143 lines
4.4 KiB
Python
143 lines
4.4 KiB
Python
import math
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from dataclasses import dataclass
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import numpy as np
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import torch
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from torch import Tensor, nn
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from .connector_edit import Qwen2Connector
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from .layers import DoubleStreamBlock, EmbedND, LastLayer, MLPEmbedder, SingleStreamBlock
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@dataclass
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class Step1XParams:
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in_channels: int
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out_channels: int
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vec_in_dim: int
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context_in_dim: int
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hidden_size: int
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mlp_ratio: float
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num_heads: int
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depth: int
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depth_single_blocks: int
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axes_dim: list[int]
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theta: int
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qkv_bias: bool
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class Step1XEdit(nn.Module):
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"""
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Transformer model for flow matching on sequences.
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"""
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def __init__(self, params: Step1XParams):
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super().__init__()
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self.params = params
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self.in_channels = params.in_channels
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self.out_channels = params.out_channels
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if params.hidden_size % params.num_heads != 0:
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raise ValueError(
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f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
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)
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pe_dim = params.hidden_size // params.num_heads
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if sum(params.axes_dim) != pe_dim:
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raise ValueError(
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f"Got {params.axes_dim} but expected positional dim {pe_dim}"
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)
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self.hidden_size = params.hidden_size
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self.num_heads = params.num_heads
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self.pe_embedder = EmbedND(
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dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim
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)
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self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
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self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
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self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
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self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
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self.double_blocks = nn.ModuleList(
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[
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DoubleStreamBlock(
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self.hidden_size,
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self.num_heads,
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mlp_ratio=params.mlp_ratio,
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qkv_bias=params.qkv_bias,
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)
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for _ in range(params.depth)
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]
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)
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self.single_blocks = nn.ModuleList(
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[
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SingleStreamBlock(
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self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio
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)
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for _ in range(params.depth_single_blocks)
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]
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)
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self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels)
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self.connector = Qwen2Connector()
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@staticmethod
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def timestep_embedding(
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t: Tensor, dim, max_period=10000, time_factor: float = 1000.0
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):
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"""
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Create sinusoidal timestep embeddings.
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:param t: a 1-D Tensor of N indices, one per batch element.
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These may be fractional.
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:param dim: the dimension of the output.
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:param max_period: controls the minimum frequency of the embeddings.
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:return: an (N, D) Tensor of positional embeddings.
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"""
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t = time_factor * t
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half = dim // 2
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freqs = torch.exp(
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-math.log(max_period)
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* torch.arange(start=0, end=half, dtype=torch.float32)
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/ half
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).to(t.device)
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args = t[:, None].float() * freqs[None]
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embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
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if dim % 2:
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embedding = torch.cat(
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[embedding, torch.zeros_like(embedding[:, :1])], dim=-1
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)
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if torch.is_floating_point(t):
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embedding = embedding.to(t)
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return embedding
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def forward(
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self,
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img: Tensor,
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img_ids: Tensor,
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txt: Tensor,
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txt_ids: Tensor,
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timesteps: Tensor,
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y: Tensor,
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) -> Tensor:
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if img.ndim != 3 or txt.ndim != 3:
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raise ValueError("Input img and txt tensors must have 3 dimensions.")
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img = self.img_in(img)
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vec = self.time_in(self.timestep_embedding(timesteps, 256))
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vec = vec + self.vector_in(y)
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txt = self.txt_in(txt)
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ids = torch.cat((txt_ids, img_ids), dim=1)
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pe = self.pe_embedder(ids)
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for block in self.double_blocks:
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img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
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img = torch.cat((txt, img), 1)
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for block in self.single_blocks:
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img = block(img, vec=vec, pe=pe)
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img = img[:, txt.shape[1] :, ...]
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img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels)
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return img |