Update utils.py

utils.py

@@ -1,25 +1,40 @@
+
+import os
+# Set Hugging Face cache dir to a safe writable location (works in Spaces & Docker)
+os.environ["TRANSFORMERS_CACHE"] = "/workspace/.cache/huggingface"
+os.makedirs("/workspace/.cache/huggingface", exist_ok=True)
+
 import torch
 from diffusers import StableDiffusionImg2ImgPipeline
 from PIL import Image
 
+
 # --- Place any download or path setup here ---
 MODEL_ID = "runwayml/stable-diffusion-v1-5"  # Can swap for custom path if using IP-Adapter
 DEVICE = "cpu"
-MODEL_CACHE = "./data"
+MODEL_CACHE = "/workspace/.cache/huggingface"
 
 # (Optional) Download IP-Adapter weights and patch pipeline if desired
 
+# Load the model ONCE at startup, not per request!
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.float32,
+    cache_dir=MODEL_CACHE,
+    safety_checker=None,  # Disable for demo/testing; enable in prod
+).to(DEVICE)
+
 def generate_sticker(input_image, prompt):
     """
     Given a user image and a prompt, generates a sticker/emoji-style portrait.
     """
     # Load the model (download if not present)
-    pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-        MODEL_ID,
-        torch_dtype=torch.float32,
-        cache_dir=MODEL_CACHE,
-        safety_checker=None,  # Disable for demo/testing
-    ).to(DEVICE)
+    # pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+    #     MODEL_ID,
+    #     torch_dtype=torch.float32,
+    #     cache_dir=MODEL_CACHE,
+    #     safety_checker=None,  # Disable for demo/testing
+    # ).to(DEVICE)
 
     # Preprocess the image (resize, etc)
     init_image = input_image.convert("RGB").resize((512, 512))