Update app.py

app.py

@@ -123,7 +123,8 @@ def process(
         shape = (1, 4, height // 8, width // 8)
         x_T = torch.randn(shape, device=device, dtype=torch.float32)
         # Create unconditional embeddings for classifier-free guidance
-        uc = model.get_learned_conditioning([""])
+        c = model.get_learned_conditioning([positive_prompt])
+        uc = model.get_learned_conditioning([negative_prompt])
 
         if not tile_diffusion:
             samples = sampler.sample_ccsr(