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: 1,059 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 | 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 (replaces CPU-only install)
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 app.py .
RUN git clone https://github.com/chuangchuangtan/NPR-DeepfakeDetection.git npr_deepfakedetection
RUN mkdir -p npr_deepfakedetection/weights && \
wget -O npr_deepfakedetection/weights/NPR.pth \
https://github.com/chuangchuangtan/NPR-DeepfakeDetection/raw/main/model_epoch_last_3090.pth
ENV MODEL_PORT=5001
ENV PRELOAD_MODEL=false
ENV MODEL_TIMEOUT=600
EXPOSE 5001
RUN adduser --disabled-password --gecos '' appuser
USER appuser
CMD ["python", "app.py"]
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