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# Copyright 2024 Black Forest Labs, The HuggingFace Team and The InstantX Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.activations import FP32SiLU, get_activation
from diffusers.models.embeddings import Timesteps, PixArtAlphaTextProjection
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.transformers.transformer_flux import AdaLayerNormContinuous, CombinedTimestepGuidanceTextProjEmbeddings, CombinedTimestepTextProjEmbeddings, EmbedND, FluxSingleTransformerBlock, FluxTransformerBlock
from diffusers.models.modeling_outputs import Transformer2DModelOutput
from diffusers.utils import logging
import torch
from torch import nn
logger = logging.get_logger(__name__)
class FluxTransformer2DModel(ModelMixin, ConfigMixin):
"""
The Transformer model introduced in Flux.
Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
Parameters:
patch_size (`int`): Patch size to turn the input data into small patches.
in_channels (`int`, *optional*, defaults to 16): The number of channels in the input.
num_layers (`int`, *optional*, defaults to 18): The number of layers of MMDiT blocks to use.
num_single_layers (`int`, *optional*, defaults to 18): The number of layers of single DiT blocks to use.
attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
num_attention_heads (`int`, *optional*, defaults to 18): The number of heads to use for multi-head attention.
joint_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
pooled_projection_dim (`int`): Number of dimensions to use when projecting the `pooled_projections`.
guidance_embeds (`bool`, defaults to False): Whether to use guidance embeddings.
"""
_supports_gradient_checkpointing = True
_no_split_modules = ["FluxTransformerBlock", "FluxSingleTransformerBlock"]
@register_to_config
def __init__(
self,
patch_size: int = 1,
in_channels: int = 64,
num_layers: int = 19,
num_single_layers: int = 38,
attention_head_dim: int = 128,
num_attention_heads: int = 24,
joint_attention_dim: int = 4096,
pooled_projection_dim: int = 768,
guidance_embeds: bool = False,
axes_dims_rope=(16, 56, 56),
device=None
):
super().__init__()
self.out_channels = in_channels
self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim
# self.pos_embed = FluxPosEmbed(theta=10000, axes_dim=axes_dims_rope)
self.pos_embed = EmbedND(dim=self.inner_dim, theta=10000, axes_dim=axes_dims_rope).to(device)
text_time_guidance_cls = (
CombinedTimestepGuidanceTextProjEmbeddings if guidance_embeds else CombinedTimestepTextProjEmbeddings
)
self.time_text_embed = text_time_guidance_cls(
embedding_dim=self.inner_dim, pooled_projection_dim=self.config.pooled_projection_dim
).to(device)
self.context_embedder = nn.Linear(self.config.joint_attention_dim, self.inner_dim).to(device)
self.x_embedder = nn.Linear(self.config.in_channels, self.inner_dim).to(device)
self.transformer_blocks = nn.ModuleList(
[
FluxTransformerBlock(
dim=self.inner_dim,
num_attention_heads=self.config.num_attention_heads,
attention_head_dim=self.config.attention_head_dim,
).to(device)
for i in range(self.config.num_layers)
]
)
self.single_transformer_blocks = nn.ModuleList(
[
FluxSingleTransformerBlock(
dim=self.inner_dim,
num_attention_heads=self.config.num_attention_heads,
attention_head_dim=self.config.attention_head_dim,
).to(device)
for i in range(self.config.num_single_layers)
]
)
self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6).to(device)
self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True).to(device)
self.pul_id = None
self.pul_id_weight = 1.0
self.gradient_checkpointing = False
@property
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
def attn_processors(self):
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: nn.Module, processors):
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):
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: 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 _set_gradient_checkpointing(self, module, value=False):
if hasattr(module, "gradient_checkpointing"):
module.gradient_checkpointing = value
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor = None,
pooled_projections: torch.Tensor = None,
timestep: torch.LongTensor = None,
img_ids: torch.Tensor = None,
txt_ids: torch.Tensor = None,
guidance: torch.Tensor = None,
joint_attention_kwargs = None,
controlnet_block_samples=None,
controlnet_single_block_samples=None,
return_dict: bool = True
):
"""
The [`FluxTransformer2DModel`] forward method.
Args:
hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
Input `hidden_states`.
encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
from the embeddings of input conditions.
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.
"""
hidden_states = self.x_embedder(hidden_states)
timestep = timestep.to(hidden_states.dtype) * 1000
if guidance is not None:
guidance = guidance.to(hidden_states.dtype) * 1000
else:
guidance = None
temb = (
self.time_text_embed(timestep, pooled_projections)
if guidance is None
else self.time_text_embed(timestep, guidance, pooled_projections)
)
encoder_hidden_states = self.context_embedder(encoder_hidden_states)
###
# Modified by huggingface/twodgirl.
# Code from diffusers and PuLID.
ids = torch.cat((txt_ids, img_ids), dim=1)
image_rotary_emb = self.pos_embed(ids)
ca_index = 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=temb,
image_rotary_emb=image_rotary_emb,
)
if index_block % self.pulid_double_interval == 0 and self.pul_id is not None:
weighted = self.pul_id_weight * self.pulid_ca[ca_index](self.pul_id, hidden_states.to(self.pul_id.dtype))
hidden_states = hidden_states + weighted.to(hidden_states.dtype)
ca_index += 1
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,
temb=temb,
image_rotary_emb=image_rotary_emb,
)
if index_block % self.pulid_single_interval == 0 and self.pul_id is not None:
encoder_hidden_states, real_ = hidden_states[:, :encoder_hidden_states.shape[1], ...], hidden_states[:, encoder_hidden_states.shape[1]:, ...]
weighted = self.pul_id_weight * self.pulid_ca[ca_index](self.pul_id, real_.to(self.pul_id.dtype))
real_ = real_ + weighted.to(real_.dtype)
hidden_states = torch.cat([encoder_hidden_states, real_], dim=1)
ca_index += 1
hidden_states = hidden_states[:, encoder_hidden_states.shape[1] :, ...]
hidden_states = self.norm_out(hidden_states, temb)
output = self.proj_out(hidden_states)
if not return_dict:
return (output,)
return Transformer2DModelOutput(sample=output)
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