1305 lines
57 KiB
Python
1305 lines
57 KiB
Python
# Modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_flux2.py
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# Copyright 2025 Black Forest Labs, The HuggingFace Team and The InstantX Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import glob
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import inspect
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import json
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import os
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from typing import Any, Dict, List, Optional, Tuple, Union
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.loaders import FromOriginalModelMixin
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from diffusers.models.attention_processor import Attention, AttentionProcessor
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from diffusers.models.embeddings import (TimestepEmbedding, Timesteps,
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apply_rotary_emb,
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get_1d_rotary_pos_embed)
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from diffusers.models.modeling_outputs import Transformer2DModelOutput
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from diffusers.models.modeling_utils import ModelMixin
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from diffusers.models.normalization import AdaLayerNormContinuous
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from diffusers.utils import (USE_PEFT_BACKEND, is_torch_npu_available,
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is_torch_version, logging, scale_lora_layers,
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unscale_lora_layers)
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from ..dist import (Flux2MultiGPUsAttnProcessor2_0, get_sequence_parallel_rank,
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get_sequence_parallel_world_size, get_sp_group)
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from .attention_utils import attention
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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def _get_projections(attn: "Flux2Attention", hidden_states, encoder_hidden_states=None):
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query = attn.to_q(hidden_states)
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key = attn.to_k(hidden_states)
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value = attn.to_v(hidden_states)
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encoder_query = encoder_key = encoder_value = None
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if encoder_hidden_states is not None and attn.added_kv_proj_dim is not None:
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encoder_query = attn.add_q_proj(encoder_hidden_states)
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encoder_key = attn.add_k_proj(encoder_hidden_states)
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encoder_value = attn.add_v_proj(encoder_hidden_states)
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return query, key, value, encoder_query, encoder_key, encoder_value
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def _get_qkv_projections(attn: "Flux2Attention", hidden_states, encoder_hidden_states=None):
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return _get_projections(attn, hidden_states, encoder_hidden_states)
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def apply_rotary_emb(
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x: torch.Tensor,
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freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]],
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use_real: bool = True,
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use_real_unbind_dim: int = -1,
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sequence_dim: int = 2,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings
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to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are
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reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting
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tensors contain rotary embeddings and are returned as real tensors.
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Args:
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x (`torch.Tensor`):
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Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply
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freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],)
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Returns:
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Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.
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"""
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if use_real:
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cos, sin = freqs_cis # [S, D]
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if sequence_dim == 2:
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cos = cos[None, None, :, :]
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sin = sin[None, None, :, :]
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elif sequence_dim == 1:
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cos = cos[None, :, None, :]
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sin = sin[None, :, None, :]
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else:
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raise ValueError(f"`sequence_dim={sequence_dim}` but should be 1 or 2.")
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cos, sin = cos.to(x.device), sin.to(x.device)
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if use_real_unbind_dim == -1:
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# Used for flux, cogvideox, hunyuan-dit
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x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, H, S, D//2]
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x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)
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elif use_real_unbind_dim == -2:
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# Used for Stable Audio, OmniGen, CogView4 and Cosmos
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x_real, x_imag = x.reshape(*x.shape[:-1], 2, -1).unbind(-2) # [B, H, S, D//2]
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x_rotated = torch.cat([-x_imag, x_real], dim=-1)
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else:
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raise ValueError(f"`use_real_unbind_dim={use_real_unbind_dim}` but should be -1 or -2.")
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out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype)
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return out
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else:
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# used for lumina
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x_rotated = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
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freqs_cis = freqs_cis.unsqueeze(2)
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x_out = torch.view_as_real(x_rotated * freqs_cis).flatten(3)
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return x_out.type_as(x)
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class Flux2SwiGLU(nn.Module):
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"""
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Flux 2 uses a SwiGLU-style activation in the transformer feedforward sub-blocks, but with the linear projection
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layer fused into the first linear layer of the FF sub-block. Thus, this module has no trainable parameters.
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"""
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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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x = self.gate_fn(x1) * x2
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return x
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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: Optional[int] = None,
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mult: float = 3.0,
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inner_dim: Optional[int] = 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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# Flux2SwiGLU will reduce the dimension by half
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self.linear_in = nn.Linear(dim, inner_dim * 2, bias=bias)
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self.act_fn = Flux2SwiGLU()
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self.linear_out = nn.Linear(inner_dim, dim_out, bias=bias)
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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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x = self.linear_out(x)
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return x
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class Flux2AttnProcessor:
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_attention_backend = None
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_parallel_config = None
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def __init__(self):
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if not hasattr(F, "scaled_dot_product_attention"):
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raise ImportError(f"{self.__class__.__name__} requires PyTorch 2.0. Please upgrade your pytorch version.")
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def __call__(
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self,
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attn: Union["Flux2Attention", "Flux2ParallelSelfAttention"],
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hidden_states: torch.Tensor,
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encoder_hidden_states: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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image_rotary_emb: Optional[torch.Tensor] = None,
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text_seq_len: int = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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"""
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Unified processor for both Flux2Attention and Flux2ParallelSelfAttention.
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Args:
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attn: Attention module (either Flux2Attention or Flux2ParallelSelfAttention)
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hidden_states: Input hidden states
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encoder_hidden_states: Optional encoder hidden states (only for Flux2Attention)
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attention_mask: Optional attention mask
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image_rotary_emb: Optional rotary embeddings
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Returns:
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For Flux2Attention with encoder_hidden_states: (hidden_states, encoder_hidden_states)
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For Flux2Attention without encoder_hidden_states: hidden_states
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For Flux2ParallelSelfAttention: hidden_states
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"""
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# Determine which type of attention we're processing
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is_parallel_self_attn = hasattr(attn, 'to_qkv_mlp_proj') and attn.to_qkv_mlp_proj is not None
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if is_parallel_self_attn:
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# ============================================
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# Parallel Self-Attention Path (with MLP)
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# ============================================
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# Parallel in (QKV + MLP in) projection
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hidden_states = attn.to_qkv_mlp_proj(hidden_states)
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qkv, mlp_hidden_states = torch.split(
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hidden_states, [3 * attn.inner_dim, attn.mlp_hidden_dim * attn.mlp_mult_factor], dim=-1
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)
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# Handle the attention logic
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query, key, value = qkv.chunk(3, dim=-1)
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else:
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# ============================================
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# Standard Attention Path (possibly with encoder)
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# ============================================
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query, key, value, encoder_query, encoder_key, encoder_value = _get_qkv_projections(
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attn, hidden_states, encoder_hidden_states
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)
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# Common processing for query, key, value
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query = query.unflatten(-1, (attn.heads, -1))
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key = key.unflatten(-1, (attn.heads, -1))
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value = value.unflatten(-1, (attn.heads, -1))
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query = attn.norm_q(query)
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key = attn.norm_k(key)
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# Handle encoder projections (only for standard attention)
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if not is_parallel_self_attn and attn.added_kv_proj_dim is not None:
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encoder_query = encoder_query.unflatten(-1, (attn.heads, -1))
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encoder_key = encoder_key.unflatten(-1, (attn.heads, -1))
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encoder_value = encoder_value.unflatten(-1, (attn.heads, -1))
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encoder_query = attn.norm_added_q(encoder_query)
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encoder_key = attn.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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# Apply rotary embeddings
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if image_rotary_emb is not None:
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query = apply_rotary_emb(query, image_rotary_emb, sequence_dim=1)
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key = apply_rotary_emb(key, image_rotary_emb, sequence_dim=1)
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# Perform attention
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hidden_states = attention(
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query, key, value, attn_mask=attention_mask,
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)
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hidden_states = hidden_states.flatten(2, 3)
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hidden_states = hidden_states.to(query.dtype)
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if is_parallel_self_attn:
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# ============================================
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# Parallel Self-Attention Output Path
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# ============================================
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# Handle the feedforward (FF) logic
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mlp_hidden_states = attn.mlp_act_fn(mlp_hidden_states)
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# Concatenate and parallel output projection
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hidden_states = torch.cat([hidden_states, mlp_hidden_states], dim=-1)
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hidden_states = attn.to_out(hidden_states)
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return hidden_states
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else:
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# ============================================
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# Standard Attention Output Path
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# ============================================
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# Split encoder and latent hidden states if encoder was used
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if encoder_hidden_states is not None:
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encoder_hidden_states, hidden_states = hidden_states.split_with_sizes(
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[encoder_hidden_states.shape[1], hidden_states.shape[1] - encoder_hidden_states.shape[1]], dim=1
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)
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encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
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# Project output
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hidden_states = attn.to_out[0](hidden_states)
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hidden_states = attn.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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else:
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return hidden_states
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class Flux2Attention(torch.nn.Module):
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_default_processor_cls = Flux2AttnProcessor
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_available_processors = [Flux2AttnProcessor]
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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: Optional[int] = None,
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added_proj_bias: Optional[bool] = 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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processor=None,
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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.use_bias = bias
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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.added_proj_bias = added_proj_bias
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self.to_q = torch.nn.Linear(query_dim, self.inner_dim, bias=bias)
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self.to_k = torch.nn.Linear(query_dim, self.inner_dim, bias=bias)
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self.to_v = torch.nn.Linear(query_dim, self.inner_dim, bias=bias)
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# QK Norm
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self.norm_q = torch.nn.RMSNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
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self.norm_k = torch.nn.RMSNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
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self.to_out = torch.nn.ModuleList([])
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self.to_out.append(torch.nn.Linear(self.inner_dim, self.out_dim, bias=out_bias))
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self.to_out.append(torch.nn.Dropout(dropout))
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if added_kv_proj_dim is not None:
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self.norm_added_q = torch.nn.RMSNorm(dim_head, eps=eps)
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self.norm_added_k = torch.nn.RMSNorm(dim_head, eps=eps)
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self.add_q_proj = torch.nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias)
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self.add_k_proj = torch.nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias)
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self.add_v_proj = torch.nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias)
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self.to_add_out = torch.nn.Linear(self.inner_dim, query_dim, bias=out_bias)
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if processor is None:
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processor = self._default_processor_cls()
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self.set_processor(processor)
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def set_processor(self, processor: AttentionProcessor) -> None:
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"""
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Set the attention processor to use.
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Args:
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processor (`AttnProcessor`):
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The attention processor to use.
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"""
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# if current processor is in `self._modules` and if passed `processor` is not, we need to
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# pop `processor` from `self._modules`
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if (
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hasattr(self, "processor")
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and isinstance(self.processor, torch.nn.Module)
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and not isinstance(processor, torch.nn.Module)
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):
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logger.info(f"You are removing possibly trained weights of {self.processor} with {processor}")
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self._modules.pop("processor")
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self.processor = processor
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def get_processor(self, return_deprecated_lora: bool = False) -> "AttentionProcessor":
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"""
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Get the attention processor in use.
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Args:
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return_deprecated_lora (`bool`, *optional*, defaults to `False`):
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Set to `True` to return the deprecated LoRA attention processor.
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Returns:
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"AttentionProcessor": The attention processor in use.
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"""
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if not return_deprecated_lora:
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return self.processor
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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: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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image_rotary_emb: Optional[torch.Tensor] = None,
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**kwargs,
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) -> torch.Tensor:
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attn_parameters = set(inspect.signature(self.processor.__call__).parameters.keys())
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unused_kwargs = [k for k, _ in kwargs.items() if k not in attn_parameters]
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if len(unused_kwargs) > 0:
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logger.warning(
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f"joint_attention_kwargs {unused_kwargs} are not expected by {self.processor.__class__.__name__} and will be ignored."
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)
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kwargs = {k: w for k, w in kwargs.items() if k in attn_parameters}
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return self.processor(self, hidden_states, encoder_hidden_states, attention_mask, image_rotary_emb, **kwargs)
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class Flux2ParallelSelfAttention(torch.nn.Module):
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"""
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Flux 2 parallel self-attention for the Flux 2 single-stream transformer blocks.
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This implements a parallel transformer block, where the attention QKV projections are fused to the feedforward (FF)
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input projections, and the attention output projections are fused to the FF output projections. See the [ViT-22B
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paper](https://arxiv.org/abs/2302.05442) for a visual depiction of this type of transformer block.
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"""
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_default_processor_cls = Flux2AttnProcessor
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_available_processors = [Flux2AttnProcessor]
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# Does not support QKV fusion as the QKV projections are always fused
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_supports_qkv_fusion = False
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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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processor=None,
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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.use_bias = bias
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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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# Fused QKV projections + MLP input projection
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self.to_qkv_mlp_proj = torch.nn.Linear(
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self.query_dim, self.inner_dim * 3 + self.mlp_hidden_dim * self.mlp_mult_factor, bias=bias
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)
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self.mlp_act_fn = Flux2SwiGLU()
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# QK Norm
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|
self.norm_q = torch.nn.RMSNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
|
|
self.norm_k = torch.nn.RMSNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
|
|
|
|
# Fused attention output projection + MLP output projection
|
|
self.to_out = torch.nn.Linear(self.inner_dim + self.mlp_hidden_dim, self.out_dim, bias=out_bias)
|
|
|
|
if processor is None:
|
|
processor = self._default_processor_cls()
|
|
self.set_processor(processor)
|
|
|
|
def set_processor(self, processor: AttentionProcessor) -> None:
|
|
"""
|
|
Set the attention processor to use.
|
|
|
|
Args:
|
|
processor (`AttnProcessor`):
|
|
The attention processor to use.
|
|
"""
|
|
# if current processor is in `self._modules` and if passed `processor` is not, we need to
|
|
# pop `processor` from `self._modules`
|
|
if (
|
|
hasattr(self, "processor")
|
|
and isinstance(self.processor, torch.nn.Module)
|
|
and not isinstance(processor, torch.nn.Module)
|
|
):
|
|
logger.info(f"You are removing possibly trained weights of {self.processor} with {processor}")
|
|
self._modules.pop("processor")
|
|
|
|
self.processor = processor
|
|
|
|
def get_processor(self, return_deprecated_lora: bool = False) -> "AttentionProcessor":
|
|
"""
|
|
Get the attention processor in use.
|
|
|
|
Args:
|
|
return_deprecated_lora (`bool`, *optional*, defaults to `False`):
|
|
Set to `True` to return the deprecated LoRA attention processor.
|
|
|
|
Returns:
|
|
"AttentionProcessor": The attention processor in use.
|
|
"""
|
|
if not return_deprecated_lora:
|
|
return self.processor
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
image_rotary_emb: Optional[torch.Tensor] = None,
|
|
**kwargs,
|
|
) -> torch.Tensor:
|
|
attn_parameters = set(inspect.signature(self.processor.__call__).parameters.keys())
|
|
unused_kwargs = [k for k, _ in kwargs.items() if k not in attn_parameters]
|
|
if len(unused_kwargs) > 0:
|
|
logger.warning(
|
|
f"joint_attention_kwargs {unused_kwargs} are not expected by {self.processor.__class__.__name__} and will be ignored."
|
|
)
|
|
kwargs = {k: w for k, w in kwargs.items() if k in attn_parameters}
|
|
return self.processor(self, hidden_states, encoder_hidden_states, attention_mask, image_rotary_emb, **kwargs)
|
|
|
|
|
|
class Flux2SingleTransformerBlock(nn.Module):
|
|
def __init__(
|
|
self,
|
|
dim: int,
|
|
num_attention_heads: int,
|
|
attention_head_dim: int,
|
|
mlp_ratio: float = 3.0,
|
|
eps: float = 1e-6,
|
|
bias: bool = False,
|
|
):
|
|
super().__init__()
|
|
|
|
self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
|
|
|
|
# Note that the MLP in/out linear layers are fused with the attention QKV/out projections, respectively; this
|
|
# is often called a "parallel" transformer block. See the [ViT-22B paper](https://arxiv.org/abs/2302.05442)
|
|
# for a visual depiction of this type of transformer block.
|
|
self.attn = Flux2ParallelSelfAttention(
|
|
query_dim=dim,
|
|
dim_head=attention_head_dim,
|
|
heads=num_attention_heads,
|
|
out_dim=dim,
|
|
bias=bias,
|
|
out_bias=bias,
|
|
eps=eps,
|
|
mlp_ratio=mlp_ratio,
|
|
mlp_mult_factor=2,
|
|
processor=Flux2AttnProcessor(),
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
encoder_hidden_states: Optional[torch.Tensor],
|
|
temb_mod_params: Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
|
|
image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
|
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
# If encoder_hidden_states is None, hidden_states is assumed to have encoder_hidden_states already
|
|
# concatenated
|
|
if encoder_hidden_states is not None:
|
|
text_seq_len = encoder_hidden_states.shape[1]
|
|
hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
|
|
|
|
mod_shift, mod_scale, mod_gate = temb_mod_params
|
|
|
|
norm_hidden_states = self.norm(hidden_states)
|
|
norm_hidden_states = (1 + mod_scale) * norm_hidden_states + mod_shift
|
|
|
|
joint_attention_kwargs = joint_attention_kwargs or {}
|
|
attn_output = self.attn(
|
|
hidden_states=norm_hidden_states,
|
|
image_rotary_emb=image_rotary_emb,
|
|
text_seq_len=text_seq_len,
|
|
**joint_attention_kwargs,
|
|
)
|
|
|
|
hidden_states = hidden_states + mod_gate * attn_output
|
|
if hidden_states.dtype == torch.float16:
|
|
hidden_states = hidden_states.clip(-65504, 65504)
|
|
|
|
encoder_hidden_states, hidden_states = hidden_states[:, :text_seq_len], hidden_states[:, text_seq_len:]
|
|
return encoder_hidden_states, hidden_states
|
|
|
|
|
|
class Flux2TransformerBlock(nn.Module):
|
|
def __init__(
|
|
self,
|
|
dim: int,
|
|
num_attention_heads: int,
|
|
attention_head_dim: int,
|
|
mlp_ratio: float = 3.0,
|
|
eps: float = 1e-6,
|
|
bias: bool = False,
|
|
):
|
|
super().__init__()
|
|
self.mlp_hidden_dim = int(dim * mlp_ratio)
|
|
|
|
self.norm1 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
|
|
self.norm1_context = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
|
|
|
|
self.attn = Flux2Attention(
|
|
query_dim=dim,
|
|
added_kv_proj_dim=dim,
|
|
dim_head=attention_head_dim,
|
|
heads=num_attention_heads,
|
|
out_dim=dim,
|
|
bias=bias,
|
|
added_proj_bias=bias,
|
|
out_bias=bias,
|
|
eps=eps,
|
|
processor=Flux2AttnProcessor(),
|
|
)
|
|
|
|
self.norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
|
|
self.ff = Flux2FeedForward(dim=dim, dim_out=dim, mult=mlp_ratio, bias=bias)
|
|
|
|
self.norm2_context = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
|
|
self.ff_context = Flux2FeedForward(dim=dim, dim_out=dim, mult=mlp_ratio, bias=bias)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
encoder_hidden_states: torch.Tensor,
|
|
temb_mod_params_img: Tuple[Tuple[torch.Tensor, torch.Tensor, torch.Tensor], ...],
|
|
temb_mod_params_txt: Tuple[Tuple[torch.Tensor, torch.Tensor, torch.Tensor], ...],
|
|
image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
|
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
joint_attention_kwargs = joint_attention_kwargs or {}
|
|
|
|
# Modulation parameters shape: [1, 1, self.dim]
|
|
(shift_msa, scale_msa, gate_msa), (shift_mlp, scale_mlp, gate_mlp) = temb_mod_params_img
|
|
(c_shift_msa, c_scale_msa, c_gate_msa), (c_shift_mlp, c_scale_mlp, c_gate_mlp) = temb_mod_params_txt
|
|
|
|
# Img stream
|
|
norm_hidden_states = self.norm1(hidden_states)
|
|
norm_hidden_states = (1 + scale_msa) * norm_hidden_states + shift_msa
|
|
|
|
# Conditioning txt stream
|
|
norm_encoder_hidden_states = self.norm1_context(encoder_hidden_states)
|
|
norm_encoder_hidden_states = (1 + c_scale_msa) * norm_encoder_hidden_states + c_shift_msa
|
|
|
|
# Attention on concatenated img + txt stream
|
|
attention_outputs = self.attn(
|
|
hidden_states=norm_hidden_states,
|
|
encoder_hidden_states=norm_encoder_hidden_states,
|
|
image_rotary_emb=image_rotary_emb,
|
|
**joint_attention_kwargs,
|
|
)
|
|
|
|
attn_output, context_attn_output = attention_outputs
|
|
|
|
# Process attention outputs for the image stream (`hidden_states`).
|
|
attn_output = gate_msa * attn_output
|
|
hidden_states = hidden_states + attn_output
|
|
|
|
norm_hidden_states = self.norm2(hidden_states)
|
|
norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
|
|
|
|
ff_output = self.ff(norm_hidden_states)
|
|
hidden_states = hidden_states + gate_mlp * ff_output
|
|
|
|
# Process attention outputs for the text stream (`encoder_hidden_states`).
|
|
context_attn_output = c_gate_msa * context_attn_output
|
|
encoder_hidden_states = encoder_hidden_states + context_attn_output
|
|
|
|
norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
|
|
norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp) + c_shift_mlp
|
|
|
|
context_ff_output = self.ff_context(norm_encoder_hidden_states)
|
|
encoder_hidden_states = encoder_hidden_states + c_gate_mlp * context_ff_output
|
|
if encoder_hidden_states.dtype == torch.float16:
|
|
encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504)
|
|
|
|
return encoder_hidden_states, hidden_states
|
|
|
|
|
|
class Flux2PosEmbed(nn.Module):
|
|
# modified from https://github.com/black-forest-labs/flux/blob/c00d7c60b085fce8058b9df845e036090873f2ce/src/flux/modules/layers.py#L11
|
|
def __init__(self, theta: int, axes_dim: List[int]):
|
|
super().__init__()
|
|
self.theta = theta
|
|
self.axes_dim = axes_dim
|
|
|
|
def forward(self, ids: torch.Tensor) -> torch.Tensor:
|
|
# Expected ids shape: [S, len(self.axes_dim)]
|
|
cos_out = []
|
|
sin_out = []
|
|
pos = ids.float()
|
|
is_mps = ids.device.type == "mps"
|
|
is_npu = ids.device.type == "npu"
|
|
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)):
|
|
cos, sin = get_1d_rotary_pos_embed(
|
|
self.axes_dim[i],
|
|
pos[..., i],
|
|
theta=self.theta,
|
|
repeat_interleave_real=True,
|
|
use_real=True,
|
|
freqs_dtype=freqs_dtype,
|
|
)
|
|
cos_out.append(cos)
|
|
sin_out.append(sin)
|
|
freqs_cos = torch.cat(cos_out, dim=-1).to(ids.device)
|
|
freqs_sin = torch.cat(sin_out, dim=-1).to(ids.device)
|
|
return freqs_cos, freqs_sin
|
|
|
|
|
|
class Flux2TimestepGuidanceEmbeddings(nn.Module):
|
|
def __init__(self, in_channels: int = 256, embedding_dim: int = 6144, bias: bool = False):
|
|
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
|
|
)
|
|
|
|
self.guidance_embedder = TimestepEmbedding(
|
|
in_channels=in_channels, time_embed_dim=embedding_dim, sample_proj_bias=bias
|
|
)
|
|
|
|
def forward(self, timestep: torch.Tensor, guidance: torch.Tensor) -> torch.Tensor:
|
|
timesteps_proj = self.time_proj(timestep)
|
|
timesteps_emb = self.timestep_embedder(timesteps_proj.to(timestep.dtype)) # (N, D)
|
|
|
|
guidance_proj = self.time_proj(guidance)
|
|
guidance_emb = self.guidance_embedder(guidance_proj.to(guidance.dtype)) # (N, D)
|
|
|
|
time_guidance_emb = timesteps_emb + guidance_emb
|
|
|
|
return time_guidance_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(
|
|
ModelMixin,
|
|
ConfigMixin,
|
|
FromOriginalModelMixin,
|
|
):
|
|
"""
|
|
The Transformer model introduced in Flux 2.
|
|
|
|
Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
|
|
|
|
Args:
|
|
patch_size (`int`, defaults to `1`):
|
|
Patch size to turn the input data into small patches.
|
|
in_channels (`int`, defaults to `128`):
|
|
The number of channels in the input.
|
|
out_channels (`int`, *optional*, defaults to `None`):
|
|
The number of channels in the output. If not specified, it defaults to `in_channels`.
|
|
num_layers (`int`, defaults to `8`):
|
|
The number of layers of dual stream DiT blocks to use.
|
|
num_single_layers (`int`, defaults to `48`):
|
|
The number of layers of single stream DiT blocks to use.
|
|
attention_head_dim (`int`, defaults to `128`):
|
|
The number of dimensions to use for each attention head.
|
|
num_attention_heads (`int`, defaults to `48`):
|
|
The number of attention heads to use.
|
|
joint_attention_dim (`int`, defaults to `15360`):
|
|
The number of dimensions to use for the joint attention (embedding/channel dimension of
|
|
`encoder_hidden_states`).
|
|
pooled_projection_dim (`int`, defaults to `768`):
|
|
The number of dimensions to use for the pooled projection.
|
|
guidance_embeds (`bool`, defaults to `True`):
|
|
Whether to use guidance embeddings for guidance-distilled variant of the model.
|
|
axes_dims_rope (`Tuple[int]`, defaults to `(32, 32, 32, 32)`):
|
|
The dimensions to use for the rotary positional embeddings.
|
|
"""
|
|
|
|
_supports_gradient_checkpointing = True
|
|
# _no_split_modules = ["Flux2TransformerBlock", "Flux2SingleTransformerBlock"]
|
|
# _skip_layerwise_casting_patterns = ["pos_embed", "norm"]
|
|
# _repeated_blocks = ["Flux2TransformerBlock", "Flux2SingleTransformerBlock"]
|
|
|
|
@register_to_config
|
|
def __init__(
|
|
self,
|
|
patch_size: int = 1,
|
|
in_channels: int = 128,
|
|
out_channels: Optional[int] = 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,
|
|
):
|
|
super().__init__()
|
|
self.out_channels = out_channels or in_channels
|
|
self.inner_dim = num_attention_heads * attention_head_dim
|
|
|
|
# 1. Sinusoidal positional embedding for RoPE on image and text tokens
|
|
self.pos_embed = Flux2PosEmbed(theta=rope_theta, axes_dim=axes_dims_rope)
|
|
|
|
# 2. Combined timestep + guidance embedding
|
|
self.time_guidance_embed = Flux2TimestepGuidanceEmbeddings(
|
|
in_channels=timestep_guidance_channels, embedding_dim=self.inner_dim, bias=False
|
|
)
|
|
|
|
# 3. Modulation (double stream and single stream blocks share modulation parameters, resp.)
|
|
# Two sets of shift/scale/gate modulation parameters for the double stream attn and FF sub-blocks
|
|
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)
|
|
# Only one set of modulation parameters as the attn and FF sub-blocks are run in parallel for single stream
|
|
self.single_stream_modulation = Flux2Modulation(self.inner_dim, mod_param_sets=1, bias=False)
|
|
|
|
# 4. Input projections
|
|
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)
|
|
|
|
# 5. Double Stream Transformer Blocks
|
|
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)
|
|
]
|
|
)
|
|
|
|
# 6. Single Stream Transformer Blocks
|
|
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)
|
|
]
|
|
)
|
|
|
|
# 7. Output 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)
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
self.sp_world_size = 1
|
|
self.sp_world_rank = 0
|
|
|
|
def _set_gradient_checkpointing(self, *args, **kwargs):
|
|
if "value" in kwargs:
|
|
self.gradient_checkpointing = kwargs["value"]
|
|
elif "enable" in kwargs:
|
|
self.gradient_checkpointing = kwargs["enable"]
|
|
else:
|
|
raise ValueError("Invalid set gradient checkpointing")
|
|
|
|
def enable_multi_gpus_inference(self,):
|
|
self.sp_world_size = get_sequence_parallel_world_size()
|
|
self.sp_world_rank = get_sequence_parallel_rank()
|
|
self.all_gather = get_sp_group().all_gather
|
|
self.set_attn_processor(Flux2MultiGPUsAttnProcessor2_0())
|
|
|
|
@property
|
|
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
|
|
def attn_processors(self) -> Dict[str, AttentionProcessor]:
|
|
r"""
|
|
Returns:
|
|
`dict` of attention processors: A dictionary containing all attention processors used in the model with
|
|
indexed by its weight name.
|
|
"""
|
|
# set recursively
|
|
processors = {}
|
|
|
|
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
|
|
if hasattr(module, "get_processor"):
|
|
processors[f"{name}.processor"] = module.get_processor()
|
|
|
|
for sub_name, child in module.named_children():
|
|
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
|
|
|
|
return processors
|
|
|
|
for name, module in self.named_children():
|
|
fn_recursive_add_processors(name, module, processors)
|
|
|
|
return processors
|
|
|
|
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
|
|
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
|
|
r"""
|
|
Sets the attention processor to use to compute attention.
|
|
|
|
Parameters:
|
|
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
|
|
The instantiated processor class or a dictionary of processor classes that will be set as the processor
|
|
for **all** `Attention` layers.
|
|
|
|
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
|
|
processor. This is strongly recommended when setting trainable attention processors.
|
|
|
|
"""
|
|
count = len(self.attn_processors.keys())
|
|
|
|
if isinstance(processor, dict) and len(processor) != count:
|
|
raise ValueError(
|
|
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
|
|
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
|
|
)
|
|
|
|
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
|
|
if hasattr(module, "set_processor"):
|
|
if not isinstance(processor, dict):
|
|
module.set_processor(processor)
|
|
else:
|
|
module.set_processor(processor.pop(f"{name}.processor"))
|
|
|
|
for sub_name, child in module.named_children():
|
|
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
|
|
|
|
for name, module in self.named_children():
|
|
fn_recursive_attn_processor(name, module, processor)
|
|
|
|
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,
|
|
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
return_dict: bool = True,
|
|
) -> Union[torch.Tensor, Transformer2DModelOutput]:
|
|
"""
|
|
The [`FluxTransformer2DModel`] forward method.
|
|
|
|
Args:
|
|
hidden_states (`torch.Tensor` of shape `(batch_size, image_sequence_length, in_channels)`):
|
|
Input `hidden_states`.
|
|
encoder_hidden_states (`torch.Tensor` of shape `(batch_size, text_sequence_length, joint_attention_dim)`):
|
|
Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
|
|
timestep ( `torch.LongTensor`):
|
|
Used to indicate denoising step.
|
|
block_controlnet_hidden_states: (`list` of `torch.Tensor`):
|
|
A list of tensors that if specified are added to the residuals of transformer blocks.
|
|
joint_attention_kwargs (`dict`, *optional*):
|
|
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
|
`self.processor` in
|
|
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
|
|
tuple.
|
|
|
|
Returns:
|
|
If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
|
|
`tuple` where the first element is the sample tensor.
|
|
"""
|
|
# 0. Handle input arguments
|
|
if joint_attention_kwargs is not None:
|
|
joint_attention_kwargs = joint_attention_kwargs.copy()
|
|
lora_scale = joint_attention_kwargs.pop("scale", 1.0)
|
|
else:
|
|
lora_scale = 1.0
|
|
|
|
num_txt_tokens = encoder_hidden_states.shape[1]
|
|
|
|
# 1. Calculate timestep embedding and modulation parameters
|
|
timestep = timestep.to(hidden_states.dtype) * 1000
|
|
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]
|
|
|
|
# 2. Input projection for image (hidden_states) and conditioning text (encoder_hidden_states)
|
|
hidden_states = self.x_embedder(hidden_states)
|
|
encoder_hidden_states = self.context_embedder(encoder_hidden_states)
|
|
|
|
# 3. Calculate RoPE embeddings from image and text tokens
|
|
# NOTE: the below logic means that we can't support batched inference with images of different resolutions or
|
|
# text prompts of differents lengths. Is this a use case we want to support?
|
|
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),
|
|
)
|
|
|
|
# Context Parallel
|
|
if self.sp_world_size > 1:
|
|
hidden_states = torch.chunk(hidden_states, self.sp_world_size, dim=1)[self.sp_world_rank]
|
|
if concat_rotary_emb is not None:
|
|
txt_rotary_emb = (
|
|
concat_rotary_emb[0][:encoder_hidden_states.shape[1]],
|
|
concat_rotary_emb[1][:encoder_hidden_states.shape[1]]
|
|
)
|
|
concat_rotary_emb = (
|
|
torch.chunk(concat_rotary_emb[0][encoder_hidden_states.shape[1]:], self.sp_world_size, dim=0)[self.sp_world_rank],
|
|
torch.chunk(concat_rotary_emb[1][encoder_hidden_states.shape[1]:], self.sp_world_size, dim=0)[self.sp_world_rank],
|
|
)
|
|
concat_rotary_emb = [torch.cat([_txt_rotary_emb, _image_rotary_emb], dim=0) \
|
|
for _txt_rotary_emb, _image_rotary_emb in zip(txt_rotary_emb, concat_rotary_emb)]
|
|
|
|
# 4. Double Stream Transformer Blocks
|
|
for index_block, block in enumerate(self.transformer_blocks):
|
|
if torch.is_grad_enabled() and self.gradient_checkpointing:
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
return module(*inputs)
|
|
|
|
return custom_forward
|
|
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
|
|
encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(block),
|
|
hidden_states,
|
|
encoder_hidden_states,
|
|
double_stream_mod_img,
|
|
double_stream_mod_txt,
|
|
concat_rotary_emb,
|
|
joint_attention_kwargs,
|
|
**ckpt_kwargs,
|
|
)
|
|
else:
|
|
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,
|
|
)
|
|
|
|
# 5. Single Stream Transformer Blocks
|
|
for index_block, block in enumerate(self.single_transformer_blocks):
|
|
if torch.is_grad_enabled() and self.gradient_checkpointing:
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
return module(*inputs)
|
|
|
|
return custom_forward
|
|
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
|
|
encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(block),
|
|
hidden_states,
|
|
encoder_hidden_states,
|
|
single_stream_mod,
|
|
concat_rotary_emb,
|
|
joint_attention_kwargs,
|
|
**ckpt_kwargs,
|
|
)
|
|
else:
|
|
encoder_hidden_states, hidden_states = block(
|
|
hidden_states=hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
temb_mod_params=single_stream_mod,
|
|
image_rotary_emb=concat_rotary_emb,
|
|
joint_attention_kwargs=joint_attention_kwargs,
|
|
)
|
|
|
|
# 6. Output layers
|
|
hidden_states = self.norm_out(hidden_states, temb)
|
|
output = self.proj_out(hidden_states)
|
|
|
|
if self.sp_world_size > 1:
|
|
output = self.all_gather(output, dim=1)
|
|
|
|
if not return_dict:
|
|
return (output,)
|
|
|
|
return Transformer2DModelOutput(sample=output)
|
|
|
|
@classmethod
|
|
def from_pretrained(
|
|
cls, pretrained_model_path, subfolder=None, transformer_additional_kwargs=None,
|
|
low_cpu_mem_usage=False, torch_dtype=torch.bfloat16
|
|
):
|
|
transformer_additional_kwargs = {} if transformer_additional_kwargs is None else dict(transformer_additional_kwargs)
|
|
if subfolder is not None:
|
|
pretrained_model_path = os.path.join(pretrained_model_path, subfolder)
|
|
print(f"loaded 3D transformer's pretrained weights from {pretrained_model_path} ...")
|
|
|
|
config_file = os.path.join(pretrained_model_path, 'config.json')
|
|
if not os.path.isfile(config_file):
|
|
raise RuntimeError(f"{config_file} does not exist")
|
|
with open(config_file, "r") as f:
|
|
config = json.load(f)
|
|
|
|
from diffusers.utils import WEIGHTS_NAME
|
|
model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)
|
|
model_file_safetensors = model_file.replace(".bin", ".safetensors")
|
|
|
|
if "dict_mapping" in transformer_additional_kwargs.keys():
|
|
dict_mapping = transformer_additional_kwargs.pop("dict_mapping")
|
|
for key in dict_mapping:
|
|
transformer_additional_kwargs[dict_mapping[key]] = config[key]
|
|
|
|
if low_cpu_mem_usage:
|
|
try:
|
|
import re
|
|
|
|
from diffusers import __version__ as diffusers_version
|
|
from packaging import version as pkg_version
|
|
if pkg_version.parse(diffusers_version) >= pkg_version.parse("0.33.0"):
|
|
from diffusers.models.model_loading_utils import \
|
|
load_model_dict_into_meta
|
|
else:
|
|
from diffusers.models.modeling_utils import \
|
|
load_model_dict_into_meta
|
|
from diffusers.utils import is_accelerate_available
|
|
if is_accelerate_available():
|
|
import accelerate
|
|
|
|
# Instantiate model with empty weights
|
|
with accelerate.init_empty_weights():
|
|
model = cls.from_config(config, **transformer_additional_kwargs)
|
|
|
|
param_device = "cpu"
|
|
if os.path.exists(model_file):
|
|
state_dict = torch.load(model_file, map_location="cpu", weights_only=True)
|
|
elif os.path.exists(model_file_safetensors):
|
|
from safetensors.torch import load_file, safe_open
|
|
state_dict = load_file(model_file_safetensors)
|
|
else:
|
|
from safetensors.torch import load_file, safe_open
|
|
model_files_safetensors = glob.glob(os.path.join(pretrained_model_path, "*.safetensors"))
|
|
state_dict = {}
|
|
print(model_files_safetensors)
|
|
for _model_file_safetensors in model_files_safetensors:
|
|
_state_dict = load_file(_model_file_safetensors)
|
|
for key in _state_dict:
|
|
state_dict[key] = _state_dict[key]
|
|
if len(state_dict) == 0:
|
|
raise FileNotFoundError(f"No weights found in {pretrained_model_path}")
|
|
|
|
model_state_dict = model.state_dict()
|
|
filtered_state_dict = {}
|
|
for key in state_dict:
|
|
if key in model_state_dict and model_state_dict[key].size() == state_dict[key].size():
|
|
filtered_state_dict[key] = state_dict[key]
|
|
else:
|
|
print(f"Skipping key '{key}' due to size mismatch or absence in model.")
|
|
|
|
model_keys = set(model_state_dict.keys())
|
|
loaded_keys = set(filtered_state_dict.keys())
|
|
missing_keys = model_keys - loaded_keys
|
|
|
|
def initialize_missing_parameters(missing_keys, model_state_dict, torch_dtype=None):
|
|
initialized_dict = {}
|
|
|
|
with torch.no_grad():
|
|
for key in missing_keys:
|
|
param_shape = model_state_dict[key].shape
|
|
param_dtype = torch_dtype if torch_dtype is not None else model_state_dict[key].dtype
|
|
if "control" in key and key.replace("control_", "") in filtered_state_dict.keys() and model_state_dict[key].size() == filtered_state_dict[key.replace("control_", "")].size():
|
|
initialized_dict[key] = filtered_state_dict[key.replace("control_", "")].clone()
|
|
print(f"Initializing missing parameter '{key}' with model.state_dict().")
|
|
elif "after_proj" in key or "before_proj" in key:
|
|
initialized_dict[key] = torch.zeros(param_shape, dtype=param_dtype)
|
|
print(f"Initializing missing parameter '{key}' with zero.")
|
|
elif 'weight' in key:
|
|
if any(norm_type in key for norm_type in ['norm', 'ln_', 'layer_norm', 'group_norm', 'batch_norm']):
|
|
initialized_dict[key] = torch.ones(param_shape, dtype=param_dtype)
|
|
elif 'embedding' in key or 'embed' in key:
|
|
initialized_dict[key] = torch.randn(param_shape, dtype=param_dtype) * 0.02
|
|
elif 'head' in key or 'output' in key or 'proj_out' in key:
|
|
initialized_dict[key] = torch.zeros(param_shape, dtype=param_dtype)
|
|
elif len(param_shape) >= 2:
|
|
initialized_dict[key] = torch.empty(param_shape, dtype=param_dtype)
|
|
nn.init.xavier_uniform_(initialized_dict[key])
|
|
else:
|
|
initialized_dict[key] = torch.randn(param_shape, dtype=param_dtype) * 0.02
|
|
elif 'bias' in key:
|
|
initialized_dict[key] = torch.zeros(param_shape, dtype=param_dtype)
|
|
elif 'running_mean' in key:
|
|
initialized_dict[key] = torch.zeros(param_shape, dtype=param_dtype)
|
|
elif 'running_var' in key:
|
|
initialized_dict[key] = torch.ones(param_shape, dtype=param_dtype)
|
|
elif 'num_batches_tracked' in key:
|
|
initialized_dict[key] = torch.zeros(param_shape, dtype=torch.long)
|
|
else:
|
|
initialized_dict[key] = torch.zeros(param_shape, dtype=param_dtype)
|
|
|
|
return initialized_dict
|
|
|
|
if missing_keys:
|
|
print(f"Missing keys will be initialized: {sorted(missing_keys)}")
|
|
initialized_params = initialize_missing_parameters(
|
|
missing_keys,
|
|
model_state_dict,
|
|
torch_dtype
|
|
)
|
|
filtered_state_dict.update(initialized_params)
|
|
|
|
if pkg_version.parse(diffusers_version) >= pkg_version.parse("0.33.0"):
|
|
# Diffusers has refactored `load_model_dict_into_meta` since version 0.33.0 in this commit:
|
|
# https://github.com/huggingface/diffusers/commit/f5929e03060d56063ff34b25a8308833bec7c785.
|
|
load_model_dict_into_meta(
|
|
model,
|
|
filtered_state_dict,
|
|
dtype=torch_dtype,
|
|
model_name_or_path=pretrained_model_path,
|
|
)
|
|
else:
|
|
model._convert_deprecated_attention_blocks(filtered_state_dict)
|
|
unexpected_keys = load_model_dict_into_meta(
|
|
model,
|
|
filtered_state_dict,
|
|
device=param_device,
|
|
dtype=torch_dtype,
|
|
model_name_or_path=pretrained_model_path,
|
|
)
|
|
|
|
if cls._keys_to_ignore_on_load_unexpected is not None:
|
|
for pat in cls._keys_to_ignore_on_load_unexpected:
|
|
unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
|
|
|
|
if len(unexpected_keys) > 0:
|
|
print(
|
|
f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
|
|
)
|
|
|
|
params = [p.numel() if "." in n else 0 for n, p in model.named_parameters()]
|
|
print(f"### All Parameters: {sum(params) / 1e6} M")
|
|
|
|
params = [p.numel() if "attn1." in n else 0 for n, p in model.named_parameters()]
|
|
print(f"### attn1 Parameters: {sum(params) / 1e6} M")
|
|
return model
|
|
except Exception as e:
|
|
import traceback
|
|
traceback.print_exc()
|
|
print(
|
|
f"The low_cpu_mem_usage mode is not work because {e}. Use low_cpu_mem_usage=False instead."
|
|
)
|
|
|
|
model = cls.from_config(config, **transformer_additional_kwargs)
|
|
if os.path.exists(model_file):
|
|
state_dict = torch.load(model_file, map_location="cpu", weights_only=True)
|
|
elif os.path.exists(model_file_safetensors):
|
|
from safetensors.torch import load_file, safe_open
|
|
state_dict = load_file(model_file_safetensors)
|
|
else:
|
|
from safetensors.torch import load_file, safe_open
|
|
model_files_safetensors = glob.glob(os.path.join(pretrained_model_path, "*.safetensors"))
|
|
state_dict = {}
|
|
for _model_file_safetensors in model_files_safetensors:
|
|
_state_dict = load_file(_model_file_safetensors)
|
|
for key in _state_dict:
|
|
state_dict[key] = _state_dict[key]
|
|
if len(state_dict) == 0:
|
|
raise FileNotFoundError(f"No weights found in {pretrained_model_path}")
|
|
|
|
model_state_dict = model.state_dict()
|
|
tmp_state_dict = {}
|
|
for key in state_dict:
|
|
if key in model_state_dict.keys() and model_state_dict[key].size() == state_dict[key].size():
|
|
tmp_state_dict[key] = state_dict[key]
|
|
else:
|
|
print(key, "Size don't match, skip")
|
|
|
|
for key in model_state_dict:
|
|
if "control" in key and key.replace("control_", "") in state_dict.keys() and model_state_dict[key].size() == state_dict[key.replace("control_", "")].size():
|
|
tmp_state_dict[key] = state_dict[key.replace("control_", "")].clone()
|
|
print(f"Initializing missing parameter '{key}' with model.state_dict().")
|
|
|
|
state_dict = tmp_state_dict
|
|
|
|
m, u = model.load_state_dict(state_dict, strict=False)
|
|
print(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};")
|
|
print(m)
|
|
|
|
params = [p.numel() if "." in n else 0 for n, p in model.named_parameters()]
|
|
print(f"### All Parameters: {sum(params) / 1e6} M")
|
|
|
|
params = [p.numel() if "attn1." in n else 0 for n, p in model.named_parameters()]
|
|
print(f"### attn1 Parameters: {sum(params) / 1e6} M")
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model = model.to(torch_dtype)
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return model |