Instructions to use ivanmikhnenkov/tinydit-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ivanmikhnenkov/tinydit-256 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ivanmikhnenkov/tinydit-256", 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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# tinydit-256
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A 210M-parameter text-to-image diffusion transformer trained from scratch on one GPU (3.5 days, 400k steps,
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4.2M images at 256² area with aspect-ratio buckets). Frozen FLUX.2 autoencoder (32-channel latent) and frozen
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flan-t5-base text encoder; the transformer, recipe, data pipeline and evaluation are original.
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# tinydit-256
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A 210M-parameter text-to-image diffusion transformer trained from scratch on one GPU (RTX Pro 6000, 3.5 days, 400k steps,
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4.2M images at 256² area with aspect-ratio buckets). Frozen FLUX.2 autoencoder (32-channel latent) and frozen
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flan-t5-base text encoder; the transformer, recipe, data pipeline and evaluation are original.
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