246 lines
8.8 KiB
Python
246 lines
8.8 KiB
Python
import math
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from functools import partial
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import torch
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from einops import rearrange, repeat
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from scepter.modules.model.base_model import BaseModel
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from scepter.modules.model.registry import BACKBONES
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from scepter.modules.utils.config import dict_to_yaml
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from scepter.modules.utils.distribute import we
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from scepter.modules.utils.file_system import FS
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from torch import Tensor, nn
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from torch.utils.checkpoint import checkpoint_sequential
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from .layers import (DoubleStreamBlock, EmbedND, LastLayer,
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MLPEmbedder, SingleStreamBlock,
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timestep_embedding)
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@BACKBONES.register_class()
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class Flux(BaseModel):
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"""
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Transformer backbone Diffusion model with RoPE.
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"""
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para_dict = {
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"IN_CHANNELS": {
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"value": 64,
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"description": "model's input channels."
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},
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"OUT_CHANNELS": {
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"value": 64,
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"description": "model's output channels."
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},
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"HIDDEN_SIZE": {
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"value": 1024,
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"description": "model's hidden size."
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},
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"NUM_HEADS": {
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"value": 16,
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"description": "number of heads in the transformer."
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},
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"AXES_DIM": {
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"value": [16, 56, 56],
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"description": "dimensions of the axes of the positional encoding."
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},
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"THETA": {
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"value": 10_000,
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"description": "theta for positional encoding."
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},
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"VEC_IN_DIM": {
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"value": 768,
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"description": "dimension of the vector input."
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},
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"GUIDANCE_EMBED": {
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"value": False,
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"description": "whether to use guidance embedding."
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},
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"CONTEXT_IN_DIM": {
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"value": 4096,
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"description": "dimension of the context input."
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},
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"MLP_RATIO": {
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"value": 4.0,
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"description": "ratio of mlp hidden size to hidden size."
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},
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"QKV_BIAS": {
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"value": True,
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"description": "whether to use bias in qkv projection."
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},
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"DEPTH": {
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"value": 19,
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"description": "number of transformer blocks."
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},
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"DEPTH_SINGLE_BLOCKS": {
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"value": 38,
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"description": "number of transformer blocks in the single stream block."
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},
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"USE_GRAD_CHECKPOINT": {
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"value": False,
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"description": "whether to use gradient checkpointing."
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}
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}
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def __init__(
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self,
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cfg,
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logger = None
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):
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super().__init__(cfg, logger=logger)
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self.in_channels = cfg.IN_CHANNELS
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self.out_channels = cfg.get("OUT_CHANNELS", self.in_channels)
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hidden_size = cfg.get("HIDDEN_SIZE", 1024)
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num_heads = cfg.get("NUM_HEADS", 16)
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axes_dim = cfg.AXES_DIM
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theta = cfg.THETA
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vec_in_dim = cfg.VEC_IN_DIM
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self.guidance_embed = cfg.GUIDANCE_EMBED
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context_in_dim = cfg.CONTEXT_IN_DIM
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mlp_ratio = cfg.MLP_RATIO
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qkv_bias = cfg.QKV_BIAS
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depth = cfg.DEPTH
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depth_single_blocks = cfg.DEPTH_SINGLE_BLOCKS
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self.use_grad_checkpoint = cfg.get("USE_GRAD_CHECKPOINT", False)
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if hidden_size % num_heads != 0:
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raise ValueError(
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f"Hidden size {hidden_size} must be divisible by num_heads {num_heads}"
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)
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pe_dim = hidden_size // num_heads
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if sum(axes_dim) != pe_dim:
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raise ValueError(f"Got {axes_dim} but expected positional dim {pe_dim}")
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self.hidden_size = hidden_size
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self.num_heads = num_heads
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self.pe_embedder = EmbedND(dim=pe_dim, theta=theta, axes_dim= axes_dim)
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self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
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self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
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self.vector_in = MLPEmbedder(vec_in_dim, self.hidden_size)
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self.guidance_in = (
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MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if self.guidance_embed else nn.Identity()
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)
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self.txt_in = nn.Linear(context_in_dim, self.hidden_size)
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self.double_blocks = nn.ModuleList(
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[
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DoubleStreamBlock(
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self.hidden_size,
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self.num_heads,
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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)
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for _ in range(depth)
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]
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)
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self.single_blocks = nn.ModuleList(
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[
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SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=mlp_ratio)
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for _ in range(depth_single_blocks)
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]
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)
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self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels)
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def prepare_input(self, x, context, y, x_shape=None):
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# x.shape [6, 16, 16, 16] target is [6, 16, 768, 1360]
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bs, c, h, w = x.shape
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x = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
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x_id = torch.zeros(h // 2, w // 2, 3)
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x_id[..., 1] = x_id[..., 1] + torch.arange(h // 2)[:, None]
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x_id[..., 2] = x_id[..., 2] + torch.arange(w // 2)[None, :]
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x_ids = repeat(x_id, "h w c -> b (h w) c", b=bs)
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txt_ids = torch.zeros(bs, context.shape[1], 3)
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return x, x_ids.to(x), context.to(x), txt_ids.to(x), y.to(x), h, w
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def unpack(self, x: Tensor, height: int, width: int) -> Tensor:
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return rearrange(
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x,
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"b (h w) (c ph pw) -> b c (h ph) (w pw)",
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h=math.ceil(height/2),
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w=math.ceil(width/2),
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ph=2,
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pw=2,
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)
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def load_pretrained_model(self, pretrained_model):
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if next(self.parameters()).device.type == 'meta':
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map_location = we.device_id
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else:
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map_location = "cpu"
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if pretrained_model is not None:
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with FS.get_from(pretrained_model, wait_finish=True) as local_model:
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if local_model.endswith('safetensors'):
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from safetensors.torch import load_file as load_safetensors
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sd = load_safetensors(local_model, device=map_location)
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else:
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sd = torch.load(local_model, map_location=map_location)
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missing, unexpected = self.load_state_dict(sd, strict=False, assign=True)
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self.logger.info(
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f'Restored from {pretrained_model} with {len(missing)} missing and {len(unexpected)} unexpected keys'
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)
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if len(missing) > 0:
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self.logger.info(f'Missing Keys:\n {missing}')
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if len(unexpected) > 0:
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self.logger.info(f'\nUnexpected Keys:\n {unexpected}')
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def forward(
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self,
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x: Tensor,
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t: Tensor,
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cond: dict = {},
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guidance: Tensor | None = None,
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gc_seg: int = 0
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) -> Tensor:
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x, x_ids, txt, txt_ids, y, h, w = self.prepare_input(x, cond["context"], cond["y"])
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# running on sequences img
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x = self.img_in(x)
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vec = self.time_in(timestep_embedding(t, 256))
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if self.guidance_embed:
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if guidance is None:
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raise ValueError("Didn't get guidance strength for guidance distilled model.")
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vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
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vec = vec + self.vector_in(y)
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txt = self.txt_in(txt)
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ids = torch.cat((txt_ids, x_ids), dim=1)
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pe = self.pe_embedder(ids)
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kwargs = dict(
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vec=vec,
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pe=pe,
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txt_length=txt.shape[1],
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)
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x = torch.cat((txt, x), 1)
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if self.use_grad_checkpoint and gc_seg >= 0:
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x = checkpoint_sequential(
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functions=[partial(block, **kwargs) for block in self.double_blocks],
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segments=gc_seg if gc_seg > 0 else len(self.double_blocks),
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input=x,
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use_reentrant=False
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)
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else:
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for block in self.double_blocks:
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x = block(x, **kwargs)
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kwargs = dict(
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vec=vec,
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pe=pe,
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)
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if self.use_grad_checkpoint and gc_seg >= 0:
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x = checkpoint_sequential(
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functions=[partial(block, **kwargs) for block in self.single_blocks],
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segments=gc_seg if gc_seg > 0 else len(self.single_blocks),
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input=x,
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use_reentrant=False
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)
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else:
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for block in self.single_blocks:
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x = block(x, **kwargs)
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x = x[:, txt.shape[1] :, ...]
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x = self.final_layer(x, vec) # (N, T, patch_size ** 2 * out_channels) 6 64 64
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x = self.unpack(x, h, w)
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return x
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@staticmethod
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def get_config_template():
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return dict_to_yaml('MODEL',
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__class__.__name__,
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Flux.para_dict,
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set_name=True)
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