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import os
from dataclasses import dataclass
import torch
from einops import rearrange
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file as load_sft
from .model import Flux, FluxParams
from .controlnet import ControlNetFlux
from .modules.autoencoder import AutoEncoder, AutoEncoderParams
from .modules.conditioner import HFEmbedder
from safetensors import safe_open
def load_safetensors(path):
tensors = {}
with safe_open(path, framework="pt", device="cpu") as f:
for key in f.keys():
tensors[key] = f.get_tensor(key)
return tensors
@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="ae.safetensors",
ckpt_path=os.getenv("FLUX_DEV"),
params=FluxParams(
in_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="ae.safetensors",
ckpt_path=os.getenv("FLUX_SCHNELL"),
params=FluxParams(
in_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=False,
),
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,
),
),
}
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 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)
with torch.device("meta" if ckpt_path is not None else device):
model = Flux(configs[name].params).to(torch.bfloat16)
if ckpt_path is not None:
print("Loading checkpoint")
# load_sft doesn't support torch.device
sd = load_sft(ckpt_path, device=str(device))
missing, unexpected = model.load_state_dict(sd, strict=False, assign=True)
print_load_warning(missing, unexpected)
return model
def load_flow_model2(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.replace("sft", "safetensors"))
with torch.device("meta" if ckpt_path is not None else device):
model = Flux(configs[name].params)
if ckpt_path is not None:
print("Loading checkpoint")
# load_sft doesn't support torch.device
sd = load_sft(ckpt_path, device=str(device))
missing, unexpected = model.load_state_dict(sd, strict=False, assign=True)
print_load_warning(missing, unexpected)
return model
def load_controlnet(name, device, transformer=None):
with torch.device(device):
controlnet = ControlNetFlux(configs[name].params)
if transformer is not None:
controlnet.load_state_dict(transformer.state_dict(), strict=False)
return controlnet
def load_t5(device: str | torch.device = "cuda", max_length: int = 512) -> HFEmbedder:
# max length 64, 128, 256 and 512 should work (if your sequence is short enough)
return HFEmbedder("google/t5-v1_1-xxl", max_length=max_length, torch_dtype=torch.bfloat16).to(device)
def load_clip(device: str | torch.device = "cuda") -> HFEmbedder:
return HFEmbedder("openai/clip-vit-large-patch14", max_length=77, torch_dtype=torch.bfloat16).to(device)
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 None
and configs[name].repo_id is not None
and configs[name].repo_ae is not None
and hf_download
):
ckpt_path = hf_hub_download(configs[name].repo_id, configs[name].repo_ae)
# Loading the autoencoder
print("Init AE")
with torch.device("meta" if ckpt_path is not None else device):
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)
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 choosen 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:]]
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