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
Update trainer.py
Browse files- trainer.py +11 -1
trainer.py
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# =====================================================================================
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# SD1.5 Flow-Matching Trainer — David-Driven Block Penalties (HF-loaded)
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#
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# - BaseConfig at top
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# - Functionality (teacher/student/david/assessor/fusion/trainer)
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# - Activations at bottom
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# =====================================================================================
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from __future__ import annotations
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import os, json, math, random, re
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from dataclasses import dataclass, asdict
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# =====================================================================================
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# SD1.5 Flow-Matching Trainer — David-Driven Block Penalties (HF-loaded)
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# Author: AbstractPhil
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# Assistant: Claude Sonnet 4.5 + GPT 4o
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# - BaseConfig at top
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# - Functionality (teacher/student/david/assessor/fusion/trainer)
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# - Activations at bottom
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# =====================================================================================
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# try:
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# !pip uninstall -qy geometricvocab
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# except:
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# pass
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#
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# !pip install -q git+https://github.com/AbstractEyes/lattice_vocabulary.git
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#
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# =====================================================================================
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from __future__ import annotations
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import os, json, math, random, re
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from dataclasses import dataclass, asdict
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