| from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig |
| import torch |
|
|
|
|
| vram_config = { |
| "offload_dtype": torch.bfloat16, |
| "offload_device": "cpu", |
| "onload_dtype": torch.bfloat16, |
| "onload_device": "cuda", |
| "preparing_dtype": torch.bfloat16, |
| "preparing_device": "cuda", |
| "computation_dtype": torch.bfloat16, |
| "computation_device": "cuda", |
| } |
| pipe = Flux2ImagePipeline.from_pretrained( |
| torch_dtype=torch.bfloat16, |
| device="cuda", |
| model_configs=[ |
| ModelConfig(model_id="black-forest-labs/FLUX.2-dev", origin_file_pattern="text_encoder/*.safetensors", **vram_config), |
| ModelConfig(model_id="black-forest-labs/FLUX.2-dev", origin_file_pattern="transformer/*.safetensors", **vram_config), |
| ModelConfig(model_id="black-forest-labs/FLUX.2-dev", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), |
| ], |
| tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-dev", origin_file_pattern="tokenizer/"), |
| ) |
| pipe.load_lora(pipe.dit, "./models/train/FLUX.2-dev-LoRA-splited/epoch-4.safetensors") |
| prompt = "a dog" |
| image = pipe(prompt, seed=0) |
| image.save("image_FLUX.2-dev_lora.jpg") |
|
|
