Create app.py

app.py

@@ -0,0 +1,34 @@
+import gradio as gr
+import modin.pandas as pd
+import torch
+import numpy as np
+from PIL import Image
+from diffusers import DiffusionPipeline
+from diffusers.utils import load_image
+import math
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = DiffusionPipeline.from_pretrained("radames/stable-diffusion-v1-5-img2img", torch_dtype=torch.float16) if torch.cuda.is_available() else AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo")
+pipe = pipe.to(device)
+
+def resize(value,img):
+    img = Image.open(img)
+    img = img.resize((value,value))
+    return img
+
+def infer(source_img, prompt, steps, seed, Strength):
+    generator = torch.Generator(device).manual_seed(seed)  
+    if int(steps * Strength) < 1:
+        steps = math.ceil(1 / max(0.10, Strength))
+    source_image = resize(512, source_img)
+    source_image.save('source.png')
+    image = pipe(prompt, image=source_image, strength=Strength, guidance_scale=0.0, num_inference_steps=steps).images[0]
+    return image
+
+gr.Interface(fn=infer, inputs=[
+    gr.Image(sources=["upload", "webcam", "clipboard"], type="filepath", label="Raw Image."), 
+    gr.Textbox(label = 'Creative Touch(prompt)'),
+    gr.Slider(1, 5, value = 2, step = 1, label = 'Number of Iterations'),
+    gr.Slider(label = "Seed", minimum = 0, maximum = 987654321987654321, step = 1, randomize = True), 
+    gr.Slider(label='Strength', minimum = 0.1, maximum = 1, step = .05, value = .5)], 
+    outputs='image', title = "Creative Touch").queue(max_size=10).launch()