diff --git a/webui.py b/webui.py
index 0e60d914..fbe76d11 100644
--- a/webui.py
+++ b/webui.py
@@ -683,7 +683,7 @@ def wrap_gradio_call(func):
             print("Arguments:", args, kwargs, file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
 
-            res = [None, f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
+            res = [None, '', f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
 
         elapsed = time.perf_counter() - t
 
@@ -1027,6 +1027,18 @@ class KDiffusionSampler:
         if hasattr(k_diffusion.sampling, 'trange'):
             k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
 
+        def cb(d):
+            n = d['i']
+            img = d['denoised']
+
+            x_samples_ddim = sd_model.decode_first_stage(img)
+            x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
+            for i, x_sample in enumerate(x_samples_ddim):
+                x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+                x_sample = x_sample.astype(np.uint8)
+                image = Image.fromarray(x_sample)
+                image.save(f'a/{n}.png')
+
         samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False)
         return samples_ddim
 
@@ -1989,7 +2001,7 @@ else:
     sd_model = sd_model.to(device)
 
 model_hijack = StableDiffusionModelHijack()
-model_hijack.hijack(sd_model)
+#model_hijack.hijack(sd_model)
 
 with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:
     css = file.read()