inference.py

import os
import torch
import traceback
import gradio as gr  # ✅ Needed for gr.Error
from diffusers import AutoPipelineForImage2Image

# ✅ Cache models and tokenizers inside persistent storage
os.environ["HF_HOME"] = "/data/.cache/huggingface"

# Load SDXL pipeline with LoRA
pipe = AutoPipelineForImage2Image.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16,
    variant="fp16",
    use_safetensors=True,
    token=os.getenv("HF_TOKEN")  # ✅ Your token from Space secrets
).to("cuda")

pipe.load_lora_weights("theoracle/sdxl-lora-headshot")

def generate_with_lora(image, prompt, negative_prompt, strength, guidance_scale):
    try:
        if image is None:
            raise ValueError("Uploaded image is None. Please upload a valid image.")

        print("[INFO] Received image size:", image.size)
        image = image.convert("RGB").resize((1024, 1024))  # ✅ Safer with convert("RGB")
        print("[INFO] Starting pipeline with prompt:", prompt)

        result = pipe(
            prompt=prompt,
            negative_prompt=negative_prompt or "",
            image=image,
            strength=strength,
            guidance_scale=guidance_scale,
            num_inference_steps=50
        ).images[0]

        print("[INFO] Generation successful.")
        return result

    except Exception as e:
        print("[ERROR] Exception in generate_with_lora:\n", traceback.format_exc())
        raise gr.Error(f"Image generation failed: {str(e)}")