Update app.py

app.py

@@ -122,18 +122,12 @@ def process(
     for _ in tqdm(range(num_samples)):
         shape = (1, 4, height // 8, width // 8)
         x_T = torch.randn(shape, device=device, dtype=torch.float32)
-        # Create unconditional embeddings for classifier-free guidance
-        c = model.get_learned_conditioning([positive_prompt])
-        uc = model.get_learned_conditioning([negative_prompt])
-
         if not tile_diffusion:
             samples = sampler.sample_ccsr(
                 steps=steps, t_max=0.6667, t_min=0.3333, shape=shape, cond_img=control,
                 positive_prompt=positive_prompt, negative_prompt=negative_prompt, x_T=x_T,
                 cfg_scale=cfg_scale,
-                color_fix_type="adain" if use_color_fix else "none",
-                # Pass unconditional embeddings to the sampler
-                unconditional_conditioning=uc,
+                color_fix_type="adain" if use_color_fix else "none"
             )
         else:
             samples = sampler.sample_with_tile_ccsr(
@@ -141,9 +135,7 @@ def process(
                 steps=steps, t_max=0.6667, t_min=0.3333, shape=shape, cond_img=control,
                 positive_prompt=positive_prompt, negative_prompt=negative_prompt, x_T=x_T,
                 cfg_scale=cfg_scale,
-                color_fix_type="adain" if use_color_fix else "none",
-                # Pass unconditional embeddings to the sampler
-                unconditional_conditioning=uc,
+                color_fix_type="adain" if use_color_fix else "none"
             )
 
         x_samples = samples.clamp(0, 1)