Create README.md

README.md

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+---
+datasets:
+- eurecom-ds/celeba
+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-celeba-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")
+```