Generates cinematic images.Disclaimer for Cinematic LoRAThe LoRA image generation model produces outputs based on probabilistic training data and does not guarantee accuracy, originality, or freedom from bias. Users assume all risks associated with its use, including potential misuse of intellectual property or unintended content. By using this tool, you agree to indemnify and hold harmless the developers and distributors from any claims arising from generated images or related usage.

Cinematic Photography - Z Image Turbo

CinematicPhotography_Zit_2_c1n3m4t1c_st7000.safetensors

v1.7000


# 

## 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-Turbo](https://modelscope.ai/models/Tongyi-MAI/Z-Image-Turbo)
* Training Code: [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio)
* Data amount：75
* Total training steps：7000
* 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-Turbo", origin_file_pattern="transformer/*.safetensors"),
        ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="text_encoder/*.safetensors"),
        ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
    ],
    tokenizer_config=ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
)
pipe.load_lora(pipe.dit, ModelConfig(model_id="Trapdaar/CinematicPhotography_Zit_2", origin_file_pattern="CinematicPhotography_Zit_2_c1-st7000.safetensors"))
prompt = "a cat"
image = pipe(prompt=prompt, num_inference_steps=8, cfg_scale=1)
image.save("image.jpg")
```


Files with SHA256: 69019b644d3540b534227376d1952b85d3cfa39208dbbc20283d254128dcb803