
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def block_wrap(args): |
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out = {} |
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out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"]) |
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return out |
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out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe}, {"original_block": block_wrap}) |
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txt = out["txt"] |
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img = out["img"] |
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else: |
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img, txt = block(img=img, txt=txt, vec=vec, pe=pe) |
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if control is not None: |
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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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if ("single_block", i) in blocks_replace: |
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def block_wrap(args): |
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out = {} |
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out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"]) |
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return out |
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out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe}, {"original_block": block_wrap}) |
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img = out["img"] |
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else: |
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img = block(img, vec=vec, pe=pe) |
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if control is not None: |
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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) |
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return img |
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def forward(self, x, timestep, context, y, guidance, control=None, transformer_options={}, **kwargs): |
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bs, c, h, w = x.shape |
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patch_size = self.patch_size |
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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).unsqueeze(1) |
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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).unsqueeze(0) |
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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, transformer_options) |
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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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