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---
datasets:
- eurecom-ds/multi_dsprites
library_name: diffusers
pipeline_tag: unconditional-image-generation
---
```python
# !pip install diffusers
from diffusers import DiffusionPipeline
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_id = "eurecom-ds/scoresdeve-ema-multi-dsprites-64"
# load model and scheduler
pipe = DiffusionPipeline.from_pretrained(model_id, trust_remote_code=True)
pipe.to(device)
# run pipeline in inference (sample random noise and denoise)
generator = torch.Generator(device=device).manual_seed(46)
image = pipe(
generator=generator,
batch_size=1,
num_inference_steps=1000
).images
# save image
image[0].save("sde_ve_generated_image.png")
```
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