Training: 8000 step, 100 imagesCfg:1.0 "tattoo_stencil This is a digital black&amp;white image of..."Example images generated with Viggle Turbo 0.3 Q6_K Qwen Image 2.1 on a RTX 4060 8GB.Note: this run was about getting images that could be made into stencils, not about making the actual stencils. Give me a bit of time, I'm learning as I go; the goal IS to produce stencils either via image-to-image or directly.This model is trained using the <a rel="ugc" href="https://modelscope.ai/civision/modelTraining">Civision/model training</a>, training resources provided by ModelScope.

Tattoo for Stencil Qwen21

tattoo_for_stencil_c1-st8000.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-2.1](https://modelscope.ai/models/Qwen/Qwen-Image-2.1)
* Training Code: [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio)
* Data amount：100
* 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.qwen_image_21 import QwenImage21Pipeline, ModelConfig
import torch

pipe = QwenImage21Pipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="Qwen/Qwen-Image-2.1", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
        ModelConfig(model_id="Qwen/Qwen-Image-2.1", origin_file_pattern="text_encoder/model*.safetensors"),
        ModelConfig(model_id="Qwen/Qwen-Image-2.1", origin_file_pattern="vae/diffusion_pytorch_model*.safetensors"),
    ],
    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-2.1", origin_file_pattern="processor/"),
)
pipe.load_lora(pipe.dit, ModelConfig(model_id="joeblm/tattoo_for_stencil", origin_file_pattern="tattoo_for_stencil_c1-st8000.safetensors"))

prompt = "a cat"
image = pipe(prompt, seed=0, num_inference_steps=50)
image.save("image.png")
```


Files with SHA256: fe06ede05147043151270de5c09d1324b3cf6bb0f9989cd31c3f2d8f9ffb6e62