Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import torch
 from diffusers import StableDiffusionXLImg2ImgPipeline
 from diffusers.utils import load_image
-
+'''
 pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
     "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 )
@@ -13,11 +13,29 @@ def run_fn(img_url):
     prompt = "a photo of an astronaut riding a horse on mars"
     image = pipe(prompt, image=init_image).images
     return image
-gr.Interface(fn=run_fn,
-            inputs=[
-                gr.Textbox()
-            ],
-            outputs=[
-                gr.Image()
-            ]).launch()
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
+pipe = pipe.to(device)
+'''
+def resize(value,img):
+    img = Image.open(img)
+    img = img.resize((value,value))
+    return img
+
+def infer(source_img, prompt, negative_prompt, guide, steps, seed, Strength):
+    source_img = load_image(source_img).convert("RGB")
+    
+    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]
+    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)], 
+    outputs='image').launch()