App files

app.py

@@ -0,0 +1,41 @@
+import replicate
+import gradio as gr
+from PIL import Image
+import requests
+from io import BytesIO
+
+api = replicate.Client(api_token="r8_9BTRdfQjCrVHkVMyQ6xAYJS52S6mLzx4YP6VA")
+
+# Setting up Replicate AI API for stable diffusion
+def generate_image(input_text, width=768, height=768, guidance_scale=7.5, num_inference_steps=50):
+    guidance_scale = float(guidance_scale)
+    output = api.run(
+        "stability-ai/stable-diffusion:ac732df83cea7fff18b8472768c88ad041fa750ff7682a21affe81863cbe77e4",
+        input={
+            "width": width,
+            "height": height,
+            "prompt": input_text,
+            "scheduler": "K_EULER",
+            "num_outputs": 1,
+            "guidance_scale": guidance_scale,
+            "num_inference_steps": num_inference_steps
+        }
+    )
+
+    image_url = output[0]
+    response = requests.get(image_url)
+    image = Image.open(BytesIO(response.content))
+    return image
+
+# Setting up a gradio interface
+
+iface = gr.Interface(
+    fn=generate_image,
+    inputs=[gr.Textbox(label="Prompt"),
+            gr.Slider(label="Width", minimum = 64,  maximum = 768, step = 64),
+            gr.Slider(label="Height", minimum = 64,  maximum = 768, step = 64),
+            gr.Slider(label="Guidance Scale", minimum = 0,  maximum = 20, step = 0.5),
+            gr.Radio([10,20,30,40,50], label="Inference Steps", info="Choose the number of Inference Steps")],
+    outputs="image")
+
+iface.launch(debug=True)

requirements.txt

@@ -0,0 +1,2 @@
+replicate
+gradio