Update app.py

app.py

@@ -3,22 +3,41 @@ import torch
 from diffusers import AutoPipelineForImage2Image
 from diffusers.utils import make_image_grid, load_image
 
-gr.load("models/NSTiwari/SDXL_LoRA_model").launch()
+# gr.load("models/NSTiwari/SDXL_LoRA_model").launch()
 
 pipeline = AutoPipelineForImage2Image.from_pretrained(
     "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 )
-pipeline.load_lora_weights('/content/SDXL_LoRA_model/pytorch_lora_weights.safetensors')
+pipeline.load_lora_weights('pytorch_lora_weights_00.safetensors')
 _ = pipeline.to("cuda")
 
 pipeline.enable_model_cpu_offload()
 
 url = "https://img.onmanorama.com/content/dam/mm/en/lifestyle/decor/images/2020/12/1/25-lakh-living-hall.jpg.transform/576x300/image.jpg"
-init_image = load_image(url)
-image = init_image.resize((1024, 576))
+# init_image = load_image(url)
+# image = init_image.resize((1024, 576))
 
 prompt = "A cozy Indian living room glows with morning sunshine on Republic Day, its walls decked in saffron, white, and green tapestries and art, while colorful cushions and festive garlands add a vibrant, celebratory air."
 
 # pass prompt and image to pipeline
 image_out = pipeline(prompt, image=image, strength=0.5).images[0]
-make_image_grid([image, image_out], rows=1, cols=2)
+# make_image_grid([image, image_out], rows=1, cols=2)
+
+
+# Define the image generation function
+def generate_image(prompt, image_url):
+    init_image = load_image(image_url)
+    image = init_image.resize((1024, 576))
+    image_out = pipeline(prompt, image=image, strength=0.5).images[0]
+    return image_out
+
+    
+# Set up Gradio interface
+iface = gr.Interface(
+    fn=generate_image, 
+    inputs=[gr.Textbox(label="Prompt"), gr.Textbox(label="Image URL")], 
+    outputs="image"
+)
+
+# Launch the Gradio app
+iface.launch()