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
Update README.md
Browse files
README.md
CHANGED
|
@@ -46,3 +46,9 @@ image = pipe(prompt).images[0]
|
|
| 46 |
# Save or display
|
| 47 |
image.save("generated_em.png")
|
| 48 |
image.show()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 46 |
# Save or display
|
| 47 |
image.save("generated_em.png")
|
| 48 |
image.show()
|
| 49 |
+
```
|
| 50 |
+
---
|
| 51 |
+
|
| 52 |
+
## 🖼️ Example Outputs
|
| 53 |
+
|
| 54 |
+

|