test gradio

app.py

@@ -16,22 +16,19 @@ login(token=token)
 
 
 pipeline = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16).to("cuda")
+pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter-plus-face_sdxl_vit-h.safetensors")
 
 
 
 @spaces.GPU
-def generate_image(prompt, reference_images, controlnet_conditioning_scale):
-    pipeline.load_ip_adapter(["h94/IP-Adapter"]*len(reference_images), subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
-    style_images = [Image.open(reference_image) for reference_image in reference_images]
-    # reference_image.resize((512, 512))
-    scale = {
-        "up": {"block_0": [0.0, controlnet_conditioning_scale/len(reference_images), 0.0]},
-    }
-    pipeline.set_ip_adapter_scale([scale]*len(reference_images))
+def generate_image(prompt, reference_image, controlnet_conditioning_scale):
+    style_images = [load_image(f.name) for f in reference_image]
+
+    pipeline.set_ip_adapter_scale(controlnet_conditioning_scale)
 
     image = pipeline(
         prompt=prompt,
-        ip_adapter_image=style_images,
+        ip_adapter_image=[style_images],
         negative_prompt="",
         guidance_scale=5,
         num_inference_steps=30,
@@ -44,8 +41,9 @@ interface = gr.Interface(
     fn=generate_image,
     inputs=[
         gr.Textbox(label="Prompt"),
-        gr.inputs.File(file_count="multiple"),
-        gr.Slider(label="Control Net Conditioning Scale", minimum=0, maximum=1.0, step=0.1, value=0.6),
+        # gr.Image( type= "filepath",label="Reference Image (Style)"),
+        gr.inputs.File(file_count="multiple",label="Reference Image (Style)"),
+        gr.Slider(label="Control Net Conditioning Scale", minimum=0, maximum=1.0, step=0.1, value=1.0),
     ],
     outputs="image",
     title="Image Generation with Stable Diffusion 3 medium and ControlNet",