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
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# UniEM-Gen
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## 📘 Model Summary
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This is the text-to-image diffusion model trained on the complete **[UniEM-3M](https://huggingface.co/datasets/NNNan/UniEM-3M)** dataset.
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## 🖼️ Example Outputs
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# UniEM-Gen
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## 🖼️ Example Outputs
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## 📘 Model Summary
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This is the text-to-image diffusion model trained on the complete **[UniEM-3M](https://huggingface.co/datasets/NNNan/UniEM-3M)** dataset.
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