| | 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) |
| | ] |
| | ) |
| |
|
| | |
| | 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): |
| | |
| | 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.") |
| |
|
| | |
| | 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 |
| |
|
