Text-to-Image
Diffusers
Safetensors
StableDiffusionXLPipeline
materials
microstructure
electron_micrograph
characterization
scientific_figure_understanding
Instructions to use UniParser/EM3M-Gen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use UniParser/EM3M-Gen with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("UniParser/EM3M-Gen", 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
Download examples/figure1.png from UniParser/EM3M-Gen: direct link, hf CLI and curl.
- Browser
- Download file 2.22 MB
-
https://huggingface.co/UniParser/EM3M-Gen/resolve/main/examples/figure1.png
- Command line
-
hf download hf://UniParser/EM3M-Gen/examples/figure1.png
-
curl -L -o figure1.png https://huggingface.co/UniParser/EM3M-Gen/resolve/main/examples/figure1.png
2.22 MB

- Xet hash:
- 60f2343603f08eed99c036574071517262cd024e73d297bb4d2a0a5ed64abe1e
- Size of remote file:
- 2.22 MB
- SHA256:
- b0f034ce41e7e45600e5eaee4a225aef5abdf7469b6cb2d99dcf69c11a54a80a
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