Update app.py

app.py

@@ -16,7 +16,7 @@ pipe.enable_model_cpu_offload()
 pipe.vae.enable_slicing()
 pipe.vae.enable_tiling()
 
-def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed):
+def genie (Model, Prompt, negative_prompt, scale, steps, seed):
     generator = np.random.seed(0) #if seed == 0 else torch.manual_seed(seed)
        
     image = pipe(
@@ -24,15 +24,15 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed):
         guidance_scale=scale,
         num_images_per_prompt=1,
         num_inference_steps=steps,
-        width=width,
-        height=height, generator=generator).images[0]
+        width=1024,
+        height=1024, generator=generator).images[0]
 
     return image
     
 gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'), 
                                gr.Textbox(label='What you Do Not want the AI to generate. 77 Token Limit'),
-                               gr.Slider(512, 1024, 768, step=128, label='Height'),
-                               gr.Slider(512, 1024, 768, step=128, label='Width'),
+                               #gr.Slider(512, 1024, 768, step=128, label='Height'),
+                               #gr.Slider(512, 1024, 768, step=128, label='Width'),
                                gr.Slider(3, maximum=12, value=5, step=.25, label='Guidance Scale', info="5-7 for PhotoReal and 7-10 for Animagine"), 
                                gr.Slider(25, maximum=50, value=25, step=25, label='Number of Iterations'), 
                                gr.Slider(minimum=0, step=1, maximum=9999999999999999, randomize=True, label='Seed: 0 is Random'),