Last updated: March 17, 2026

Deep Floyd

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Introduction:

Contribute to deep-floyd/IF development by creating an account on GitHub.

Launch Date:

October 8, 2007

Monthly Visits:

GitHub

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Deep Floyd Overview

What is Deep Floyd?

Deep Floyd is a tool that seems to be related to scraping data, but it currently faces issues as indicated by the error message: 'Scrape timed out'.

How to use Deep Floyd?

To use Deep Floyd, ensure that the scraping target is accessible and try again. If timeout issues persist, consider adjusting timeout settings or checking network conditions.

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Deep Floyd Q&A

The minimum requirements to use all IF models are: 16GB vRAM for IF-I-XL (4.3B text to 64x64 base module) & IF-II-L (1.2B to 256x256 upscaler module), and 24GB vRAM for IF-I-XL (4.3B text to 64x64 base module) & IF-II-L (1.2B to 256x256 upscaler module) & Stable x4 (to 1024x1024 upscaler). You also need to have xformers and set the environment variable FORCE_MEM_EFFICIENT_ATTN=1.

To quickly start using the IF models, you can use Google Colab or Hugging Face Spaces. You can also install the necessary packages using pip: pip install deepfloyd_if==1.0.2rc0, pip install xformers==0.0.16, and pip install git+https://github.com/openai/CLIP.git --no-deps.

IF is integrated with the 🤗 Hugging Face Diffusers library, allowing users to customize the image generation process and inspect intermediate results easily. Each stage runs individually, providing flexibility in the generation pipeline.

To run the code locally, you need to load the models into VRAM using the following code: from deepfloyd_if.modules import IFStageI, IFStageII, StableStageIII. You can then create instances of these classes and run the model with your desired parameters.

The different modes available in IF include Dream (text-to-image), Zero-shot Image-to-Image Translation, Super Resolution, and Zero-shot Inpainting.

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