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d09dc68 39c903a d09dc68 39c903a d09dc68 880c288 d09dc68 880c288 d09dc68 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 | #!/usr/bin/env python3
# Model for SupraMNiST-IMG-200k
from __future__ import annotations
from dataclasses import dataclass
from typing import Any, Optional
import torch
from diffusers import UNet2DConditionModel
from transformers import PreTrainedModel
from transformers.utils import ModelOutput
from .configuration import DigitDiffusionConfig
@dataclass
class DigitDiffusionOutput(ModelOutput):
sample: torch.FloatTensor | None = None
class DigitDiffusionModel(PreTrainedModel):
config_class = DigitDiffusionConfig
base_model_prefix = "unet"
main_input_name = "noisy_images"
all_tied_weights_keys = {}
def __init__(self, config: DigitDiffusionConfig) -> None:
super().__init__(config)
block_count = len(config.block_out_channels)
self.unet = UNet2DConditionModel(
sample_size=config.sample_size,
in_channels=config.in_channels,
out_channels=config.out_channels,
layers_per_block=config.layers_per_block,
block_out_channels=tuple(config.block_out_channels),
down_block_types=("DownBlock2D",) * block_count,
up_block_types=("UpBlock2D",) * block_count,
mid_block_type="UNetMidBlock2D",
norm_num_groups=config.norm_num_groups,
num_class_embeds=config.num_classes,
cross_attention_dim=config.cross_attention_dim,
class_embed_type=config.class_embed_type,
)
self.post_init()
def _init_weights(self, module):
# Diffusers initializes the UNet internally, so there is nothing extra
# to initialize here.
return
def _make_dummy_context(
self,
batch_size: int,
device: torch.device,
dtype: torch.dtype,
) -> torch.Tensor:
return torch.zeros(
batch_size,
1,
self.config.cross_attention_dim,
device=device,
dtype=dtype,
)
def _normalize_inputs(
self,
noisy_images: Optional[torch.Tensor] = None,
timesteps: Optional[torch.Tensor | int] = None,
sample: Optional[torch.Tensor] = None,
timestep: Optional[torch.Tensor | int] = None,
) -> tuple[torch.Tensor, torch.Tensor]:
if noisy_images is None:
noisy_images = sample
if timesteps is None:
timesteps = timestep
if noisy_images is None:
raise ValueError("Either `noisy_images` or `sample` must be provided.")
if timesteps is None:
raise ValueError("Either `timesteps` or `timestep` must be provided.")
if not torch.is_tensor(timesteps):
timesteps = torch.tensor(
timesteps,
device=noisy_images.device,
dtype=torch.long,
)
if timesteps.ndim == 0:
timesteps = timesteps.expand(noisy_images.shape[0])
elif timesteps.shape[0] != noisy_images.shape[0]:
timesteps = timesteps.reshape(-1)
if timesteps.numel() == 1:
timesteps = timesteps.expand(noisy_images.shape[0])
elif timesteps.shape[0] != noisy_images.shape[0]:
raise ValueError(
"Timesteps must be a scalar, a batch-sized tensor, or a single-value tensor."
)
return noisy_images, timesteps.to(device=noisy_images.device, dtype=torch.long)
def forward(
self,
noisy_images: Optional[torch.Tensor] = None,
timesteps: Optional[torch.Tensor | int] = None,
class_labels: Optional[torch.Tensor] = None,
sample: Optional[torch.Tensor] = None,
timestep: Optional[torch.Tensor | int] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
return_dict: bool = True,
**kwargs: Any,
):
noisy_images, timesteps = self._normalize_inputs(
noisy_images=noisy_images,
timesteps=timesteps,
sample=sample,
timestep=timestep,
)
batch_size = noisy_images.shape[0]
if class_labels is None:
class_labels = torch.zeros(
batch_size,
device=noisy_images.device,
dtype=torch.long,
)
else:
class_labels = class_labels.to(device=noisy_images.device, dtype=torch.long)
if encoder_hidden_states is None:
encoder_hidden_states = self._make_dummy_context(
batch_size=batch_size,
device=noisy_images.device,
dtype=noisy_images.dtype,
)
noise_pred = self.unet(
sample=noisy_images,
timestep=timesteps,
encoder_hidden_states=encoder_hidden_states,
class_labels=class_labels,
return_dict=True,
**kwargs,
).sample
if return_dict:
return DigitDiffusionOutput(sample=noise_pred)
return (noise_pred,)
def load_state_dict(self, state_dict, strict: bool = True, assign: bool = False):
if state_dict:
keys = list(state_dict.keys())
has_prefixed = any(k.startswith("unet.") for k in keys)
has_plain_unet = any(
k.startswith(
(
"conv_in.",
"conv_norm_out.",
"conv_out.",
"time_embedding.",
"class_embedding.",
"down_blocks.",
"up_blocks.",
"mid_block.",
)
)
for k in keys
)
if has_plain_unet and not has_prefixed:
state_dict = {f"unet.{k}": v for k, v in state_dict.items()}
return super().load_state_dict(state_dict, strict=strict, assign=assign)
DigitDiffusionModel.register_for_auto_class("AutoModel")
|