Update app.py

app.py

@@ -15,16 +15,17 @@ def resize(value,img):
     img = img.resize((value,value))
     return img
 
-def infer(source_img, prompt, steps, seed, Strength):
+def infer(source_img, prompt, steps, seed):
     generator = torch.Generator(device).manual_seed(seed)     
     source_image = resize(512, source_img)
     source_image.save('source.png')
-    image = pipe(prompt, image=source_image, strength=Strength, guidance_scale=0.0, num_inference_steps=steps).images[0]
+    image = pipe(prompt, image=source_image, strength=0.5, guidance_scale=0.0, num_inference_steps=steps).images[0]
     return image
 
-gr.Interface(fn=infer, inputs=[gr.Image(source="upload", type="filepath", label="Raw Image. Must Be .png"), gr.Textbox(label = 'Prompt Input Text. 77 Token (Keyword or Symbol) Maximum'),
+gr.Interface(fn=infer, inputs=[gr.Image(source="upload", type="filepath", label="Raw Image. Must Be .png"), 
+    gr.Textbox(label = 'Prompt Input Text. 77 Token (Keyword or Symbol) Maximum'),
     #gr.Slider(2, 15, value = 7, label = 'Guidance Scale'),
     gr.Slider(1, 5, value = 2, step = 1, label = 'Number of Iterations'),
     gr.Slider(label = "Seed", minimum = 0, maximum = 987654321987654321, step = 1, randomize = True), 
-    gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .5)], 
+    #gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .5)], 
     outputs='image', title = "Stable Diffusion XL 1.0 Image to Image Pipeline CPU", description = "For more information on Stable Diffusion XL 1.0 see https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0 <br><br>Upload an Image (<b>MUST Be .PNG and 512x512 or 768x768</b>) enter a Prompt, or let it just do its Thing, then click submit. 10 Iterations takes about ~900-1200 seconds currently. For more informationon about Stable Diffusion or Suggestions for prompts, keywords, artists or styles see https://github.com/Maks-s/sd-akashic", article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").queue(max_size=5).launch()