Update app.py

app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-#import torch
+import torch
 #from torch import autocast // only for GPU
 
 from PIL import Image
@@ -12,7 +12,7 @@ MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
 from diffusers import StableDiffusionImg2ImgPipeline
 
 def empty_checker(images, **kwargs): return images, False
-print("start generating")
+print("hello")
 
 YOUR_TOKEN=MY_SECRET_TOKEN
 
@@ -35,21 +35,26 @@ def resize(value,img):
   return img
 
 
-def infer(source_img, prompt): 
+def infer(source_img, prompt, guide, steps, seed, strength): 
          
     source_image = resize(512, source_img)
     source_image.save('source.png')
-    images_list = img_pipe([prompt] * 1, guidance_scale=7.5, num_inference_steps=25, init_image=source_image,  strength=0.75)
+    images_list = img_pipe([prompt] * 1,  init_image=source_image, strength=strength, guidance_scale=guide, num_inference_steps=steps)
     images = []
 
     for i, image in enumerate(images_list["images"]):
             images.append(image)    
     return images
 
-print("okay")
+print("done")
 
 title="Marco Generation Sketch"
-description="Draw and use 'mkmk woman' to get Marco pics. <br />Warning: Slow process... about 10 min inference time." 
+description="<p style='text-align: center;'>Draw and use 'mkmk woman' to get Marco pics. <br />Warning: Slow process... about 10 min inference time.</p>" 
 
 
-gr.Interface(fn=infer, inputs=[source_img, "text"], outputs=gallery,title=title,description=description).queue(max_size=100).launch(enable_queue=True)
+gr.Interface(fn=infer, inputs=[source_img,
+    "text",
+    gr.Slider(2, 15, value = 7, label = 'Guidence Scale'),
+    gr.Slider(10, 50, value = 25, step = 1, label = 'Number of Iterations'),
+    gr.Slider(label = "Seed", minimum = 0, maximum = 2147483647, step = 1, randomize = True),
+    gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .75)], outputs=gallery,title=title,description=description).queue(max_size=100).launch(enable_queue=True)