Instructions to use BigDannyPt/FP8-E5M2-Collection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BigDannyPt/FP8-E5M2-Collection with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BigDannyPt/FP8-E5M2-Collection", 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
- Local Apps
- Draw Things
- DiffusionBee
metadata
license: apache-2.0
library_name: diffusers
tags:
- text-to-image
inference: false
Just a collection of some models that I've converted to fp8_e5m2 for better compatability with my RX6800
The ones that end with _All means that can be used without CLIP and VAE, so it is the whole model
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if you would like to help me, it seems that runpod has a Refer thing - https://runpod.io?ref=d2452mau
| You get | I get |
|---|---|
| - A one-time credit of $5 when they sign up with your link and adds $10 for the first time - Instant access to Runpod's GPU resources |
- A one-time credit of $5 when a user signs up with your link and adds $10 for the first time - Credits on referred user spend during their first 6 months. (5% Serverless and 3% Pods) |