Instructions to use deepsafe/deepsafe-services with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use deepsafe/deepsafe-services with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("deepsafe/deepsafe-services", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 1,693 Bytes
4b0b144 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | FROM nvidia/cuda:12.1.1-cudnn8-runtime-ubuntu22.04
ENV DEBIAN_FRONTEND=noninteractive
ENV PYTHONUNBUFFERED=1
WORKDIR /app
RUN apt-get update && apt-get install -y --no-install-recommends \
python3 python3-pip git wget \
&& rm -rf /var/lib/apt/lists/*
RUN ln -sf /usr/bin/python3 /usr/bin/python
# Install PyTorch with CUDA 12.1 (upgraded from 1.11/CUDA 11.3 for Blackwell support)
RUN pip install --no-cache-dir \
torch==2.5.1 torchvision==0.20.1 \
--index-url https://download.pytorch.org/whl/cu121
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
# Copy application code
COPY app.py .
# Clone the repository
RUN git clone https://github.com/WisconsinAIVision/UniversalFakeDetect.git universalfakedetect && \
mkdir -p universalfakedetect/pretrained_weights
RUN python -c "import torch; import os; os.makedirs('/root/.cache/clip', exist_ok=True)" && \
wget -O /root/.cache/clip/ViT-L-14.pt https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt
# Download the weights file separately to avoid timeouts during startup
RUN wget -O universalfakedetect/pretrained_weights/fc_weights.pth \
https://github.com/WisconsinAIVision/UniversalFakeDetect/raw/main/pretrained_weights/fc_weights.pth
# Set environment variables
ENV MODEL_PORT=5003
ENV USE_GPU=false
ENV TIMEOUT=600
# Expose port
EXPOSE 5003
# Drop root privileges
RUN adduser --disabled-password --gecos '' appuser
USER appuser
# Run the application with gunicorn for better timeout handling
CMD ["python", "-m", "uvicorn", "app:app", "--host", "0.0.0.0", "--port", "5003", "--timeout-keep-alive", "600"] |