Update app.py

app.py

@@ -18,16 +18,12 @@ def resize(value,img):
     img = img.resize((value,value))
     return img
 
-def infer(source_img, prompt, negative_prompt, guide, steps, seed, Strength):
-    generator = torch.Generator(device).manual_seed(seed)     
-    source_image = resize(768, source_img)
-    source_image.save('source.png')
-    image = pipe(prompt, negative_prompt=negative_prompt, image=source_image, strength=Strength, guidance_scale=guide, num_inference_steps=steps).images[0]
+def infer(prompt, source_img):
+    generator = torch.Generator(device).manual_seed(seed)
+    if not isinstance(source_image, Image.Image):
+        source_image = Image.open(source_image).convert("RGB")
+    image = pipe(prompt, image=source_image).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.Textbox(label='What you Do Not want the AI to generate.'),
-    gr.Slider(2, 15, value = 7, label = 'Guidance Scale'),
-    gr.Slider(1, 25, value = 10, 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.Interface(fn=infer, inputs=[gr.Text(label="Prompt"), gr.Image(label="Initial Image", type="pil")], 
     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 = "CuongTran").queue(max_size=5).launch()