mirror of
https://github.com/kohya-ss/sd-scripts.git
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1330 lines
48 KiB
Python
1330 lines
48 KiB
Python
# copy from FLUX repo: https://github.com/black-forest-labs/flux
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# license: Apache-2.0 License
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import math
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import os
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import time
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from concurrent.futures import Future, ThreadPoolExecutor
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from dataclasses import dataclass
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from typing import Dict, List, Optional, Union
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from library import utils
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from library.device_utils import clean_memory_on_device, init_ipex
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init_ipex()
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import torch
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from einops import rearrange
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from torch import Tensor, nn
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from torch.utils.checkpoint import checkpoint
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from library import custom_offloading_utils
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# USE_REENTRANT = True
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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[int]
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theta: int
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qkv_bias: bool
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guidance_embed: bool
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# region autoencoder
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@dataclass
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class AutoEncoderParams:
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resolution: int
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in_channels: int
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ch: int
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out_ch: int
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ch_mult: list[int]
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num_res_blocks: int
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z_channels: int
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scale_factor: float
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shift_factor: float
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def swish(x: Tensor) -> Tensor:
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return x * torch.sigmoid(x)
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class AttnBlock(nn.Module):
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def __init__(self, in_channels: int):
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super().__init__()
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self.in_channels = in_channels
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self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
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self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1)
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self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1)
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self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1)
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self.proj_out = nn.Conv2d(in_channels, in_channels, kernel_size=1)
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def attention(self, h_: Tensor) -> Tensor:
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h_ = self.norm(h_)
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q = self.q(h_)
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k = self.k(h_)
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v = self.v(h_)
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b, c, h, w = q.shape
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q = rearrange(q, "b c h w -> b 1 (h w) c").contiguous()
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k = rearrange(k, "b c h w -> b 1 (h w) c").contiguous()
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v = rearrange(v, "b c h w -> b 1 (h w) c").contiguous()
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h_ = nn.functional.scaled_dot_product_attention(q, k, v)
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return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b)
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def forward(self, x: Tensor) -> Tensor:
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return x + self.proj_out(self.attention(x))
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class ResnetBlock(nn.Module):
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def __init__(self, in_channels: int, out_channels: int):
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super().__init__()
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self.in_channels = in_channels
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out_channels = in_channels if out_channels is None else out_channels
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self.out_channels = out_channels
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self.norm1 = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
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self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
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self.norm2 = nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True)
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self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
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if self.in_channels != self.out_channels:
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self.nin_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
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def forward(self, x):
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h = x
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h = self.norm1(h)
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h = swish(h)
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h = self.conv1(h)
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h = self.norm2(h)
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h = swish(h)
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h = self.conv2(h)
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if self.in_channels != self.out_channels:
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x = self.nin_shortcut(x)
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return x + h
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class Downsample(nn.Module):
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def __init__(self, in_channels: int):
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super().__init__()
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# no asymmetric padding in torch conv, must do it ourselves
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self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
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def forward(self, x: Tensor):
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pad = (0, 1, 0, 1)
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x = nn.functional.pad(x, pad, mode="constant", value=0)
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x = self.conv(x)
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return x
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class Upsample(nn.Module):
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def __init__(self, in_channels: int):
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super().__init__()
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self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
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def forward(self, x: Tensor):
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x = nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
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x = self.conv(x)
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return x
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class Encoder(nn.Module):
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def __init__(
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self,
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resolution: int,
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in_channels: int,
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ch: int,
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ch_mult: list[int],
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num_res_blocks: int,
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z_channels: int,
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):
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super().__init__()
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self.ch = ch
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self.num_resolutions = len(ch_mult)
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self.num_res_blocks = num_res_blocks
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self.resolution = resolution
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self.in_channels = in_channels
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# downsampling
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self.conv_in = nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1)
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curr_res = resolution
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in_ch_mult = (1,) + tuple(ch_mult)
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self.in_ch_mult = in_ch_mult
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self.down = nn.ModuleList()
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block_in = self.ch
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for i_level in range(self.num_resolutions):
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block = nn.ModuleList()
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attn = nn.ModuleList()
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block_in = ch * in_ch_mult[i_level]
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block_out = ch * ch_mult[i_level]
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for _ in range(self.num_res_blocks):
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block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
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block_in = block_out
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down = nn.Module()
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down.block = block
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down.attn = attn
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if i_level != self.num_resolutions - 1:
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down.downsample = Downsample(block_in)
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curr_res = curr_res // 2
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self.down.append(down)
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# middle
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self.mid = nn.Module()
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self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
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self.mid.attn_1 = AttnBlock(block_in)
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self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
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# end
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self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
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self.conv_out = nn.Conv2d(block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1)
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def forward(self, x: Tensor) -> Tensor:
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# downsampling
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hs = [self.conv_in(x)]
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for i_level in range(self.num_resolutions):
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for i_block in range(self.num_res_blocks):
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h = self.down[i_level].block[i_block](hs[-1])
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if len(self.down[i_level].attn) > 0:
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h = self.down[i_level].attn[i_block](h)
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hs.append(h)
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if i_level != self.num_resolutions - 1:
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hs.append(self.down[i_level].downsample(hs[-1]))
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# middle
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h = hs[-1]
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h = self.mid.block_1(h)
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h = self.mid.attn_1(h)
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h = self.mid.block_2(h)
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# end
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h = self.norm_out(h)
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h = swish(h)
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h = self.conv_out(h)
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return h
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class Decoder(nn.Module):
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def __init__(
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self,
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ch: int,
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out_ch: int,
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ch_mult: list[int],
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num_res_blocks: int,
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in_channels: int,
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resolution: int,
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z_channels: int,
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):
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super().__init__()
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self.ch = ch
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self.num_resolutions = len(ch_mult)
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self.num_res_blocks = num_res_blocks
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self.resolution = resolution
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self.in_channels = in_channels
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self.ffactor = 2 ** (self.num_resolutions - 1)
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# compute in_ch_mult, block_in and curr_res at lowest res
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block_in = ch * ch_mult[self.num_resolutions - 1]
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curr_res = resolution // 2 ** (self.num_resolutions - 1)
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self.z_shape = (1, z_channels, curr_res, curr_res)
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# z to block_in
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self.conv_in = nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1)
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# middle
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self.mid = nn.Module()
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self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
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self.mid.attn_1 = AttnBlock(block_in)
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self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
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# upsampling
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self.up = nn.ModuleList()
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for i_level in reversed(range(self.num_resolutions)):
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block = nn.ModuleList()
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attn = nn.ModuleList()
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block_out = ch * ch_mult[i_level]
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for _ in range(self.num_res_blocks + 1):
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block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
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block_in = block_out
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up = nn.Module()
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up.block = block
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up.attn = attn
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if i_level != 0:
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up.upsample = Upsample(block_in)
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curr_res = curr_res * 2
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self.up.insert(0, up) # prepend to get consistent order
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# end
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self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
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self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1)
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def forward(self, z: Tensor) -> Tensor:
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# z to block_in
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h = self.conv_in(z)
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# middle
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h = self.mid.block_1(h)
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h = self.mid.attn_1(h)
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h = self.mid.block_2(h)
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# upsampling
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for i_level in reversed(range(self.num_resolutions)):
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for i_block in range(self.num_res_blocks + 1):
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h = self.up[i_level].block[i_block](h)
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if len(self.up[i_level].attn) > 0:
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h = self.up[i_level].attn[i_block](h)
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if i_level != 0:
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h = self.up[i_level].upsample(h)
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# end
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h = self.norm_out(h)
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h = swish(h)
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h = self.conv_out(h)
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return h
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class DiagonalGaussian(nn.Module):
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def __init__(self, sample: bool = True, chunk_dim: int = 1):
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super().__init__()
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self.sample = sample
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self.chunk_dim = chunk_dim
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def forward(self, z: Tensor) -> Tensor:
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mean, logvar = torch.chunk(z, 2, dim=self.chunk_dim)
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if self.sample:
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std = torch.exp(0.5 * logvar)
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return mean + std * torch.randn_like(mean)
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else:
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return mean
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class AutoEncoder(nn.Module):
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def __init__(self, params: AutoEncoderParams):
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super().__init__()
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self.encoder = Encoder(
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resolution=params.resolution,
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in_channels=params.in_channels,
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ch=params.ch,
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ch_mult=params.ch_mult,
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num_res_blocks=params.num_res_blocks,
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z_channels=params.z_channels,
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)
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self.decoder = Decoder(
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resolution=params.resolution,
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in_channels=params.in_channels,
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ch=params.ch,
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out_ch=params.out_ch,
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ch_mult=params.ch_mult,
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num_res_blocks=params.num_res_blocks,
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z_channels=params.z_channels,
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)
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self.reg = DiagonalGaussian()
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self.scale_factor = params.scale_factor
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self.shift_factor = params.shift_factor
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@property
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def device(self) -> torch.device:
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return next(self.parameters()).device
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@property
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def dtype(self) -> torch.dtype:
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return next(self.parameters()).dtype
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def encode(self, x: Tensor) -> Tensor:
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z = self.reg(self.encoder(x))
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z = self.scale_factor * (z - self.shift_factor)
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return z
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def decode(self, z: Tensor) -> Tensor:
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z = z / self.scale_factor + self.shift_factor
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return self.decoder(z)
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def forward(self, x: Tensor) -> Tensor:
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return self.decode(self.encode(x))
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# endregion
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# region config
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@dataclass
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class ModelSpec:
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params: FluxParams
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ae_params: AutoEncoderParams
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ckpt_path: str | None
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ae_path: str | None
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# repo_id: str | None
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# repo_flow: str | None
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# repo_ae: str | None
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configs = {
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"dev": ModelSpec(
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# repo_id="black-forest-labs/FLUX.1-dev",
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# repo_flow="flux1-dev.sft",
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# repo_ae="ae.sft",
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ckpt_path=None, # os.getenv("FLUX_DEV"),
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params=FluxParams(
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in_channels=64,
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vec_in_dim=768,
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context_in_dim=4096,
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hidden_size=3072,
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mlp_ratio=4.0,
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num_heads=24,
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depth=19,
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depth_single_blocks=38,
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axes_dim=[16, 56, 56],
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theta=10_000,
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qkv_bias=True,
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guidance_embed=True,
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),
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ae_path=None, # os.getenv("AE"),
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ae_params=AutoEncoderParams(
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resolution=256,
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in_channels=3,
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ch=128,
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out_ch=3,
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ch_mult=[1, 2, 4, 4],
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num_res_blocks=2,
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z_channels=16,
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scale_factor=0.3611,
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shift_factor=0.1159,
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),
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),
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"schnell": ModelSpec(
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# repo_id="black-forest-labs/FLUX.1-schnell",
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# repo_flow="flux1-schnell.sft",
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# repo_ae="ae.sft",
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ckpt_path=None, # os.getenv("FLUX_SCHNELL"),
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params=FluxParams(
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in_channels=64,
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vec_in_dim=768,
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context_in_dim=4096,
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hidden_size=3072,
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mlp_ratio=4.0,
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num_heads=24,
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depth=19,
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depth_single_blocks=38,
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axes_dim=[16, 56, 56],
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theta=10_000,
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qkv_bias=True,
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guidance_embed=False,
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),
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ae_path=None, # os.getenv("AE"),
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ae_params=AutoEncoderParams(
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resolution=256,
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in_channels=3,
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ch=128,
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out_ch=3,
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ch_mult=[1, 2, 4, 4],
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num_res_blocks=2,
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z_channels=16,
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scale_factor=0.3611,
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shift_factor=0.1159,
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),
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),
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}
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# endregion
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# region math
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def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, attn_mask: Optional[Tensor] = None) -> Tensor:
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q, k = apply_rope(q, k, pe)
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x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
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x = rearrange(x, "B H L D -> B L (H D)")
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return x
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def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
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assert dim % 2 == 0
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scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
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omega = 1.0 / (theta**scale)
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out = torch.einsum("...n,d->...nd", pos, omega)
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out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
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out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
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return out.float()
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def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
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xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
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xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
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xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
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xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
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return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
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# endregion
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# region layers
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# for cpu_offload_checkpointing
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def to_cuda(x):
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if isinstance(x, torch.Tensor):
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return x.cuda()
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elif isinstance(x, (list, tuple)):
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return [to_cuda(elem) for elem in x]
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elif isinstance(x, dict):
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return {k: to_cuda(v) for k, v in x.items()}
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else:
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return x
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|
|
def to_cpu(x):
|
|
if isinstance(x, torch.Tensor):
|
|
return x.cpu()
|
|
elif isinstance(x, (list, tuple)):
|
|
return [to_cpu(elem) for elem in x]
|
|
elif isinstance(x, dict):
|
|
return {k: to_cpu(v) for k, v in x.items()}
|
|
else:
|
|
return x
|
|
|
|
|
|
class EmbedND(nn.Module):
|
|
def __init__(self, dim: int, theta: int, axes_dim: list[int]):
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.theta = theta
|
|
self.axes_dim = axes_dim
|
|
|
|
def forward(self, ids: Tensor) -> Tensor:
|
|
n_axes = ids.shape[-1]
|
|
emb = torch.cat(
|
|
[rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
|
|
dim=-3,
|
|
)
|
|
|
|
return emb.unsqueeze(1)
|
|
|
|
|
|
def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
|
|
"""
|
|
Create sinusoidal timestep embeddings.
|
|
:param t: a 1-D Tensor of N indices, one per batch element.
|
|
These may be fractional.
|
|
:param dim: the dimension of the output.
|
|
:param max_period: controls the minimum frequency of the embeddings.
|
|
:return: an (N, D) Tensor of positional embeddings.
|
|
"""
|
|
t = time_factor * t
|
|
half = dim // 2
|
|
freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(t.device)
|
|
|
|
args = t[:, None].float() * freqs[None]
|
|
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
|
|
if dim % 2:
|
|
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
|
|
if torch.is_floating_point(t):
|
|
embedding = embedding.to(t)
|
|
return embedding
|
|
|
|
|
|
class MLPEmbedder(nn.Module):
|
|
def __init__(self, in_dim: int, hidden_dim: int):
|
|
super().__init__()
|
|
self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
|
|
self.silu = nn.SiLU()
|
|
self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
def enable_gradient_checkpointing(self):
|
|
self.gradient_checkpointing = True
|
|
|
|
def disable_gradient_checkpointing(self):
|
|
self.gradient_checkpointing = False
|
|
|
|
def _forward(self, x: Tensor) -> Tensor:
|
|
return self.out_layer(self.silu(self.in_layer(x)))
|
|
|
|
def forward(self, *args, **kwargs):
|
|
if self.training and self.gradient_checkpointing:
|
|
return checkpoint(self._forward, *args, use_reentrant=False, **kwargs)
|
|
else:
|
|
return self._forward(*args, **kwargs)
|
|
|
|
# def forward(self, x):
|
|
# if self.training and self.gradient_checkpointing:
|
|
# def create_custom_forward(func):
|
|
# def custom_forward(*inputs):
|
|
# return func(*inputs)
|
|
# return custom_forward
|
|
# return torch.utils.checkpoint.checkpoint(create_custom_forward(self._forward), x, use_reentrant=USE_REENTRANT)
|
|
# else:
|
|
# return self._forward(x)
|
|
|
|
|
|
class RMSNorm(torch.nn.Module):
|
|
def __init__(self, dim: int):
|
|
super().__init__()
|
|
self.scale = nn.Parameter(torch.ones(dim))
|
|
|
|
def forward(self, x: Tensor):
|
|
x_dtype = x.dtype
|
|
x = x.float()
|
|
rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
|
|
# return (x * rrms).to(dtype=x_dtype) * self.scale
|
|
return ((x * rrms) * self.scale.float()).to(dtype=x_dtype)
|
|
|
|
|
|
class QKNorm(torch.nn.Module):
|
|
def __init__(self, dim: int):
|
|
super().__init__()
|
|
self.query_norm = RMSNorm(dim)
|
|
self.key_norm = RMSNorm(dim)
|
|
|
|
def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]:
|
|
q = self.query_norm(q)
|
|
k = self.key_norm(k)
|
|
return q.to(v), k.to(v)
|
|
|
|
|
|
class SelfAttention(nn.Module):
|
|
def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False):
|
|
super().__init__()
|
|
self.num_heads = num_heads
|
|
head_dim = dim // num_heads
|
|
|
|
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
|
self.norm = QKNorm(head_dim)
|
|
self.proj = nn.Linear(dim, dim)
|
|
|
|
# this is not called from DoubleStreamBlock/SingleStreamBlock because they uses attention function directly
|
|
def forward(self, x: Tensor, pe: Tensor) -> Tensor:
|
|
qkv = self.qkv(x)
|
|
q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
|
|
q, k = self.norm(q, k, v)
|
|
x = attention(q, k, v, pe=pe)
|
|
x = self.proj(x)
|
|
return x
|
|
|
|
|
|
@dataclass
|
|
class ModulationOut:
|
|
shift: Tensor
|
|
scale: Tensor
|
|
gate: Tensor
|
|
|
|
|
|
class Modulation(nn.Module):
|
|
def __init__(self, dim: int, double: bool):
|
|
super().__init__()
|
|
self.is_double = double
|
|
self.multiplier = 6 if double else 3
|
|
self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)
|
|
|
|
def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]:
|
|
out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
|
|
|
|
return (
|
|
ModulationOut(*out[:3]),
|
|
ModulationOut(*out[3:]) if self.is_double else None,
|
|
)
|
|
|
|
|
|
class DoubleStreamBlock(nn.Module):
|
|
def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False):
|
|
super().__init__()
|
|
|
|
mlp_hidden_dim = int(hidden_size * mlp_ratio)
|
|
self.num_heads = num_heads
|
|
self.hidden_size = hidden_size
|
|
self.img_mod = Modulation(hidden_size, double=True)
|
|
self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
|
|
|
|
self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.img_mlp = nn.Sequential(
|
|
nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
|
|
nn.GELU(approximate="tanh"),
|
|
nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
|
|
)
|
|
|
|
self.txt_mod = Modulation(hidden_size, double=True)
|
|
self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
|
|
|
|
self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.txt_mlp = nn.Sequential(
|
|
nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
|
|
nn.GELU(approximate="tanh"),
|
|
nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
|
|
)
|
|
|
|
self.gradient_checkpointing = False
|
|
self.cpu_offload_checkpointing = False
|
|
|
|
def enable_gradient_checkpointing(self, cpu_offload: bool = False):
|
|
self.gradient_checkpointing = True
|
|
self.cpu_offload_checkpointing = cpu_offload
|
|
|
|
def disable_gradient_checkpointing(self):
|
|
self.gradient_checkpointing = False
|
|
self.cpu_offload_checkpointing = False
|
|
|
|
def _forward(
|
|
self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, txt_attention_mask: Optional[Tensor] = None
|
|
) -> tuple[Tensor, Tensor]:
|
|
img_mod1, img_mod2 = self.img_mod(vec)
|
|
txt_mod1, txt_mod2 = self.txt_mod(vec)
|
|
|
|
# prepare image for attention
|
|
img_modulated = self.img_norm1(img)
|
|
img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
|
|
img_qkv = self.img_attn.qkv(img_modulated)
|
|
img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
|
|
img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
|
|
|
|
# prepare txt for attention
|
|
txt_modulated = self.txt_norm1(txt)
|
|
txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
|
|
txt_qkv = self.txt_attn.qkv(txt_modulated)
|
|
txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
|
|
txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
|
|
|
|
# run actual attention
|
|
q = torch.cat((txt_q, img_q), dim=2)
|
|
k = torch.cat((txt_k, img_k), dim=2)
|
|
v = torch.cat((txt_v, img_v), dim=2)
|
|
|
|
# make attention mask if not None
|
|
attn_mask = None
|
|
if txt_attention_mask is not None:
|
|
# F.scaled_dot_product_attention expects attn_mask to be bool for binary mask
|
|
attn_mask = txt_attention_mask.to(torch.bool) # b, seq_len
|
|
attn_mask = torch.cat(
|
|
(attn_mask, torch.ones(attn_mask.shape[0], img.shape[1], device=attn_mask.device, dtype=torch.bool)), dim=1
|
|
) # b, seq_len + img_len
|
|
|
|
# broadcast attn_mask to all heads
|
|
attn_mask = attn_mask[:, None, None, :].expand(-1, q.shape[1], q.shape[2], -1)
|
|
|
|
attn = attention(q, k, v, pe=pe, attn_mask=attn_mask)
|
|
txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
|
|
|
|
# calculate the img blocks
|
|
img = img + img_mod1.gate * self.img_attn.proj(img_attn)
|
|
img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
|
|
|
|
# calculate the txt blocks
|
|
txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
|
|
txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
|
|
return img, txt
|
|
|
|
def forward(
|
|
self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, txt_attention_mask: Optional[Tensor] = None
|
|
) -> tuple[Tensor, Tensor]:
|
|
if self.training and self.gradient_checkpointing:
|
|
if not self.cpu_offload_checkpointing:
|
|
return checkpoint(self._forward, img, txt, vec, pe, txt_attention_mask, use_reentrant=False)
|
|
# cpu offload checkpointing
|
|
|
|
def create_custom_forward(func):
|
|
def custom_forward(*inputs):
|
|
cuda_inputs = to_cuda(inputs)
|
|
outputs = func(*cuda_inputs)
|
|
return to_cpu(outputs)
|
|
|
|
return custom_forward
|
|
|
|
return torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(self._forward), img, txt, vec, pe, txt_attention_mask, use_reentrant=False
|
|
)
|
|
|
|
else:
|
|
return self._forward(img, txt, vec, pe, txt_attention_mask)
|
|
|
|
|
|
class SingleStreamBlock(nn.Module):
|
|
"""
|
|
A DiT block with parallel linear layers as described in
|
|
https://arxiv.org/abs/2302.05442 and adapted modulation interface.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
hidden_size: int,
|
|
num_heads: int,
|
|
mlp_ratio: float = 4.0,
|
|
qk_scale: float | None = None,
|
|
):
|
|
super().__init__()
|
|
self.hidden_dim = hidden_size
|
|
self.num_heads = num_heads
|
|
head_dim = hidden_size // num_heads
|
|
self.scale = qk_scale or head_dim**-0.5
|
|
|
|
self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
|
|
# qkv and mlp_in
|
|
self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
|
|
# proj and mlp_out
|
|
self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
|
|
|
|
self.norm = QKNorm(head_dim)
|
|
|
|
self.hidden_size = hidden_size
|
|
self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
|
|
self.mlp_act = nn.GELU(approximate="tanh")
|
|
self.modulation = Modulation(hidden_size, double=False)
|
|
|
|
self.gradient_checkpointing = False
|
|
self.cpu_offload_checkpointing = False
|
|
|
|
def enable_gradient_checkpointing(self, cpu_offload: bool = False):
|
|
self.gradient_checkpointing = True
|
|
self.cpu_offload_checkpointing = cpu_offload
|
|
|
|
def disable_gradient_checkpointing(self):
|
|
self.gradient_checkpointing = False
|
|
self.cpu_offload_checkpointing = False
|
|
|
|
def _forward(self, x: Tensor, vec: Tensor, pe: Tensor, txt_attention_mask: Optional[Tensor] = None) -> Tensor:
|
|
mod, _ = self.modulation(vec)
|
|
x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
|
|
qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
|
|
|
|
q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
|
|
q, k = self.norm(q, k, v)
|
|
|
|
# make attention mask if not None
|
|
attn_mask = None
|
|
if txt_attention_mask is not None:
|
|
# F.scaled_dot_product_attention expects attn_mask to be bool for binary mask
|
|
attn_mask = txt_attention_mask.to(torch.bool) # b, seq_len
|
|
attn_mask = torch.cat(
|
|
(
|
|
attn_mask,
|
|
torch.ones(
|
|
attn_mask.shape[0], x.shape[1] - txt_attention_mask.shape[1], device=attn_mask.device, dtype=torch.bool
|
|
),
|
|
),
|
|
dim=1,
|
|
) # b, seq_len + img_len = x_len
|
|
|
|
# broadcast attn_mask to all heads
|
|
attn_mask = attn_mask[:, None, None, :].expand(-1, q.shape[1], q.shape[2], -1)
|
|
|
|
# compute attention
|
|
attn = attention(q, k, v, pe=pe, attn_mask=attn_mask)
|
|
|
|
# compute activation in mlp stream, cat again and run second linear layer
|
|
output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
|
|
return x + mod.gate * output
|
|
|
|
def forward(self, x: Tensor, vec: Tensor, pe: Tensor, txt_attention_mask: Optional[Tensor] = None) -> Tensor:
|
|
if self.training and self.gradient_checkpointing:
|
|
if not self.cpu_offload_checkpointing:
|
|
return checkpoint(self._forward, x, vec, pe, txt_attention_mask, use_reentrant=False)
|
|
|
|
# cpu offload checkpointing
|
|
|
|
def create_custom_forward(func):
|
|
def custom_forward(*inputs):
|
|
cuda_inputs = to_cuda(inputs)
|
|
outputs = func(*cuda_inputs)
|
|
return to_cpu(outputs)
|
|
|
|
return custom_forward
|
|
|
|
return torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(self._forward), x, vec, pe, txt_attention_mask, use_reentrant=False
|
|
)
|
|
else:
|
|
return self._forward(x, vec, pe, txt_attention_mask)
|
|
|
|
|
|
class LastLayer(nn.Module):
|
|
def __init__(self, hidden_size: int, patch_size: int, out_channels: int):
|
|
super().__init__()
|
|
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
|
|
self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
|
|
|
|
def forward(self, x: Tensor, vec: Tensor) -> Tensor:
|
|
shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
|
|
x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
|
|
x = self.linear(x)
|
|
return x
|
|
|
|
|
|
# endregion
|
|
|
|
|
|
class Flux(nn.Module):
|
|
"""
|
|
Transformer model for flow matching on sequences.
|
|
"""
|
|
|
|
def __init__(self, params: FluxParams):
|
|
super().__init__()
|
|
|
|
self.params = params
|
|
self.in_channels = params.in_channels
|
|
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 = nn.Linear(self.in_channels, self.hidden_size, bias=True)
|
|
self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
|
|
self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
|
|
self.guidance_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity()
|
|
self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
|
|
|
|
self.double_blocks = nn.ModuleList(
|
|
[
|
|
DoubleStreamBlock(
|
|
self.hidden_size,
|
|
self.num_heads,
|
|
mlp_ratio=params.mlp_ratio,
|
|
qkv_bias=params.qkv_bias,
|
|
)
|
|
for _ in range(params.depth)
|
|
]
|
|
)
|
|
|
|
self.single_blocks = nn.ModuleList(
|
|
[
|
|
SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio)
|
|
for _ in range(params.depth_single_blocks)
|
|
]
|
|
)
|
|
|
|
self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels)
|
|
|
|
self.gradient_checkpointing = False
|
|
self.cpu_offload_checkpointing = False
|
|
self.blocks_to_swap = None
|
|
|
|
self.offloader_double = None
|
|
self.offloader_single = None
|
|
self.num_double_blocks = len(self.double_blocks)
|
|
self.num_single_blocks = len(self.single_blocks)
|
|
|
|
def get_model_type(self) -> str:
|
|
return "flux"
|
|
|
|
@property
|
|
def device(self):
|
|
return next(self.parameters()).device
|
|
|
|
@property
|
|
def dtype(self):
|
|
return next(self.parameters()).dtype
|
|
|
|
def enable_gradient_checkpointing(self, cpu_offload: bool = False):
|
|
self.gradient_checkpointing = True
|
|
self.cpu_offload_checkpointing = cpu_offload
|
|
|
|
self.time_in.enable_gradient_checkpointing()
|
|
self.vector_in.enable_gradient_checkpointing()
|
|
if self.guidance_in.__class__ != nn.Identity:
|
|
self.guidance_in.enable_gradient_checkpointing()
|
|
|
|
for block in self.double_blocks + self.single_blocks:
|
|
block.enable_gradient_checkpointing(cpu_offload=cpu_offload)
|
|
|
|
print(f"FLUX: Gradient checkpointing enabled. CPU offload: {cpu_offload}")
|
|
|
|
def disable_gradient_checkpointing(self):
|
|
self.gradient_checkpointing = False
|
|
self.cpu_offload_checkpointing = False
|
|
|
|
self.time_in.disable_gradient_checkpointing()
|
|
self.vector_in.disable_gradient_checkpointing()
|
|
if self.guidance_in.__class__ != nn.Identity:
|
|
self.guidance_in.disable_gradient_checkpointing()
|
|
|
|
for block in self.double_blocks + self.single_blocks:
|
|
block.disable_gradient_checkpointing()
|
|
|
|
print("FLUX: Gradient checkpointing disabled.")
|
|
|
|
def enable_block_swap(self, num_blocks: int, device: torch.device):
|
|
self.blocks_to_swap = num_blocks
|
|
double_blocks_to_swap = num_blocks // 2
|
|
single_blocks_to_swap = (num_blocks - double_blocks_to_swap) * 2
|
|
|
|
assert double_blocks_to_swap <= self.num_double_blocks - 2 and single_blocks_to_swap <= self.num_single_blocks - 2, (
|
|
f"Cannot swap more than {self.num_double_blocks - 2} double blocks and {self.num_single_blocks - 2} single blocks. "
|
|
f"Requested {double_blocks_to_swap} double blocks and {single_blocks_to_swap} single blocks."
|
|
)
|
|
|
|
self.offloader_double = custom_offloading_utils.ModelOffloader(
|
|
self.double_blocks, double_blocks_to_swap, device # , debug=True
|
|
)
|
|
self.offloader_single = custom_offloading_utils.ModelOffloader(
|
|
self.single_blocks, single_blocks_to_swap, device # , debug=True
|
|
)
|
|
print(
|
|
f"FLUX: Block swap enabled. Swapping {num_blocks} blocks, double blocks: {double_blocks_to_swap}, single blocks: {single_blocks_to_swap}."
|
|
)
|
|
|
|
def move_to_device_except_swap_blocks(self, device: torch.device):
|
|
# assume model is on cpu. do not move blocks to device to reduce temporary memory usage
|
|
if self.blocks_to_swap:
|
|
save_double_blocks = self.double_blocks
|
|
save_single_blocks = self.single_blocks
|
|
self.double_blocks = None
|
|
self.single_blocks = None
|
|
|
|
self.to(device)
|
|
|
|
if self.blocks_to_swap:
|
|
self.double_blocks = save_double_blocks
|
|
self.single_blocks = save_single_blocks
|
|
|
|
def prepare_block_swap_before_forward(self):
|
|
if self.blocks_to_swap is None or self.blocks_to_swap == 0:
|
|
return
|
|
self.offloader_double.prepare_block_devices_before_forward(self.double_blocks)
|
|
self.offloader_single.prepare_block_devices_before_forward(self.single_blocks)
|
|
|
|
def get_mod_vectors(self, timesteps: Tensor, guidance: Tensor | None = None, batch_size: int | None = None) -> Tensor:
|
|
return None # FLUX.1 does not use mod_vectors, but Chroma does.
|
|
|
|
def forward(
|
|
self,
|
|
img: Tensor,
|
|
img_ids: Tensor,
|
|
txt: Tensor,
|
|
txt_ids: Tensor,
|
|
timesteps: Tensor,
|
|
y: Tensor,
|
|
block_controlnet_hidden_states=None,
|
|
block_controlnet_single_hidden_states=None,
|
|
guidance: Tensor | None = None,
|
|
txt_attention_mask: Tensor | None = None,
|
|
mod_vectors: Tensor | None = None,
|
|
) -> Tensor:
|
|
if img.ndim != 3 or txt.ndim != 3:
|
|
raise ValueError("Input img and txt tensors must have 3 dimensions.")
|
|
|
|
# running on sequences img
|
|
img = self.img_in(img)
|
|
vec = self.time_in(timestep_embedding(timesteps, 256))
|
|
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))
|
|
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)
|
|
if block_controlnet_hidden_states is not None:
|
|
controlnet_depth = len(block_controlnet_hidden_states)
|
|
if block_controlnet_single_hidden_states is not None:
|
|
controlnet_single_depth = len(block_controlnet_single_hidden_states)
|
|
|
|
if not self.blocks_to_swap:
|
|
for block_idx, block in enumerate(self.double_blocks):
|
|
img, txt = block(img=img, txt=txt, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask)
|
|
if block_controlnet_hidden_states is not None and controlnet_depth > 0:
|
|
img = img + block_controlnet_hidden_states[block_idx % controlnet_depth]
|
|
|
|
img = torch.cat((txt, img), 1)
|
|
for block_idx, block in enumerate(self.single_blocks):
|
|
img = block(img, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask)
|
|
if block_controlnet_single_hidden_states is not None and controlnet_single_depth > 0:
|
|
img = img + block_controlnet_single_hidden_states[block_idx % controlnet_single_depth]
|
|
else:
|
|
for block_idx, block in enumerate(self.double_blocks):
|
|
self.offloader_double.wait_for_block(block_idx)
|
|
|
|
img, txt = block(img=img, txt=txt, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask)
|
|
if block_controlnet_hidden_states is not None and controlnet_depth > 0:
|
|
img = img + block_controlnet_hidden_states[block_idx % controlnet_depth]
|
|
|
|
self.offloader_double.submit_move_blocks(self.double_blocks, block_idx)
|
|
|
|
img = torch.cat((txt, img), 1)
|
|
|
|
for block_idx, block in enumerate(self.single_blocks):
|
|
self.offloader_single.wait_for_block(block_idx)
|
|
|
|
img = block(img, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask)
|
|
if block_controlnet_single_hidden_states is not None and controlnet_single_depth > 0:
|
|
img = img + block_controlnet_single_hidden_states[block_idx % controlnet_single_depth]
|
|
|
|
self.offloader_single.submit_move_blocks(self.single_blocks, block_idx)
|
|
|
|
img = img[:, txt.shape[1] :, ...]
|
|
|
|
if self.training and self.cpu_offload_checkpointing:
|
|
img = img.to(self.device)
|
|
vec = vec.to(self.device)
|
|
|
|
img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels)
|
|
|
|
return img
|
|
|
|
|
|
def zero_module(module):
|
|
for p in module.parameters():
|
|
nn.init.zeros_(p)
|
|
return module
|
|
|
|
|
|
class ControlNetFlux(nn.Module):
|
|
"""
|
|
Transformer model for flow matching on sequences.
|
|
"""
|
|
|
|
def __init__(self, params: FluxParams, controlnet_depth=2, controlnet_single_depth=0):
|
|
super().__init__()
|
|
|
|
self.params = params
|
|
self.in_channels = params.in_channels
|
|
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 = nn.Linear(self.in_channels, self.hidden_size, bias=True)
|
|
self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
|
|
self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
|
|
self.guidance_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity()
|
|
self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
|
|
|
|
self.double_blocks = nn.ModuleList(
|
|
[
|
|
DoubleStreamBlock(
|
|
self.hidden_size,
|
|
self.num_heads,
|
|
mlp_ratio=params.mlp_ratio,
|
|
qkv_bias=params.qkv_bias,
|
|
)
|
|
for _ in range(controlnet_depth)
|
|
]
|
|
)
|
|
|
|
self.single_blocks = nn.ModuleList(
|
|
[
|
|
SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio)
|
|
for _ in range(controlnet_single_depth)
|
|
]
|
|
)
|
|
|
|
self.gradient_checkpointing = False
|
|
self.cpu_offload_checkpointing = False
|
|
self.blocks_to_swap = None
|
|
|
|
self.offloader_double = None
|
|
self.offloader_single = None
|
|
self.num_double_blocks = len(self.double_blocks)
|
|
self.num_single_blocks = len(self.single_blocks)
|
|
|
|
# add ControlNet blocks
|
|
self.controlnet_blocks = nn.ModuleList([])
|
|
for _ in range(controlnet_depth):
|
|
controlnet_block = nn.Linear(self.hidden_size, self.hidden_size)
|
|
controlnet_block = zero_module(controlnet_block)
|
|
self.controlnet_blocks.append(controlnet_block)
|
|
self.controlnet_blocks_for_single = nn.ModuleList([])
|
|
for _ in range(controlnet_single_depth):
|
|
controlnet_block = nn.Linear(self.hidden_size, self.hidden_size)
|
|
controlnet_block = zero_module(controlnet_block)
|
|
self.controlnet_blocks_for_single.append(controlnet_block)
|
|
self.pos_embed_input = nn.Linear(self.in_channels, self.hidden_size, bias=True)
|
|
self.gradient_checkpointing = False
|
|
self.input_hint_block = nn.Sequential(
|
|
nn.Conv2d(3, 16, 3, padding=1),
|
|
nn.SiLU(),
|
|
nn.Conv2d(16, 16, 3, padding=1),
|
|
nn.SiLU(),
|
|
nn.Conv2d(16, 16, 3, padding=1, stride=2),
|
|
nn.SiLU(),
|
|
nn.Conv2d(16, 16, 3, padding=1),
|
|
nn.SiLU(),
|
|
nn.Conv2d(16, 16, 3, padding=1, stride=2),
|
|
nn.SiLU(),
|
|
nn.Conv2d(16, 16, 3, padding=1),
|
|
nn.SiLU(),
|
|
nn.Conv2d(16, 16, 3, padding=1, stride=2),
|
|
nn.SiLU(),
|
|
zero_module(nn.Conv2d(16, 16, 3, padding=1)),
|
|
)
|
|
|
|
@property
|
|
def device(self):
|
|
return next(self.parameters()).device
|
|
|
|
@property
|
|
def dtype(self):
|
|
return next(self.parameters()).dtype
|
|
|
|
def enable_gradient_checkpointing(self, cpu_offload: bool = False):
|
|
self.gradient_checkpointing = True
|
|
self.cpu_offload_checkpointing = cpu_offload
|
|
|
|
self.time_in.enable_gradient_checkpointing()
|
|
self.vector_in.enable_gradient_checkpointing()
|
|
if self.guidance_in.__class__ != nn.Identity:
|
|
self.guidance_in.enable_gradient_checkpointing()
|
|
|
|
for block in self.double_blocks + self.single_blocks:
|
|
block.enable_gradient_checkpointing(cpu_offload=cpu_offload)
|
|
|
|
print(f"FLUX: Gradient checkpointing enabled. CPU offload: {cpu_offload}")
|
|
|
|
def disable_gradient_checkpointing(self):
|
|
self.gradient_checkpointing = False
|
|
self.cpu_offload_checkpointing = False
|
|
|
|
self.time_in.disable_gradient_checkpointing()
|
|
self.vector_in.disable_gradient_checkpointing()
|
|
if self.guidance_in.__class__ != nn.Identity:
|
|
self.guidance_in.disable_gradient_checkpointing()
|
|
|
|
for block in self.double_blocks + self.single_blocks:
|
|
block.disable_gradient_checkpointing()
|
|
|
|
print("FLUX: Gradient checkpointing disabled.")
|
|
|
|
def enable_block_swap(self, num_blocks: int, device: torch.device):
|
|
self.blocks_to_swap = num_blocks
|
|
double_blocks_to_swap = num_blocks // 2
|
|
single_blocks_to_swap = (num_blocks - double_blocks_to_swap) * 2
|
|
|
|
assert double_blocks_to_swap <= self.num_double_blocks - 2 and single_blocks_to_swap <= self.num_single_blocks - 2, (
|
|
f"Cannot swap more than {self.num_double_blocks - 2} double blocks and {self.num_single_blocks - 2} single blocks. "
|
|
f"Requested {double_blocks_to_swap} double blocks and {single_blocks_to_swap} single blocks."
|
|
)
|
|
|
|
self.offloader_double = custom_offloading_utils.ModelOffloader(
|
|
self.double_blocks, double_blocks_to_swap, device # , debug=True
|
|
)
|
|
self.offloader_single = custom_offloading_utils.ModelOffloader(
|
|
self.single_blocks, single_blocks_to_swap, device # , debug=True
|
|
)
|
|
print(
|
|
f"FLUX: Block swap enabled. Swapping {num_blocks} blocks, double blocks: {double_blocks_to_swap}, single blocks: {single_blocks_to_swap}."
|
|
)
|
|
|
|
def move_to_device_except_swap_blocks(self, device: torch.device):
|
|
# assume model is on cpu. do not move blocks to device to reduce temporary memory usage
|
|
if self.blocks_to_swap:
|
|
save_double_blocks = self.double_blocks
|
|
save_single_blocks = self.single_blocks
|
|
self.double_blocks = nn.ModuleList()
|
|
self.single_blocks = nn.ModuleList()
|
|
|
|
self.to(device)
|
|
|
|
if self.blocks_to_swap:
|
|
self.double_blocks = save_double_blocks
|
|
self.single_blocks = save_single_blocks
|
|
|
|
def prepare_block_swap_before_forward(self):
|
|
if self.blocks_to_swap is None or self.blocks_to_swap == 0:
|
|
return
|
|
self.offloader_double.prepare_block_devices_before_forward(self.double_blocks)
|
|
self.offloader_single.prepare_block_devices_before_forward(self.single_blocks)
|
|
|
|
def forward(
|
|
self,
|
|
img: Tensor,
|
|
img_ids: Tensor,
|
|
controlnet_cond: Tensor,
|
|
txt: Tensor,
|
|
txt_ids: Tensor,
|
|
timesteps: Tensor,
|
|
y: Tensor,
|
|
guidance: Tensor | None = None,
|
|
txt_attention_mask: Tensor | None = None,
|
|
) -> tuple[tuple[Tensor]]:
|
|
if img.ndim != 3 or txt.ndim != 3:
|
|
raise ValueError("Input img and txt tensors must have 3 dimensions.")
|
|
|
|
# running on sequences img
|
|
img = self.img_in(img)
|
|
controlnet_cond = self.input_hint_block(controlnet_cond)
|
|
controlnet_cond = rearrange(controlnet_cond, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
|
|
controlnet_cond = self.pos_embed_input(controlnet_cond)
|
|
img = img + controlnet_cond
|
|
vec = self.time_in(timestep_embedding(timesteps, 256))
|
|
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))
|
|
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)
|
|
|
|
block_samples = ()
|
|
block_single_samples = ()
|
|
if not self.blocks_to_swap:
|
|
for block in self.double_blocks:
|
|
img, txt = block(img=img, txt=txt, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask)
|
|
block_samples = block_samples + (img,)
|
|
|
|
img = torch.cat((txt, img), 1)
|
|
for block in self.single_blocks:
|
|
img = block(img, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask)
|
|
block_single_samples = block_single_samples + (img,)
|
|
else:
|
|
for block_idx, block in enumerate(self.double_blocks):
|
|
self.offloader_double.wait_for_block(block_idx)
|
|
|
|
img, txt = block(img=img, txt=txt, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask)
|
|
block_samples = block_samples + (img,)
|
|
|
|
self.offloader_double.submit_move_blocks(self.double_blocks, block_idx)
|
|
|
|
img = torch.cat((txt, img), 1)
|
|
|
|
for block_idx, block in enumerate(self.single_blocks):
|
|
self.offloader_single.wait_for_block(block_idx)
|
|
|
|
img = block(img, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask)
|
|
block_single_samples = block_single_samples + (img,)
|
|
|
|
self.offloader_single.submit_move_blocks(self.single_blocks, block_idx)
|
|
|
|
controlnet_block_samples = ()
|
|
controlnet_single_block_samples = ()
|
|
for block_sample, controlnet_block in zip(block_samples, self.controlnet_blocks):
|
|
block_sample = controlnet_block(block_sample)
|
|
controlnet_block_samples = controlnet_block_samples + (block_sample,)
|
|
for block_sample, controlnet_block in zip(block_samples, self.controlnet_blocks_for_single):
|
|
block_sample = controlnet_block(block_sample)
|
|
controlnet_single_block_samples = controlnet_single_block_samples + (block_sample,)
|
|
|
|
return controlnet_block_samples, controlnet_single_block_samples
|