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from dataclasses import dataclass
import torch
from einops import rearrange
from huggingface_hub import hf_hub_download
# from imwatermark import WatermarkEncoder
from safetensors.torch import load_file as load_sft
from flux.model import Flux, FluxParams
from flux.modules.autoencoder import AutoEncoder, AutoEncoderParams
from flux.modules.conditioner import HFEmbedder
from transformers import (CLIPTextModel, CLIPTokenizer, T5EncoderModel,
T5Tokenizer, BitsAndBytesConfig) # <--- Added BitsAndBytesConfig
@dataclass
class ModelSpec:
params: FluxParams
ae_params: AutoEncoderParams
ckpt_path: str | None
ae_path: str | None
repo_id: str | None
repo_flow: str | None
repo_ae: str | None
configs = {
"flux-dev": ModelSpec(
repo_id="black-forest-labs/FLUX.1-dev",
repo_flow="flux1-dev.safetensors",
repo_ae=None,
ckpt_path=os.getenv("FLUX_DEV"),
params=FluxParams(
in_channels=64,
out_channels=64,
vec_in_dim=768,
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,
),
ae_path=os.getenv("AE"),
ae_params=AutoEncoderParams(
resolution=256,
in_channels=3,
ch=128,
out_ch=3,
ch_mult=[1, 2, 4, 4],
num_res_blocks=2,
z_channels=16,
scale_factor=0.3611,
shift_factor=0.1159,
),
),
"flux-fill-dev": ModelSpec(
repo_id="black-forest-labs/FLUX.1-Fill-dev",
repo_flow="flux1-fill-dev.safetensors",
repo_ae="ae.safetensors",
ckpt_path=os.getenv("FLUX_FILL_DEV"),
params=FluxParams(
in_channels=64,
out_channels=384,
vec_in_dim=768,
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,
),
ae_path=os.getenv("AE"),
ae_params=AutoEncoderParams(
resolution=256,
in_channels=3,
ch=128,
out_ch=3,
ch_mult=[1, 2, 4, 4],
num_res_blocks=2,
z_channels=16,
scale_factor=0.3611,
shift_factor=0.1159,
),
),
"flux-kontext-dev": ModelSpec(
repo_id="black-forest-labs/FLUX.1-Kontext-dev",
repo_flow="flux1-kontext-dev.safetensors",
repo_ae="ae.safetensors",
ckpt_path=os.getenv("FLUX_FILL_DEV"),
params=FluxParams(
in_channels=64,
out_channels=64,
vec_in_dim=768,
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,
),
ae_path=os.getenv("AE"),
ae_params=AutoEncoderParams(
resolution=256,
in_channels=3,
ch=128,
out_ch=3,
ch_mult=[1, 2, 4, 4],
num_res_blocks=2,
z_channels=16,
scale_factor=0.3611,
shift_factor=0.1159,
),
),
"flux-schnell": ModelSpec(
repo_id="black-forest-labs/FLUX.1-schnell",
repo_flow="flux1-schnell.safetensors",
repo_ae="black-forest-labs/FLUX.1-schnell",
ckpt_path=os.getenv("FLUX_SCHNELL"),
params=FluxParams(
in_channels=64, # ArtiAgent custom input dimension logic handled in load_flow_model
out_channels=64,
vec_in_dim=768,
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=10000.0,
qkv_bias=True,
guidance_embed=False,
),
ae_path="ae.safetensors",
ae_params=AutoEncoderParams(
resolution=256,
in_channels=3,
ch=128,
out_ch=3,
ch_mult=[1, 2, 4, 4],
num_res_blocks=2,
z_channels=16,
scale_factor=0.3611,
shift_factor=0.1159,
),
),
}
def print_load_warning(missing: list[str], unexpected: list[str]) -> None:
if len(missing) > 0 and len(unexpected) > 0:
print(f"Got {len(missing)} missing keys:\n\t" + "\n\t".join(missing))
print("\n" + "-" * 79 + "\n")
print(f"Got {len(unexpected)} unexpected keys:\n\t" + "\n\t".join(unexpected))
elif len(missing) > 0:
print(f"Got {len(missing)} missing keys:\n\t" + "\n\t".join(missing))
elif len(unexpected) > 0:
print(f"Got {len(unexpected)} unexpected keys:\n\t" + "\n\t".join(unexpected))
def _replace_linear_with_4bit(module, compute_dtype=torch.bfloat16):
"""Recursively replace all nn.Linear with bitsandbytes 4-bit layers"""
import bitsandbytes as bnb
for name, child in module.named_children():
if name == "img_in":
continue # Skip img_in to preserve ArtiAgent's custom shape handling
if isinstance(child, torch.nn.Linear):
has_bias = child.bias is not None
new_layer = bnb.nn.Linear4bit(
child.in_features,
child.out_features,
bias=has_bias,
compute_dtype=compute_dtype,
compress_statistics=True,
quant_type="nf4",
)
new_layer.weight = bnb.nn.Params4bit(
child.weight.data,
requires_grad=False,
quant_type="nf4",
)
if has_bias:
new_layer.bias = torch.nn.Parameter(child.bias.data)
setattr(module, name, new_layer)
else:
_replace_linear_with_4bit(child, compute_dtype)
def load_flow_model(name: str, device: str | torch.device = "cuda", hf_download: bool = True):
# Loading Flux
print("Init model")
ckpt_path = configs[name].ckpt_path
if (
ckpt_path is None
and configs[name].repo_id is not None
and configs[name].repo_flow is not None
and hf_download
):
ckpt_path = hf_hub_download(configs[name].repo_id, configs[name].repo_flow)
# Initialize model directly on CPU or target device (avoids meta-tensor shape replacement)
target_device = torch.device(device)
model = Flux(configs[name].params).to(dtype=torch.bfloat16)
if ckpt_path is not None:
print("Loading checkpoint")
# load_sft doesn't support torch.device
sd = load_sft(ckpt_path, device="cpu")
# --- ADD THIS LINE TO STRIP FP8 / COMFYUI KEY PREFIXES ---
sd = {k.replace("model.diffusion_model.", ""): v for k, v in sd.items()}
# ---------------------------------------------------------
# --- FIX: HANDLE EXPANDED IMG_IN (384 channels vs 64 channels) ---
img_in_weight = sd.pop("img_in.weight", None)
img_in_bias = sd.pop("img_in.bias", None)
# Load all standard layers safely
missing, unexpected = model.load_state_dict(sd, strict=False, assign=True)
print_load_warning(missing, unexpected)
# Copy base 64 channels into ArtiAgent's expanded 384-channel input layer
# In src/flux/util.py inside load_flow_model():
if img_in_weight is not None:
with torch.no_grad():
w = img_in_weight.to(device=device, dtype=torch.bfloat16)
# Check if model.img_in weight expects 384 channels while checkpoint has 64
if model.img_in.weight.shape[1] != w.shape[1]:
# Slice model.img_in.weight to match the 64-channel input tensor
model.img_in.weight = torch.nn.Parameter(model.img_in.weight[:, :w.shape[1]])
model.img_in.weight.copy_(w)
if img_in_bias is not None and getattr(model.img_in, "bias", None) is not None:
with torch.no_grad():
b = img_in_bias.to(device=device, dtype=torch.bfloat16)
model.img_in.bias.copy_(b)
# Quantize all Linear layers to NF4 on CPU before moving to GPU
print("Quantizing model to NF4 (this may take a minute)...")
_replace_linear_with_4bit(model, compute_dtype=torch.bfloat16)
print("NF4 quantization complete.")
# Move model to target CUDA device
model = model.to(target_device)
return model
def load_t5(device: str | torch.device = "cuda", max_length: int = 512) -> HFEmbedder:
# Force T5 onto CPU; sampling.py already moves the encoded txt tensor to GPU
return HFEmbedder(
"google/t5-v1_1-xxl",
max_length=max_length,
is_clip=False,
torch_dtype=torch.bfloat16,
device_map="cpu"
)
def load_clip(device: str | torch.device = "cuda") -> HFEmbedder:
# Keep on CPU; sampling.py moves vec to GPU after encoding
return HFEmbedder("openai/clip-vit-large-patch14", max_length=77, is_clip=True, torch_dtype=torch.bfloat16)
def load_ae(name: str, device: str | torch.device = "cuda", hf_download: bool = True) -> AutoEncoder:
ckpt_path = configs[name].ae_path
# If ckpt_path is just a filename and doesn't exist locally, download it
if ckpt_path is not None and not os.path.exists(ckpt_path) and hf_download:
repo_id = configs[name].repo_ae or configs[name].repo_id
ckpt_path = hf_hub_download(repo_id, ckpt_path)
elif ckpt_path is None and configs[name].repo_id is not None and hf_download:
repo_id = configs[name].repo_ae or configs[name].repo_id
ckpt_path = hf_hub_download(repo_id, "ae.safetensors")
# Loading the autoencoder
print("Init AE")
# Initialize directly on CPU to avoid meta-tensor initialization issues
ae = AutoEncoder(configs[name].ae_params)
if ckpt_path is not None:
sd = load_sft(ckpt_path, device=str(device))
missing, unexpected = ae.load_state_dict(sd, strict=False, assign=True)
print_load_warning(missing, unexpected)
ae = ae.to(device)
return ae
# class WatermarkEmbedder:
# def __init__(self, watermark):
# self.watermark = watermark
# self.num_bits = len(WATERMARK_BITS)
# self.encoder = WatermarkEncoder()
# self.encoder.set_watermark("bits", self.watermark)
# def __call__(self, image: torch.Tensor) -> torch.Tensor:
# """
# Adds a predefined watermark to the input image
# Args:
# image: ([N,] B, RGB, H, W) in range [-1, 1]
# Returns:
# same as input but watermarked
# """
# image = 0.5 * image + 0.5
# squeeze = len(image.shape) == 4
# if squeeze:
# image = image[None, ...]
# n = image.shape[0]
# image_np = rearrange((255 * image).detach().cpu(), "n b c h w -> (n b) h w c").numpy()[:, :, :, ::-1]
# # torch (b, c, h, w) in [0, 1] -> numpy (b, h, w, c) [0, 255]
# # watermarking libary expects input as cv2 BGR format
# for k in range(image_np.shape[0]):
# image_np[k] = self.encoder.encode(image_np[k], "dwtDct")
# image = torch.from_numpy(rearrange(image_np[:, :, :, ::-1], "(n b) h w c -> n b c h w", n=n)).to(
# image.device
# )
# image = torch.clamp(image / 255, min=0.0, max=1.0)
# if squeeze:
# image = image[0]
# image = 2 * image - 1
# return image
# # A fixed 48-bit message that was chosen at random
# WATERMARK_MESSAGE = 0b001010101111111010000111100111001111010100101110
# # bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
# WATERMARK_BITS = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
# embed_watermark = WatermarkEmbedder(WATERMARK_BITS)
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