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import os
# Set Hugging Face cache dir to a safe writable location (works in Spaces & Docker)
os.environ["HF_HOME"] = "/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
ADAPTER_PATH = "/workspace/.cache/huggingface/ip_adapter/ip-adapter_sd15.bin"
ADAPTER_DIR = "/workspace/.cache/huggingface/ip_adapter"
DEVICE = "cpu"
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)
pipe.load_ip_adapter(
pretrained_model_name_or_path=ADAPTER_DIR,
subfolder=".", # The weights file is directly in ADAPTER_DIR
weight_name="ip-adapter_sd15.bin"
# Optionally: subfolder="models" if using the repo, not a direct path
)
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)
# Preprocess the image (resize, etc)
init_image = input_image.convert("RGB").resize((512, 512))
# Run inference (low strength for identity preservation)
result = pipe(
prompt=prompt,
image=init_image,
strength=0.65,
guidance_scale=7.5,
num_inference_steps=30
)
# Return the generated image (as PIL)
return result.images[0]
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