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Zero
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import math
from dataclasses import dataclass
import numpy as np
import torch
from torch import Tensor, nn
from .connector_edit import Qwen2Connector
from .layers import DoubleStreamBlock, EmbedND, LastLayer, MLPEmbedder, SingleStreamBlock
@dataclass
class Step1XParams:
in_channels: int
out_channels: int
vec_in_dim: int
context_in_dim: int
hidden_size: int
mlp_ratio: float
num_heads: int
depth: int
depth_single_blocks: int
axes_dim: list[int]
theta: int
qkv_bias: bool
class Step1XEdit(nn.Module):
"""
Transformer model for flow matching on sequences.
"""
def __init__(self, params: Step1XParams):
super().__init__()
self.params = params
self.in_channels = params.in_channels
self.out_channels = params.out_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.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.connector = Qwen2Connector()
@staticmethod
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
def forward(
self,
img: Tensor,
img_ids: Tensor,
txt: Tensor,
txt_ids: Tensor,
timesteps: Tensor,
y: Tensor,
) -> Tensor:
if img.ndim != 3 or txt.ndim != 3:
raise ValueError("Input img and txt tensors must have 3 dimensions.")
img = self.img_in(img)
vec = self.time_in(self.timestep_embedding(timesteps, 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)
for block in self.double_blocks:
img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
img = torch.cat((txt, img), 1)
for block in self.single_blocks:
img = block(img, vec=vec, pe=pe)
img = img[:, txt.shape[1] :, ...]
img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels)
return img |