| import torch | |
| from diffsynth.pipelines.ernie_image import ErnieImagePipeline, ModelConfig | |
| from diffsynth.core.loader.file import load_state_dict | |
| pipe = ErnieImagePipeline.from_pretrained( | |
| torch_dtype=torch.bfloat16, | |
| device="cuda", | |
| model_configs=[ | |
| ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), | |
| ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="text_encoder/model.safetensors"), | |
| ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), | |
| ], | |
| ) | |
| lora_state_dict = load_state_dict('./models/train/Ernie-Image-T2I_split/epoch-4.safetensors', torch_dtype=torch.bfloat16, device="cuda") | |
| pipe.load_lora(pipe.dit, state_dict=lora_state_dict, alpha=1.0) | |
| image = pipe( | |
| prompt="a professional photo of a cute dog", | |
| seed=0, | |
| num_inference_steps=50, | |
| cfg_scale=4.0, | |
| ) | |
| image.save('split_training_ERNIE-Image.jpg') | |
| print("LoRA validation image saved to image_lora.jpg") | |