Instructions to use minaj546/CardiB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use minaj546/CardiB with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("minaj546/CardiB") prompt = "ASCII\u0000\u0000\u0000photo of (ohwx woman) wearing a blue hoodie, <lora:CardiB:1>" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Cardi B SDXL LoRA

- Prompt
- ASCIIphoto of (ohwx woman) wearing a blue hoodie, <lora:CardiB:1>
- Negative Prompt
- cleavage, nsfw
Model description
This model was testing a couple of things. One being the larger training image dataset, hence the larger amount of steps. Two being, yes, a non-1.7GB LoRA. Only future tests/outputs/comparisons will tell if there is a quality loss in this or not. I decided to scale down the size per high demand and preliminary speculation based on observations that hopefully it wont diminish the overall quality of the model. Worth noting if you are also training SDXL LoRAs yourself that this method of training to achieve 800mb files also lowers your overall GPU VRAM usage from ~18gb to ~15gb.
Most images were on DreamShaper XL A2 in A1111/ComfyUI. Hi-res fix with R-ESRGAN (1.25) and 0.2-0.4 denoising strength. Upscaled using "4x_NickelbackFS_72000_G" or "4x_NMKD-Siax_200k"
Trigger words
You should use ohwx woman to trigger the image generation.
You should use ohwx to trigger the image generation.
Download model
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