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from dataclasses import dataclass
import torch
import torch.nn as nn
from typing import Optional
from diffusers.models.embeddings import Timesteps
import math
import craftsman
from craftsman.models.transformers.attention import ResidualAttentionBlock
from craftsman.models.transformers.utils import init_linear, MLP
from craftsman.utils.base import BaseModule
class UNetDiffusionTransformer(nn.Module):
def __init__(
self,
*,
n_ctx: int,
width: int,
layers: int,
heads: int,
init_scale: float = 0.25,
qkv_bias: bool = False,
skip_ln: bool = False,
use_checkpoint: bool = False
):
super().__init__()
self.n_ctx = n_ctx
self.width = width
self.layers = layers
self.encoder = nn.ModuleList()
for _ in range(layers):
resblock = ResidualAttentionBlock(
n_ctx=n_ctx,
width=width,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
use_checkpoint=use_checkpoint
)
self.encoder.append(resblock)
self.middle_block = ResidualAttentionBlock(
n_ctx=n_ctx,
width=width,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
use_checkpoint=use_checkpoint
)
self.decoder = nn.ModuleList()
for _ in range(layers):
resblock = ResidualAttentionBlock(
n_ctx=n_ctx,
width=width,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
use_checkpoint=use_checkpoint
)
linear = nn.Linear(width * 2, width)
init_linear(linear, init_scale)
layer_norm = nn.LayerNorm(width) if skip_ln else None
self.decoder.append(nn.ModuleList([resblock, linear, layer_norm]))
def forward(self, x: torch.Tensor):
enc_outputs = []
for block in self.encoder:
x = block(x)
enc_outputs.append(x)
x = self.middle_block(x)
for i, (resblock, linear, layer_norm) in enumerate(self.decoder):
x = torch.cat([enc_outputs.pop(), x], dim=-1)
x = linear(x)
if layer_norm is not None:
x = layer_norm(x)
x = resblock(x)
return x
@craftsman.register("simple-denoiser")
class SimpleDenoiser(BaseModule):
@dataclass
class Config(BaseModule.Config):
pretrained_model_name_or_path: Optional[str] = None
input_channels: int = 32
output_channels: int = 32
n_ctx: int = 512
width: int = 768
layers: int = 6
heads: int = 12
context_dim: int = 1024
context_ln: bool = True
skip_ln: bool = False
init_scale: float = 0.25
flip_sin_to_cos: bool = False
use_checkpoint: bool = False
cfg: Config
def configure(self) -> None:
super().configure()
init_scale = self.cfg.init_scale * math.sqrt(1.0 / self.cfg.width)
self.backbone = UNetDiffusionTransformer(
n_ctx=self.cfg.n_ctx,
width=self.cfg.width,
layers=self.cfg.layers,
heads=self.cfg.heads,
skip_ln=self.cfg.skip_ln,
init_scale=init_scale,
use_checkpoint=self.cfg.use_checkpoint
)
self.ln_post = nn.LayerNorm(self.cfg.width)
self.input_proj = nn.Linear(self.cfg.input_channels, self.cfg.width)
self.output_proj = nn.Linear(self.cfg.width, self.cfg.output_channels)
# timestep embedding
self.time_embed = Timesteps(self.cfg.width, flip_sin_to_cos=self.cfg.flip_sin_to_cos, downscale_freq_shift=0)
self.time_proj = MLP(width=self.cfg.width, init_scale=init_scale)
if self.cfg.context_ln:
self.context_embed = nn.Sequential(
nn.LayerNorm(self.cfg.context_dim),
nn.Linear(self.cfg.context_dim, self.cfg.width),
)
else:
self.context_embed = nn.Linear(self.cfg.context_dim, self.cfg.width)
if self.cfg.pretrained_model_name_or_path:
pretrained_ckpt = torch.load(self.cfg.pretrained_model_name_or_path, map_location="cpu")
_pretrained_ckpt = {}
for k, v in pretrained_ckpt.items():
if k.startswith('denoiser_model.'):
_pretrained_ckpt[k.replace('denoiser_model.', '')] = v
pretrained_ckpt = _pretrained_ckpt
if 'state_dict' in pretrained_ckpt:
_pretrained_ckpt = {}
for k, v in pretrained_ckpt['state_dict'].items():
if k.startswith('denoiser_model.'):
_pretrained_ckpt[k.replace('denoiser_model.', '')] = v
pretrained_ckpt = _pretrained_ckpt
self.load_state_dict(pretrained_ckpt, strict=True)
def forward(self,
model_input: torch.FloatTensor,
timestep: torch.LongTensor,
context: torch.FloatTensor):
r"""
Args:
model_input (torch.FloatTensor): [bs, n_data, c]
timestep (torch.LongTensor): [bs,]
context (torch.FloatTensor): [bs, context_tokens, c]
Returns:
sample (torch.FloatTensor): [bs, n_data, c]
"""
_, n_data, _ = model_input.shape
# 1. time
t_emb = self.time_proj(self.time_embed(timestep)).unsqueeze(dim=1)
# 2. conditions projector
context = self.context_embed(context)
# 3. denoiser
x = self.input_proj(model_input)
x = torch.cat([t_emb, context, x], dim=1)
x = self.backbone(x)
x = self.ln_post(x)
x = x[:, -n_data:] # B, n_data, width
sample = self.output_proj(x) # B, n_data, embed_dim
return sample |