test gradio

app.py

@@ -4,6 +4,7 @@ from diffusers import StableDiffusion3ControlNetPipeline, SD3ControlNetModel, Un
 from huggingface_hub import login
 import os
 import spaces
+
 # Log in to Hugging Face with your token
 token = os.getenv("HF_TOKEN")
 login(token=token)
@@ -21,9 +22,9 @@ pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
 pipe = pipe.to("cuda")
 
 @spaces.GPU
-def generate_image(prompt, reference_image,controlnet_conditioning_scale):
+def generate_image(prompt, reference_image, controlnet_conditioning_scale):
     # Prepare the reference image for ControlNet
-    reference_image = reference_image.convert("RGB").resize((1024, 1024))
+    reference_image = reference_image.convert("RGB").resize((1024, 1024), Image.LANCZOS)
 
     # Generate the image with ControlNet conditioning
     generated_image = pipe(
@@ -31,7 +32,7 @@ def generate_image(prompt, reference_image,controlnet_conditioning_scale):
         control_image=reference_image,
         controlnet_conditioning_scale=controlnet_conditioning_scale,
         guidance_scale=7.5,
-        num_inference_steps=50
+        num_inference_steps=75  # Increased from 50 to refine quality
     ).images[0]
     return generated_image
 
@@ -41,7 +42,7 @@ interface = gr.Interface(
     inputs=[
         gr.Textbox(label="Prompt"),
         gr.Image(type="pil", label="Reference Image (Style)"),
-        gr.Slider(label="Control Net Conditioning Scale",minimum=0,maximum=1),
+        gr.Slider(label="Control Net Conditioning Scale", minimum=0.5, maximum=2.0, step=0.1, value=1.0),
     ],
     outputs="image",
     title="Image Generation with Stable Diffusion 3.5 and ControlNet",