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# Copyright 2025 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 dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
import torch.nn as nn
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import PeftAdapterMixin, FromOriginalModelMixin
from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, logging, scale_lora_layers, unscale_lora_layers
from diffusers.models.attention_processor import AttentionProcessor
from diffusers.models.cache_utils import CacheMixin
from diffusers.models.controlnets.controlnet import zero_module
from diffusers.models.modeling_outputs import Transformer2DModelOutput
from diffusers.models.modeling_utils import ModelMixin
# from diffusers.models.transformers.transformer_qwenimage import QwenImageTransformerBlock, QwenTimestepProjEmbeddings, QwenEmbedRope, RMSNorm
from transformer_qwenimage import QwenImageTransformerBlock, QwenTimestepProjEmbeddings, QwenEmbedRope, RMSNorm
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
@dataclass
class QwenImageControlNetOutput(BaseOutput):
controlnet_block_samples: Tuple[torch.Tensor]
class QwenImageControlNetModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, CacheMixin):
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
patch_size: int = 2,
in_channels: int = 64,
out_channels: Optional[int] = 16,
num_layers: int = 60,
attention_head_dim: int = 128,
num_attention_heads: int = 24,
joint_attention_dim: int = 3584,
axes_dims_rope: Tuple[int, int, int] = (16, 56, 56),
extra_condition_channels: int = 0, # for controlnet-inpainting
):
super().__init__()
self.out_channels = out_channels or in_channels
self.inner_dim = num_attention_heads * attention_head_dim
self.pos_embed = QwenEmbedRope(theta=10000, axes_dim=list(axes_dims_rope), scale_rope=True)
self.time_text_embed = QwenTimestepProjEmbeddings(embedding_dim=self.inner_dim)
self.txt_norm = RMSNorm(joint_attention_dim, eps=1e-6)
self.img_in = nn.Linear(in_channels, self.inner_dim)
self.txt_in = nn.Linear(joint_attention_dim, self.inner_dim)
self.transformer_blocks = nn.ModuleList(
[
QwenImageTransformerBlock(
dim=self.inner_dim,
num_attention_heads=num_attention_heads,
attention_head_dim=attention_head_dim,
)
for _ in range(num_layers)
]
)
# controlnet_blocks
self.controlnet_blocks = nn.ModuleList([])
for _ in range(len(self.transformer_blocks)):
self.controlnet_blocks.append(zero_module(nn.Linear(self.inner_dim, self.inner_dim)))
self.controlnet_x_embedder = zero_module(torch.nn.Linear(in_channels + extra_condition_channels, self.inner_dim))
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: 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):
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)
@classmethod
def from_transformer(
cls,
transformer,
num_layers: int = 5,
attention_head_dim: int = 128,
num_attention_heads: int = 24,
load_weights_from_transformer=True,
extra_condition_channels: int = 0,
):
config = dict(transformer.config)
config["num_layers"] = num_layers
config["attention_head_dim"] = attention_head_dim
config["num_attention_heads"] = num_attention_heads
config["extra_condition_channels"] = extra_condition_channels
controlnet = cls.from_config(config)
if load_weights_from_transformer:
controlnet.pos_embed.load_state_dict(transformer.pos_embed.state_dict())
controlnet.time_text_embed.load_state_dict(transformer.time_text_embed.state_dict())
controlnet.img_in.load_state_dict(transformer.img_in.state_dict())
controlnet.txt_in.load_state_dict(transformer.txt_in.state_dict())
controlnet.transformer_blocks.load_state_dict(transformer.transformer_blocks.state_dict(), strict=False)
controlnet.controlnet_x_embedder = zero_module(controlnet.controlnet_x_embedder)
return controlnet
def forward(
self,
hidden_states: torch.Tensor,
controlnet_cond: torch.Tensor,
conditioning_scale: float = 1.0,
encoder_hidden_states: torch.Tensor = None,
encoder_hidden_states_mask: torch.Tensor = None,
timestep: torch.LongTensor = None,
img_shapes: Optional[List[Tuple[int, int, int]]] = None,
txt_seq_lens: Optional[List[int]] = None,
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
return_dict: bool = True,
) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
"""
The [`FluxTransformer2DModel`] forward method.
Args:
hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
Input `hidden_states`.
controlnet_cond (`torch.Tensor`):
The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
conditioning_scale (`float`, defaults to `1.0`):
The scale factor for ControlNet outputs.
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.
"""
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
if USE_PEFT_BACKEND:
# weight the lora layers by setting `lora_scale` for each PEFT layer
scale_lora_layers(self, lora_scale)
else:
if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
logger.warning(
"Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
)
hidden_states = self.img_in(hidden_states)
# add
hidden_states = hidden_states + self.controlnet_x_embedder(controlnet_cond)
temb = self.time_text_embed(timestep, hidden_states)
image_rotary_emb = self.pos_embed(img_shapes, txt_seq_lens, device=hidden_states.device)
timestep = timestep.to(hidden_states.dtype)
encoder_hidden_states = self.txt_norm(encoder_hidden_states)
encoder_hidden_states = self.txt_in(encoder_hidden_states)
block_samples = ()
for index_block, block in enumerate(self.transformer_blocks):
if torch.is_grad_enabled() and self.gradient_checkpointing:
encoder_hidden_states, hidden_states = self._gradient_checkpointing_func(
block,
hidden_states,
encoder_hidden_states,
encoder_hidden_states_mask,
temb,
image_rotary_emb,
)
else:
encoder_hidden_states, hidden_states = block(
hidden_states=hidden_states,
encoder_hidden_states=encoder_hidden_states,
encoder_hidden_states_mask=encoder_hidden_states_mask,
temb=temb,
image_rotary_emb=image_rotary_emb,
joint_attention_kwargs=joint_attention_kwargs,
)
block_samples = block_samples + (hidden_states,)
# controlnet block
controlnet_block_samples = ()
for block_sample, controlnet_block in zip(block_samples, self.controlnet_blocks):
block_sample = controlnet_block(block_sample)
controlnet_block_samples = controlnet_block_samples + (block_sample,)
# scaling
controlnet_block_samples = [sample * conditioning_scale for sample in controlnet_block_samples]
controlnet_block_samples = None if len(controlnet_block_samples) == 0 else controlnet_block_samples
if USE_PEFT_BACKEND:
# remove `lora_scale` from each PEFT layer
unscale_lora_layers(self, lora_scale)
if not return_dict:
return (controlnet_block_samples)
return QwenImageControlNetOutput(
controlnet_block_samples=controlnet_block_samples,
)
class QwenImageMultiControlNetModel(ModelMixin):
r"""
`QwenImageMultiControlNetModel` wrapper class for Multi-QwenImageControlNetModel
This module is a wrapper for multiple instances of the `QwenImageControlNetModel`. The `forward()` API is designed to be
compatible with `QwenImageControlNetModel`.
Args:
controlnets (`List[QwenImageControlNetModel]`):
Provides additional conditioning to the unet during the denoising process. You must set multiple
`QwenImageControlNetModel` as a list.
"""
def __init__(self, controlnets):
super().__init__()
self.nets = nn.ModuleList(controlnets)
def forward(
self,
hidden_states: torch.FloatTensor,
controlnet_cond: List[torch.tensor],
conditioning_scale: List[float],
encoder_hidden_states: torch.Tensor = None,
encoder_hidden_states_mask: torch.Tensor = None,
timestep: torch.LongTensor = None,
img_shapes: Optional[List[Tuple[int, int, int]]] = None,
txt_seq_lens: Optional[List[int]] = None,
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
return_dict: bool = True,
) -> Union[QwenImageControlNetOutput, Tuple]:
# ControlNet-Union with multiple conditions
# only load one ControlNet for saving memories
if len(self.nets) == 1:
controlnet = self.nets[0]
for i, (image, scale) in enumerate(zip(controlnet_cond, conditioning_scale)):
block_samples = controlnet(
hidden_states=hidden_states,
controlnet_cond=image,
conditioning_scale=scale,
encoder_hidden_states=encoder_hidden_states,
encoder_hidden_states_mask=encoder_hidden_states_mask,
timestep=timestep,
img_shapes=img_shapes,
txt_seq_lens=txt_seq_lens,
joint_attention_kwargs=joint_attention_kwargs,
return_dict=return_dict,
)
# merge samples
if i == 0:
control_block_samples = block_samples
else:
if block_samples is not None and control_block_samples is not None:
control_block_samples = [
control_block_sample + block_sample
for control_block_sample, block_sample in zip(control_block_samples, block_samples)
]
else:
raise ValueError("QwenImageMultiControlNetModel only supports controlnet-union now.")
return control_block_samples |