Create app.py

app.py

@@ -0,0 +1,31 @@
+from contextlib import nullcontext
+import gradio as gr
+import torch
+from torch import autocast
+from diffusers import StableDiffusionPipeline
+
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+context = autocast if device == "cuda" else nullcontext
+dtype = torch.float16 if device == "cuda" else torch.float32
+
+pipe = StableDiffusionPipeline.from_pretrained("ringhyacinth/nail-set-diffuser", torch_dtype=dtype)
+pipe = pipe.to(device)
+
+
+# Disable nsfw checker
+disable_safety = True
+
+if disable_safety:
+  def null_safety(images, **kwargs):
+      return images, False
+  pipe.safety_checker = null_safety
+
+
+def infer(prompt, n_samples, steps, scale):
+
+    with context("cuda"):
+        images = pipe(n_samples*[prompt], guidance_scale=scale, num_inference_steps=steps).images
+
+    return images
+