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add SD3 reference implementation from https://github.com/mcmonkey4eva/sd3-ref/

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Alex "mcmonkey" Goodwin 2024-06-16 07:13:57 +03:00
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### This file contains impls for MM-DiT, the core model component of SD3
import math
from typing import Dict, Optional
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange, repeat
from other_impls import attention, Mlp
class PatchEmbed(nn.Module):
""" 2D Image to Patch Embedding"""
def __init__(
self,
img_size: Optional[int] = 224,
patch_size: int = 16,
in_chans: int = 3,
embed_dim: int = 768,
flatten: bool = True,
bias: bool = True,
strict_img_size: bool = True,
dynamic_img_pad: bool = False,
dtype=None,
device=None,
):
super().__init__()
self.patch_size = (patch_size, patch_size)
if img_size is not None:
self.img_size = (img_size, img_size)
self.grid_size = tuple([s // p for s, p in zip(self.img_size, self.patch_size)])
self.num_patches = self.grid_size[0] * self.grid_size[1]
else:
self.img_size = None
self.grid_size = None
self.num_patches = None
# flatten spatial dim and transpose to channels last, kept for bwd compat
self.flatten = flatten
self.strict_img_size = strict_img_size
self.dynamic_img_pad = dynamic_img_pad
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device)
def forward(self, x):
B, C, H, W = x.shape
x = self.proj(x)
if self.flatten:
x = x.flatten(2).transpose(1, 2) # NCHW -> NLC
return x
def modulate(x, shift, scale):
if shift is None:
shift = torch.zeros_like(scale)
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
#################################################################################
# Sine/Cosine Positional Embedding Functions #
#################################################################################
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0, scaling_factor=None, offset=None):
"""
grid_size: int of the grid height and width
return:
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
grid_h = np.arange(grid_size, dtype=np.float32)
grid_w = np.arange(grid_size, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
if scaling_factor is not None:
grid = grid / scaling_factor
if offset is not None:
grid = grid - offset
grid = grid.reshape([2, 1, grid_size, grid_size])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token and extra_tokens > 0:
pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
return pos_embed
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
assert embed_dim % 2 == 0
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
return emb
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float64)
omega /= embed_dim / 2.0
omega = 1.0 / 10000**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product
emb_sin = np.sin(out) # (M, D/2)
emb_cos = np.cos(out) # (M, D/2)
return np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
#################################################################################
# Embedding Layers for Timesteps and Class Labels #
#################################################################################
class TimestepEmbedder(nn.Module):
"""Embeds scalar timesteps into vector representations."""
def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
)
self.frequency_embedding_size = frequency_embedding_size
@staticmethod
def timestep_embedding(t, dim, max_period=10000):
"""
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.
"""
half = dim // 2
freqs = torch.exp(
-math.log(max_period)
* torch.arange(start=0, end=half, dtype=torch.float32)
/ half
).to(device=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(dtype=t.dtype)
return embedding
def forward(self, t, dtype, **kwargs):
t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
t_emb = self.mlp(t_freq)
return t_emb
class VectorEmbedder(nn.Module):
"""Embeds a flat vector of dimension input_dim"""
def __init__(self, input_dim: int, hidden_size: int, dtype=None, device=None):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(input_dim, hidden_size, bias=True, dtype=dtype, device=device),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.mlp(x)
#################################################################################
# Core DiT Model #
#################################################################################
def split_qkv(qkv, head_dim):
qkv = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, -1, head_dim).movedim(2, 0)
return qkv[0], qkv[1], qkv[2]
def optimized_attention(qkv, num_heads):
return attention(qkv[0], qkv[1], qkv[2], num_heads)
class SelfAttention(nn.Module):
ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug")
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = False,
qk_scale: Optional[float] = None,
attn_mode: str = "xformers",
pre_only: bool = False,
qk_norm: Optional[str] = None,
rmsnorm: bool = False,
dtype=None,
device=None,
):
super().__init__()
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
if not pre_only:
self.proj = nn.Linear(dim, dim, dtype=dtype, device=device)
assert attn_mode in self.ATTENTION_MODES
self.attn_mode = attn_mode
self.pre_only = pre_only
if qk_norm == "rms":
self.ln_q = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
self.ln_k = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
elif qk_norm == "ln":
self.ln_q = nn.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
self.ln_k = nn.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
elif qk_norm is None:
self.ln_q = nn.Identity()
self.ln_k = nn.Identity()
else:
raise ValueError(qk_norm)
def pre_attention(self, x: torch.Tensor):
B, L, C = x.shape
qkv = self.qkv(x)
q, k, v = split_qkv(qkv, self.head_dim)
q = self.ln_q(q).reshape(q.shape[0], q.shape[1], -1)
k = self.ln_k(k).reshape(q.shape[0], q.shape[1], -1)
return (q, k, v)
def post_attention(self, x: torch.Tensor) -> torch.Tensor:
assert not self.pre_only
x = self.proj(x)
return x
def forward(self, x: torch.Tensor) -> torch.Tensor:
(q, k, v) = self.pre_attention(x)
x = attention(q, k, v, self.num_heads)
x = self.post_attention(x)
return x
class RMSNorm(torch.nn.Module):
def __init__(
self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None
):
"""
Initialize the RMSNorm normalization layer.
Args:
dim (int): The dimension of the input tensor.
eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
Attributes:
eps (float): A small value added to the denominator for numerical stability.
weight (nn.Parameter): Learnable scaling parameter.
"""
super().__init__()
self.eps = eps
self.learnable_scale = elementwise_affine
if self.learnable_scale:
self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
else:
self.register_parameter("weight", None)
def _norm(self, x):
"""
Apply the RMSNorm normalization to the input tensor.
Args:
x (torch.Tensor): The input tensor.
Returns:
torch.Tensor: The normalized tensor.
"""
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
"""
Forward pass through the RMSNorm layer.
Args:
x (torch.Tensor): The input tensor.
Returns:
torch.Tensor: The output tensor after applying RMSNorm.
"""
x = self._norm(x)
if self.learnable_scale:
return x * self.weight.to(device=x.device, dtype=x.dtype)
else:
return x
class SwiGLUFeedForward(nn.Module):
def __init__(
self,
dim: int,
hidden_dim: int,
multiple_of: int,
ffn_dim_multiplier: Optional[float] = None,
):
"""
Initialize the FeedForward module.
Args:
dim (int): Input dimension.
hidden_dim (int): Hidden dimension of the feedforward layer.
multiple_of (int): Value to ensure hidden dimension is a multiple of this value.
ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None.
Attributes:
w1 (ColumnParallelLinear): Linear transformation for the first layer.
w2 (RowParallelLinear): Linear transformation for the second layer.
w3 (ColumnParallelLinear): Linear transformation for the third layer.
"""
super().__init__()
hidden_dim = int(2 * hidden_dim / 3)
# custom dim factor multiplier
if ffn_dim_multiplier is not None:
hidden_dim = int(ffn_dim_multiplier * hidden_dim)
hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
self.w1 = nn.Linear(dim, hidden_dim, bias=False)
self.w2 = nn.Linear(hidden_dim, dim, bias=False)
self.w3 = nn.Linear(dim, hidden_dim, bias=False)
def forward(self, x):
return self.w2(nn.functional.silu(self.w1(x)) * self.w3(x))
class DismantledBlock(nn.Module):
"""A DiT block with gated adaptive layer norm (adaLN) conditioning."""
ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug")
def __init__(
self,
hidden_size: int,
num_heads: int,
mlp_ratio: float = 4.0,
attn_mode: str = "xformers",
qkv_bias: bool = False,
pre_only: bool = False,
rmsnorm: bool = False,
scale_mod_only: bool = False,
swiglu: bool = False,
qk_norm: Optional[str] = None,
dtype=None,
device=None,
**block_kwargs,
):
super().__init__()
assert attn_mode in self.ATTENTION_MODES
if not rmsnorm:
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
else:
self.norm1 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, attn_mode=attn_mode, pre_only=pre_only, qk_norm=qk_norm, rmsnorm=rmsnorm, dtype=dtype, device=device)
if not pre_only:
if not rmsnorm:
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
else:
self.norm2 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)
mlp_hidden_dim = int(hidden_size * mlp_ratio)
if not pre_only:
if not swiglu:
self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=nn.GELU(approximate="tanh"), dtype=dtype, device=device)
else:
self.mlp = SwiGLUFeedForward(dim=hidden_size, hidden_dim=mlp_hidden_dim, multiple_of=256)
self.scale_mod_only = scale_mod_only
if not scale_mod_only:
n_mods = 6 if not pre_only else 2
else:
n_mods = 4 if not pre_only else 1
self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, n_mods * hidden_size, bias=True, dtype=dtype, device=device))
self.pre_only = pre_only
def pre_attention(self, x: torch.Tensor, c: torch.Tensor):
assert x is not None, "pre_attention called with None input"
if not self.pre_only:
if not self.scale_mod_only:
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1)
else:
shift_msa = None
shift_mlp = None
scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(4, dim=1)
qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))
return qkv, (x, gate_msa, shift_mlp, scale_mlp, gate_mlp)
else:
if not self.scale_mod_only:
shift_msa, scale_msa = self.adaLN_modulation(c).chunk(2, dim=1)
else:
shift_msa = None
scale_msa = self.adaLN_modulation(c)
qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))
return qkv, None
def post_attention(self, attn, x, gate_msa, shift_mlp, scale_mlp, gate_mlp):
assert not self.pre_only
x = x + gate_msa.unsqueeze(1) * self.attn.post_attention(attn)
x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
return x
def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
assert not self.pre_only
(q, k, v), intermediates = self.pre_attention(x, c)
attn = attention(q, k, v, self.attn.num_heads)
return self.post_attention(attn, *intermediates)
def block_mixing(context, x, context_block, x_block, c):
assert context is not None, "block_mixing called with None context"
context_qkv, context_intermediates = context_block.pre_attention(context, c)
x_qkv, x_intermediates = x_block.pre_attention(x, c)
o = []
for t in range(3):
o.append(torch.cat((context_qkv[t], x_qkv[t]), dim=1))
q, k, v = tuple(o)
attn = attention(q, k, v, x_block.attn.num_heads)
context_attn, x_attn = (attn[:, : context_qkv[0].shape[1]], attn[:, context_qkv[0].shape[1] :])
if not context_block.pre_only:
context = context_block.post_attention(context_attn, *context_intermediates)
else:
context = None
x = x_block.post_attention(x_attn, *x_intermediates)
return context, x
class JointBlock(nn.Module):
"""just a small wrapper to serve as a fsdp unit"""
def __init__(self, *args, **kwargs):
super().__init__()
pre_only = kwargs.pop("pre_only")
qk_norm = kwargs.pop("qk_norm", None)
self.context_block = DismantledBlock(*args, pre_only=pre_only, qk_norm=qk_norm, **kwargs)
self.x_block = DismantledBlock(*args, pre_only=False, qk_norm=qk_norm, **kwargs)
def forward(self, *args, **kwargs):
return block_mixing(*args, context_block=self.context_block, x_block=self.x_block, **kwargs)
class FinalLayer(nn.Module):
"""
The final layer of DiT.
"""
def __init__(self, hidden_size: int, patch_size: int, out_channels: int, total_out_channels: Optional[int] = None, dtype=None, device=None):
super().__init__()
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
self.linear = (
nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
if (total_out_channels is None)
else nn.Linear(hidden_size, total_out_channels, bias=True, dtype=dtype, device=device)
)
self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device))
def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
x = modulate(self.norm_final(x), shift, scale)
x = self.linear(x)
return x
class MMDiT(nn.Module):
"""Diffusion model with a Transformer backbone."""
def __init__(
self,
input_size: int = 32,
patch_size: int = 2,
in_channels: int = 4,
depth: int = 28,
mlp_ratio: float = 4.0,
learn_sigma: bool = False,
adm_in_channels: Optional[int] = None,
context_embedder_config: Optional[Dict] = None,
register_length: int = 0,
attn_mode: str = "torch",
rmsnorm: bool = False,
scale_mod_only: bool = False,
swiglu: bool = False,
out_channels: Optional[int] = None,
pos_embed_scaling_factor: Optional[float] = None,
pos_embed_offset: Optional[float] = None,
pos_embed_max_size: Optional[int] = None,
num_patches = None,
qk_norm: Optional[str] = None,
qkv_bias: bool = True,
dtype = None,
device = None,
):
super().__init__()
print(f"mmdit initializing with: {input_size=}, {patch_size=}, {in_channels=}, {depth=}, {mlp_ratio=}, {learn_sigma=}, {adm_in_channels=}, {context_embedder_config=}, {register_length=}, {attn_mode=}, {rmsnorm=}, {scale_mod_only=}, {swiglu=}, {out_channels=}, {pos_embed_scaling_factor=}, {pos_embed_offset=}, {pos_embed_max_size=}, {num_patches=}, {qk_norm=}, {qkv_bias=}, {dtype=}, {device=}")
self.dtype = dtype
self.learn_sigma = learn_sigma
self.in_channels = in_channels
default_out_channels = in_channels * 2 if learn_sigma else in_channels
self.out_channels = out_channels if out_channels is not None else default_out_channels
self.patch_size = patch_size
self.pos_embed_scaling_factor = pos_embed_scaling_factor
self.pos_embed_offset = pos_embed_offset
self.pos_embed_max_size = pos_embed_max_size
# apply magic --> this defines a head_size of 64
hidden_size = 64 * depth
num_heads = depth
self.num_heads = num_heads
self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, bias=True, strict_img_size=self.pos_embed_max_size is None, dtype=dtype, device=device)
self.t_embedder = TimestepEmbedder(hidden_size, dtype=dtype, device=device)
if adm_in_channels is not None:
assert isinstance(adm_in_channels, int)
self.y_embedder = VectorEmbedder(adm_in_channels, hidden_size, dtype=dtype, device=device)
self.context_embedder = nn.Identity()
if context_embedder_config is not None:
if context_embedder_config["target"] == "torch.nn.Linear":
self.context_embedder = nn.Linear(**context_embedder_config["params"], dtype=dtype, device=device)
self.register_length = register_length
if self.register_length > 0:
self.register = nn.Parameter(torch.randn(1, register_length, hidden_size, dtype=dtype, device=device))
# num_patches = self.x_embedder.num_patches
# Will use fixed sin-cos embedding:
# just use a buffer already
if num_patches is not None:
self.register_buffer(
"pos_embed",
torch.zeros(1, num_patches, hidden_size, dtype=dtype, device=device),
)
else:
self.pos_embed = None
self.joint_blocks = nn.ModuleList(
[
JointBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, attn_mode=attn_mode, pre_only=i == depth - 1, rmsnorm=rmsnorm, scale_mod_only=scale_mod_only, swiglu=swiglu, qk_norm=qk_norm, dtype=dtype, device=device)
for i in range(depth)
]
)
self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels, dtype=dtype, device=device)
def cropped_pos_embed(self, hw):
assert self.pos_embed_max_size is not None
p = self.x_embedder.patch_size[0]
h, w = hw
# patched size
h = h // p
w = w // p
assert h <= self.pos_embed_max_size, (h, self.pos_embed_max_size)
assert w <= self.pos_embed_max_size, (w, self.pos_embed_max_size)
top = (self.pos_embed_max_size - h) // 2
left = (self.pos_embed_max_size - w) // 2
spatial_pos_embed = rearrange(
self.pos_embed,
"1 (h w) c -> 1 h w c",
h=self.pos_embed_max_size,
w=self.pos_embed_max_size,
)
spatial_pos_embed = spatial_pos_embed[:, top : top + h, left : left + w, :]
spatial_pos_embed = rearrange(spatial_pos_embed, "1 h w c -> 1 (h w) c")
return spatial_pos_embed
def unpatchify(self, x, hw=None):
"""
x: (N, T, patch_size**2 * C)
imgs: (N, H, W, C)
"""
c = self.out_channels
p = self.x_embedder.patch_size[0]
if hw is None:
h = w = int(x.shape[1] ** 0.5)
else:
h, w = hw
h = h // p
w = w // p
assert h * w == x.shape[1]
x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
x = torch.einsum("nhwpqc->nchpwq", x)
imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
return imgs
def forward_core_with_concat(self, x: torch.Tensor, c_mod: torch.Tensor, context: Optional[torch.Tensor] = None) -> torch.Tensor:
if self.register_length > 0:
context = torch.cat((repeat(self.register, "1 ... -> b ...", b=x.shape[0]), context if context is not None else torch.Tensor([]).type_as(x)), 1)
# context is B, L', D
# x is B, L, D
for block in self.joint_blocks:
context, x = block(context, x, c=c_mod)
x = self.final_layer(x, c_mod) # (N, T, patch_size ** 2 * out_channels)
return x
def forward(self, x: torch.Tensor, t: torch.Tensor, y: Optional[torch.Tensor] = None, context: Optional[torch.Tensor] = None) -> torch.Tensor:
"""
Forward pass of DiT.
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
t: (N,) tensor of diffusion timesteps
y: (N,) tensor of class labels
"""
hw = x.shape[-2:]
x = self.x_embedder(x) + self.cropped_pos_embed(hw)
c = self.t_embedder(t, dtype=x.dtype) # (N, D)
if y is not None:
y = self.y_embedder(y) # (N, D)
c = c + y # (N, D)
context = self.context_embedder(context)
x = self.forward_core_with_concat(x, c, context)
x = self.unpatchify(x, hw=hw) # (N, out_channels, H, W)
return x

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### This file contains impls for underlying related models (CLIP, T5, etc)
import torch, math
from torch import nn
from transformers import CLIPTokenizer, T5TokenizerFast
#################################################################################################
### Core/Utility
#################################################################################################
def attention(q, k, v, heads, mask=None):
"""Convenience wrapper around a basic attention operation"""
b, _, dim_head = q.shape
dim_head //= heads
q, k, v = map(lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2), (q, k, v))
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
return out.transpose(1, 2).reshape(b, -1, heads * dim_head)
class Mlp(nn.Module):
""" MLP as used in Vision Transformer, MLP-Mixer and related networks"""
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, dtype=None, device=None):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features, bias=bias, dtype=dtype, device=device)
self.act = act_layer
self.fc2 = nn.Linear(hidden_features, out_features, bias=bias, dtype=dtype, device=device)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.fc2(x)
return x
#################################################################################################
### CLIP
#################################################################################################
class CLIPAttention(torch.nn.Module):
def __init__(self, embed_dim, heads, dtype, device):
super().__init__()
self.heads = heads
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
def forward(self, x, mask=None):
q = self.q_proj(x)
k = self.k_proj(x)
v = self.v_proj(x)
out = attention(q, k, v, self.heads, mask)
return self.out_proj(out)
ACTIVATIONS = {
"quick_gelu": lambda a: a * torch.sigmoid(1.702 * a),
"gelu": torch.nn.functional.gelu,
}
class CLIPLayer(torch.nn.Module):
def __init__(self, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device):
super().__init__()
self.layer_norm1 = nn.LayerNorm(embed_dim, dtype=dtype, device=device)
self.self_attn = CLIPAttention(embed_dim, heads, dtype, device)
self.layer_norm2 = nn.LayerNorm(embed_dim, dtype=dtype, device=device)
#self.mlp = CLIPMLP(embed_dim, intermediate_size, intermediate_activation, dtype, device)
self.mlp = Mlp(embed_dim, intermediate_size, embed_dim, act_layer=ACTIVATIONS[intermediate_activation], dtype=dtype, device=device)
def forward(self, x, mask=None):
x += self.self_attn(self.layer_norm1(x), mask)
x += self.mlp(self.layer_norm2(x))
return x
class CLIPEncoder(torch.nn.Module):
def __init__(self, num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device):
super().__init__()
self.layers = torch.nn.ModuleList([CLIPLayer(embed_dim, heads, intermediate_size, intermediate_activation, dtype, device) for i in range(num_layers)])
def forward(self, x, mask=None, intermediate_output=None):
if intermediate_output is not None:
if intermediate_output < 0:
intermediate_output = len(self.layers) + intermediate_output
intermediate = None
for i, l in enumerate(self.layers):
x = l(x, mask)
if i == intermediate_output:
intermediate = x.clone()
return x, intermediate
class CLIPEmbeddings(torch.nn.Module):
def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None):
super().__init__()
self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim, dtype=dtype, device=device)
self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
def forward(self, input_tokens):
return self.token_embedding(input_tokens) + self.position_embedding.weight
class CLIPTextModel_(torch.nn.Module):
def __init__(self, config_dict, dtype, device):
num_layers = config_dict["num_hidden_layers"]
embed_dim = config_dict["hidden_size"]
heads = config_dict["num_attention_heads"]
intermediate_size = config_dict["intermediate_size"]
intermediate_activation = config_dict["hidden_act"]
super().__init__()
self.embeddings = CLIPEmbeddings(embed_dim, dtype=torch.float32, device=device)
self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device)
self.final_layer_norm = nn.LayerNorm(embed_dim, dtype=dtype, device=device)
def forward(self, input_tokens, intermediate_output=None, final_layer_norm_intermediate=True):
x = self.embeddings(input_tokens)
causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1)
x, i = self.encoder(x, mask=causal_mask, intermediate_output=intermediate_output)
x = self.final_layer_norm(x)
if i is not None and final_layer_norm_intermediate:
i = self.final_layer_norm(i)
pooled_output = x[torch.arange(x.shape[0], device=x.device), input_tokens.to(dtype=torch.int, device=x.device).argmax(dim=-1),]
return x, i, pooled_output
class CLIPTextModel(torch.nn.Module):
def __init__(self, config_dict, dtype, device):
super().__init__()
self.num_layers = config_dict["num_hidden_layers"]
self.text_model = CLIPTextModel_(config_dict, dtype, device)
embed_dim = config_dict["hidden_size"]
self.text_projection = nn.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device)
self.text_projection.weight.copy_(torch.eye(embed_dim))
self.dtype = dtype
def get_input_embeddings(self):
return self.text_model.embeddings.token_embedding
def set_input_embeddings(self, embeddings):
self.text_model.embeddings.token_embedding = embeddings
def forward(self, *args, **kwargs):
x = self.text_model(*args, **kwargs)
out = self.text_projection(x[2])
return (x[0], x[1], out, x[2])
class SDTokenizer:
def __init__(self, max_length=77, pad_with_end=True, tokenizer=None, has_start_token=True, pad_to_max_length=True, min_length=None):
self.tokenizer = tokenizer
self.max_length = max_length
self.min_length = min_length
empty = self.tokenizer('')["input_ids"]
if has_start_token:
self.tokens_start = 1
self.start_token = empty[0]
self.end_token = empty[1]
else:
self.tokens_start = 0
self.start_token = None
self.end_token = empty[0]
self.pad_with_end = pad_with_end
self.pad_to_max_length = pad_to_max_length
vocab = self.tokenizer.get_vocab()
self.inv_vocab = {v: k for k, v in vocab.items()}
self.max_word_length = 8
def tokenize_with_weights(self, text:str):
"""Tokenize the text, with weight values - presume 1.0 for all and ignore other features here. The details aren't relevant for a reference impl, and weights themselves has weak effect on SD3."""
if self.pad_with_end:
pad_token = self.end_token
else:
pad_token = 0
batch = []
if self.start_token is not None:
batch.append((self.start_token, 1.0))
to_tokenize = text.replace("\n", " ").split(' ')
to_tokenize = [x for x in to_tokenize if x != ""]
for word in to_tokenize:
batch.extend([(t, 1) for t in self.tokenizer(word)["input_ids"][self.tokens_start:-1]])
batch.append((self.end_token, 1.0))
if self.pad_to_max_length:
batch.extend([(pad_token, 1.0)] * (self.max_length - len(batch)))
if self.min_length is not None and len(batch) < self.min_length:
batch.extend([(pad_token, 1.0)] * (self.min_length - len(batch)))
return [batch]
class SDXLClipGTokenizer(SDTokenizer):
def __init__(self, tokenizer):
super().__init__(pad_with_end=False, tokenizer=tokenizer)
class SD3Tokenizer:
def __init__(self):
clip_tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
self.clip_l = SDTokenizer(tokenizer=clip_tokenizer)
self.clip_g = SDXLClipGTokenizer(clip_tokenizer)
self.t5xxl = T5XXLTokenizer()
def tokenize_with_weights(self, text:str):
out = {}
out["g"] = self.clip_g.tokenize_with_weights(text)
out["l"] = self.clip_l.tokenize_with_weights(text)
out["t5xxl"] = self.t5xxl.tokenize_with_weights(text)
return out
class ClipTokenWeightEncoder:
def encode_token_weights(self, token_weight_pairs):
tokens = list(map(lambda a: a[0], token_weight_pairs[0]))
out, pooled = self([tokens])
if pooled is not None:
first_pooled = pooled[0:1].cpu()
else:
first_pooled = pooled
output = [out[0:1]]
return torch.cat(output, dim=-2).cpu(), first_pooled
class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
"""Uses the CLIP transformer encoder for text (from huggingface)"""
LAYERS = ["last", "pooled", "hidden"]
def __init__(self, device="cpu", max_length=77, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=CLIPTextModel,
special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, return_projected_pooled=True):
super().__init__()
assert layer in self.LAYERS
self.transformer = model_class(textmodel_json_config, dtype, device)
self.num_layers = self.transformer.num_layers
self.max_length = max_length
self.transformer = self.transformer.eval()
for param in self.parameters():
param.requires_grad = False
self.layer = layer
self.layer_idx = None
self.special_tokens = special_tokens
self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))
self.layer_norm_hidden_state = layer_norm_hidden_state
self.return_projected_pooled = return_projected_pooled
if layer == "hidden":
assert layer_idx is not None
assert abs(layer_idx) < self.num_layers
self.set_clip_options({"layer": layer_idx})
self.options_default = (self.layer, self.layer_idx, self.return_projected_pooled)
def set_clip_options(self, options):
layer_idx = options.get("layer", self.layer_idx)
self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled)
if layer_idx is None or abs(layer_idx) > self.num_layers:
self.layer = "last"
else:
self.layer = "hidden"
self.layer_idx = layer_idx
def forward(self, tokens):
backup_embeds = self.transformer.get_input_embeddings()
device = backup_embeds.weight.device
tokens = torch.LongTensor(tokens).to(device)
outputs = self.transformer(tokens, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state)
self.transformer.set_input_embeddings(backup_embeds)
if self.layer == "last":
z = outputs[0]
else:
z = outputs[1]
pooled_output = None
if len(outputs) >= 3:
if not self.return_projected_pooled and len(outputs) >= 4 and outputs[3] is not None:
pooled_output = outputs[3].float()
elif outputs[2] is not None:
pooled_output = outputs[2].float()
return z.float(), pooled_output
class SDXLClipG(SDClipModel):
"""Wraps the CLIP-G model into the SD-CLIP-Model interface"""
def __init__(self, config, device="cpu", layer="penultimate", layer_idx=None, dtype=None):
if layer == "penultimate":
layer="hidden"
layer_idx=-2
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0}, layer_norm_hidden_state=False)
class T5XXLModel(SDClipModel):
"""Wraps the T5-XXL model into the SD-CLIP-Model interface for convenience"""
def __init__(self, config, device="cpu", layer="last", layer_idx=None, dtype=None):
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=T5)
#################################################################################################
### T5 implementation, for the T5-XXL text encoder portion, largely pulled from upstream impl
#################################################################################################
class T5XXLTokenizer(SDTokenizer):
"""Wraps the T5 Tokenizer from HF into the SDTokenizer interface"""
def __init__(self):
super().__init__(pad_with_end=False, tokenizer=T5TokenizerFast.from_pretrained("google/t5-v1_1-xxl"), has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=77)
class T5LayerNorm(torch.nn.Module):
def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None):
super().__init__()
self.weight = torch.nn.Parameter(torch.ones(hidden_size, dtype=dtype, device=device))
self.variance_epsilon = eps
def forward(self, x):
variance = x.pow(2).mean(-1, keepdim=True)
x = x * torch.rsqrt(variance + self.variance_epsilon)
return self.weight.to(device=x.device, dtype=x.dtype) * x
class T5DenseGatedActDense(torch.nn.Module):
def __init__(self, model_dim, ff_dim, dtype, device):
super().__init__()
self.wi_0 = nn.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
self.wi_1 = nn.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
self.wo = nn.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
def forward(self, x):
hidden_gelu = torch.nn.functional.gelu(self.wi_0(x), approximate="tanh")
hidden_linear = self.wi_1(x)
x = hidden_gelu * hidden_linear
x = self.wo(x)
return x
class T5LayerFF(torch.nn.Module):
def __init__(self, model_dim, ff_dim, dtype, device):
super().__init__()
self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, dtype, device)
self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device)
def forward(self, x):
forwarded_states = self.layer_norm(x)
forwarded_states = self.DenseReluDense(forwarded_states)
x += forwarded_states
return x
class T5Attention(torch.nn.Module):
def __init__(self, model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device):
super().__init__()
# Mesh TensorFlow initialization to avoid scaling before softmax
self.q = nn.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.k = nn.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.v = nn.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.o = nn.Linear(inner_dim, model_dim, bias=False, dtype=dtype, device=device)
self.num_heads = num_heads
self.relative_attention_bias = None
if relative_attention_bias:
self.relative_attention_num_buckets = 32
self.relative_attention_max_distance = 128
self.relative_attention_bias = torch.nn.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device)
@staticmethod
def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
"""
Adapted from Mesh Tensorflow:
https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
Translate relative position to a bucket number for relative attention. The relative position is defined as
memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
This should allow for more graceful generalization to longer sequences than the model has been trained on
Args:
relative_position: an int32 Tensor
bidirectional: a boolean - whether the attention is bidirectional
num_buckets: an integer
max_distance: an integer
Returns:
a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
"""
relative_buckets = 0
if bidirectional:
num_buckets //= 2
relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
relative_position = torch.abs(relative_position)
else:
relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
# now relative_position is in the range [0, inf)
# half of the buckets are for exact increments in positions
max_exact = num_buckets // 2
is_small = relative_position < max_exact
# The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
relative_position_if_large = max_exact + (
torch.log(relative_position.float() / max_exact)
/ math.log(max_distance / max_exact)
* (num_buckets - max_exact)
).to(torch.long)
relative_position_if_large = torch.min(relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1))
relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
return relative_buckets
def compute_bias(self, query_length, key_length, device):
"""Compute binned relative position bias"""
context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
relative_position = memory_position - context_position # shape (query_length, key_length)
relative_position_bucket = self._relative_position_bucket(
relative_position, # shape (query_length, key_length)
bidirectional=True,
num_buckets=self.relative_attention_num_buckets,
max_distance=self.relative_attention_max_distance,
)
values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads)
values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length)
return values
def forward(self, x, past_bias=None):
q = self.q(x)
k = self.k(x)
v = self.v(x)
if self.relative_attention_bias is not None:
past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device)
if past_bias is not None:
mask = past_bias
out = attention(q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask)
return self.o(out), past_bias
class T5LayerSelfAttention(torch.nn.Module):
def __init__(self, model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device):
super().__init__()
self.SelfAttention = T5Attention(model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device)
self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device)
def forward(self, x, past_bias=None):
output, past_bias = self.SelfAttention(self.layer_norm(x), past_bias=past_bias)
x += output
return x, past_bias
class T5Block(torch.nn.Module):
def __init__(self, model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device):
super().__init__()
self.layer = torch.nn.ModuleList()
self.layer.append(T5LayerSelfAttention(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device))
self.layer.append(T5LayerFF(model_dim, ff_dim, dtype, device))
def forward(self, x, past_bias=None):
x, past_bias = self.layer[0](x, past_bias)
x = self.layer[-1](x)
return x, past_bias
class T5Stack(torch.nn.Module):
def __init__(self, num_layers, model_dim, inner_dim, ff_dim, num_heads, vocab_size, dtype, device):
super().__init__()
self.embed_tokens = torch.nn.Embedding(vocab_size, model_dim, device=device)
self.block = torch.nn.ModuleList([T5Block(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias=(i == 0), dtype=dtype, device=device) for i in range(num_layers)])
self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device)
def forward(self, input_ids, intermediate_output=None, final_layer_norm_intermediate=True):
intermediate = None
x = self.embed_tokens(input_ids)
past_bias = None
for i, l in enumerate(self.block):
x, past_bias = l(x, past_bias)
if i == intermediate_output:
intermediate = x.clone()
x = self.final_layer_norm(x)
if intermediate is not None and final_layer_norm_intermediate:
intermediate = self.final_layer_norm(intermediate)
return x, intermediate
class T5(torch.nn.Module):
def __init__(self, config_dict, dtype, device):
super().__init__()
self.num_layers = config_dict["num_layers"]
self.encoder = T5Stack(self.num_layers, config_dict["d_model"], config_dict["d_model"], config_dict["d_ff"], config_dict["num_heads"], config_dict["vocab_size"], dtype, device)
self.dtype = dtype
def get_input_embeddings(self):
return self.encoder.embed_tokens
def set_input_embeddings(self, embeddings):
self.encoder.embed_tokens = embeddings
def forward(self, *args, **kwargs):
return self.encoder(*args, **kwargs)

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### Impls of the SD3 core diffusion model and VAE
import torch, math, einops
from mmdit import MMDiT
from PIL import Image
#################################################################################################
### MMDiT Model Wrapping
#################################################################################################
class ModelSamplingDiscreteFlow(torch.nn.Module):
"""Helper for sampler scheduling (ie timestep/sigma calculations) for Discrete Flow models"""
def __init__(self, shift=1.0):
super().__init__()
self.shift = shift
timesteps = 1000
ts = self.sigma(torch.arange(1, timesteps + 1, 1))
self.register_buffer('sigmas', ts)
@property
def sigma_min(self):
return self.sigmas[0]
@property
def sigma_max(self):
return self.sigmas[-1]
def timestep(self, sigma):
return sigma * 1000
def sigma(self, timestep: torch.Tensor):
timestep = timestep / 1000.0
if self.shift == 1.0:
return timestep
return self.shift * timestep / (1 + (self.shift - 1) * timestep)
def calculate_denoised(self, sigma, model_output, model_input):
sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
return model_input - model_output * sigma
def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
return sigma * noise + (1.0 - sigma) * latent_image
class BaseModel(torch.nn.Module):
"""Wrapper around the core MM-DiT model"""
def __init__(self, shift=1.0, device=None, dtype=torch.float32, file=None, prefix=""):
super().__init__()
# Important configuration values can be quickly determined by checking shapes in the source file
# Some of these will vary between models (eg 2B vs 8B primarily differ in their depth, but also other details change)
patch_size = file.get_tensor(f"{prefix}x_embedder.proj.weight").shape[2]
depth = file.get_tensor(f"{prefix}x_embedder.proj.weight").shape[0] // 64
num_patches = file.get_tensor(f"{prefix}pos_embed").shape[1]
pos_embed_max_size = round(math.sqrt(num_patches))
adm_in_channels = file.get_tensor(f"{prefix}y_embedder.mlp.0.weight").shape[1]
context_shape = file.get_tensor(f"{prefix}context_embedder.weight").shape
context_embedder_config = {
"target": "torch.nn.Linear",
"params": {
"in_features": context_shape[1],
"out_features": context_shape[0]
}
}
self.diffusion_model = MMDiT(input_size=None, pos_embed_scaling_factor=None, pos_embed_offset=None, pos_embed_max_size=pos_embed_max_size, patch_size=patch_size, in_channels=16, depth=depth, num_patches=num_patches, adm_in_channels=adm_in_channels, context_embedder_config=context_embedder_config, device=device, dtype=dtype)
self.model_sampling = ModelSamplingDiscreteFlow(shift=shift)
def apply_model(self, x, sigma, c_crossattn=None, y=None):
dtype = self.get_dtype()
timestep = self.model_sampling.timestep(sigma).float()
model_output = self.diffusion_model(x.to(dtype), timestep, context=c_crossattn.to(dtype), y=y.to(dtype)).float()
return self.model_sampling.calculate_denoised(sigma, model_output, x)
def forward(self, *args, **kwargs):
return self.apply_model(*args, **kwargs)
def get_dtype(self):
return self.diffusion_model.dtype
class CFGDenoiser(torch.nn.Module):
"""Helper for applying CFG Scaling to diffusion outputs"""
def __init__(self, model):
super().__init__()
self.model = model
def forward(self, x, timestep, cond, uncond, cond_scale):
# Run cond and uncond in a batch together
batched = self.model.apply_model(torch.cat([x, x]), torch.cat([timestep, timestep]), c_crossattn=torch.cat([cond["c_crossattn"], uncond["c_crossattn"]]), y=torch.cat([cond["y"], uncond["y"]]))
# Then split and apply CFG Scaling
pos_out, neg_out = batched.chunk(2)
scaled = neg_out + (pos_out - neg_out) * cond_scale
return scaled
class SD3LatentFormat:
"""Latents are slightly shifted from center - this class must be called after VAE Decode to correct for the shift"""
def __init__(self):
self.scale_factor = 1.5305
self.shift_factor = 0.0609
def process_in(self, latent):
return (latent - self.shift_factor) * self.scale_factor
def process_out(self, latent):
return (latent / self.scale_factor) + self.shift_factor
def decode_latent_to_preview(self, x0):
"""Quick RGB approximate preview of sd3 latents"""
factors = torch.tensor([
[-0.0645, 0.0177, 0.1052], [ 0.0028, 0.0312, 0.0650],
[ 0.1848, 0.0762, 0.0360], [ 0.0944, 0.0360, 0.0889],
[ 0.0897, 0.0506, -0.0364], [-0.0020, 0.1203, 0.0284],
[ 0.0855, 0.0118, 0.0283], [-0.0539, 0.0658, 0.1047],
[-0.0057, 0.0116, 0.0700], [-0.0412, 0.0281, -0.0039],
[ 0.1106, 0.1171, 0.1220], [-0.0248, 0.0682, -0.0481],
[ 0.0815, 0.0846, 0.1207], [-0.0120, -0.0055, -0.0867],
[-0.0749, -0.0634, -0.0456], [-0.1418, -0.1457, -0.1259]
], device="cpu")
latent_image = x0[0].permute(1, 2, 0).cpu() @ factors
latents_ubyte = (((latent_image + 1) / 2)
.clamp(0, 1) # change scale from -1..1 to 0..1
.mul(0xFF) # to 0..255
.byte()).cpu()
return Image.fromarray(latents_ubyte.numpy())
#################################################################################################
### K-Diffusion Sampling
#################################################################################################
def append_dims(x, target_dims):
"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
dims_to_append = target_dims - x.ndim
return x[(...,) + (None,) * dims_to_append]
def to_d(x, sigma, denoised):
"""Converts a denoiser output to a Karras ODE derivative."""
return (x - denoised) / append_dims(sigma, x.ndim)
@torch.no_grad()
@torch.autocast("cuda", dtype=torch.float16)
def sample_euler(model, x, sigmas, extra_args=None):
"""Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
for i in range(len(sigmas) - 1):
sigma_hat = sigmas[i]
denoised = model(x, sigma_hat * s_in, **extra_args)
d = to_d(x, sigma_hat, denoised)
dt = sigmas[i + 1] - sigma_hat
# Euler method
x = x + d * dt
return x
#################################################################################################
### VAE
#################################################################################################
def Normalize(in_channels, num_groups=32, dtype=torch.float32, device=None):
return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
class ResnetBlock(torch.nn.Module):
def __init__(self, *, in_channels, out_channels=None, dtype=torch.float32, device=None):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.norm1 = Normalize(in_channels, dtype=dtype, device=device)
self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device)
self.norm2 = Normalize(out_channels, dtype=dtype, device=device)
self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device)
if self.in_channels != self.out_channels:
self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, dtype=dtype, device=device)
else:
self.nin_shortcut = None
self.swish = torch.nn.SiLU(inplace=True)
def forward(self, x):
hidden = x
hidden = self.norm1(hidden)
hidden = self.swish(hidden)
hidden = self.conv1(hidden)
hidden = self.norm2(hidden)
hidden = self.swish(hidden)
hidden = self.conv2(hidden)
if self.in_channels != self.out_channels:
x = self.nin_shortcut(x)
return x + hidden
class AttnBlock(torch.nn.Module):
def __init__(self, in_channels, dtype=torch.float32, device=None):
super().__init__()
self.norm = Normalize(in_channels, dtype=dtype, device=device)
self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0, dtype=dtype, device=device)
self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0, dtype=dtype, device=device)
self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0, dtype=dtype, device=device)
self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0, dtype=dtype, device=device)
def forward(self, x):
hidden = self.norm(x)
q = self.q(hidden)
k = self.k(hidden)
v = self.v(hidden)
b, c, h, w = q.shape
q, k, v = map(lambda x: einops.rearrange(x, "b c h w -> b 1 (h w) c").contiguous(), (q, k, v))
hidden = torch.nn.functional.scaled_dot_product_attention(q, k, v) # scale is dim ** -0.5 per default
hidden = einops.rearrange(hidden, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b)
hidden = self.proj_out(hidden)
return x + hidden
class Downsample(torch.nn.Module):
def __init__(self, in_channels, dtype=torch.float32, device=None):
super().__init__()
self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0, dtype=dtype, device=device)
def forward(self, x):
pad = (0,1,0,1)
x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
x = self.conv(x)
return x
class Upsample(torch.nn.Module):
def __init__(self, in_channels, dtype=torch.float32, device=None):
super().__init__()
self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device)
def forward(self, x):
x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
x = self.conv(x)
return x
class VAEEncoder(torch.nn.Module):
def __init__(self, ch=128, ch_mult=(1,2,4,4), num_res_blocks=2, in_channels=3, z_channels=16, dtype=torch.float32, device=None):
super().__init__()
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
# downsampling
self.conv_in = torch.nn.Conv2d(in_channels, ch, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device)
in_ch_mult = (1,) + tuple(ch_mult)
self.in_ch_mult = in_ch_mult
self.down = torch.nn.ModuleList()
for i_level in range(self.num_resolutions):
block = torch.nn.ModuleList()
attn = torch.nn.ModuleList()
block_in = ch*in_ch_mult[i_level]
block_out = ch*ch_mult[i_level]
for i_block in range(num_res_blocks):
block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, dtype=dtype, device=device))
block_in = block_out
down = torch.nn.Module()
down.block = block
down.attn = attn
if i_level != self.num_resolutions - 1:
down.downsample = Downsample(block_in, dtype=dtype, device=device)
self.down.append(down)
# middle
self.mid = torch.nn.Module()
self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, dtype=dtype, device=device)
self.mid.attn_1 = AttnBlock(block_in, dtype=dtype, device=device)
self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, dtype=dtype, device=device)
# end
self.norm_out = Normalize(block_in, dtype=dtype, device=device)
self.conv_out = torch.nn.Conv2d(block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device)
self.swish = torch.nn.SiLU(inplace=True)
def forward(self, x):
# downsampling
hs = [self.conv_in(x)]
for i_level in range(self.num_resolutions):
for i_block in range(self.num_res_blocks):
h = self.down[i_level].block[i_block](hs[-1])
hs.append(h)
if i_level != self.num_resolutions-1:
hs.append(self.down[i_level].downsample(hs[-1]))
# middle
h = hs[-1]
h = self.mid.block_1(h)
h = self.mid.attn_1(h)
h = self.mid.block_2(h)
# end
h = self.norm_out(h)
h = self.swish(h)
h = self.conv_out(h)
return h
class VAEDecoder(torch.nn.Module):
def __init__(self, ch=128, out_ch=3, ch_mult=(1, 2, 4, 4), num_res_blocks=2, resolution=256, z_channels=16, dtype=torch.float32, device=None):
super().__init__()
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
block_in = ch * ch_mult[self.num_resolutions - 1]
curr_res = resolution // 2 ** (self.num_resolutions - 1)
# z to block_in
self.conv_in = torch.nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device)
# middle
self.mid = torch.nn.Module()
self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, dtype=dtype, device=device)
self.mid.attn_1 = AttnBlock(block_in, dtype=dtype, device=device)
self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, dtype=dtype, device=device)
# upsampling
self.up = torch.nn.ModuleList()
for i_level in reversed(range(self.num_resolutions)):
block = torch.nn.ModuleList()
block_out = ch * ch_mult[i_level]
for i_block in range(self.num_res_blocks + 1):
block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, dtype=dtype, device=device))
block_in = block_out
up = torch.nn.Module()
up.block = block
if i_level != 0:
up.upsample = Upsample(block_in, dtype=dtype, device=device)
curr_res = curr_res * 2
self.up.insert(0, up) # prepend to get consistent order
# end
self.norm_out = Normalize(block_in, dtype=dtype, device=device)
self.conv_out = torch.nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device)
self.swish = torch.nn.SiLU(inplace=True)
def forward(self, z):
# z to block_in
hidden = self.conv_in(z)
# middle
hidden = self.mid.block_1(hidden)
hidden = self.mid.attn_1(hidden)
hidden = self.mid.block_2(hidden)
# upsampling
for i_level in reversed(range(self.num_resolutions)):
for i_block in range(self.num_res_blocks + 1):
hidden = self.up[i_level].block[i_block](hidden)
if i_level != 0:
hidden = self.up[i_level].upsample(hidden)
# end
hidden = self.norm_out(hidden)
hidden = self.swish(hidden)
hidden = self.conv_out(hidden)
return hidden
class SDVAE(torch.nn.Module):
def __init__(self, dtype=torch.float32, device=None):
super().__init__()
self.encoder = VAEEncoder(dtype=dtype, device=device)
self.decoder = VAEDecoder(dtype=dtype, device=device)
@torch.autocast("cuda", dtype=torch.float16)
def decode(self, latent):
return self.decoder(latent)
@torch.autocast("cuda", dtype=torch.float16)
def encode(self, image):
hidden = self.encoder(image)
mean, logvar = torch.chunk(hidden, 2, dim=1)
logvar = torch.clamp(logvar, -30.0, 20.0)
std = torch.exp(0.5 * logvar)
return mean + std * torch.randn_like(mean)