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", torch_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
File size: 932 Bytes
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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.
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