Instructions to use bghira/pseudo-journey with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bghira/pseudo-journey with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bghira/pseudo-journey", 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
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Check out the documentation for more information.
Deprecation notice
This model was a research project focused on the effect of fine-tuning the OpenCLIP text encoder.
It has been deprecated in favour of a newer checkpoint that continued training this model.
This model remains accessible as a test comparison and possible base model for fine-tuning.
Training data
Base model: stabilityai/stable-diffusion-2-1
Data: 3300 midjourney 5.1 upscaled images with their captions.
Training parameters
Duration: 4000 steps LR scheduler: polynomial Batch size: 3 Text encoder: Thawed Optimizer: 8bit ADAM No prior loss preservation
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