# ## Model Introduction This model is trained using the [Civision/model training](https://modelscope.ai/civision/modelTraining) environment and computing resources provided by ModelScope. * Model Type: LoRA * Base Model: [Tongyi-MAI/Z-Image](https://modelscope.ai/models/Tongyi-MAI/Z-Image) * Training Code: [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) * Data amount：41 * Total training steps：8000 * License: Apache-2.0 ## Inference Code Install [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio): ```bash pip install diffsynth ``` Inference: ```python from diffsynth.pipelines.z_image import ZImagePipeline, ModelConfig import torch pipe = ZImagePipeline.from_pretrained( torch_dtype=torch.bfloat16, device="cuda", model_configs=[ ModelConfig(model_id="Tongyi-MAI/Z-Image", origin_file_pattern="transformer/*.safetensors"), ModelConfig(model_id="Tongyi-MAI/Z-Image", origin_file_pattern="text_encoder/*.safetensors"), ModelConfig(model_id="Tongyi-MAI/Z-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), ], tokenizer_config=ModelConfig(model_id="Tongyi-MAI/Z-Image", origin_file_pattern="tokenizer/"), ) pipe.load_lora(pipe.dit, ModelConfig(model_id="cat34243/testlorarank8", origin_file_pattern="testlorarank8_c1-st8000.safetensors")) prompt = "a cat" image = pipe(prompt=prompt, num_inference_steps=50, cfg_scale=4) image.save("image.jpg") ```