780 lines
29 KiB
Python
780 lines
29 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Iterable
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from types import SimpleNamespace
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from typing import Any
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import torch
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from diffusers.models.embeddings import (
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TimestepEmbedding,
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Timesteps,
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get_1d_rotary_pos_embed,
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)
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from diffusers.models.modeling_outputs import Transformer2DModelOutput
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from diffusers.models.normalization import AdaLayerNormContinuous
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from diffusers.utils import is_torch_npu_available
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from torch import nn
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (
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ColumnParallelLinear,
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MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm_omni.diffusion.attention.backends.abstract import AttentionMetadata
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from vllm_omni.diffusion.attention.layer import Attention
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from vllm_omni.diffusion.layers.rope import RotaryEmbedding
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class Flux2SwiGLU(nn.Module):
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"""SwiGLU activation used by Flux2."""
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def __init__(self):
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super().__init__()
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self.gate_fn = nn.SiLU()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x1, x2 = x.chunk(2, dim=-1)
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return self.gate_fn(x1) * x2
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class Flux2FeedForward(nn.Module):
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def __init__(
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self,
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dim: int,
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dim_out: int | None = None,
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mult: float = 3.0,
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inner_dim: int | None = None,
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bias: bool = False,
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):
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super().__init__()
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if inner_dim is None:
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inner_dim = int(dim * mult)
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dim_out = dim_out or dim
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self.linear_in = MergedColumnParallelLinear(
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dim,
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[inner_dim, inner_dim],
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bias=bias,
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return_bias=False,
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)
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self.act_fn = Flux2SwiGLU()
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self.linear_out = RowParallelLinear(
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inner_dim,
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dim_out,
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bias=bias,
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input_is_parallel=True,
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return_bias=False,
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = self.linear_in(x)
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x = self.act_fn(x)
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return self.linear_out(x)
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class Flux2Attention(nn.Module):
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def __init__(
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self,
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query_dim: int,
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heads: int = 8,
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dim_head: int = 64,
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dropout: float = 0.0,
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bias: bool = False,
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added_kv_proj_dim: int | None = None,
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added_proj_bias: bool | None = True,
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out_bias: bool = True,
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eps: float = 1e-5,
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out_dim: int = None,
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elementwise_affine: bool = True,
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):
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super().__init__()
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self.head_dim = dim_head
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self.inner_dim = out_dim if out_dim is not None else dim_head * heads
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self.query_dim = query_dim
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self.out_dim = out_dim if out_dim is not None else query_dim
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self.heads = out_dim // dim_head if out_dim is not None else heads
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self.dropout = dropout
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self.added_kv_proj_dim = added_kv_proj_dim
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self.to_qkv = QKVParallelLinear(
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hidden_size=query_dim,
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head_size=self.head_dim,
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total_num_heads=self.heads,
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bias=bias,
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)
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self.query_num_heads = self.to_qkv.num_heads
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self.kv_num_heads = self.to_qkv.num_kv_heads
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self.norm_q = RMSNorm(dim_head, eps=eps)
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self.norm_k = RMSNorm(dim_head, eps=eps)
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self.to_out = nn.ModuleList(
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[
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RowParallelLinear(
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self.inner_dim,
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self.out_dim,
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bias=out_bias,
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input_is_parallel=True,
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return_bias=False,
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),
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nn.Dropout(dropout),
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]
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)
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if added_kv_proj_dim is not None:
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self.norm_added_q = RMSNorm(dim_head, eps=eps)
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self.norm_added_k = RMSNorm(dim_head, eps=eps)
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self.add_kv_proj = QKVParallelLinear(
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hidden_size=added_kv_proj_dim,
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head_size=self.head_dim,
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total_num_heads=self.heads,
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bias=added_proj_bias,
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)
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self.add_query_num_heads = self.add_kv_proj.num_heads
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self.add_kv_num_heads = self.add_kv_proj.num_kv_heads
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self.to_add_out = RowParallelLinear(
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self.inner_dim,
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query_dim,
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bias=out_bias,
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input_is_parallel=True,
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return_bias=False,
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)
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self.rope = RotaryEmbedding(is_neox_style=False)
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self.attn = Attention(
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num_heads=self.query_num_heads,
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head_size=self.head_dim,
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softmax_scale=1.0 / (self.head_dim**0.5),
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causal=False,
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num_kv_heads=self.kv_num_heads,
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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encoder_hidden_states: torch.Tensor | None = None,
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attention_mask: torch.Tensor | None = None,
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image_rotary_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
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**kwargs,
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) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
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qkv, _ = self.to_qkv(hidden_states)
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query, key, value = qkv.chunk(3, dim=-1)
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encoder_query = encoder_key = encoder_value = None
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if encoder_hidden_states is not None and self.added_kv_proj_dim is not None:
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encoder_qkv, _ = self.add_kv_proj(encoder_hidden_states)
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encoder_query, encoder_key, encoder_value = encoder_qkv.chunk(3, dim=-1)
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query = query.unflatten(-1, (self.query_num_heads, -1))
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key = key.unflatten(-1, (self.kv_num_heads, -1))
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value = value.unflatten(-1, (self.kv_num_heads, -1))
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query = self.norm_q(query)
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key = self.norm_k(key)
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if encoder_hidden_states is not None and self.added_kv_proj_dim is not None:
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encoder_query = encoder_query.unflatten(-1, (self.add_query_num_heads, -1))
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encoder_key = encoder_key.unflatten(-1, (self.add_kv_num_heads, -1))
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encoder_value = encoder_value.unflatten(-1, (self.add_kv_num_heads, -1))
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encoder_query = self.norm_added_q(encoder_query)
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encoder_key = self.norm_added_k(encoder_key)
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query = torch.cat([encoder_query, query], dim=1)
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key = torch.cat([encoder_key, key], dim=1)
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value = torch.cat([encoder_value, value], dim=1)
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if image_rotary_emb is not None:
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cos, sin = image_rotary_emb
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cos = cos.to(query.dtype)
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sin = sin.to(query.dtype)
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query = self.rope(query, cos, sin)
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key = self.rope(key, cos, sin)
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attn_metadata = None
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if attention_mask is not None:
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if attention_mask.dim() == 3:
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attention_mask = attention_mask.unsqueeze(1)
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attn_metadata = AttentionMetadata(attn_mask=attention_mask)
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hidden_states = self.attn(query, key, value, attn_metadata)
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hidden_states = hidden_states.flatten(2, 3).to(query.dtype)
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if encoder_hidden_states is not None:
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context_len = encoder_hidden_states.shape[1]
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encoder_hidden_states, hidden_states = hidden_states.split_with_sizes(
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[context_len, hidden_states.shape[1] - context_len],
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dim=1,
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)
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# Contiguous for FP8 quantization in RowParallelLinear
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encoder_hidden_states = self.to_add_out(encoder_hidden_states.contiguous())
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hidden_states = self.to_out[0](hidden_states.contiguous())
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hidden_states = self.to_out[1](hidden_states)
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if encoder_hidden_states is not None:
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return hidden_states, encoder_hidden_states
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return hidden_states
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class Flux2ParallelSelfAttention(nn.Module):
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"""
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Parallel attention block that fuses QKV projections with MLP input projections.
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"""
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def __init__(
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self,
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query_dim: int,
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heads: int = 8,
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dim_head: int = 64,
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dropout: float = 0.0,
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bias: bool = False,
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out_bias: bool = True,
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eps: float = 1e-5,
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out_dim: int = None,
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elementwise_affine: bool = True,
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mlp_ratio: float = 4.0,
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mlp_mult_factor: int = 2,
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):
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super().__init__()
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self.head_dim = dim_head
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self.inner_dim = out_dim if out_dim is not None else dim_head * heads
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self.query_dim = query_dim
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self.out_dim = out_dim if out_dim is not None else query_dim
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self.heads = out_dim // dim_head if out_dim is not None else heads
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self.dropout = dropout
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self.mlp_ratio = mlp_ratio
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self.mlp_hidden_dim = int(query_dim * self.mlp_ratio)
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self.mlp_mult_factor = mlp_mult_factor
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self.to_qkv_mlp_proj = ColumnParallelLinear(
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self.query_dim,
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self.inner_dim * 3 + self.mlp_hidden_dim * self.mlp_mult_factor,
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bias=bias,
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gather_output=True,
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)
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self.mlp_act_fn = Flux2SwiGLU()
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self.norm_q = RMSNorm(dim_head, eps=eps)
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self.norm_k = RMSNorm(dim_head, eps=eps)
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self.to_out = ColumnParallelLinear(
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self.inner_dim + self.mlp_hidden_dim,
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self.out_dim,
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bias=out_bias,
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gather_output=True,
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)
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self.rope = RotaryEmbedding(is_neox_style=False)
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self.attn = Attention(
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num_heads=self.heads,
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head_size=self.head_dim,
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softmax_scale=1.0 / (self.head_dim**0.5),
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causal=False,
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: torch.Tensor | None = None,
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image_rotary_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
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**kwargs,
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) -> torch.Tensor:
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hidden_states, _ = self.to_qkv_mlp_proj(hidden_states)
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qkv, mlp_hidden_states = torch.split(
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hidden_states,
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[3 * self.inner_dim, self.mlp_hidden_dim * self.mlp_mult_factor],
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dim=-1,
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)
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query, key, value = qkv.chunk(3, dim=-1)
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query = query.unflatten(-1, (self.heads, -1))
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key = key.unflatten(-1, (self.heads, -1))
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value = value.unflatten(-1, (self.heads, -1))
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query = self.norm_q(query)
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key = self.norm_k(key)
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if image_rotary_emb is not None:
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cos, sin = image_rotary_emb
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cos = cos.to(query.dtype)
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sin = sin.to(query.dtype)
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query = self.rope(query, cos, sin)
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key = self.rope(key, cos, sin)
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attn_metadata = None
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if attention_mask is not None:
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if attention_mask.dim() == 3:
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attention_mask = attention_mask.unsqueeze(1)
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attn_metadata = AttentionMetadata(attn_mask=attention_mask)
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attn_output = self.attn(query, key, value, attn_metadata)
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attn_output = attn_output.flatten(2, 3).to(query.dtype)
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mlp_hidden_states = self.mlp_act_fn(mlp_hidden_states)
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hidden_states = torch.cat([attn_output, mlp_hidden_states], dim=-1)
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hidden_states, _ = self.to_out(hidden_states)
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return hidden_states
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class Flux2SingleTransformerBlock(nn.Module):
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def __init__(
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self,
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dim: int,
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num_attention_heads: int,
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attention_head_dim: int,
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mlp_ratio: float = 3.0,
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eps: float = 1e-6,
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bias: bool = False,
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):
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super().__init__()
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self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
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self.attn = Flux2ParallelSelfAttention(
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query_dim=dim,
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dim_head=attention_head_dim,
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heads=num_attention_heads,
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out_dim=dim,
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bias=bias,
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out_bias=bias,
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eps=eps,
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mlp_ratio=mlp_ratio,
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mlp_mult_factor=2,
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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encoder_hidden_states: torch.Tensor | None,
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temb_mod_params: tuple[torch.Tensor, torch.Tensor, torch.Tensor],
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image_rotary_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
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joint_attention_kwargs: dict[str, Any] | None = None,
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split_hidden_states: bool = False,
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text_seq_len: int | None = None,
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) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
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if encoder_hidden_states is not None:
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text_seq_len = encoder_hidden_states.shape[1]
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hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
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mod_shift, mod_scale, mod_gate = temb_mod_params
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norm_hidden_states = self.norm(hidden_states)
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norm_hidden_states = (1 + mod_scale) * norm_hidden_states + mod_shift
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joint_attention_kwargs = joint_attention_kwargs or {}
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attn_output = self.attn(
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hidden_states=norm_hidden_states,
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image_rotary_emb=image_rotary_emb,
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**joint_attention_kwargs,
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)
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hidden_states = hidden_states + mod_gate * attn_output
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if hidden_states.dtype == torch.float16:
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hidden_states = hidden_states.clip(-65504, 65504)
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if split_hidden_states:
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encoder_hidden_states, hidden_states = hidden_states[:, :text_seq_len], hidden_states[:, text_seq_len:]
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return encoder_hidden_states, hidden_states
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return hidden_states
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class Flux2TransformerBlock(nn.Module):
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def __init__(
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self,
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dim: int,
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num_attention_heads: int,
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attention_head_dim: int,
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mlp_ratio: float = 3.0,
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eps: float = 1e-6,
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bias: bool = False,
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):
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super().__init__()
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self.mlp_hidden_dim = int(dim * mlp_ratio)
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self.norm1 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
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self.norm1_context = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
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self.attn = Flux2Attention(
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query_dim=dim,
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added_kv_proj_dim=dim,
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dim_head=attention_head_dim,
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heads=num_attention_heads,
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out_dim=dim,
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bias=bias,
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added_proj_bias=bias,
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out_bias=bias,
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eps=eps,
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)
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self.norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
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self.ff = Flux2FeedForward(dim=dim, dim_out=dim, mult=mlp_ratio, bias=bias)
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self.norm2_context = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
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self.ff_context = Flux2FeedForward(dim=dim, dim_out=dim, mult=mlp_ratio, bias=bias)
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def forward(
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self,
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hidden_states: torch.Tensor,
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encoder_hidden_states: torch.Tensor,
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temb_mod_params_img: tuple[tuple[torch.Tensor, torch.Tensor, torch.Tensor], ...],
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temb_mod_params_txt: tuple[tuple[torch.Tensor, torch.Tensor, torch.Tensor], ...],
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image_rotary_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
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joint_attention_kwargs: dict[str, Any] | None = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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joint_attention_kwargs = joint_attention_kwargs or {}
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(shift_msa, scale_msa, gate_msa), (shift_mlp, scale_mlp, gate_mlp) = temb_mod_params_img
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(c_shift_msa, c_scale_msa, c_gate_msa), (c_shift_mlp, c_scale_mlp, c_gate_mlp) = temb_mod_params_txt
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norm_hidden_states = self.norm1(hidden_states)
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norm_hidden_states = (1 + scale_msa) * norm_hidden_states + shift_msa
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norm_encoder_hidden_states = self.norm1_context(encoder_hidden_states)
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norm_encoder_hidden_states = (1 + c_scale_msa) * norm_encoder_hidden_states + c_shift_msa
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attn_output, context_attn_output = self.attn(
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hidden_states=norm_hidden_states,
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encoder_hidden_states=norm_encoder_hidden_states,
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image_rotary_emb=image_rotary_emb,
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**joint_attention_kwargs,
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)
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attn_output = gate_msa * attn_output
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hidden_states = hidden_states + attn_output
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norm_hidden_states = self.norm2(hidden_states)
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norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
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ff_output = self.ff(norm_hidden_states)
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hidden_states = hidden_states + gate_mlp * ff_output
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context_attn_output = c_gate_msa * context_attn_output
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encoder_hidden_states = encoder_hidden_states + context_attn_output
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norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
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norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp) + c_shift_mlp
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context_ff_output = self.ff_context(norm_encoder_hidden_states)
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encoder_hidden_states = encoder_hidden_states + c_gate_mlp * context_ff_output
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if encoder_hidden_states.dtype == torch.float16:
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encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504)
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return encoder_hidden_states, hidden_states
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|
|
class Flux2PosEmbed(nn.Module):
|
|
def __init__(self, theta: int, axes_dim: list[int]):
|
|
super().__init__()
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|
self.theta = theta
|
|
self.axes_dim = axes_dim
|
|
|
|
def forward(self, ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
|
|
# Expected ids shape: [S, len(self.axes_dim)]
|
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cos_out = []
|
|
sin_out = []
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|
pos = ids.float()
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|
is_mps = ids.device.type == "mps"
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|
is_npu = ids.device.type == "npu"
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|
freqs_dtype = torch.float32 if (is_mps or is_npu) else torch.float64
|
|
# Unlike Flux 1, loop over len(self.axes_dim) rather than ids.shape[-1]
|
|
for i in range(len(self.axes_dim)):
|
|
freqs_cis = get_1d_rotary_pos_embed(
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|
self.axes_dim[i],
|
|
pos[..., i],
|
|
theta=self.theta,
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use_real=False,
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|
freqs_dtype=freqs_dtype,
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|
)
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|
cos_out.append(freqs_cis.real)
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|
sin_out.append(freqs_cis.imag)
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freqs_cos = torch.cat(cos_out, dim=-1).to(ids.device)
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|
freqs_sin = torch.cat(sin_out, dim=-1).to(ids.device)
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|
return freqs_cos, freqs_sin
|
|
|
|
|
|
class Flux2TimestepGuidanceEmbeddings(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels: int = 256,
|
|
embedding_dim: int = 6144,
|
|
bias: bool = False,
|
|
guidance_embeds: bool = True,
|
|
):
|
|
super().__init__()
|
|
self.time_proj = Timesteps(num_channels=in_channels, flip_sin_to_cos=True, downscale_freq_shift=0)
|
|
self.timestep_embedder = TimestepEmbedding(
|
|
in_channels=in_channels,
|
|
time_embed_dim=embedding_dim,
|
|
sample_proj_bias=bias,
|
|
)
|
|
|
|
if guidance_embeds:
|
|
self.guidance_embedder = TimestepEmbedding(
|
|
in_channels=in_channels,
|
|
time_embed_dim=embedding_dim,
|
|
sample_proj_bias=bias,
|
|
)
|
|
else:
|
|
self.guidance_embedder = None
|
|
|
|
def forward(self, timestep: torch.Tensor, guidance: torch.Tensor | None) -> torch.Tensor:
|
|
timesteps_proj = self.time_proj(timestep)
|
|
timesteps_emb = self.timestep_embedder(timesteps_proj.to(timestep.dtype))
|
|
|
|
if guidance is not None and self.guidance_embedder is not None:
|
|
guidance_proj = self.time_proj(guidance)
|
|
guidance_emb = self.guidance_embedder(guidance_proj.to(guidance.dtype))
|
|
return timesteps_emb + guidance_emb
|
|
return timesteps_emb
|
|
|
|
|
|
class Flux2Modulation(nn.Module):
|
|
def __init__(self, dim: int, mod_param_sets: int = 2, bias: bool = False):
|
|
super().__init__()
|
|
self.mod_param_sets = mod_param_sets
|
|
|
|
self.linear = nn.Linear(dim, dim * 3 * self.mod_param_sets, bias=bias)
|
|
self.act_fn = nn.SiLU()
|
|
|
|
def forward(self, temb: torch.Tensor) -> tuple[tuple[torch.Tensor, torch.Tensor, torch.Tensor], ...]:
|
|
mod = self.act_fn(temb)
|
|
mod = self.linear(mod)
|
|
|
|
if mod.ndim == 2:
|
|
mod = mod.unsqueeze(1)
|
|
mod_params = torch.chunk(mod, 3 * self.mod_param_sets, dim=-1)
|
|
# Return tuple of 3-tuples of modulation params shift/scale/gate
|
|
return tuple(mod_params[3 * i : 3 * (i + 1)] for i in range(self.mod_param_sets))
|
|
|
|
|
|
class Flux2Transformer2DModel(nn.Module):
|
|
"""
|
|
The Transformer model introduced in Flux 2.
|
|
"""
|
|
|
|
_repeated_blocks = ["Flux2TransformerBlock", "Flux2SingleTransformerBlock"]
|
|
|
|
@staticmethod
|
|
def _is_transformer_block(name: str, module) -> bool:
|
|
return ("transformer_blocks" in name or "single_transformer_blocks" in name) and name.split(".")[-1].isdigit()
|
|
|
|
_hsdp_shard_conditions = [_is_transformer_block]
|
|
|
|
def __init__(
|
|
self,
|
|
patch_size: int = 1,
|
|
in_channels: int = 128,
|
|
out_channels: int | None = None,
|
|
num_layers: int = 8,
|
|
num_single_layers: int = 48,
|
|
attention_head_dim: int = 128,
|
|
num_attention_heads: int = 48,
|
|
joint_attention_dim: int = 15360,
|
|
timestep_guidance_channels: int = 256,
|
|
mlp_ratio: float = 3.0,
|
|
axes_dims_rope: tuple[int, ...] = (32, 32, 32, 32),
|
|
rope_theta: int = 2000,
|
|
eps: float = 1e-6,
|
|
guidance_embeds: bool = True,
|
|
):
|
|
super().__init__()
|
|
self.guidance_embeds = guidance_embeds
|
|
self.stacked_params_mapping = None
|
|
self.out_channels = out_channels or in_channels
|
|
self.inner_dim = num_attention_heads * attention_head_dim
|
|
|
|
self.config = SimpleNamespace(
|
|
patch_size=patch_size,
|
|
in_channels=in_channels,
|
|
out_channels=self.out_channels,
|
|
num_layers=num_layers,
|
|
num_single_layers=num_single_layers,
|
|
attention_head_dim=attention_head_dim,
|
|
num_attention_heads=num_attention_heads,
|
|
joint_attention_dim=joint_attention_dim,
|
|
timestep_guidance_channels=timestep_guidance_channels,
|
|
mlp_ratio=mlp_ratio,
|
|
axes_dims_rope=axes_dims_rope,
|
|
rope_theta=rope_theta,
|
|
eps=eps,
|
|
guidance_embeds=guidance_embeds,
|
|
)
|
|
|
|
self.pos_embed = Flux2PosEmbed(theta=rope_theta, axes_dim=axes_dims_rope)
|
|
self.time_guidance_embed = Flux2TimestepGuidanceEmbeddings(
|
|
in_channels=timestep_guidance_channels,
|
|
embedding_dim=self.inner_dim,
|
|
bias=False,
|
|
guidance_embeds=guidance_embeds,
|
|
)
|
|
|
|
self.double_stream_modulation_img = Flux2Modulation(self.inner_dim, mod_param_sets=2, bias=False)
|
|
self.double_stream_modulation_txt = Flux2Modulation(self.inner_dim, mod_param_sets=2, bias=False)
|
|
self.single_stream_modulation = Flux2Modulation(self.inner_dim, mod_param_sets=1, bias=False)
|
|
|
|
self.x_embedder = nn.Linear(in_channels, self.inner_dim, bias=False)
|
|
self.context_embedder = nn.Linear(joint_attention_dim, self.inner_dim, bias=False)
|
|
|
|
self.transformer_blocks = nn.ModuleList(
|
|
[
|
|
Flux2TransformerBlock(
|
|
dim=self.inner_dim,
|
|
num_attention_heads=num_attention_heads,
|
|
attention_head_dim=attention_head_dim,
|
|
mlp_ratio=mlp_ratio,
|
|
eps=eps,
|
|
bias=False,
|
|
)
|
|
for _ in range(num_layers)
|
|
]
|
|
)
|
|
|
|
self.single_transformer_blocks = nn.ModuleList(
|
|
[
|
|
Flux2SingleTransformerBlock(
|
|
dim=self.inner_dim,
|
|
num_attention_heads=num_attention_heads,
|
|
attention_head_dim=attention_head_dim,
|
|
mlp_ratio=mlp_ratio,
|
|
eps=eps,
|
|
bias=False,
|
|
)
|
|
for _ in range(num_single_layers)
|
|
]
|
|
)
|
|
|
|
self.norm_out = AdaLayerNormContinuous(
|
|
self.inner_dim, self.inner_dim, elementwise_affine=False, eps=eps, bias=False
|
|
)
|
|
self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=False)
|
|
|
|
@property
|
|
def dtype(self) -> torch.dtype:
|
|
return next(self.parameters()).dtype
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
encoder_hidden_states: torch.Tensor = None,
|
|
timestep: torch.LongTensor = None,
|
|
img_ids: torch.Tensor = None,
|
|
txt_ids: torch.Tensor = None,
|
|
guidance: torch.Tensor | None = None,
|
|
joint_attention_kwargs: dict[str, Any] | None = None,
|
|
return_dict: bool = True,
|
|
) -> torch.Tensor | Transformer2DModelOutput:
|
|
joint_attention_kwargs = joint_attention_kwargs or {}
|
|
|
|
num_txt_tokens = encoder_hidden_states.shape[1]
|
|
|
|
timestep = timestep.to(hidden_states.dtype) * 1000
|
|
if guidance is not None:
|
|
guidance = guidance.to(hidden_states.dtype) * 1000
|
|
|
|
temb = self.time_guidance_embed(timestep, guidance)
|
|
|
|
double_stream_mod_img = self.double_stream_modulation_img(temb)
|
|
double_stream_mod_txt = self.double_stream_modulation_txt(temb)
|
|
single_stream_mod = self.single_stream_modulation(temb)[0]
|
|
|
|
hidden_states = self.x_embedder(hidden_states)
|
|
encoder_hidden_states = self.context_embedder(encoder_hidden_states)
|
|
|
|
if img_ids.ndim == 3:
|
|
img_ids = img_ids[0]
|
|
if txt_ids.ndim == 3:
|
|
txt_ids = txt_ids[0]
|
|
|
|
if is_torch_npu_available():
|
|
freqs_cos_image, freqs_sin_image = self.pos_embed(img_ids.cpu())
|
|
image_rotary_emb = (freqs_cos_image.npu(), freqs_sin_image.npu())
|
|
freqs_cos_text, freqs_sin_text = self.pos_embed(txt_ids.cpu())
|
|
text_rotary_emb = (freqs_cos_text.npu(), freqs_sin_text.npu())
|
|
else:
|
|
image_rotary_emb = self.pos_embed(img_ids)
|
|
text_rotary_emb = self.pos_embed(txt_ids)
|
|
concat_rotary_emb = (
|
|
torch.cat([text_rotary_emb[0], image_rotary_emb[0]], dim=0),
|
|
torch.cat([text_rotary_emb[1], image_rotary_emb[1]], dim=0),
|
|
)
|
|
|
|
for index_block, block in enumerate(self.transformer_blocks):
|
|
encoder_hidden_states, hidden_states = block(
|
|
hidden_states=hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
temb_mod_params_img=double_stream_mod_img,
|
|
temb_mod_params_txt=double_stream_mod_txt,
|
|
image_rotary_emb=concat_rotary_emb,
|
|
joint_attention_kwargs=joint_attention_kwargs,
|
|
)
|
|
|
|
hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
|
|
|
|
for index_block, block in enumerate(self.single_transformer_blocks):
|
|
hidden_states = block(
|
|
hidden_states=hidden_states,
|
|
encoder_hidden_states=None,
|
|
temb_mod_params=single_stream_mod,
|
|
image_rotary_emb=concat_rotary_emb,
|
|
joint_attention_kwargs=joint_attention_kwargs,
|
|
)
|
|
|
|
hidden_states = hidden_states[:, num_txt_tokens:, ...]
|
|
hidden_states = self.norm_out(hidden_states, temb)
|
|
output = self.proj_out(hidden_states)
|
|
|
|
if not return_dict:
|
|
return (output,)
|
|
|
|
return Transformer2DModelOutput(sample=output)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
stacked_params_mapping = [
|
|
(".to_qkv.", ".to_q.", "q"),
|
|
(".to_qkv.", ".to_k.", "k"),
|
|
(".to_qkv.", ".to_v.", "v"),
|
|
(".add_kv_proj", ".add_q_proj", "q"),
|
|
(".add_kv_proj", ".add_k_proj", "k"),
|
|
(".add_kv_proj", ".add_v_proj", "v"),
|
|
]
|
|
# Expose packed shard mappings for LoRA handling of fused projections.
|
|
self.stacked_params_mapping = stacked_params_mapping
|
|
|
|
params_dict = dict(self.named_parameters())
|
|
|
|
for name, buffer in self.named_buffers():
|
|
if name.endswith(".beta") or name.endswith(".eps"):
|
|
params_dict[name] = buffer
|
|
|
|
loaded_params: set[str] = set()
|
|
for name, loaded_weight in weights:
|
|
original_name = name
|
|
mapped = False
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
if weight_name not in original_name:
|
|
continue
|
|
name = original_name.replace(weight_name, param_name)
|
|
param = params_dict.get(name)
|
|
if param is None:
|
|
break
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
loaded_params.add(name)
|
|
mapped = True
|
|
break
|
|
if mapped:
|
|
continue
|
|
|
|
name = original_name
|
|
if name not in params_dict and ".to_out.0." in name:
|
|
name = name.replace(".to_out.0.", ".to_out.")
|
|
# Some GGUF checkpoints include quantized tensors for modules that
|
|
# are intentionally left unquantized in this model.
|
|
param = params_dict.get(name)
|
|
if param is None:
|
|
continue
|
|
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
return loaded_params
|