test gradio

app.py

@@ -2,14 +2,10 @@ import gradio as gr
 from huggingface_hub import login
 import os
 import spaces
-import torch
-from diffusers import StableDiffusionXLPipeline
-from PIL import Image
-import torch
-from diffusers import AutoPipelineForText2Image, DDIMScheduler
 from diffusers import AutoPipelineForText2Image
 from diffusers.utils import load_image
 import torch
+import tempfile
 
 token = os.getenv("HF_TOKEN")
 login(token=token)
@@ -22,7 +18,13 @@ pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name=
 
 @spaces.GPU
 def generate_image(prompt, reference_image, controlnet_conditioning_scale):
-    style_images = [load_image(f.file.name) for f in reference_image]
+    style_image_paths = []
+    for f in reference_image:
+        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg")  # Adjust suffix if using another format
+        temp_file.write(f.read())
+        temp_file.close()
+        style_image_paths.append(temp_file.name)
+    style_images = [load_image(path) for path in style_image_paths]
 
     pipeline.set_ip_adapter_scale(controlnet_conditioning_scale)
 

app_image_style.py

@@ -0,0 +1,54 @@
+import gradio as gr
+from huggingface_hub import login
+import os
+import spaces
+import torch
+from diffusers import StableDiffusionXLPipeline
+from PIL import Image
+import torch
+from diffusers import AutoPipelineForText2Image, DDIMScheduler
+from diffusers import AutoPipelineForText2Image
+from diffusers.utils import load_image
+import torch
+
+token = os.getenv("HF_TOKEN")
+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_sdxl.bin")
+
+
+
+@spaces.GPU
+def generate_image(prompt, reference_image, controlnet_conditioning_scale):
+    style_images = [load_image(f.file.name) for f in reference_image]
+
+    pipeline.set_ip_adapter_scale(controlnet_conditioning_scale)
+
+    image = pipeline(
+        prompt=prompt,
+        ip_adapter_image=[style_images],
+        negative_prompt="",
+        guidance_scale=5,
+        num_inference_steps=30,
+    ).images[0]
+
+    return image
+
+# Set up Gradio interface
+interface = gr.Interface(
+    fn=generate_image,
+    inputs=[
+        gr.Textbox(label="Prompt"),
+        # gr.Image( type= "filepath",label="Reference Image (Style)"),
+        gr.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",
+    description="Generates an image based on a text prompt and a reference image using Stable Diffusion 3 medium with ControlNet."
+
+)
+
+interface.launch()