Create app.py

app.py

@@ -0,0 +1,43 @@
+import torch
+from diffusers.utils import load_image
+from diffusers import FluxControlNetModel
+from diffusers.pipelines import FluxControlNetPipeline
+
+# Load pipeline
+controlnet = FluxControlNetModel.from_pretrained(
+  "jasperai/Flux.1-dev-Controlnet-Upscaler",
+  torch_dtype=torch.bfloat16
+)
+pipe = FluxControlNetPipeline.from_pretrained(
+  "black-forest-labs/FLUX.1-dev",
+  controlnet=controlnet,
+  torch_dtype=torch.bfloat16
+)
+pipe.to("cuda")
+
+# Load a control image
+
+uploaded_file = st.file_uploader("Choose an image", type=["png", "jpg"])
+
+control_image = None;
+if uploaded_file is not None:
+    bytes_data = uploaded_file.getvalue
+    control_image = bytes_data
+    st.write(f"filename: {uploaded_file.name}")
+    st.image(bytes_data)
+
+w, h = control_image.size
+
+# Upscale x4
+control_image = control_image.resize((w * 4, h * 4))
+
+image = pipe(
+    prompt="", 
+    control_image=control_image,
+    controlnet_conditioning_scale=0.6,
+    num_inference_steps=28, 
+    guidance_scale=3.5,
+    height=control_image.size[1],
+    width=control_image.size[0]
+).images[0]
+st.image(image)