
# 

## 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: [HiDream-ai/HiDream-O1-Image](https://modelscope.ai/models/HiDream-ai/HiDream-O1-Image)
* Training Code: [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio)
* Data amount：100
* 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.hidream_o1_image import HiDreamO1ImagePipeline, ModelConfig
import torch

pipe = HiDreamO1ImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="HiDream-ai/HiDream-O1-Image", origin_file_pattern="*.safetensors"),
    ],
    processor_config=ModelConfig(model_id="HiDream-ai/HiDream-O1-Image"),
)
pipe.load_lora(pipe.dit, ModelConfig(model_id="Jagerfly/RetroAnalogVHS-HiDream", origin_file_pattern="RetroAnalogVHS-HiDream_c1-st7000.safetensors"))
prompt = "a cat"
image = pipe(prompt=prompt, num_inference_steps=30, cfg_scale=4)
image.save("image.jpg")
```


RetroAnalogVHS-HiDream

RetroAnalogVHS-HiDream_c1-st3000.safetensors

c1-st3000

c1-st1000

Files with SHA256: 814a883b169f361435273ed81a58a52b394ec9ee4d70514131be672c335afc04