This model is trained using the <a target="_blank" rel="ugc" href="https://modelscope.ai/civision/modelTraining">Civision/model training</a> environment and computing resources provided by ModelScope.<ul><li>Model Type: LoRA</li><li>Base Model: <a target="_blank" rel="ugc" href="https://modelscope.ai/models/Qwen/Qwen-Image-2512">Qwen/Qwen-Image-2512</a></li><li>Training Code: <a target="_blank" rel="ugc" href="https://github.com/modelscope/DiffSynth-Studio">DiffSynth-Studio</a></li><li>Data amount：94</li><li>Total training steps：10000</li><li>License: Apache-2.0</li></ul>

Retro_Futuristic_Painterly

retro_futuristic_painterly_c2-st10000.safetensors

v1.0


# 

## 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: [Qwen/Qwen-Image-2512](https://modelscope.ai/models/Qwen/Qwen-Image-2512)
* Training Code: [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio)
* Data amount：94
* Total training steps：10000
* License: Apache-2.0

## Inference Code

Install [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio):

```bash
pip install diffsynth
```

Inference:

```python
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
import torch

pipe = QwenImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="Qwen/Qwen-Image-2512", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
    ],
    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
)
pipe.load_lora(pipe.dit, ModelConfig(model_id="CosmicCrafter/retro_futuristic_painterly", origin_file_pattern="retro_futuristic_painterly_c2-st10000.safetensors"))
prompt = "a cat"
image = pipe(prompt)
image.save("image.jpg")
```


Files with SHA256: 9763f9b0dcea91783655aa6f373039994bea7c31408f51d8df0f56b332e32a4d