Reinforcement Learning
Diffusers
Safetensors
English
image-quality-assessment
vision-language
image-editing
Instructions to use RobinY99/MR-IQA-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use RobinY99/MR-IQA-2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("RobinY99/MR-IQA-2", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| # MR-IQA-2 single-image code bundle | |
| This directory contains the minimal source files needed by the released | |
| single-image Actor, Editor, and Judge pipeline. It mirrors the public GitHub | |
| implementation while keeping the model repository lightweight. | |
| From this directory, create the two pinned inference environments and run one | |
| image: | |
| ```bash | |
| bash scripts/setup_envs.sh --profile inference | |
| python examples/quick_start.py /absolute/path/to/input.jpg | |
| ``` | |
| The command downloads Actor, Editor, and Judge weights from | |
| `RobinY99/MR-IQA-2` as they are needed. Add `--local-files-only` when all three | |
| model folders are already cached. | |
| The bundle includes: | |
| - `examples/quick_start.py`: sequential Actor, Editor, and Judge entry point; | |
| - `examples/actor_to_editor.py`: Actor generation and strict output parsing; | |
| - `actor/plugin/`: public Actor output and prompt contracts; | |
| - `judge/`: frozen E5 Judge contract and local inference implementation; | |
| - `environment/`, `requirements/`, `scripts/setup_envs.sh`: pinned setup files. | |
| Use the full [GitHub repository](https://github.com/RobinY99/MR-IQA-2) for | |
| training, complete evaluation, datasets, launchers, and the full contract test | |
| suite. | |