| 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 |
|
|
