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
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library_name: diffusers
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pipeline_tag: text-to-image
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# More faults more problems still managed to salvage the real one
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How absurd and difficult anything SD15 has been to debug.
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library_name: diffusers
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pipeline_tag: text-to-image
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# Most checkpoints are default sd15 after testing
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I will remove the faulty checkpoints and the correct checkpoints, which are early training and incompatible with correct inference, will be present.
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It's basically just blobs as expected, so don't expect much yet. It has a long way to go.
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# More faults more problems still managed to salvage the real one
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How absurd and difficult anything SD15 has been to debug.
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