add img2img

app.py

@@ -5,8 +5,10 @@ import re
 import gradio as gr
 from diffusers import AutoPipelineForText2Image
 import torch
-
+from diffusers import AutoPipelineForImage2Image
+from diffusers.utils import load_image, make_image_grid
 pipeline_text2image = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
+pipeline_image2image = AutoPipelineForImage2Image.from_pipe(pipeline_text2image).to("cuda")
 pipeline_text2image = pipeline_text2image.to("cuda")
 
 
@@ -14,6 +16,12 @@ def text2img(prompt = "A cinematic shot of a baby racoon wearing an intricate it
     image = pipeline_text2image(prompt=prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
     return image
 
+def img2img(image,prompt=prompt, guidance_scale=0.0, num_inference_steps=1,strength=0.5):
+    init_image = load_image(image)
+    init_image = init_image.resize((512, 512))
+    image = pipeline_image2image(prompt, image=init_image, strength=strength, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
+    return image
+
 gradio_app_text2img = gr.Interface(
     fn=text2img,
     inputs=[
@@ -24,8 +32,18 @@ gradio_app_text2img = gr.Interface(
     outputs="image",
 )
 
+gradio_app_img2img = gr.Interface(
+    fn=text2img,
+    inputs=[
+        gr.Image(type='filepath'),
+        gr.Text(),
+        gr.Slider(0.0, 10.0, value=1,step=0.1),
+        gr.Slider(0.0, 10.0, value=1,step=1)
+    ],
+    outputs="image",
+)
 
-demo = gr.TabbedInterface([gradio_app_text2img], ["text2img"])
+demo = gr.TabbedInterface([gradio_app_text2img,gradio_app_img2img], ["text2img","img2img"])
 
 if __name__ == "__main__":
     demo.launch()