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 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 for training, complete evaluation, datasets, launchers, and the full contract test suite.