Instructions to use clem/maxdekdt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use clem/maxdekdt with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("clem/maxdekdt", torch_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 Settings
- Draw Things
- DiffusionBee
File size: 314 Bytes
60ac1e4 b10babd 60ac1e4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ---
tags:
- autotrain
- stable-diffusion
- text-to-image
datasets:
- clem/autotrain-data-maxdekdt-AER2SE090K
co2_eq_emissions:
emissions: 86.12664300406121
license: openrail
language:
- en
---
# Model Trained Using AutoTrain
- Problem type: Dreambooth
- Model ID: 2604679068
- CO2 Emissions (in grams): 86.1266 |