up

app.py

@@ -96,8 +96,6 @@ def txt_to_img(
     neg_prompt,
     guidance,
     steps,
-    width,
-    height,
     generator,
 ):
     pipe = MODELS[model_name].pipe_t2i
@@ -111,15 +109,13 @@ def txt_to_img(
         negative_prompt=neg_prompt,
         num_inference_steps=int(steps),
         guidance_scale=guidance,
-        width=width,
-        height=height,
         generator=generator,
         output_type="latent",
     ).images
 
     with torch.no_grad():
         low_res_image = pipe.decode_latents(low_res_latents)
-        low_res_image = pipe.numpy_to_pil(low_res_image)[0]
+        low_res_image = pipe.numpy_to_pil(low_res_image)
 
     up_res_image = UPSCALER(
         prompt=prompt,
@@ -128,7 +124,7 @@ def txt_to_img(
         num_inference_steps=20,
         guidance_scale=0,
         generator=generator,
-    ).images[0]
+    ).images
 
     pipe.to("cpu")
     torch.cuda.empty_cache()
@@ -225,14 +221,6 @@ with gr.Blocks(css="style.css") as demo:
                             step=1,
                         )
 
-                    with gr.Row():
-                        width = gr.Slider(
-                            label="Width", value=512, minimum=64, maximum=1024, step=8
-                        )
-                        height = gr.Slider(
-                            label="Height", value=512, minimum=64, maximum=1024, step=8
-                        )
-
                     seed = gr.Slider(
                         0, 2147483647, label="Seed (0 = random)", value=0, step=1
                     )
@@ -242,8 +230,6 @@ with gr.Blocks(css="style.css") as demo:
         prompt,
         guidance,
         steps,
-        width,
-        height,
         seed,
         neg_prompt,
     ]