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import torch
from diffusers import (
AutoPipelineForText2Image,
DiffusionPipeline,
AutoencoderKL,
FluxControlNetModel,
FluxMultiControlNetModel,
)
from diffusers.schedulers import *
def load_flux():
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device = "cuda" if torch.cuda.is_available() else "cpu"
# Models
models = [
{
"repo_id": "black-forest-labs/FLUX.1-dev",
"loader": "flux",
"compute_type": torch.bfloat16,
}
]
for model in models:
try:
model["pipeline"] = AutoPipelineForText2Image.from_pretrained(
model['repo_id'],
vae=AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=torch.bfloat16).to(device),
torch_dtype=model['compute_type'],
safety_checker=None,
variant="fp16"
).to(device)
except:
model["pipeline"] = AutoPipelineForText2Image.from_pretrained(
model['repo_id'],
vae=AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=torch.bfloat16).to(device),
torch_dtype=model['compute_type'],
safety_checker=None
).to(device)
model["pipeline"].enable_model_cpu_offload()
# VAE n Refiner
flux_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=torch.bfloat16).to(device)
# ControlNet
controlnet = FluxMultiControlNetModel([FluxControlNetModel.from_pretrained(
"Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro",
torch_dtype=torch.bfloat16
).to(device)])
return device, models, flux_vae, controlnet
device, models, flux_vae, controlnet = load_flux()
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