Create new file

app.py

@@ -0,0 +1,42 @@
+import gradio as gr
+import torch
+import streamlit as st
+from PIL import Image
+import numpy as np
+from io import BytesIO
+from diffusers import StableDiffusionImg2ImgPipeline
+
+device="cuda"
+
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=st.secrets['USER_TOKEN'])
+pipe.to(device)
+
+def resize(value,img):
+  img = Image.open(img)
+  img = img.resize((value,value), Image.Resampling.LANCZOS)
+  return img
+
+def infer(source_img, prompt, guide, steps, seed, Strength): 
+    generator = torch.Generator('cuda').manual_seed(seed)     
+    source_image = resize(512, source_img)
+    source_image.save('source.png')
+    image_list = pipe([prompt], init_image=source_image, strength=Strength, guidance_scale=guide, num_inference_steps=steps)
+    images = []
+    safe_image = Image.open(r"unsafe.png")
+    for i, image in enumerate(image_list["sample"]):
+        if(image_list["nsfw_content_detected"][i]):
+            images.append(safe_image)
+        else:
+            images.append(image)    
+    return image
+
+gr.Interface(fn=infer, inputs=[gr.Image(source="upload", type="filepath", label="Raw Image"), gr.Textbox(label = 'Prompt Input 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 = .5)
+    ], outputs='image').queue(max_size=10).launch(enable_queue=True)