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 Settings
- Draw Things
- DiffusionBee
Update README.md
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README.md
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pipeline_tag: text-to-image
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# Plan Update: 11/1/2025
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I'm sticking to the positive spectrum here, knowing that 6 million samples isn't enough to converge sd15.
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# Earlier updates
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This is the config for the PT, you want the student unless you want to train it
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I KNOW I KNOW, I'll get it worked out. For now this is every epoch 9+ if you see a PT for this particular model.
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pipeline_tag: text-to-image
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# Plan Update: 11/1/2025
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I'm sticking to the positive spectrum here, knowing that 6 million samples isn't enough to converge sd15.
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# Earlier updates
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```
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This is the config for the PT, you want the student unless you want to train it
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I KNOW I KNOW, I'll get it worked out. For now this is every epoch 9+ if you see a PT for this particular model.
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