Instructions to use BlithFok/ddpm-finetuned-model-for-testing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BlithFok/ddpm-finetuned-model-for-testing with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BlithFok/ddpm-finetuned-model-for-testing", 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
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("BlithFok/ddpm-finetuned-model-for-testing", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Example Fine-Tuned Model for Unit 2 of the Diffusion Models Class 🧨
Self learning exercise. A finetuned diffusion model to generate butterfly images. Ony for testing since there is insufficient time to train long enough
Usage
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('BlithFok/ddpm-finetuned-model-for-testing')
image = pipeline().images[0]
image
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