
# 

## 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：100
* 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="anthropic/ANT-Fable-YoutubeV2", origin_file_pattern="ANT-Fable-YoutubeV2_c2-st10000.safetensors"))
prompt = "a cat"
image = pipe(prompt)
image.save("image.jpg")
```


ANT-Fable-YoutubeV2

ANT-Fable-YoutubeV2_c2-st1000.safetensors

c2-st1000

c2-st2000

c2-st3000

c2-st4000

c2-st5000

c2-st6000

c2-st7000

c2-st8000

c2-st9000

c2-st10000

Files with SHA256: 64ab4b7ef8d352c31aeb12f5a85e939159ca5a02ad7e488ee13e77559ee1302d