Text-to-Image
Diffusers
TensorBoard
stable-diffusion
diffusion
distillation
flow-matching
geometric-deep-learning
research
Instructions to use AbstractPhil/sd15-flow-matching with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AbstractPhil/sd15-flow-matching with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("AbstractPhil/sd15-flow-matching", 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
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README.md
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# Most original checkpoints are default sd15 after testing
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For those who downloaded the models that either exhibit blobs or don't use flow matching noise - my sincerest apologies. They are defective.
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The CURRENT e8 has no clip or vae, so it's just sitting there standalone. This is the currently newest valid one and it functions as expected - by making blobs due to early pretraining.
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pipeline_tag: text-to-image
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# Most original checkpoints are default sd15 after testing
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For those who downloaded the models that either exhibit blobs or don't use flow matching noise - my sincerest apologies. They are defective. Blobs are expected, standard noise is not.
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The CURRENT e8 has no clip or vae, so it's just sitting there standalone. This is the currently newest valid one and it functions as expected - by making blobs due to early pretraining.
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