diff --git a/frontend/frontend.py b/frontend/frontend.py
index de34742..a9e6c6a 100644
--- a/frontend/frontend.py
+++ b/frontend/frontend.py
@@ -38,8 +38,11 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda
                                                 value=txt2img_defaults['cfg_scale'], elem_id='cfg_slider')
                         txt2img_seed = gr.Textbox(label="Seed (blank to randomize)", lines=1, max_lines=1,
                                                   value=txt2img_defaults["seed"])
+                        txt2img_batch_size = gr.Slider(minimum=1, maximum=50, step=1,
+                                                       label='Images per batch',
+                                                       value=txt2img_defaults['batch_size'])
                         txt2img_batch_count = gr.Slider(minimum=1, maximum=50, step=1,
-                                                        label='Number of images to generate',
+                                                        label='Number of batches to generate',
                                                         value=txt2img_defaults['n_iter'])
 
                         txt2img_job_ui = job_manager.draw_gradio_ui() if job_manager else None
@@ -93,9 +96,6 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda
                             with gr.TabItem('Advanced'):
                                 txt2img_toggles = gr.CheckboxGroup(label='', choices=txt2img_toggles,
                                                                    value=txt2img_toggle_defaults, type="index")
-                                txt2img_batch_size = gr.Slider(minimum=1, maximum=8, step=1,
-                                                               label='Batch size (how many images are in a batch; memory-hungry)',
-                                                               value=txt2img_defaults['batch_size'])
                                 txt2img_realesrgan_model_name = gr.Dropdown(label='RealESRGAN model',
                                                                             choices=['RealESRGAN_x4plus',
                                                                                      'RealESRGAN_x4plus_anime_6B'],
diff --git a/scripts/webui.py b/scripts/webui.py
index 31f35bb..0de96dd 100644
--- a/scripts/webui.py
+++ b/scripts/webui.py
@@ -969,11 +969,10 @@ def process_images(
             if opt.optimized:
                 modelFS.to(device)
 
+            for i in range(len(samples_ddim)):
+                x_samples_ddim = (model if not opt.optimized else modelFS).decode_first_stage(samples_ddim[i].unsqueeze(0))
+                x_sample = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
-
-            x_samples_ddim = (model if not opt.optimized else modelFS).decode_first_stage(samples_ddim)
-            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):
                 sanitized_prompt = prompts[i].replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})
                 if variant_seed != None and variant_seed != '':
                     if variant_amount == 0.0:
@@ -1005,7 +1004,7 @@ def process_images(
                 filename = filename.replace("[SEED]", seed_used)
                 filename = filename.replace("[VARIANT_AMOUNT]", f"{cur_variant_amount:.2f}")
 
-                x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+                x_sample = 255. * rearrange(x_sample[0].cpu().numpy(), 'c h w -> h w c')
                 x_sample = x_sample.astype(np.uint8)
                 metadata = ImageMetadata(prompt=prompts[i], seed=seeds[i], height=height, width=width, steps=steps,
                                     cfg_scale=cfg_scale, normalize_prompt_weights=normalize_prompt_weights, denoising_strength=denoising_strength,