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: 1,773 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 50 51 52 53 54 55 56 | 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 (downgraded from 2.6.0 -- no cu121 wheels for 2.6+)
RUN pip install --no-cache-dir \
torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 \
--index-url https://download.pytorch.org/whl/cu121
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
RUN git clone https://github.com/yermandy/deepfake-detection.git model_code
RUN mkdir -p model_code/weights && \
wget https://huggingface.co/yermandy/deepfake-detection/resolve/main/model.ckpt -O model_code/weights/model.ckpt
ENV HF_HOME=/app/huggingface_cache
ENV TRANSFORMERS_CACHE=/app/huggingface_cache
ENV HF_HUB_CACHE=/app/huggingface_cache
RUN mkdir -p $HF_HOME
RUN pip install --no-cache-dir --upgrade transformers
RUN python -c "from transformers import CLIPProcessor, CLIPModel; \
CLIPProcessor.from_pretrained('openai/clip-vit-base-patch16'); \
CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'); \
CLIPProcessor.from_pretrained('openai/clip-vit-large-patch14'); \
CLIPProcessor.from_pretrained('openai/clip-vit-large-patch14-336'); \
CLIPModel.from_pretrained('openai/clip-vit-large-patch14')"
COPY app.py .
ENV MODEL_PORT=5002
ENV MODEL_PATH=model_code/weights/model.ckpt
ENV PRELOAD_MODEL="false"
ENV MODEL_TIMEOUT="600"
ENV PYTHONPATH=/app/model_code:/app/model_code/src:$PYTHONPATH
EXPOSE ${MODEL_PORT}
RUN adduser --disabled-password --gecos '' appuser
USER appuser
CMD ["python", "app.py"]
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