Created app.py

app.py

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+import gradio as gr
+import requests
+import torch
+from PIL import Image
+from io import BytesIO
+from diffusers import StableDiffusionImg2ImgPipeline
+
+device = "cuda"
+model_id_or_path = "runwayml/stable-diffusion-v1-5"
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
+pipe = pipe.to(device)
+
+
+def transform (init_image, textPrompt, strength=0.5, guidance_scale=15):
+  init_image = Image.open(init_image).convert("RGB")
+  init_image = init_image.resize((768, 512))
+  images = pipe(prompt=textPrompt, image=init_image, strength=strength, guidance_scale=guidance_scale).images
+  image = images[0]
+  return image
+
+demo = gr.Interface(
+    fn=transform,
+    inputs=[gr.Image(type='filepath'), "text", gr.Slider(0,1), gr.Slider(1,30)],
+    outputs=["image"],
+    allow_flagging="never"
+)
+demo.launch()