from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
import torch


pipe = Flux2ImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="text_encoder/*.safetensors"),
        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-base-9B", origin_file_pattern="transformer/*.safetensors"),
        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
    ],
    tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="tokenizer/"),
)
pipe.load_lora(pipe.dit, "./models/train/FLUX.2-klein-base-9B_lora/epoch-4.safetensors")
prompt = "a dog"
image = pipe(prompt=prompt, seed=0, num_inference_steps=40, cfg_scale=4, height=768, width=768)
image.save("image.jpg")