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#Original code can be found on: https://github.com/black-forest-labs/flux
from dataclasses import dataclass
import torch
from torch import Tensor, nn
from einops import rearrange, repeat
import comfy.ldm.common_dit
from comfy.ldm.flux.layers import (
EmbedND,
timestep_embedding,
)
from .layers import (
DoubleStreamBlock,
LastLayer,
SingleStreamBlock,
Approximator,
ChromaModulationOut,
)
@dataclass
class ChromaParams:
in_channels: int
out_channels: int
context_in_dim: int
hidden_size: int
mlp_ratio: float
num_heads: int
depth: int
depth_single_blocks: int
axes_dim: list
theta: int
patch_size: int
qkv_bias: bool
in_dim: int
out_dim: int
hidden_dim: int
n_layers: int
class Chroma(nn.Module):
"""
Transformer model for flow matching on sequences.
"""
def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs):
super().__init__()
self.dtype = dtype
params = ChromaParams(**kwargs)
self.params = params
self.patch_size = params.patch_size
self.in_channels = params.in_channels
self.out_channels = params.out_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.in_dim = params.in_dim
self.out_dim = params.out_dim
self.hidden_dim = params.hidden_dim
self.n_layers = params.n_layers
self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device)
# set as nn identity for now, will overwrite it later.
self.distilled_guidance_layer = Approximator(
in_dim=self.in_dim,
hidden_dim=self.hidden_dim,
out_dim=self.out_dim,
n_layers=self.n_layers,
dtype=dtype, device=device, operations=operations
)
self.double_blocks = nn.ModuleList(
[
DoubleStreamBlock(
self.hidden_size,
self.num_heads,
mlp_ratio=params.mlp_ratio,
qkv_bias=params.qkv_bias,
dtype=dtype, device=device, operations=operations
)
for _ in range(params.depth)
]
)
self.single_blocks = nn.ModuleList(
[
SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations)
for _ in range(params.depth_single_blocks)
]
)
if final_layer:
self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations)
self.skip_mmdit = []
self.skip_dit = []
self.lite = False
def get_modulations(self, tensor: torch.Tensor, block_type: str, *, idx: int = 0):
# This function slices up the modulations tensor which has the following layout:
# single : num_single_blocks * 3 elements
# double_img : num_double_blocks * 6 elements
# double_txt : num_double_blocks * 6 elements
# final : 2 elements
if block_type == "final":
return (tensor[:, -2:-1, :], tensor[:, -1:, :])
single_block_count = self.params.depth_single_blocks
double_block_count = self.params.depth
offset = 3 * idx
if block_type == "single":
return ChromaModulationOut.from_offset(tensor, offset)
# Double block modulations are 6 elements so we double 3 * idx.
offset *= 2
if block_type in {"double_img", "double_txt"}:
# Advance past the single block modulations.
offset += 3 * single_block_count
if block_type == "double_txt":
# Advance past the double block img modulations.
offset += 6 * double_block_count
return (
ChromaModulationOut.from_offset(tensor, offset),
ChromaModulationOut.from_offset(tensor, offset + 3),
)
raise ValueError("Bad block_type")
def forward_orig(
self,
img: Tensor,
img_ids: Tensor,
txt: Tensor,
txt_ids: Tensor,
timesteps: Tensor,
guidance: Tensor = None,
control = None,
transformer_options={},
attn_mask: Tensor = None,
) -> Tensor:
patches_replace = transformer_options.get("patches_replace", {})
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)
# distilled vector guidance
mod_index_length = 344
distill_timestep = timestep_embedding(timesteps.detach().clone(), 16).to(img.device, img.dtype)
# guidance = guidance *
distil_guidance = timestep_embedding(guidance.detach().clone(), 16).to(img.device, img.dtype)
# get all modulation index
modulation_index = timestep_embedding(torch.arange(mod_index_length, device=img.device), 32).to(img.device, img.dtype)
# 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).to(img.device, img.dtype)
# and we need to broadcast timestep and guidance along too
timestep_guidance = torch.cat([distill_timestep, distil_guidance], dim=1).unsqueeze(1).repeat(1, mod_index_length, 1).to(img.dtype).to(img.device, img.dtype)
# then and only then we could concatenate it together
input_vec = torch.cat([timestep_guidance, modulation_index], dim=-1).to(img.device, img.dtype)
mod_vectors = self.distilled_guidance_layer(input_vec)
txt = self.txt_in(txt)
ids = torch.cat((txt_ids, img_ids), dim=1)
pe = self.pe_embedder(ids)
blocks_replace = patches_replace.get("dit", {})
for i, block in enumerate(self.double_blocks):
if i not in self.skip_mmdit:
double_mod = (
self.get_modulations(mod_vectors, "double_img", idx=i),
self.get_modulations(mod_vectors, "double_txt", idx=i),
)
if ("double_block", i) in blocks_replace:
def block_wrap(args):
out = {}
out["img"], out["txt"] = block(img=args["img"],
txt=args["txt"],
vec=args["vec"],
pe=args["pe"],
attn_mask=args.get("attn_mask"))
return out
out = blocks_replace[("double_block", i)]({"img": img,
"txt": txt,
"vec": double_mod,
"pe": pe,
"attn_mask": attn_mask},
{"original_block": block_wrap})
txt = out["txt"]
img = out["img"]
else:
img, txt = block(img=img,
txt=txt,
vec=double_mod,
pe=pe,
attn_mask=attn_mask)
if control is not None: # Controlnet
control_i = control.get("input")
if i < len(control_i):
add = control_i[i]
if add is not None:
img += add
img = torch.cat((txt, img), 1)
for i, block in enumerate(self.single_blocks):
if i not in self.skip_dit:
single_mod = self.get_modulations(mod_vectors, "single", idx=i)
if ("single_block", i) in blocks_replace:
def block_wrap(args):
out = {}
out["img"] = block(args["img"],
vec=args["vec"],
pe=args["pe"],
attn_mask=args.get("attn_mask"))
return out
out = blocks_replace[("single_block", i)]({"img": img,
"vec": single_mod,
"pe": pe,
"attn_mask": attn_mask},
{"original_block": block_wrap})
img = out["img"]
else:
img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask)
if control is not None: # Controlnet
control_o = control.get("output")
if i < len(control_o):
add = control_o[i]
if add is not None:
img[:, txt.shape[1] :, ...] += add
img = img[:, txt.shape[1] :, ...]
final_mod = self.get_modulations(mod_vectors, "final")
img = self.final_layer(img, vec=final_mod) # (N, T, patch_size ** 2 * out_channels)
return img
def forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs):
bs, c, h, w = x.shape
patch_size = 2
x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
h_len = ((h + (patch_size // 2)) // patch_size)
w_len = ((w + (patch_size // 2)) // patch_size)
img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
out = self.forward_orig(img, img_ids, context, txt_ids, timestep, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None))
return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w]
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