Instructions to use nerijs/pixelcascade128-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nerijs/pixelcascade128-v0.1 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-cascade", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("nerijs/pixelcascade128-v0.1") prompt = "pixel art, a cute corgi" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
PixelCascade128 v0.1
This is an early version of PixelCascade128, a LoRa for Stable Cascade Stace C for pixel art.
Disclaimer: v0.1 can't produce pixel perfect or grid aligned output
How to use
- Only tested on ComfyUI, choose one of the provided workflows (txt2img or img2img)
- Ensure you're using the correct Stable Cascade files (Stage A, B and C, effnet_encoder for img2img)
- Best results are with Stage C/B BF16 weights
How to get the best results
- Use a UNet and CLIP strength of ~1.0 to 0.7
- To force pixel art style use the "pixel art" keyword
- You can use "white background" on negative prompt to remove simple backgrounds
- Downscaling x8 times with nearest neighbors or using Astropulse's Pixel Detector can help to make them pixel perfect
- Works great for 2048x2048 outputs, best results are img2img from a 1024x1024 samples
- Euler a sampler works the best, 20 steps Stage C, 10 steps Stage B
- 2048x2048 samples might generate distorted images, use img2img with 0.7 strength
Contact
- Downloads last month
- 15
Model tree for nerijs/pixelcascade128-v0.1
Base model
stabilityai/stable-cascade
