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from dataclasses import dataclass
import torch
from torch import Tensor, nn
from einops import rearrange
from .modules.layers import (DoubleStreamBlock, EmbedND, LastLayer,
MLPEmbedder, SingleStreamBlock,
timestep_embedding)
@dataclass
class FluxParams:
in_channels: int
vec_in_dim: int
context_in_dim: int
hidden_size: int
mlp_ratio: float
num_heads: int
depth: int
depth_single_blocks: int
axes_dim: list[int]
theta: int
qkv_bias: bool
guidance_embed: bool
def zero_module(module):
for p in module.parameters():
nn.init.zeros_(p)
return module
class ControlNetFlux(nn.Module):
"""
Transformer model for flow matching on sequences.
"""
_supports_gradient_checkpointing = True
def __init__(self, params: FluxParams, controlnet_depth=2):
super().__init__()
self.params = params
self.in_channels = params.in_channels
self.out_channels = self.in_channels
if params.hidden_size % params.num_heads != 0:
raise ValueError(
f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
)
pe_dim = params.hidden_size // params.num_heads
if sum(params.axes_dim) != pe_dim:
raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
self.hidden_size = params.hidden_size
self.num_heads = params.num_heads
self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
self.guidance_in = (
MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity()
)
self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
self.double_blocks = nn.ModuleList(
[
DoubleStreamBlock(
self.hidden_size,
self.num_heads,
mlp_ratio=params.mlp_ratio,
qkv_bias=params.qkv_bias,
)
for _ in range(controlnet_depth)
]
)
# add ControlNet blocks
self.controlnet_blocks = nn.ModuleList([])
for _ in range(controlnet_depth):
controlnet_block = nn.Linear(self.hidden_size, self.hidden_size)
controlnet_block = zero_module(controlnet_block)
self.controlnet_blocks.append(controlnet_block)
self.pos_embed_input = nn.Linear(self.in_channels, self.hidden_size, bias=True)
self.gradient_checkpointing = False
self.input_hint_block = nn.Sequential(
nn.Conv2d(3, 16, 3, padding=1),
nn.SiLU(),
nn.Conv2d(16, 16, 3, padding=1),
nn.SiLU(),
nn.Conv2d(16, 16, 3, padding=1, stride=2),
nn.SiLU(),
nn.Conv2d(16, 16, 3, padding=1),
nn.SiLU(),
nn.Conv2d(16, 16, 3, padding=1, stride=2),
nn.SiLU(),
nn.Conv2d(16, 16, 3, padding=1),
nn.SiLU(),
nn.Conv2d(16, 16, 3, padding=1, stride=2),
nn.SiLU(),
zero_module(nn.Conv2d(16, 16, 3, padding=1))
)
def _set_gradient_checkpointing(self, module, value=False):
if hasattr(module, "gradient_checkpointing"):
module.gradient_checkpointing = value
@property
def attn_processors(self):
# set recursively
processors = {}
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors):
if hasattr(module, "set_processor"):
processors[f"{name}.processor"] = module.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
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)
def forward(
self,
img: Tensor,
img_ids: Tensor,
controlnet_cond: Tensor,
txt: Tensor,
txt_ids: Tensor,
timesteps: Tensor,
y: Tensor,
guidance: Tensor | None = None,
) -> Tensor:
if img.ndim != 3 or txt.ndim != 3:
raise ValueError("Input img and txt tensors must have 3 dimensions.")
# running on sequences img
img = self.img_in(img)
controlnet_cond = self.input_hint_block(controlnet_cond)
controlnet_cond = rearrange(controlnet_cond, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
controlnet_cond = self.pos_embed_input(controlnet_cond)
img = img + controlnet_cond
vec = self.time_in(timestep_embedding(timesteps, 256))
if self.params.guidance_embed:
if guidance is None:
raise ValueError("Didn't get guidance strength for guidance distilled model.")
vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
vec = vec + self.vector_in(y)
txt = self.txt_in(txt)
ids = torch.cat((txt_ids, img_ids), dim=1)
pe = self.pe_embedder(ids)
block_res_samples = ()
for block in self.double_blocks:
if self.training and self.gradient_checkpointing:
def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
if return_dict is not None:
return module(*inputs, return_dict=return_dict)
else:
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),
img,
txt,
vec,
pe,
)
else:
img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
block_res_samples = block_res_samples + (img,)
controlnet_block_res_samples = ()
for block_res_sample, controlnet_block in zip(block_res_samples, self.controlnet_blocks):
block_res_sample = controlnet_block(block_res_sample)
controlnet_block_res_samples = controlnet_block_res_samples + (block_res_sample,)
return controlnet_block_res_samples
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