A multi-modal foundation model for brain disease diagnosis and medical imaging.

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8 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

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[1] § Methods › Fine-tuning Brainfound to downstream tasks › Medical image report generation task ↔ downstream_task/report_gen/pretrain_model.py, lines 20–103 · score 0.66 · beam search, hidden layers, text decoder, vocabulary, BERT, models
[2] § Methods › Fine-tuning Brainfound to downstream tasks › ICH and midline structure segmentation task ↔ downstream_task/report_gen/pretrain.py, lines 309–419 · score 0.63 · AdamW, weight decay, location, optimizer, validation, maps
[3] § Methods › Fine-tuning Brainfound to downstream tasks › ICH and midline structure segmentation task ↔ pretrain_code/stage2/pretrain.py, lines 386–523 · score 0.63 · AdamW, weight decay, location, optimizer, validation, maps
[4] § Methods › Network architecture ↔ downstream_task/report_gen/modules/caption_modules.py, the whole file · a weak match · score 0.61 · multi head, position embedding, language model, tokens, transformer
[5] § Methods › Network architecture ↔ downstream_task/report_gen/UNet2d_condition.py, lines 814–889 · score 0.60 · UNet, cross attention, connected, deep, residual, width
[6] § Methods › Network architecture ↔ pretrain_code/stage3/UNet2d_condition.py, lines 810–885 · score 0.60 · UNet, cross attention, connected, deep, residual, width
[7] § Methods › Network architecture ↔ downstream_task/report_gen/modules/fusion_modules.py, lines 42–103 · score 0.60 · Positional encoding, transformer encoders, layer normalization, module, model
[8] § Methods › Visualization of saliency maps ↔ downstream_task/report_gen/UNet2d_condition.py, lines 1437–1535 · score 0.54 · Grad CAM, heatmap, bilinear, contour, weights

Paper

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The authors' code

Python · 1,535 lines · 75 KB · no license · 2 matches

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import safetensors
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
import cv2
import os
import torch.nn.functional as F
from diffusers.models.modeling_utils import ModelMixin
# from diffusers.models.unets.unet_2d_blocks import SimpleCrossAttnUpBlock2D
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import UNet2DConditionLoadersMixin
from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, logging, scale_lora_layers, unscale_lora_layers
from diffusers.models.activations import get_activation
from diffusers.models.attention_processor import (
ADDED_KV_ATTENTION_PROCESSORS,
CROSS_ATTENTION_PROCESSORS,
AttentionProcessor,
AttnAddedKVProcessor,
AttnProcessor,
)
from diffusers.models.embeddings import (
GaussianFourierProjection,
ImageHintTimeEmbedding,
ImageProjection,
ImageTimeEmbedding,
TextImageProjection,
TextImageTimeEmbedding,
TextTimeEmbedding,
TimestepEmbedding,
Timesteps,
)
from diffusers.models.unets.unet_2d_blocks import (
UNetMidBlock2D,
UNetMidBlock2DCrossAttn,
UNetMidBlock2DSimpleCrossAttn,
get_down_block,
get_up_block,
)
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
@dataclass
class UNet2DConditionOutput(BaseOutput):
"""
The output of [`UNet2DConditionModel`].
Args:
sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
"""
sample: torch.FloatTensor = None
class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
r"""
A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
shaped output.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Parameters:
sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
Height and width of input/output sample.
in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
Whether to flip the sin to cos in the time embedding.
freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
The tuple of downsample blocks to use.
mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or
`UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
The tuple of upsample blocks to use.
only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
Whether to include self-attention in the basic transformer blocks, see
[`~models.attention.BasicTransformerBlock`].
block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
The tuple of output channels for each block.
layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
If `None`, normalization and activation layers is skipped in post-processing.
norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
The dimension of the cross attention features.
transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):
The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
[`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
[`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None):
The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling
blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for
[`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
[`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
encoder_hid_dim (`int`, *optional*, defaults to None):
If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
dimension to `cross_attention_dim`.
encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
num_attention_heads (`int`, *optional*):
The number of attention heads. If not defined, defaults to `attention_head_dim`
resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
class_embed_type (`str`, *optional*, defaults to `None`):
The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
`"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
addition_embed_type (`str`, *optional*, defaults to `None`):
Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
"text". "text" will use the `TextTimeEmbedding` layer.
addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
Dimension for the timestep embeddings.
num_class_embeds (`int`, *optional*, defaults to `None`):
Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
class conditioning with `class_embed_type` equal to `None`.
time_embedding_type (`str`, *optional*, defaults to `positional`):
The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
time_embedding_dim (`int`, *optional*, defaults to `None`):
An optional override for the dimension of the projected time embedding.
time_embedding_act_fn (`str`, *optional*, defaults to `None`):
Optional activation function to use only once on the time embeddings before they are passed to the rest of
the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
timestep_post_act (`str`, *optional*, defaults to `None`):
The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
time_cond_proj_dim (`int`, *optional*, defaults to `None`):
The dimension of `cond_proj` layer in the timestep embedding.
conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. conv_out_kernel (`int`,
*optional*, default to `3`): The kernel size of `conv_out` layer. projection_class_embeddings_input_dim (`int`,
*optional*): The dimension of the `class_labels` input when
`class_embed_type="projection"`. Required when `class_embed_type="projection"`.
class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
embeddings with the class embeddings.
mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If
`only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
`only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
otherwise.
"""
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
sample_size: Optional[int] = None,
in_channels: int = 4,
out_channels: int = 4,
center_input_sample: bool = False,
flip_sin_to_cos: bool = True,
freq_shift: int = 0,
down_block_types: Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
),
mid_block_type: Optional[str] = "UNetMidBlock2D",
up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
only_cross_attention: Union[bool, Tuple[bool]] = False,
block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
layers_per_block: Union[int, Tuple[int]] = 2,
downsample_padding: int = 1,
mid_block_scale_factor: float = 1,
dropout: float = 0.0,
act_fn: str = "silu",
norm_num_groups: Optional[int] = 32,
norm_eps: float = 1e-5,
cross_attention_dim: Union[int, Tuple[int]] = 1280,
transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None,
encoder_hid_dim: Optional[int] = None,
encoder_hid_dim_type: Optional[str] = None,
attention_head_dim: Union[int, Tuple[int]] = 8,
num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
dual_cross_attention: bool = False,
use_linear_projection: bool = False,
class_embed_type: Optional[str] = None,
addition_embed_type: Optional[str] = None,
addition_time_embed_dim: Optional[int] = None,
num_class_embeds: Optional[int] = None,
upcast_attention: bool = False,
resnet_time_scale_shift: str = "default",
resnet_skip_time_act: bool = False,
resnet_out_scale_factor: int = 1.0,
time_embedding_type: str = "positional",
time_embedding_dim: Optional[int] = None,
time_embedding_act_fn: Optional[str] = None,
timestep_post_act: Optional[str] = None,
time_cond_proj_dim: Optional[int] = None,
conv_in_kernel: int = 3,
conv_out_kernel: int = 3,
projection_class_embeddings_input_dim: Optional[int] = None,
attention_type: str = "default",
class_embeddings_concat: bool = False,
mid_block_only_cross_attention: Optional[bool] = None,
cross_attention_norm: Optional[str] = None,
addition_embed_type_num_heads=64,
return_deep_fea=False,
):
super().__init__()
self.return_deep_fea = return_deep_fea
self.sample_size = sample_size
if num_attention_heads is not None:
raise ValueError(
"At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
)
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
num_attention_heads = num_attention_heads or attention_head_dim
# Check inputs
if len(down_block_types) != len(up_block_types):
raise ValueError(
f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
)
if len(block_out_channels) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
)
if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
)
if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
)
if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
)
if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
)
if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
)
if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None:
for layer_number_per_block in transformer_layers_per_block:
if isinstance(layer_number_per_block, list):
raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.")
# input
conv_in_padding = (conv_in_kernel - 1) // 2
self.conv_in = nn.Conv2d(
in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
)
# time
if time_embedding_type == "fourier":
time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
if time_embed_dim % 2 != 0:
raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
self.time_proj = GaussianFourierProjection(
time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
)
timestep_input_dim = time_embed_dim
elif time_embedding_type == "positional":
time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
timestep_input_dim = block_out_channels[0]
else:
raise ValueError(
f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
)
self.time_embedding = TimestepEmbedding(
timestep_input_dim,
time_embed_dim,
act_fn=act_fn,
post_act_fn=timestep_post_act,
cond_proj_dim=time_cond_proj_dim,
)
if encoder_hid_dim_type is None and encoder_hid_dim is not None:
encoder_hid_dim_type = "text_proj"
self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")
if encoder_hid_dim is None and encoder_hid_dim_type is not None:
raise ValueError(
f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
)
if encoder_hid_dim_type == "text_proj":
self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
elif encoder_hid_dim_type == "text_image_proj":
# image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
# case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
self.encoder_hid_proj = TextImageProjection(
text_embed_dim=encoder_hid_dim,
image_embed_dim=cross_attention_dim,
cross_attention_dim=cross_attention_dim,
)
elif encoder_hid_dim_type == "image_proj":
# Kandinsky 2.2
self.encoder_hid_proj = ImageProjection(
image_embed_dim=encoder_hid_dim,
cross_attention_dim=cross_attention_dim,
)
elif encoder_hid_dim_type is not None:
raise ValueError(
f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
)
else:
self.encoder_hid_proj = None
# class embedding
if class_embed_type is None and num_class_embeds is not None:
self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
elif class_embed_type == "timestep":
self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
elif class_embed_type == "identity":
self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
elif class_embed_type == "projection":
if projection_class_embeddings_input_dim is None:
raise ValueError(
"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
)
# The projection `class_embed_type` is the same as the timestep `class_embed_type` except
# 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
# 2. it projects from an arbitrary input dimension.
#
# Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
# When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
# As a result, `TimestepEmbedding` can be passed arbitrary vectors.
self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
elif class_embed_type == "simple_projection":
if projection_class_embeddings_input_dim is None:
raise ValueError(
"`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
)
self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
else:
self.class_embedding = None
if addition_embed_type == "text":
if encoder_hid_dim is not None:
text_time_embedding_from_dim = encoder_hid_dim
else:
text_time_embedding_from_dim = cross_attention_dim
self.add_embedding = TextTimeEmbedding(
text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
)
elif addition_embed_type == "text_image":
# text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
# case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
self.add_embedding = TextImageTimeEmbedding(
text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
)
elif addition_embed_type == "text_time":
self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
elif addition_embed_type == "image":
# Kandinsky 2.2
self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
elif addition_embed_type == "image_hint":
# Kandinsky 2.2 ControlNet
self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
elif addition_embed_type is not None:
raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
if time_embedding_act_fn is None:
self.time_embed_act = None
else:
self.time_embed_act = get_activation(time_embedding_act_fn)
self.down_blocks = nn.ModuleList([])
self.up_blocks = nn.ModuleList([])
if isinstance(only_cross_attention, bool):
if mid_block_only_cross_attention is None:
mid_block_only_cross_attention = only_cross_attention
only_cross_attention = [only_cross_attention] * len(down_block_types)
if mid_block_only_cross_attention is None:
mid_block_only_cross_attention = False
if isinstance(num_attention_heads, int):
num_attention_heads = (num_attention_heads,) * len(down_block_types)
if isinstance(attention_head_dim, int):
attention_head_dim = (attention_head_dim,) * len(down_block_types)
if isinstance(cross_attention_dim, int):
cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
if isinstance(layers_per_block, int):
layers_per_block = [layers_per_block] * len(down_block_types)
if isinstance(transformer_layers_per_block, int):
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
if class_embeddings_concat:
# The time embeddings are concatenated with the class embeddings. The dimension of the
# time embeddings passed to the down, middle, and up blocks is twice the dimension of the
# regular time embeddings
blocks_time_embed_dim = time_embed_dim * 2
else:
blocks_time_embed_dim = time_embed_dim
# down
output_channel = block_out_channels[0]
for i, down_block_type in enumerate(down_block_types):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
down_block = get_down_block(
down_block_type,
num_layers=layers_per_block[i],
transformer_layers_per_block=transformer_layers_per_block[i],
in_channels=input_channel,
out_channels=output_channel,
temb_channels=blocks_time_embed_dim,
add_downsample=not is_final_block,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim[i],
num_attention_heads=num_attention_heads[i],
downsample_padding=downsample_padding,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention[i],
upcast_attention=upcast_attention,
resnet_time_scale_shift=resnet_time_scale_shift,
attention_type=attention_type,
resnet_skip_time_act=resnet_skip_time_act,
resnet_out_scale_factor=resnet_out_scale_factor,
cross_attention_norm=cross_attention_norm,
attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
dropout=dropout,
)
self.down_blocks.append(down_block)
if not self.return_deep_fea:
# mid
if mid_block_type == "UNetMidBlock2DCrossAttn":
self.mid_block = UNetMidBlock2DCrossAttn(
transformer_layers_per_block=transformer_layers_per_block[-1],
in_channels=block_out_channels[-1],
temb_channels=blocks_time_embed_dim,
dropout=dropout,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_factor,
resnet_time_scale_shift=resnet_time_scale_shift,
cross_attention_dim=cross_attention_dim[-1],
num_attention_heads=num_attention_heads[-1],
resnet_groups=norm_num_groups,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
upcast_attention=upcast_attention,
attention_type=attention_type,
)
elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn":
self.mid_block = UNetMidBlock2DSimpleCrossAttn(
in_channels=block_out_channels[-1],
temb_channels=blocks_time_embed_dim,
dropout=dropout,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_factor,
cross_attention_dim=cross_attention_dim[-1],
attention_head_dim=attention_head_dim[-1],
resnet_groups=norm_num_groups,
resnet_time_scale_shift=resnet_time_scale_shift,
skip_time_act=resnet_skip_time_act,
only_cross_attention=mid_block_only_cross_attention,
cross_attention_norm=cross_attention_norm,
)
elif mid_block_type == "UNetMidBlock2D":
self.mid_block = UNetMidBlock2D(
in_channels=block_out_channels[-1],
temb_channels=blocks_time_embed_dim,
dropout=dropout,
# num_layers=0,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_factor,
resnet_groups=norm_num_groups,
resnet_time_scale_shift=resnet_time_scale_shift,
attn_groups=None,
#
add_attention=True,
attention_head_dim=attention_head_dim[-1] if attention_head_dim is not None else block_out_channels[-1],
)
elif mid_block_type is None:
self.mid_block = None
else:
raise ValueError(f"unknown mid_block_type : {mid_block_type}")
# count how many layers upsample the images
self.num_upsamplers = 0
# up
reversed_block_out_channels = list(reversed(block_out_channels))
reversed_num_attention_heads = list(reversed(num_attention_heads))
reversed_layers_per_block = list(reversed(layers_per_block))
reversed_cross_attention_dim = list(reversed(cross_attention_dim))
reversed_transformer_layers_per_block = (
list(reversed(transformer_layers_per_block))
if reverse_transformer_layers_per_block is None
else reverse_transformer_layers_per_block
)
only_cross_attention = list(reversed(only_cross_attention))
output_channel = reversed_block_out_channels[0]
for i, up_block_type in enumerate(up_block_types):
is_final_block = i == len(block_out_channels) - 1
prev_output_channel = output_channel
output_channel = reversed_block_out_channels[i]
input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
# add upsample block for all BUT final layer
if not is_final_block:
add_upsample = True
self.num_upsamplers += 1
else:
add_upsample = False
up_block = get_up_block(
up_block_type,
num_layers=reversed_layers_per_block[i] + 1,
transformer_layers_per_block=reversed_transformer_layers_per_block[i],
in_channels=input_channel,
out_channels=output_channel,
prev_output_channel=prev_output_channel,
temb_channels=blocks_time_embed_dim,
add_upsample=add_upsample,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resolution_idx=i,
resnet_groups=norm_num_groups,
cross_attention_dim=reversed_cross_attention_dim[i],
num_attention_heads=reversed_num_attention_heads[i],
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention[i],
upcast_attention=upcast_attention,
resnet_time_scale_shift=resnet_time_scale_shift,
attention_type=attention_type,
resnet_skip_time_act=resnet_skip_time_act,
resnet_out_scale_factor=resnet_out_scale_factor,
cross_attention_norm=cross_attention_norm,
attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
dropout=dropout,
)
self.up_blocks.append(up_block)
prev_output_channel = output_channel
# out
if norm_num_groups is not None:
self.conv_norm_out = nn.GroupNorm(
num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
)
self.conv_act = get_activation(act_fn)
else:
self.conv_norm_out = None
self.conv_act = None
conv_out_padding = (conv_out_kernel - 1) // 2
self.conv_out = nn.Conv2d(
block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
)
if attention_type in ["gated", "gated-text-image"]:
positive_len = 768
if isinstance(cross_attention_dim, int):
positive_len = cross_attention_dim
elif isinstance(cross_attention_dim, tuple) or isinstance(cross_attention_dim, list):
positive_len = cross_attention_dim[0]
feature_type = "text-only" if attention_type == "gated" else "text-image"
self.position_net = PositionNet(
positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type
)
@property
def attn_processors(self) -> Dict[str, AttentionProcessor]:
r"""
Returns:
`dict` of attention processors: A dictionary containing all attention processors used in the model with
indexed by its weight name.
"""
# set recursively
processors = {}
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
if hasattr(module, "get_processor"):
processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)
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: Union[AttentionProcessor, Dict[str, AttentionProcessor]], _remove_lora=False
):
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, _remove_lora=_remove_lora)
else:
module.set_processor(processor.pop(f"{name}.processor"), _remove_lora=_remove_lora)
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 set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
"""
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
processor = AttnAddedKVProcessor()
elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
processor = AttnProcessor()
else:
raise ValueError(
f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
)
self.set_attn_processor(processor, _remove_lora=True)
def set_attention_slice(self, slice_size):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module splits the input tensor in slices to compute attention in
several steps. This is useful for saving some memory in exchange for a small decrease in speed.
Args:
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
`"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
must be a multiple of `slice_size`.
"""
sliceable_head_dims = []
def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
if hasattr(module, "set_attention_slice"):
sliceable_head_dims.append(module.sliceable_head_dim)
for child in module.children():
fn_recursive_retrieve_sliceable_dims(child)
# retrieve number of attention layers
for module in self.children():
fn_recursive_retrieve_sliceable_dims(module)
num_sliceable_layers = len(sliceable_head_dims)
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = [dim // 2 for dim in sliceable_head_dims]
elif slice_size == "max":
# make smallest slice possible
slice_size = num_sliceable_layers * [1]
slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
if len(slice_size) != len(sliceable_head_dims):
raise ValueError(
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
)
for i in range(len(slice_size)):
size = slice_size[i]
dim = sliceable_head_dims[i]
if size is not None and size > dim:
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
# Recursively walk through all the children.
# Any children which exposes the set_attention_slice method
# gets the message
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
if hasattr(module, "set_attention_slice"):
module.set_attention_slice(slice_size.pop())
for child in module.children():
fn_recursive_set_attention_slice(child, slice_size)
reversed_slice_size = list(reversed(slice_size))
for module in self.children():
fn_recursive_set_attention_slice(module, reversed_slice_size)
def _set_gradient_checkpointing(self, module, value=False):
if hasattr(module, "gradient_checkpointing"):
module.gradient_checkpointing = value
def enable_freeu(self, s1, s2, b1, b2):
r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.
The suffixes after the scaling factors represent the stage blocks where they are being applied.
Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that
are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
Args:
s1 (`float`):
Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
mitigate the "oversmoothing effect" in the enhanced denoising process.
s2 (`float`):
Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
mitigate the "oversmoothing effect" in the enhanced denoising process.
b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
"""
for i, upsample_block in enumerate(self.up_blocks):
setattr(upsample_block, "s1", s1)
setattr(upsample_block, "s2", s2)
setattr(upsample_block, "b1", b1)
setattr(upsample_block, "b2", b2)
def disable_freeu(self):
"""Disables the FreeU mechanism."""
freeu_keys = {"s1", "s2", "b1", "b2"}
for i, upsample_block in enumerate(self.up_blocks):
for k in freeu_keys:
if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:
setattr(upsample_block, k, None)
def forward(
self,
sample: torch.FloatTensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
class_labels: Optional[torch.Tensor] = None,
timestep_cond: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
mid_block_additional_residual: Optional[torch.Tensor] = None,
down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
return_dict: bool = True,
) -> Union[UNet2DConditionOutput, Tuple]:
r"""
The [`UNet2DConditionModel`] forward method.
Args:
sample (`torch.FloatTensor`):
The noisy input tensor with the following shape `(batch, channel, height, width)`.
timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
encoder_hidden_states (`torch.FloatTensor`):
The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
class_labels (`torch.Tensor`, *optional*, defaults to `None`):
Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):
Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed
through the `self.time_embedding` layer to obtain the timestep embeddings.
attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
negative values to the attention scores corresponding to "discard" tokens.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
added_cond_kwargs: (`dict`, *optional*):
A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that
are passed along to the UNet blocks.
down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):
A tuple of tensors that if specified are added to the residuals of down unet blocks.
mid_block_additional_residual: (`torch.Tensor`, *optional*):
A tensor that if specified is added to the residual of the middle unet block.
encoder_attention_mask (`torch.Tensor`):
A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
`True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
which adds large negative values to the attention scores corresponding to "discard" tokens.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
tuple.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
added_cond_kwargs: (`dict`, *optional*):
A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
are passed along to the UNet blocks.
down_block_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
additional residuals to be added to UNet long skip connections from down blocks to up blocks for
example from ControlNet side model(s)
mid_block_additional_residual (`torch.Tensor`, *optional*):
additional residual to be added to UNet mid block output, for example from ControlNet side model
down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s)
Returns:
[`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise
a `tuple` is returned where the first element is the sample tensor.
"""
# By default samples have to be AT least a multiple of the overall upsampling factor.
# The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
# However, the upsampling interpolation output size can be forced to fit any upsampling size
# on the fly if necessary.
if not self.return_deep_fea:
default_overall_up_factor = 2**self.num_upsamplers
# upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
forward_upsample_size = False
upsample_size = None
for dim in sample.shape[-2:]:
if dim % default_overall_up_factor != 0:
# Forward upsample size to force interpolation output size.
forward_upsample_size = True
break
# ensure attention_mask is a bias, and give it a singleton query_tokens dimension
# expects mask of shape:
# [batch, key_tokens]
# adds singleton query_tokens dimension:
# [batch, 1, key_tokens]
# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
# [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)
# [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
if attention_mask is not None:
# assume that mask is expressed as:
# (1 = keep, 0 = discard)
# convert mask into a bias that can be added to attention scores:
# (keep = +0, discard = -10000.0)
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
attention_mask = attention_mask.unsqueeze(1)
# convert encoder_attention_mask to a bias the same way we do for attention_mask
if encoder_attention_mask is not None:
encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
# 0. center input if necessary
if self.config.center_input_sample:
sample = 2 * sample - 1.0
# 1. time
timesteps = timestep
if not torch.is_tensor(timesteps):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timesteps = timesteps.expand(sample.shape[0])
t_emb = self.time_proj(timesteps)
# `Timesteps` does not contain any weights and will always return f32 tensors
# but time_embedding might actually be running in fp16. so we need to cast here.
# there might be better ways to encapsulate this.
t_emb = t_emb.to(dtype=sample.dtype)
emb = self.time_embedding(t_emb, timestep_cond)
aug_emb = None
if self.class_embedding is not None:
if class_labels is None:
raise ValueError("class_labels should be provided when num_class_embeds > 0")
if self.config.class_embed_type == "timestep":
class_labels = self.time_proj(class_labels)
# `Timesteps` does not contain any weights and will always return f32 tensors
# there might be better ways to encapsulate this.
class_labels = class_labels.to(dtype=sample.dtype)
class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
if self.config.class_embeddings_concat:
emb = torch.cat([emb, class_emb], dim=-1)
else:
emb = emb + class_emb
if self.config.addition_embed_type == "text":
aug_emb = self.add_embedding(encoder_hidden_states)
elif self.config.addition_embed_type == "text_image":
# Kandinsky 2.1 - style
if "image_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
)
image_embs = added_cond_kwargs.get("image_embeds")
text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
aug_emb = self.add_embedding(text_embs, image_embs)
elif self.config.addition_embed_type == "text_time":
# SDXL - style
if "text_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
)
text_embeds = added_cond_kwargs.get("text_embeds")
if "time_ids" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
)
time_ids = added_cond_kwargs.get("time_ids")
time_embeds = self.add_time_proj(time_ids.flatten())
time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
add_embeds = add_embeds.to(emb.dtype)
aug_emb = self.add_embedding(add_embeds)
elif self.config.addition_embed_type == "image":
# Kandinsky 2.2 - style
if "image_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
)
image_embs = added_cond_kwargs.get("image_embeds")
aug_emb = self.add_embedding(image_embs)
elif self.config.addition_embed_type == "image_hint":
# Kandinsky 2.2 - style
if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`"
)
image_embs = added_cond_kwargs.get("image_embeds")
hint = added_cond_kwargs.get("hint")
aug_emb, hint = self.add_embedding(image_embs, hint)
sample = torch.cat([sample, hint], dim=1)
emb = emb + aug_emb if aug_emb is not None else emb
if self.time_embed_act is not None:
emb = self.time_embed_act(emb)
if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
# Kadinsky 2.1 - style
if "image_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`"
)
image_embeds = added_cond_kwargs.get("image_embeds")
encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj":
# Kandinsky 2.2 - style
if "image_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`"
)
image_embeds = added_cond_kwargs.get("image_embeds")
encoder_hidden_states = self.encoder_hid_proj(image_embeds)
# 2. pre-process
sample = self.conv_in(sample)
# 2.5 GLIGEN position net
if cross_attention_kwargs is not None and cross_attention_kwargs.get("gligen", None) is not None:
cross_attention_kwargs = cross_attention_kwargs.copy()
gligen_args = cross_attention_kwargs.pop("gligen")
cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)}
# 3. down
lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0
if USE_PEFT_BACKEND:
# weight the lora layers by setting `lora_scale` for each PEFT layer
scale_lora_layers(self, lora_scale)
is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
# using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets
is_adapter = down_intrablock_additional_residuals is not None
# maintain backward compatibility for legacy usage, where
# T2I-Adapter and ControlNet both use down_block_additional_residuals arg
# but can only use one or the other
if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None:
deprecate(
"T2I should not use down_block_additional_residuals",
"1.3.0",
"Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \
and will be removed in diffusers 1.3.0. `down_block_additional_residuals` should only be used \
for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ",
standard_warn=False,
)
down_intrablock_additional_residuals = down_block_additional_residuals
is_adapter = True
down_block_res_samples = (sample,)
for downsample_block in self.down_blocks:
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
# For t2i-adapter CrossAttnDownBlock2D
additional_residuals = {}
if is_adapter and len(down_intrablock_additional_residuals) > 0:
additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0)
sample, res_samples = downsample_block(
hidden_states=sample,
temb=emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
cross_attention_kwargs=cross_attention_kwargs,
encoder_attention_mask=encoder_attention_mask,
**additional_residuals,
)
else:
sample, res_samples = downsample_block(hidden_states=sample, temb=emb, scale=lora_scale)
if is_adapter and len(down_intrablock_additional_residuals) > 0:
sample += down_intrablock_additional_residuals.pop(0)
down_block_res_samples += res_samples
if is_controlnet:
new_down_block_res_samples = ()
for down_block_res_sample, down_block_additional_residual in zip(
down_block_res_samples, down_block_additional_residuals
):
down_block_res_sample = down_block_res_sample + down_block_additional_residual
new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)
down_block_res_samples = new_down_block_res_samples
if self.return_deep_fea:
return sample
# 4. mid
if self.mid_block is not None:
if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
sample = self.mid_block(
sample,
emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
cross_attention_kwargs=cross_attention_kwargs,
encoder_attention_mask=encoder_attention_mask,
)
else:
sample = self.mid_block(sample, emb)
# To support T2I-Adapter-XL
if (
is_adapter
and len(down_intrablock_additional_residuals) > 0
and sample.shape == down_intrablock_additional_residuals[0].shape
):
sample += down_intrablock_additional_residuals.pop(0)
if is_controlnet:
sample = sample + mid_block_additional_residual
# 5. up
for i, upsample_block in enumerate(self.up_blocks):
is_final_block = i == len(self.up_blocks) - 1
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
# if we have not reached the final block and need to forward the
# upsample size, we do it here
if not is_final_block and forward_upsample_size:
upsample_size = down_block_res_samples[-1].shape[2:]
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
sample = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_tuple=res_samples,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
upsample_size=upsample_size,
attention_mask=attention_mask,
encoder_attention_mask=encoder_attention_mask,
)
else:
sample = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_tuple=res_samples,
upsample_size=upsample_size,
scale=lora_scale,
)
# 6. post-process
if self.conv_norm_out:
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = self.conv_out(sample)
if USE_PEFT_BACKEND:
# remove `lora_scale` from each PEFT layer
unscale_lora_layers(self, lora_scale)
if not return_dict:
return (sample,)
return UNet2DConditionOutput(sample=sample)
class CLIPDDPM_2D(nn.Module):
'''
Wrapper to extract features from pretrained DDPMs.
:param steps: list of diffusion steps t.
:param blocks: list of the UNet decoder blocks.
'''
def __init__(self, model_path, num_cls=2, steps=[0], seq_len=1, clip_train=True):
super().__init__()
self.steps = steps
self._load_pretrained_model(model_path)
self.clip_train = clip_train
if not self.clip_train:
self.fc = nn.Linear(768, num_cls)
torch.nn.init.xavier_uniform_(self.fc.weight)
self.conv = nn.Conv2d(512*seq_len, 768, 3)
torch.nn.init.xavier_uniform_(self.conv.weight)
self.act = nn.ReLU()
self.avg = nn.AdaptiveAvgPool2d((1,1))
self.seq_len = seq_len
def _load_pretrained_model(self, model_path):
from diffusers import DDPMScheduler
self.noise_scheduler = DDPMScheduler(num_train_timesteps=1000)
# Needed to pass only expected args to the function
self.model = UNet2DConditionModel(
sample_size=256, # the target image resolution
in_channels=3, # the number of input channels, 3 for RGB images
out_channels=3, # the number of output channels
layers_per_block=2, # how many ResNet layers to use per UNet block
block_out_channels=(64, 64, 128, 256, 512), # the number of output channels for each UNet block
down_block_types=(
"DownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
),
up_block_types=(
"UpBlock2D",
"CrossAttnUpBlock2D",
"CrossAttnUpBlock2D",
"CrossAttnUpBlock2D",
"UpBlock2D",
),
norm_num_groups=32,
# addition_embed_type="text",
# addition_embed_type_num_heads=64,
encoder_hid_dim_type="text_proj",
encoder_hid_dim=768,
)
self.model.eval()
# @torch.no_grad()
def forward(self, x, encoder_hidden_states):
noise = torch.randn(x.shape).to(x.device)
activations = []
input_with_noise = []
t = self.steps[0]
for i in range(self.seq_len):
# Compute x_t and run DDPM
t = torch.tensor([t]).to(x.device).long()
noisy_x = self.noise_scheduler.add_noise(x, noise, t)
# input_with_noise.append(noisy_x)
direct_out = self.model(noisy_x, t, encoder_hidden_states=encoder_hidden_states)
# return sample
activations.append(direct_out)
del(noise)
del(noisy_x)
first = torch.cat(activations, dim=1)
first = self.conv(first)
first = self.act(first)
outputs = self.avg(first.contiguous())
outputs = torch.flatten(outputs, 1)
if self.clip_train:
return outputs
outputs = torch.nn.functional.dropout(outputs, p=0.5)
outputs = self.fc(outputs.contiguous())
return outputs
class CLIPDDPM_2D_CAM(nn.Module):
'''
Wrapper to extract features from pretrained DDPMs.
:param steps: list of diffusion steps t.
:param blocks: list of the UNet decoder blocks.
'''
def __init__(self, model_path, num_cls=2, steps=[0], seq_len=1, clip_train=True):
super().__init__()
self.steps = steps
self._load_pretrained_model(model_path)
self.clip_train = clip_train
if not self.clip_train:
self.fc = nn.Linear(768, num_cls)
torch.nn.init.xavier_uniform_(self.fc.weight)
self.conv = nn.Conv2d(512*seq_len, 768, 3)
torch.nn.init.xavier_uniform_(self.conv.weight)
self.act = nn.ReLU()
self.avg = nn.AdaptiveAvgPool2d((1,1))
self.seq_len = seq_len
# Grad-CAM storage
self.gradients = None
self.activations = None
def save_gradients(self, grad):
self.gradients = grad
def _load_pretrained_model(self, model_path):
from diffusers import DDPMScheduler
self.noise_scheduler = DDPMScheduler(num_train_timesteps=1000)
# Needed to pass only expected args to the function
self.model = UNet2DConditionModel(
sample_size=256, # the target image resolution
in_channels=3, # the number of input channels, 3 for RGB images
out_channels=3, # the number of output channels
layers_per_block=2, # how many ResNet layers to use per UNet block
block_out_channels=(64, 64, 128, 256, 512), # the number of output channels for each UNet block
down_block_types=(
"DownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
),
up_block_types=(
"UpBlock2D",
"CrossAttnUpBlock2D",
"CrossAttnUpBlock2D",
"CrossAttnUpBlock2D",
"UpBlock2D",
),
norm_num_groups=32,
# addition_embed_type="text",
# addition_embed_type_num_heads=64,
encoder_hid_dim_type="text_proj",
encoder_hid_dim=768,
)
self.model.eval()
# @torch.no_grad()
def forward(self, x, encoder_hidden_states):
noise = torch.randn(x.shape).to(x.device)
activations = []
input_with_noise = []
t = self.steps[0]
for i in range(self.seq_len):
# Compute x_t and run DDPM
t = torch.tensor([t]).to(x.device).long()
noisy_x = self.noise_scheduler.add_noise(x, noise, t)
# input_with_noise.append(noisy_x)
direct_out = self.model(noisy_x, t, encoder_hidden_states=encoder_hidden_states)
# return sample
activations.append(direct_out)
del(noise)
del(noisy_x)
first = torch.cat(activations, dim=1)
# Register hooks for Grad-CAM
first.requires_grad_(True)
first.register_hook(self.save_gradients)
self.activations = first
first = self.conv(first)
first = self.act(first)
outputs = self.avg(first.contiguous())
outputs = torch.flatten(outputs, 1)
if self.clip_train:
return outputs
outputs = torch.nn.functional.dropout(outputs, p=0.5)
outputs = self.fc(outputs.contiguous())
return outputs
def generate_gradcam(self, x, encoder_hidden_states,loss_):
"""Generate Grad-CAM for a specific class."""
x.requires_grad_(True)
output = self.forward(x, encoder_hidden_states)
# Backward
self.model.zero_grad()
loss_.backward()
'''# Compute Grad-CAM weights
gradients = self.gradients.detach().cpu().numpy()
activations = self.activations.detach().cpu().numpy()
# Global average pooling to compute weights
weights = np.mean(gradients, axis=(2, 3))
grad_cam = np.sum(weights[:, :, np.newaxis, np.newaxis] * activations, axis=1)
grad_cam = np.maximum(grad_cam, 0)
grad_cam = grad_cam[0]
# Normalize the Grad-CAM heatmap
grad_cam -= grad_cam.min()
grad_cam /= grad_cam.max()
return grad_cam'''
# Grad-CAM for each image in the batch
grad_cams = []
for i in range(x.size(0)): # Iterate through each sample in the batch
# Get the gradients and activations for each sample
gradients = self.gradients[i:i+1].detach().cpu().numpy() # For each sample
activations = self.activations[i:i+1].detach().cpu().numpy() # For each sample
# Compute Grad-CAM weights by averaging the gradients over the spatial dimensions (height, width)
weights = np.mean(gradients, axis=(2, 3)) # Global average pooling to compute weights
# Calculate the Grad-CAM heatmap by summing the weighted activations
grad_cam = np.sum(weights[:, :, np.newaxis, np.newaxis] * activations, axis=1)
# Apply ReLU to the heatmap to keep only positive contributions
grad_cam = np.maximum(grad_cam, 0) # Apply ReLU
# Normalize the Grad-CAM heatmap to the range [0, 1]
grad_cam -= grad_cam.min()
grad_cam /= grad_cam.max()
grad_cams.append(grad_cam)
# Return the list of Grad-CAMs for the batch
return grad_cams
def save_gradcam(self, grad_cam, input_image, save_path, file_name):
# ---------- heatmap ----------
grad_cam_resized = torch.from_numpy(grad_cam).unsqueeze(0)
grad_cam_resized = grad_cam_resized.float()
while grad_cam_resized.shape[2] < input_image.shape[2]:
grad_cam_resized = F.interpolate(grad_cam_resized, scale_factor=2, mode='bilinear')
grad_cam_resized = grad_cam_resized.squeeze().cpu().numpy()
grad_cam_resized = (grad_cam_resized - grad_cam_resized.min()) / (grad_cam_resized.max() - grad_cam_resized.min())
heatmap = grad_cam_resized
if len(heatmap.shape) == 3:
heatmap = np.mean(heatmap, axis=2)
grad_cam_resized = np.uint8(255 * grad_cam_resized)
grad_cam_resized = cv2.applyColorMap(grad_cam_resized, cv2.COLORMAP_TURBO)
save_heatmap_path = os.path.join(save_path, 'heatmap')
if not os.path.exists(save_heatmap_path):
os.makedirs(save_heatmap_path)
heatmap_name = f"{file_name}_heatmap.jpg"
save_heatmap = os.path.join(save_heatmap_path, heatmap_name)
cv2.imwrite(save_heatmap, grad_cam_resized)
save_heatmap_path = os.path.join(save_path, 'heatmap_npy')
if not os.path.exists(save_heatmap_path):
os.makedirs(save_heatmap_path)
heatmap_name = f"{file_name}_heatmap.npy"
save_heatmap = os.path.join(save_heatmap_path, heatmap_name)
np.save(save_heatmap, heatmap)
# ---------- input ----------
input_image = input_image.detach().cpu().numpy().squeeze().transpose(1, 2, 0)
input_image = (input_image - input_image.min()) / (input_image.max() - input_image.min())
base_image = input_image
if len(base_image.shape) == 3:
base_image = np.mean(base_image, axis=2)
save_input_path = os.path.join(save_path, 'original')
if not os.path.exists(save_input_path):
os.makedirs(save_input_path)
input_name = f"{file_name}.jpg"
save_input = os.path.join(save_input_path, input_name)
plt.imsave(save_input, base_image, cmap='gray')
save_input_path = os.path.join(save_path, 'original_npy')
if not os.path.exists(save_input_path):
os.makedirs(save_input_path)
input_name = f"{file_name}.npy"
save_input = os.path.join(save_input_path, input_name)
np.save(save_input, base_image)
# ---------- overlay ----------
input_image = np.uint8(255 * input_image)
overlay = cv2.addWeighted(input_image, 0.4, grad_cam_resized, 0.6, 0)
save_overlay_path = os.path.join(save_path, 'overlay')
if not os.path.exists(save_overlay_path):
os.makedirs(save_overlay_path)
overlay_name = f"{file_name}_overlay.jpg"
save_overlay = os.path.join(save_overlay_path, overlay_name)
cv2.imwrite(save_overlay, overlay)
save_overlay_path = os.path.join(save_path, 'overlay_npy')
if not os.path.exists(save_overlay_path):
os.makedirs(save_overlay_path)
overlay_name = f"{file_name}_overlay.npy"
save_overlay = os.path.join(save_overlay_path, overlay_name)
# np.save(save_overlay, overlay)
# ---------- contour ----------
fig, ax = plt.subplots(figsize=(2.56, 2.56), dpi=100)
ax.imshow(base_image, cmap='gray')
levels = [0.45, 0.55, 0.65, 0.75, 0.85]
contour = ax.contour(heatmap, levels=levels, cmap='jet', linewidths=2)
ax.axis('off')
plt.subplots_adjust(left=0, right=1, top=1, bottom=0)
contour_data = []
for collection in contour.collections:
paths = collection.get_paths()
for path in paths:
contour_data.append(path.vertices)
contour_array = np.array(contour_data, dtype=object)
save_coutour_path = os.path.join(save_path, 'coutour')
if not os.path.exists(save_coutour_path):
os.makedirs(save_coutour_path)
coutour_name = f"{file_name}_coutour.jpg"
save_coutour = os.path.join(save_coutour_path, coutour_name)
plt.savefig(save_coutour, bbox_inches='tight', pad_inches=0, transparent=True)
plt.close()
save_coutour_path = os.path.join(save_path, 'coutour_npy')
if not os.path.exists(save_coutour_path):
os.makedirs(save_coutour_path)
coutour_name = f"{file_name}_coutour.npy"
save_coutour = os.path.join(save_coutour_path, coutour_name)
# np.save(save_coutour, contour_array)
print(f"{file_name} saved.")

UNet2d_condition.py at commit d91148c, no license · at the source

Overview

Authors: Guoxun Zhang^1,2, Zebin Gao^2,3, Caohui Duan⁴, Jiaxin Liu⁵, Yuerong Lizhu^6,7, Yaou Liu^6,7, Qian Chen⁸, Ling Wang⁸, Kailun Fei⁹, Tianyun Wang³, YuJia Chen⁵, Yanchen Guo⁵, Feng Xu¹⁰, Yuchen Guo², Xin Lou⁴, Qionghai Dai^1,2

Institutions: Beijing National Research Center for Information Science and Technology (China); Tsinghua University (China); Fudan University (China); Chinese PLA General Hospital (China); Tsinghua Shenzhen International Graduate School (China); Capital Medical University (China); Beijing Tian Tan Hospital (China); Beijing Friendship Hospital (China); Chinese Academy of Medical Sciences & Peking Union Medical College (China); Cancer Hospital of Chinese Academy of Medical Sciences (China)

Journal: Patterns (New York, N.Y.), volume 7, issue 6, article 101538

Dates: received 14 September 2025; accepted 17 March 2026; published online 14 April 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1016/j.patter.2026.101538 · PMID 42328202 · PMCID PMC13280722 · OpenAlex W7154370387

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: human (organism), clinical / translational (subfield)

Methods: Connectivity, Machine learning

Keywords: multi-modal, foundation model, contrastive learning, brain disease, medical imaging, diffusion model

Topic: Medical Image Segmentation Techniques (Computer Vision and Pattern Recognition, Computer Science), according to OpenAlex

Funding: National Natural Science Foundation of China (82441013, 82441014, 62088102, 82327803, T2541076, T2541074, 82572167)

Citations: cited by 1 paper (Europe PMC); 62 references in the paper

Abstract

The precise and comprehensive diagnosis of complex brain disorders relies on non-invasive computed tomography (CT) and magnetic resonance imaging (MRI) in conjunction with multi-modal clinical information. Here, we present Brainfound, a multi-modal foundation model for brain medical imaging that integrates image-text contrastive learning with a diffusion-based generative framework. The model was pre-trained on more than 3 million brain CT slices and 7 million brain MRI slices paired with clinical reports. In multi-center evaluations, Brainfound demonstrates state-of-the-art performance across seven tasks, including brain disease diagnosis, lesion segmentation, MRI enhancement, cross-modality translation, automatic report generation, zero-shot disease classification, and human-AI dialogue. It substantially outperforms leading models in automated report generation and clinical question answering for brain imaging, and its performance approaches that of expert physicians. These findings highlight the potential of Brainfound for accelerating diagnosis, support treatment decisions, and advance human-in-the-loop brain health care.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

Zenodo 18976379

License: CC-BY-4.0

State: the link answers, verified on 29 September 2026

Evidence: files inventoried

Size: 2 files

Software Heritage: not checked

Found in: “Data and code availability”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: PyTorch (89 files), NumPy (52 files), Hugging Face Transformers (16 files), Pillow (11 files), OpenCV (10 files), Matplotlib (7 files), scikit-learn (7 files), SciPy (6 files), MONAI (5 files), scikit-image (3 files), h5py (2 files), NiBabel (2 files), TensorFlow (2 files), tifffile (2 files), pandas (1 file)

Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)

29 September 2026: the link answers (HTTP 200)

141 files

downstream_task/mcq/data_utils.py, Python, 176 lines
downstream_task/mcq/eval_utils.py, Python, 264 lines
downstream_task/mcq/evaluate_med.py, Python, 328 lines
downstream_task/mcq/main_eval_only.py, Python, 95 lines
downstream_task/mri_enhancement/functions/__init__.py, Python, 1 line
downstream_task/mri_enhancement/functions/ckpt_util.py, Python, 72 lines
downstream_task/mri_enhancement/guided_diffusion/arch_unet.py, Python, 251 lines
downstream_task/mri_enhancement/guided_diffusion/diffusion.py, Python, 640 lines
downstream_task/mri_enhancement/guided_diffusion/diffusion_DDNM.py, Python, 612 lines
downstream_task/mri_enhancement/guided_diffusion/fp16_util.py, Python, 236 lines
downstream_task/mri_enhancement/guided_diffusion/logger.py, Python, 495 lines
downstream_task/mri_enhancement/guided_diffusion/models.py, Python, 341 lines
downstream_task/mri_enhancement/guided_diffusion/nn.py, Python, 170 lines
downstream_task/mri_enhancement/guided_diffusion/script_util.py, Python, 554 lines
downstream_task/mri_enhancement/guided_diffusion/test.py, Python, 12 lines
downstream_task/mri_enhancement/guided_diffusion/test_lambda_t.py, Python, 41 lines
downstream_task/mri_enhancement/guided_diffusion/unet.py, Python, 895 lines
downstream_task/mri_enhancement/main.py, Python, 219 lines
downstream_task/mri_enhancement/script_5T.py, Python, 13 lines
downstream_task/mri_translation/backbones/__init__.py, Python, 15 lines
downstream_task/mri_translation/backbones/dense_layer.py, Python, 83 lines
downstream_task/mri_translation/backbones/discriminator.py, Python, 240 lines
downstream_task/mri_translation/backbones/generator_resnet.py, Python, 317 lines
downstream_task/mri_translation/backbones/im2im.py, Python, 182 lines
downstream_task/mri_translation/backbones/layers.py, Python, 612 lines
downstream_task/mri_translation/backbones/layerspp.py, Python, 372 lines
downstream_task/mri_translation/backbones/ncsnpp_generator_adagn.py, Python, 421 lines
downstream_task/mri_translation/backbones/up_or_down_sampling.py, Python, 262 lines
downstream_task/mri_translation/backbones/utils.py, Python, 121 lines
downstream_task/mri_translation/dataset.py, Python, 38 lines
downstream_task/mri_translation/test.py, Python, 344 lines
downstream_task/mri_translation/train.py, Python, 866 lines
downstream_task/mri_translation/utils/EMA.py, Python, 90 lines
downstream_task/mri_translation/utils/op/__init__.py, Python, 2 lines
downstream_task/mri_translation/utils/op/fused_act.py, Python, 106 lines
downstream_task/mri_translation/utils/op/fused_bias_act.cpp, C++, 28 lines
downstream_task/mri_translation/utils/op/fused_bias_act_kernel.cu, CUDA, 101 lines
downstream_task/mri_translation/utils/op/upfirdn2d.cpp, C++, 31 lines
downstream_task/mri_translation/utils/op/upfirdn2d.py, Python, 225 lines
downstream_task/mri_translation/utils/op/upfirdn2d_kernel.cu, CUDA, 371 lines
downstream_task/mri_translation/utils/utils.py, Python, 30 lines
downstream_task/report_gen/UNet2d_condition.py, Python, 1,535 lines
downstream_task/report_gen/mdataset.py, Python, 267 lines
downstream_task/report_gen/metrics/__init__.py, Python, 4 lines
downstream_task/report_gen/metrics/basic.py, Python, 203 lines
downstream_task/report_gen/metrics/caption_modules/__init__.py, Python, 4 lines
downstream_task/report_gen/metrics/caption_modules/bleu/__init__.py, Python, 1 line
downstream_task/report_gen/metrics/caption_modules/bleu/bleu.py, Python, 47 lines
downstream_task/report_gen/metrics/caption_modules/bleu/bleu_scorer.py, Python, 266 lines
downstream_task/report_gen/metrics/caption_modules/cider/__init__.py, Python, 1 line
downstream_task/report_gen/metrics/caption_modules/cider/cider.py, Python, 54 lines
downstream_task/report_gen/metrics/caption_modules/cider/cider_scorer.py, Python, 193 lines
downstream_task/report_gen/metrics/captions.py, Python, 128 lines
downstream_task/report_gen/metrics/multiclass.py, Python, 47 lines
downstream_task/report_gen/metrics/multilabel.py, Python, 235 lines
downstream_task/report_gen/modules/__init__.py, Python, 4 lines
downstream_task/report_gen/modules/bert.py, Python, 86 lines
downstream_task/report_gen/modules/build.py, Python, 101 lines
downstream_task/report_gen/modules/caption_modules.py, Python, 137 lines
downstream_task/report_gen/modules/embedding.py, Python, 96 lines
downstream_task/report_gen/modules/fusion_modules.py, Python, 142 lines
downstream_task/report_gen/modules/graph_modules.py, Python, 267 lines
downstream_task/report_gen/modules/label_corr_modules.py, Python, 369 lines
downstream_task/report_gen/modules/mae.py, Python, 499 lines
downstream_task/report_gen/modules/multi_modal_fusion_modules.py, Python, 375 lines
downstream_task/report_gen/modules/pooler.py, Python, 85 lines
downstream_task/report_gen/modules/pos_embed.py, Python, 101 lines
downstream_task/report_gen/modules/structured_encoder.py, Python, 235 lines
downstream_task/report_gen/modules/textual_encoder.py, Python, 132 lines
downstream_task/report_gen/modules/vision_transformer.py, Python, 15 lines
downstream_task/report_gen/modules/visual_encoder.py, Python, 45 lines
downstream_task/report_gen/modules/vit.py, Python, 120 lines
downstream_task/report_gen/pretrain.py, Python, 424 lines
downstream_task/report_gen/pretrain_model.py, Python, 960 lines
downstream_task/report_gen/run.sh, Shell, 20 lines
downstream_task/report_gen/utils/__init__.py, Python, 4 lines
downstream_task/report_gen/utils/basic.py, Python, 97 lines
downstream_task/report_gen/utils/link.py, Python, 110 lines
downstream_task/report_gen/utils/logger.py, Python, 169 lines
downstream_task/report_gen/utils/models.py, Python, 105 lines
downstream_task/report_gen/utils/optim_utils.py, Python, 77 lines
downstream_task/report_gen/utils/preprocess.py, Python, 73 lines
downstream_task/report_gen/utils/scheduler.py, Python, 43 lines
downstream_task/report_gen/utils/visualization.py, Python, 51 lines
downstream_task/seg/fea_ex.py, Python, 173 lines
downstream_task/seg/main.py, Python, 287 lines
downstream_task/seg/mmodels/__init__.py, Python, 1 line
downstream_task/seg/mmodels/pixel_classifier.py, Python, 208 lines
downstream_task/seg/util.py, Python, 252 lines
pretrain_code/stage1/generate.py, Python, 136 lines
pretrain_code/stage1/prtrain_ddpm.py, Python, 194 lines
pretrain_code/stage1/utils/pipeline_ddpm.py, Python, 130 lines
pretrain_code/stage1/utils/util.py, Python, 126 lines
pretrain_code/stage2/UNet2d_condition.py, Python, 1,535 lines
pretrain_code/stage2/mdataset.py, Python, 267 lines
pretrain_code/stage2/metrics/__init__.py, Python, 4 lines
pretrain_code/stage2/metrics/basic.py, Python, 203 lines
pretrain_code/stage2/metrics/caption_modules/__init__.py, Python, 4 lines
pretrain_code/stage2/metrics/caption_modules/bleu/__init__.py, Python, 1 line
pretrain_code/stage2/metrics/caption_modules/bleu/bleu.py, Python, 47 lines
pretrain_code/stage2/metrics/caption_modules/bleu/bleu_scorer.py, Python, 266 lines
pretrain_code/stage2/metrics/caption_modules/cider/__init__.py, Python, 1 line
pretrain_code/stage2/metrics/caption_modules/cider/cider.py, Python, 54 lines
pretrain_code/stage2/metrics/caption_modules/cider/cider_scorer.py, Python, 193 lines
pretrain_code/stage2/metrics/captions.py, Python, 128 lines
pretrain_code/stage2/metrics/multiclass.py, Python, 47 lines
pretrain_code/stage2/metrics/multilabel.py, Python, 235 lines
pretrain_code/stage2/modules/__init__.py, Python, 4 lines
pretrain_code/stage2/modules/bert.py, Python, 86 lines
pretrain_code/stage2/modules/build.py, Python, 101 lines
pretrain_code/stage2/modules/caption_modules.py, Python, 137 lines
pretrain_code/stage2/modules/embedding.py, Python, 96 lines
pretrain_code/stage2/modules/fusion_modules.py, Python, 142 lines
pretrain_code/stage2/modules/graph_modules.py, Python, 267 lines
pretrain_code/stage2/modules/label_corr_modules.py, Python, 369 lines
pretrain_code/stage2/modules/mae.py, Python, 499 lines
pretrain_code/stage2/modules/multi_modal_fusion_modules.py, Python, 375 lines
pretrain_code/stage2/modules/pooler.py, Python, 85 lines
pretrain_code/stage2/modules/pos_embed.py, Python, 101 lines
pretrain_code/stage2/modules/structured_encoder.py, Python, 235 lines
pretrain_code/stage2/modules/textual_encoder.py, Python, 132 lines
pretrain_code/stage2/modules/vision_transformer.py, Python, 15 lines
pretrain_code/stage2/modules/visual_encoder.py, Python, 45 lines
pretrain_code/stage2/modules/vit.py, Python, 120 lines
pretrain_code/stage2/pretrain.py, Python, 528 lines
pretrain_code/stage2/pretrain_model.py, Python, 960 lines
pretrain_code/stage2/run.sh, Shell, 20 lines
pretrain_code/stage2/run_zero_shot_eval.sh, Shell, 20 lines
pretrain_code/stage2/utils/__init__.py, Python, 4 lines
pretrain_code/stage2/utils/basic.py, Python, 97 lines
pretrain_code/stage2/utils/link.py, Python, 110 lines
pretrain_code/stage2/utils/logger.py, Python, 169 lines
pretrain_code/stage2/utils/models.py, Python, 105 lines
pretrain_code/stage2/utils/optim_utils.py, Python, 77 lines
pretrain_code/stage2/utils/preprocess.py, Python, 73 lines
pretrain_code/stage2/utils/scheduler.py, Python, 43 lines
pretrain_code/stage2/utils/visualization.py, Python, 51 lines
pretrain_code/stage2/zero_shot_cls.py, Python, 420 lines
pretrain_code/stage3/UNet2d_condition.py, Python, 1,287 lines
pretrain_code/stage3/modeling_internvl_chat.py, Python, 459 lines
README.md, Text, 55 lines

At the source: doi.org/10.5281/zenodo.18976379

Zenodo 7549620

License: CC-BY-4.0

State: the link answers, verified on 29 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: the references

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: NumPy (6 files), SciPy (2 files), ggplot2 (1 file), pandas (1 file)

Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)

29 September 2026: the link answers (HTTP 200)

8 files

Code/Benchmarks.R, R, 298 lines
Code/EGRN_Multi_Genalg.py, Python, 803 lines
Code/GRN.py, Python, 409 lines
Code/distance_functions.py, Python, 35 lines
Code/dynamics.py, Python, 52 lines
Code/mutation_functions.py, Python, 206 lines
Code/population_functions.py, Python, 62 lines
Code/utility_functions.py, Python, 99 lines

At the source:

gingerbread000/brainfound

License: none: the authors keep all their rights

State: the link answers, verified on 29 September 2026

Evidence: files inventoried

Commit: d91148c8fbe879db5d9fa8d578efdfac585d154b, 23 September 2025

Languages: Python (133), Shell (3), C++ (2), CUDA (2)

Size: 169 files, 140 scripts

Software Heritage: not archived

Found in: the Zenodo archive record

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Availability: 1 check, the latest on 29 September 2026: the link answers

29 September 2026: the link answers

141 files

downstream_task/mcq/data_utils.py, Python, 176 lines
downstream_task/mcq/eval_utils.py, Python, 264 lines
downstream_task/mcq/evaluate_med.py, Python, 328 lines
downstream_task/mcq/main_eval_only.py, Python, 95 lines
downstream_task/mri_enhancement/functions/__init__.py, Python, 1 line
downstream_task/mri_enhancement/functions/ckpt_util.py, Python, 72 lines
downstream_task/mri_enhancement/guided_diffusion/arch_unet.py, Python, 251 lines
downstream_task/mri_enhancement/guided_diffusion/diffusion.py, Python, 640 lines
downstream_task/mri_enhancement/guided_diffusion/diffusion_DDNM.py, Python, 612 lines
downstream_task/mri_enhancement/guided_diffusion/fp16_util.py, Python, 236 lines
downstream_task/mri_enhancement/guided_diffusion/logger.py, Python, 495 lines
downstream_task/mri_enhancement/guided_diffusion/models.py, Python, 341 lines
downstream_task/mri_enhancement/guided_diffusion/nn.py, Python, 170 lines
downstream_task/mri_enhancement/guided_diffusion/script_util.py, Python, 554 lines
downstream_task/mri_enhancement/guided_diffusion/test.py, Python, 12 lines
downstream_task/mri_enhancement/guided_diffusion/test_lambda_t.py, Python, 41 lines
downstream_task/mri_enhancement/guided_diffusion/unet.py, Python, 895 lines
downstream_task/mri_enhancement/main.py, Python, 219 lines
downstream_task/mri_enhancement/script_5T.py, Python, 13 lines
downstream_task/mri_translation/backbones/__init__.py, Python, 15 lines
downstream_task/mri_translation/backbones/dense_layer.py, Python, 83 lines
downstream_task/mri_translation/backbones/discriminator.py, Python, 240 lines
downstream_task/mri_translation/backbones/generator_resnet.py, Python, 317 lines
downstream_task/mri_translation/backbones/im2im.py, Python, 182 lines
downstream_task/mri_translation/backbones/layers.py, Python, 612 lines
downstream_task/mri_translation/backbones/layerspp.py, Python, 372 lines
downstream_task/mri_translation/backbones/ncsnpp_generator_adagn.py, Python, 421 lines
downstream_task/mri_translation/backbones/up_or_down_sampling.py, Python, 262 lines
downstream_task/mri_translation/backbones/utils.py, Python, 121 lines
downstream_task/mri_translation/dataset.py, Python, 38 lines
downstream_task/mri_translation/test.py, Python, 344 lines
downstream_task/mri_translation/train.py, Python, 866 lines
downstream_task/mri_translation/utils/EMA.py, Python, 90 lines
downstream_task/mri_translation/utils/op/__init__.py, Python, 2 lines
downstream_task/mri_translation/utils/op/fused_act.py, Python, 106 lines
downstream_task/mri_translation/utils/op/fused_bias_act.cpp, C++, 28 lines
downstream_task/mri_translation/utils/op/fused_bias_act_kernel.cu, CUDA, 101 lines
downstream_task/mri_translation/utils/op/upfirdn2d.cpp, C++, 31 lines
downstream_task/mri_translation/utils/op/upfirdn2d.py, Python, 225 lines
downstream_task/mri_translation/utils/op/upfirdn2d_kernel.cu, CUDA, 371 lines
downstream_task/mri_translation/utils/utils.py, Python, 30 lines
downstream_task/report_gen/UNet2d_condition.py, Python, 1,535 lines, 2 matches
downstream_task/report_gen/mdataset.py, Python, 267 lines
downstream_task/report_gen/metrics/__init__.py, Python, 4 lines
downstream_task/report_gen/metrics/basic.py, Python, 203 lines
downstream_task/report_gen/metrics/caption_modules/__init__.py, Python, 4 lines
downstream_task/report_gen/metrics/caption_modules/bleu/__init__.py, Python, 1 line
downstream_task/report_gen/metrics/caption_modules/bleu/bleu.py, Python, 47 lines
downstream_task/report_gen/metrics/caption_modules/bleu/bleu_scorer.py, Python, 266 lines
downstream_task/report_gen/metrics/caption_modules/cider/__init__.py, Python, 1 line
downstream_task/report_gen/metrics/caption_modules/cider/cider.py, Python, 54 lines
downstream_task/report_gen/metrics/caption_modules/cider/cider_scorer.py, Python, 193 lines
downstream_task/report_gen/metrics/captions.py, Python, 128 lines
downstream_task/report_gen/metrics/multiclass.py, Python, 47 lines
downstream_task/report_gen/metrics/multilabel.py, Python, 235 lines
downstream_task/report_gen/modules/__init__.py, Python, 4 lines
downstream_task/report_gen/modules/bert.py, Python, 86 lines
downstream_task/report_gen/modules/build.py, Python, 101 lines
downstream_task/report_gen/modules/caption_modules.py, Python, 137 lines, 1 match
downstream_task/report_gen/modules/embedding.py, Python, 96 lines
downstream_task/report_gen/modules/fusion_modules.py, Python, 142 lines, 1 match
downstream_task/report_gen/modules/graph_modules.py, Python, 267 lines
downstream_task/report_gen/modules/label_corr_modules.py, Python, 369 lines
downstream_task/report_gen/modules/mae.py, Python, 499 lines
downstream_task/report_gen/modules/multi_modal_fusion_modules.py, Python, 375 lines
downstream_task/report_gen/modules/pooler.py, Python, 85 lines
downstream_task/report_gen/modules/pos_embed.py, Python, 101 lines
downstream_task/report_gen/modules/structured_encoder.py, Python, 235 lines
downstream_task/report_gen/modules/textual_encoder.py, Python, 132 lines
downstream_task/report_gen/modules/vision_transformer.py, Python, 15 lines
downstream_task/report_gen/modules/visual_encoder.py, Python, 45 lines
downstream_task/report_gen/modules/vit.py, Python, 120 lines
downstream_task/report_gen/pretrain.py, Python, 424 lines, 1 match
downstream_task/report_gen/pretrain_model.py, Python, 960 lines, 1 match
downstream_task/report_gen/run.sh, Shell, 20 lines
downstream_task/report_gen/utils/__init__.py, Python, 4 lines
downstream_task/report_gen/utils/basic.py, Python, 97 lines
downstream_task/report_gen/utils/link.py, Python, 110 lines
downstream_task/report_gen/utils/logger.py, Python, 169 lines
downstream_task/report_gen/utils/models.py, Python, 105 lines
downstream_task/report_gen/utils/optim_utils.py, Python, 77 lines
downstream_task/report_gen/utils/preprocess.py, Python, 73 lines
downstream_task/report_gen/utils/scheduler.py, Python, 43 lines
downstream_task/report_gen/utils/visualization.py, Python, 51 lines
downstream_task/seg/fea_ex.py, Python, 173 lines
downstream_task/seg/main.py, Python, 287 lines
downstream_task/seg/mmodels/__init__.py, Python, 1 line
downstream_task/seg/mmodels/pixel_classifier.py, Python, 208 lines
downstream_task/seg/util.py, Python, 252 lines
pretrain_code/stage1/generate.py, Python, 136 lines
pretrain_code/stage1/prtrain_ddpm.py, Python, 194 lines
pretrain_code/stage1/utils/pipeline_ddpm.py, Python, 130 lines
pretrain_code/stage1/utils/util.py, Python, 126 lines
pretrain_code/stage2/UNet2d_condition.py, Python, 1,535 lines
pretrain_code/stage2/mdataset.py, Python, 267 lines
pretrain_code/stage2/metrics/__init__.py, Python, 4 lines
pretrain_code/stage2/metrics/basic.py, Python, 203 lines
pretrain_code/stage2/metrics/caption_modules/__init__.py, Python, 4 lines
pretrain_code/stage2/metrics/caption_modules/bleu/__init__.py, Python, 1 line
pretrain_code/stage2/metrics/caption_modules/bleu/bleu.py, Python, 47 lines
pretrain_code/stage2/metrics/caption_modules/bleu/bleu_scorer.py, Python, 266 lines
pretrain_code/stage2/metrics/caption_modules/cider/__init__.py, Python, 1 line
pretrain_code/stage2/metrics/caption_modules/cider/cider.py, Python, 54 lines
pretrain_code/stage2/metrics/caption_modules/cider/cider_scorer.py, Python, 193 lines
pretrain_code/stage2/metrics/captions.py, Python, 128 lines
pretrain_code/stage2/metrics/multiclass.py, Python, 47 lines
pretrain_code/stage2/metrics/multilabel.py, Python, 235 lines
pretrain_code/stage2/modules/__init__.py, Python, 4 lines
pretrain_code/stage2/modules/bert.py, Python, 86 lines
pretrain_code/stage2/modules/build.py, Python, 101 lines
pretrain_code/stage2/modules/caption_modules.py, Python, 137 lines
pretrain_code/stage2/modules/embedding.py, Python, 96 lines
pretrain_code/stage2/modules/fusion_modules.py, Python, 142 lines
pretrain_code/stage2/modules/graph_modules.py, Python, 267 lines
pretrain_code/stage2/modules/label_corr_modules.py, Python, 369 lines
pretrain_code/stage2/modules/mae.py, Python, 499 lines
pretrain_code/stage2/modules/multi_modal_fusion_modules.py, Python, 375 lines
pretrain_code/stage2/modules/pooler.py, Python, 85 lines
pretrain_code/stage2/modules/pos_embed.py, Python, 101 lines
pretrain_code/stage2/modules/structured_encoder.py, Python, 235 lines
pretrain_code/stage2/modules/textual_encoder.py, Python, 132 lines
pretrain_code/stage2/modules/vision_transformer.py, Python, 15 lines
pretrain_code/stage2/modules/visual_encoder.py, Python, 45 lines
pretrain_code/stage2/modules/vit.py, Python, 120 lines
pretrain_code/stage2/pretrain.py, Python, 528 lines, 1 match
pretrain_code/stage2/pretrain_model.py, Python, 960 lines
pretrain_code/stage2/run.sh, Shell, 20 lines
pretrain_code/stage2/run_zero_shot_eval.sh, Shell, 20 lines
pretrain_code/stage2/utils/__init__.py, Python, 4 lines
pretrain_code/stage2/utils/basic.py, Python, 97 lines
pretrain_code/stage2/utils/link.py, Python, 110 lines
pretrain_code/stage2/utils/logger.py, Python, 169 lines
pretrain_code/stage2/utils/models.py, Python, 105 lines
pretrain_code/stage2/utils/optim_utils.py, Python, 77 lines
pretrain_code/stage2/utils/preprocess.py, Python, 73 lines
pretrain_code/stage2/utils/scheduler.py, Python, 43 lines
pretrain_code/stage2/utils/visualization.py, Python, 51 lines
pretrain_code/stage2/zero_shot_cls.py, Python, 420 lines
pretrain_code/stage3/UNet2d_condition.py, Python, 1,287 lines, 1 match
pretrain_code/stage3/modeling_internvl_chat.py, Python, 459 lines
README.md, Text, 55 lines

At the source: github.com/gingerbread000/Brainfound/tree/zendo_update

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
288 scripts, each with its path and the digest of its content;
8 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data and code availability

The source code and pre-trained model weights of the Brainfound foundation model have been deposited in Zenodo62 and are publicly available under a persistent DOI: https://doi.org/10.5281/zenodo.18976379. The training datasets used in this study contain sensitive clinical information and cannot be publicly shared due to ethical and privacy restrictions. Access to the data could be considered upon reasonable request and subject to approval by the corresponding institutions and ethics committee. The BraTS 2023 dataset is available from the Brain Tumor Segmentation (BraTs) challenge (https://www.synapse.org/#!Synapse:syn51156910), and the RSNA Intracranial Hemorrhage dataset is available from the RSNA Intracranial Hemorrhage Detection Challenge on Kaggle (https://www.kaggle.com/competitions/rsna-intracranial-hemorrhage-detection). Access to these dataset may require registration and agreement to the respective data usage terms.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 16 authors, 6 keywords, 1 funder, 61 references.

Cite

This paper

Zhang, G., Gao, Z., Duan, C., Liu, J., Lizhu, Y., Liu, Y., Chen, Q., Wang, L., Fei, K., Wang, T., Chen, Y., Guo, Y., Xu, F., Guo, Y., Lou, X., & Dai, Q. (2026). A multi-modal foundation model for brain disease diagnosis and medical imaging. Patterns (New York, N.Y.), 7(6), 101538. https://doi.org/10.1016/j.patter.2026.101538

BibTeX

@article{zhang2026multi, author = {Zhang, Guoxun and Gao, Zebin and Duan, Caohui and Liu, Jiaxin and Lizhu, Yuerong and Liu, Yaou and Chen, Qian and Wang, Ling and Fei, Kailun and Wang, Tianyun and Chen, YuJia and Guo, Yanchen and Xu, Feng and Guo, Yuchen and Lou, Xin and Dai, Qionghai}, title = {{A multi-modal foundation model for brain disease diagnosis and medical imaging}}, journal = {Patterns (New York, N.Y.)}, year = {2026}, month = apr, volume = {7}, number = {6}, pages = {101538}, publisher = {Elsevier}, issn = {2666-3899}, doi = {10.1016/j.patter.2026.101538}, url = {https://doi.org/10.1016/j.patter.2026.101538}, pmid = {42328202}, pmcid = {PMC13280722} }

RIS

TY - JOUR AU - Zhang, Guoxun AU - Gao, Zebin AU - Duan, Caohui AU - Liu, Jiaxin AU - Lizhu, Yuerong AU - Liu, Yaou AU - Chen, Qian AU - Wang, Ling AU - Fei, Kailun AU - Wang, Tianyun AU - Chen, YuJia AU - Guo, Yanchen AU - Xu, Feng AU - Guo, Yuchen AU - Lou, Xin AU - Dai, Qionghai TI - A multi-modal foundation model for brain disease diagnosis and medical imaging T2 - Patterns (New York, N.Y.) J2 - Patterns (N Y) PY - 2026 DA - 2026/04/14 VL - 7 IS - 6 SP - 101538 SN - 2666-3899 PB - Elsevier DO - 10.1016/j.patter.2026.101538 UR - https://doi.org/10.1016/j.patter.2026.101538 LA - en ER -

CSL-JSON

{ "id": "10.1016/j.patter.2026.101538", "type": "article-journal", "title": "A multi-modal foundation model for brain disease diagnosis and medical imaging", "container-title": "Patterns (New York, N.Y.)", "author": [ { "family": "Zhang", "given": "Guoxun" }, { "family": "Gao", "given": "Zebin" }, { "family": "Duan", "given": "Caohui" }, { "family": "Liu", "given": "Jiaxin" }, { "family": "Lizhu", "given": "Yuerong" }, { "family": "Liu", "given": "Yaou" }, { "family": "Chen", "given": "Qian" }, { "family": "Wang", "given": "Ling" }, { "family": "Fei", "given": "Kailun" }, { "family": "Wang", "given": "Tianyun" }, { "family": "Chen", "given": "YuJia" }, { "family": "Guo", "given": "Yanchen" }, { "family": "Xu", "given": "Feng" }, { "family": "Guo", "given": "Yuchen" }, { "family": "Lou", "given": "Xin" }, { "family": "Dai", "given": "Qionghai" } ], "container-title-short": "Patterns (N Y)", "volume": "7", "issue": "6", "page": "101538", "DOI": "10.1016/j.patter.2026.101538", "PMID": "42328202", "PMCID": "PMC13280722", "ISSN": "2666-3899", "publisher": "Elsevier", "URL": "https://doi.org/10.1016/j.patter.2026.101538", "language": "en", "issued": { "date-parts": [ [ 2026, 4, 14 ] ] } }

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A multi-modal foundation model for brain disease diagnosis and medical imaging.

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