Create app.py

app.py

@@ -0,0 +1,66 @@
+import streamlit as st
+from io import BytesIO
+import base64
+import os
+from replicate import Client
+
+illuse = Client(api_token=os.getenv('REPLICATE'))
+model_name = "andreasjansson/illusion:75d51a73fce3c00de31ed9ab4358c73e8fc0f627dc8ce975818e653317cb919b"
+example_image = "https://replicate.delivery/pbxt/hHJNV9QteKX8DK2ckkUeXsqbEIKNGFXU1fN0MJoizz3iPlOjA/output-0.png"
+
+def generate(prompt, negative_prompt, qr_content, pattern_image, num_inference_steps, guidance_scale, width, height, seed, num_outputs, controlnet_conditioning_scale, border, qrcode_background):
+    try:
+        inputs = {
+                'prompt': prompt,
+                'negative_prompt': negative_prompt,
+                'qr_code_content': qr_content,
+                'num_inference_steps': num_inference_steps,
+                'guidance_scale': guidance_scale,
+                'width': width,
+                'height': height,
+                'seed': seed,
+                'num_outputs': num_outputs,
+                'controlnet_conditioning_scale': controlnet_conditioning_scale,
+                'border': border,
+                'qrcode_background': qrcode_background
+            }
+        if pattern_image is not None:
+            inputs['image'] = open(pattern_image, 'rb')
+
+        result = illuse.run(
+            model_name,
+            input=inputs
+        )
+        return result
+    except Exception as e:
+        print(e)
+        st.error(str(e))
+        return
+
+st.title("Illusion Diffusion Fast Demo powered by replicate")
+
+prompt = st.text_input("Prompt")
+negative_prompt = st.text_input("Negative")
+
+qr_content = st.text_input("QR Code Content", "https://youtube.com/")
+pattern_input = st.file_uploader("Pattern Image (if used, QR Code Content won't be used)", type=["jpg", "png", "jpeg"])
+
+num_inference_steps = st.slider("num_inference_steps", min_value=20, max_value=100, step=1, value=50)
+guidance_scale = st.slider("guidance_scale", min_value=0.1, max_value=30.0, step=0.01, value=7.5)
+
+width = st.slider("width", min_value=128, max_value=1024, step=8, value=768)
+height = st.slider("height", min_value=128, max_value=1024, step=8, value=768)
+
+seed = st.number_input("seed", value=-1)
+num_outputs = st.slider("num_outputs", min_value=1, max_value=4, step=1, value=1)
+
+controlnet_conditioning_scale = st.slider("controlnet_conditioning_scale", min_value=0, max_value=4, step=1, value=1)
+border = st.slider("border", min_value=0, max_value=4, step=1, value=4)
+qrcode_background = st.selectbox("qrcode_background", options=['gray', 'white'], index=1)
+
+if st.button("Run"):
+    result = generate(prompt, negative_prompt, qr_content, pattern_input, num_inference_steps, guidance_scale, width, height, seed, num_outputs, controlnet_conditioning_scale, border, qrcode_background)
+    if result:
+        st.image(result['output'])
+
+st.image(example_image, caption='Example Image', use_column_width=True)