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
model card: drop demo link until the Space exists
Browse files
README.md
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@@ -23,7 +23,6 @@ A 210M-parameter text-to-image diffusion transformer trained from scratch on one
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flan-t5-base text encoder; the transformer, recipe, data pipeline and evaluation are original.
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- Code, figures and every design decision with sources: https://github.com/ivanmikhnenkov/tinydit (tag `v1-pretrain`)
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- Try it: https://huggingface.co/spaces/ivanmikhnenkov/tinydit
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- Author: https://ivanmikhnenkov.com
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## Results (held-out prompts, 20 Euler steps, CFG 4, shift 2.8)
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flan-t5-base text encoder; the transformer, recipe, data pipeline and evaluation are original.
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- Code, figures and every design decision with sources: https://github.com/ivanmikhnenkov/tinydit (tag `v1-pretrain`)
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- Author: https://ivanmikhnenkov.com
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## Results (held-out prompts, 20 Euler steps, CFG 4, shift 2.8)
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