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Added support for Lodestone Rock's Chroma model

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Jaret Burkett
2025-04-05 13:21:36 -06:00
parent 2b901cca39
commit 7c21eac1b3
9 changed files with 1499 additions and 0 deletions
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extensions_built_in/diffusion_models/chroma/src/model.py Normal file
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from dataclasses import dataclass
import torch
from torch import Tensor, nn
import torch.utils.checkpoint as ckpt
from .layers import (
DoubleStreamBlock,
EmbedND,
LastLayer,
SingleStreamBlock,
timestep_embedding,
Approximator,
distribute_modulations,
)
@dataclass
class ChromaParams:
in_channels: int
context_in_dim: int
hidden_size: int
mlp_ratio: float
num_heads: int
depth: int
depth_single_blocks: int
axes_dim: list[int]
theta: int
qkv_bias: bool
guidance_embed: bool
approximator_in_dim: int
approximator_depth: int
approximator_hidden_size: int
_use_compiled: bool
chroma_params = ChromaParams(
in_channels=64,
context_in_dim=4096,
hidden_size=3072,
mlp_ratio=4.0,
num_heads=24,
depth=19,
depth_single_blocks=38,
axes_dim=[16, 56, 56],
theta=10_000,
qkv_bias=True,
guidance_embed=True,
approximator_in_dim=64,
approximator_depth=5,
approximator_hidden_size=5120,
_use_compiled=False,
)
def modify_mask_to_attend_padding(mask, max_seq_length, num_extra_padding=8):
"""
Modifies attention mask to allow attention to a few extra padding tokens.
Args:
mask: Original attention mask (1 for tokens to attend to, 0 for masked tokens)
max_seq_length: Maximum sequence length of the model
num_extra_padding: Number of padding tokens to unmask
Returns:
Modified mask
"""
# Get the actual sequence length from the mask
seq_length = mask.sum(dim=-1)
batch_size = mask.shape[0]
modified_mask = mask.clone()
for i in range(batch_size):
current_seq_len = int(seq_length[i].item())
# Only add extra padding tokens if there's room
if current_seq_len < max_seq_length:
# Calculate how many padding tokens we can unmask
available_padding = max_seq_length - current_seq_len
tokens_to_unmask = min(num_extra_padding, available_padding)
# Unmask the specified number of padding tokens right after the sequence
modified_mask[i, current_seq_len : current_seq_len + tokens_to_unmask] = 1
return modified_mask
class Chroma(nn.Module):
"""
Transformer model for flow matching on sequences.
"""
def __init__(self, params: ChromaParams):
super().__init__()
self.params = params
self.in_channels = params.in_channels
self.out_channels = self.in_channels
if params.hidden_size % params.num_heads != 0:
raise ValueError(
f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
)
pe_dim = params.hidden_size // params.num_heads
if sum(params.axes_dim) != pe_dim:
raise ValueError(
f"Got {params.axes_dim} but expected positional dim {pe_dim}"
)
self.hidden_size = params.hidden_size
self.num_heads = params.num_heads
self.pe_embedder = EmbedND(
dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim
)
self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
# TODO: need proper mapping for this approximator output!
# currently the mapping is hardcoded in distribute_modulations function
self.distilled_guidance_layer = Approximator(
params.approximator_in_dim,
self.hidden_size,
params.approximator_hidden_size,
params.approximator_depth,
)
self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
self.double_blocks = nn.ModuleList(
[
DoubleStreamBlock(
self.hidden_size,
self.num_heads,
mlp_ratio=params.mlp_ratio,
qkv_bias=params.qkv_bias,
use_compiled=params._use_compiled,
)
for _ in range(params.depth)
]
)
self.single_blocks = nn.ModuleList(
[
SingleStreamBlock(
self.hidden_size,
self.num_heads,
mlp_ratio=params.mlp_ratio,
use_compiled=params._use_compiled,
)
for _ in range(params.depth_single_blocks)
]
)
self.final_layer = LastLayer(
self.hidden_size,
1,
self.out_channels,
use_compiled=params._use_compiled,
)
# TODO: move this hardcoded value to config
self.mod_index_length = 344
# self.mod_index = torch.tensor(list(range(self.mod_index_length)), device=0)
self.register_buffer(
"mod_index",
torch.tensor(list(range(self.mod_index_length)), device="cpu"),
persistent=False,
)
@property
def device(self):
# Get the device of the module (assumes all parameters are on the same device)
return next(self.parameters()).device
def forward(
self,
img: Tensor,
img_ids: Tensor,
txt: Tensor,
txt_ids: Tensor,
txt_mask: Tensor,
timesteps: Tensor,
guidance: Tensor,
attn_padding: int = 1,
) -> Tensor:
if img.ndim != 3 or txt.ndim != 3:
raise ValueError("Input img and txt tensors must have 3 dimensions.")
# running on sequences img
img = self.img_in(img)
txt = self.txt_in(txt)
# TODO:
# need to fix grad accumulation issue here for now it's in no grad mode
# besides, i don't want to wash out the PFP that's trained on this model weights anyway
# the fan out operation here is deleting the backward graph
# alternatively doing forward pass for every block manually is doable but slow
# custom backward probably be better
with torch.no_grad():
distill_timestep = timestep_embedding(timesteps, 16)
# TODO: need to add toggle to omit this from schnell but that's not a priority
distil_guidance = timestep_embedding(guidance, 16)
# get all modulation index
modulation_index = timestep_embedding(self.mod_index, 32)
# we need to broadcast the modulation index here so each batch has all of the index
modulation_index = modulation_index.unsqueeze(0).repeat(img.shape[0], 1, 1)
# and we need to broadcast timestep and guidance along too
timestep_guidance = (
torch.cat([distill_timestep, distil_guidance], dim=1)
.unsqueeze(1)
.repeat(1, self.mod_index_length, 1)
)
# then and only then we could concatenate it together
input_vec = torch.cat([timestep_guidance, modulation_index], dim=-1)
mod_vectors = self.distilled_guidance_layer(input_vec.requires_grad_(True))
mod_vectors_dict = distribute_modulations(mod_vectors)
ids = torch.cat((txt_ids, img_ids), dim=1)
pe = self.pe_embedder(ids)
# compute mask
# assume max seq length from the batched input
max_len = txt.shape[1]
# mask
with torch.no_grad():
txt_mask_w_padding = modify_mask_to_attend_padding(
txt_mask, max_len, attn_padding
)
txt_img_mask = torch.cat(
[
txt_mask_w_padding,
torch.ones([img.shape[0], img.shape[1]], device=txt_mask.device),
],
dim=1,
)
txt_img_mask = txt_img_mask.float().T @ txt_img_mask.float()
txt_img_mask = (
txt_img_mask[None, None, ...]
.repeat(txt.shape[0], self.num_heads, 1, 1)
.int()
.bool()
)
# txt_mask_w_padding[txt_mask_w_padding==False] = True
for i, block in enumerate(self.double_blocks):
# the guidance replaced by FFN output
img_mod = mod_vectors_dict[f"double_blocks.{i}.img_mod.lin"]
txt_mod = mod_vectors_dict[f"double_blocks.{i}.txt_mod.lin"]
double_mod = [img_mod, txt_mod]
# just in case in different GPU for simple pipeline parallel
if self.training:
img.requires_grad_(True)
img, txt = ckpt.checkpoint(
block, img, txt, pe, double_mod, txt_img_mask
)
else:
img, txt = block(
img=img, txt=txt, pe=pe, distill_vec=double_mod, mask=txt_img_mask
)
img = torch.cat((txt, img), 1)
for i, block in enumerate(self.single_blocks):
single_mod = mod_vectors_dict[f"single_blocks.{i}.modulation.lin"]
if self.training:
img.requires_grad_(True)
img = ckpt.checkpoint(block, img, pe, single_mod, txt_img_mask)
else:
img = block(img, pe=pe, distill_vec=single_mod, mask=txt_img_mask)
img = img[:, txt.shape[1] :, ...]
final_mod = mod_vectors_dict["final_layer.adaLN_modulation.1"]
img = self.final_layer(
img, distill_vec=final_mod
) # (N, T, patch_size ** 2 * out_channels)
return img