Instructions to use climba/MinorPerception-R2I-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use climba/MinorPerception-R2I-LoRA with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("climba/MinorPerception-R2I-LoRA") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| library_name: diffusers | |
| tags: | |
| - text-to-image | |
| - lora | |
| - sd3.5 | |
| - flux | |
| - r2i | |
| - grpo | |
| pipeline_tag: text-to-image | |
| # MinorPerception R2I LoRA Adapters | |
| This repository contains selected LoRA adapters from the MinorPerception R2I diffusion experiments. | |
| The base model weights are not included. | |
| ## Contents | |
| - `sd35_v3_qwenvl_hybrid_grpo_lora/` | |
| - Selected Stable Diffusion 3.5 LoRA from the Qwen-VL hybrid GRPO reward experiment. | |
| - Intended base model: `stabilityai/stable-diffusion-3.5-medium`. | |
| - `flux_v3_clip_grpo_stage3init_memfix_lora/` | |
| - Selected FLUX LoRA from the stage3 warm-start, memory-fixed CLIP-GRPO experiment. | |
| - Intended base model: `black-forest-labs/FLUX.1-dev`. | |
| ## Notes | |
| These adapters were trained for R2I-style prompt-to-resolved-caption alignment experiments. | |
| They are experimental research artifacts and should be evaluated against the exact inference scripts | |
| and prompts used in the repository. | |