# DeepFloyd IF
DeepFloyd IF is a cascaded pixel-diffusion text-to-image model released in research form by [Stability AI](https://www.edgepedia.org/stability-ai) and its multimodal research lab DeepFloyd in late April 2023, notable for rendering legible text inside images. Unlike [Stable Diffusion](https://www.edgepedia.org/stable-diffusion), which diffuses in a compressed latent space, IF performs diffusion directly on pixels: a base model generates a 64x64 pixel image from the text prompt, and two super-resolution stages raise it to 256x256 and then 1024x1024 pixels.[1](https://github.com/deep-floyd/IF) • [2](https://stability.ai/news-updates/deepfloyd-if-text-to-image-model)
| Key fact | Detail |
|---|---|
| Class | Cascaded pixel-diffusion text-to-image model, research release April 26–28, 2023[3](https://pypi.org/project/deepfloyd-if/) |
| Maker | Stability AI's DeepFloyd lab[2](https://stability.ai/news-updates/deepfloyd-if-text-to-image-model) |
| Stages | IF-I base (400M/900M/4.3B), IF-II upscalers (450M/1.2B), Stable x4 suggested as third-stage substitute[1](https://github.com/deep-floyd/IF) • [4](https://huggingface.co/docs/diffusers/en/api/pipelines/deepfloyd_if) |
| Text encoder | Frozen T5-XXL-1.1[2](https://stability.ai/news-updates/deepfloyd-if-text-to-image-model) |
| Vendor benchmark | Zero-shot COCO FID 6.66 for IF-I-XL (4.3B)[1](https://github.com/deep-floyd/IF) |
| Training data | Custom LAION-A set of 1B image-text pairs[2](https://stability.ai/news-updates/deepfloyd-if-text-to-image-model) |
| License | Restricted, research-purposes-only at initial release[1](https://github.com/deep-floyd/IF) |
| Local hardware | 24GB VRAM for the full three-stage pipeline, 16GB for two stages[3](https://pypi.org/project/deepfloyd-if/) |
## Architecture and training as published
The published pipeline has three cascaded diffusion modules plus a frozen text encoder. Stage 1 generates a 64x64 pixel image from text; Stage 2 upscales it to 256x256; Stage 3 was specified as a 256-to-1024 super-resolution model.[1](https://github.com/deep-floyd/IF) • [4](https://huggingface.co/docs/diffusers/en/api/pipelines/deepfloyd_if) The vendor released Stage 1 in three sizes: IF-I-M at 400M parameters, IF-I-L at 900M, and IF-I-XL at 4.3B. Stage 2 came in two sizes, IF-II-M at 450M and IF-II-L at 1.2B.[1](https://github.com/deep-floyd/IF)
**Frozen components.** All stages use a frozen text encoder based on the T5 transformer, specifically T5-XXL-1.1 in the announced pipeline. In the Diffusers implementation, the T5 embeddings feed a UNet architecture enhanced with cross-attention and attention pooling in Stages 1 and 2.[2](https://stability.ai/news-updates/deepfloyd-if-text-to-image-model) • [4](https://huggingface.co/docs/diffusers/en/api/pipelines/deepfloyd_if) The vendor credited this large language-model encoder for IF's ability to generate coherent, clear text alongside objects in various spatial relations, which was the model's headline capability.[2](https://stability.ai/news-updates/deepfloyd-if-text-to-image-model)
The third-stage 700M-parameter IF-III model was not released at announcement; the vendor noted that IF's modularity allowed substituting another upscaler, suggesting the Stable Diffusion x4 Upscaler in its place.[2](https://stability.ai/news-updates/deepfloyd-if-text-to-image-model)
Training used a custom dataset the vendor called LAION-A, containing 1 billion (image, text) pairs, described as an aesthetic deduplicated subset of English LAION-5B with watermarked, NSFW, and inappropriate content filtered out. The vendor also listed zero-shot image-to-image translation without fine-tuning and aspect-ratio shifting (vertical, horizontal, square) among the release features.[2](https://stability.ai/news-updates/deepfloyd-if-text-to-image-model)
## Benchmarks: vendor claims versus independent results
The benchmark numbers available in the sources are all vendor-reported. According to the model's GitHub table, IF-I-XL (4.3B) achieves a zero-shot FID of 6.66 on the COCO dataset, against 8.06 for the 900M IF-I-L and 8.86 for the 400M IF-I-M; the table also lists a batch size of 3072 and 2.42M steps for the XL model.[1](https://github.com/deep-floyd/IF) A 2026 retrospective repeats the 6.66 figure and compares it with [DALL-E 2](https://www.edgepedia.org/dall-e-2)'s 10.39 on COCO-30K, citing the DeepFloyd paper on arXiv.[5](https://tasarim.ai/en/models/deepfloyd-if)
Readers should treat the FID figures as the vendor's own reported numbers.[1](https://github.com/deep-floyd/IF)
## Licensing, availability and practical use
The initial release was under a restricted, research-purposes-only (non-commercial) license. The vendor stated this was temporary, to gather feedback, and that it intended to release a fully open-source model afterwards, in line with other Stability AI models.[1](https://github.com/deep-floyd/IF) • [2](https://stability.ai/news-updates/deepfloyd-if-text-to-image-model) The sources do not record whether that fully open release ever happened.
The model was distributed through [Hugging Face](https://www.edgepedia.org/hugging-face) with a Diffusers implementation, and as a Python package on PyPI, where releases ran from 0.0.1rc5 on April 26, 2023 to v1.0.1 on April 28, 2023.[4](https://huggingface.co/docs/diffusers/en/api/pipelines/deepfloyd_if) • [3](https://pypi.org/project/deepfloyd-if/)
Hardware was a practical barrier. Running the largest configuration locally, IF-I-XL plus IF-II-L plus the Stable x4 upscaler, required 24GB of VRAM; the two-stage setup (stopping at 256x256) required 16GB.[3](https://pypi.org/project/deepfloyd-if/)
## References
1. [deep-floyd/IF GitHub repository](https://github.com/deep-floyd/IF)
2. [Stability AI releases DeepFloyd IF — official announcement](https://stability.ai/news-updates/deepfloyd-if-text-to-image-model)
3. [deepfloyd-if v1.0.1 — PyPI](https://pypi.org/project/deepfloyd-if/)
4. [DeepFloyd IF — Hugging Face Diffusers documentation](https://huggingface.co/docs/diffusers/en/api/pipelines/deepfloyd_if)
5. [DeepFloyd IF: Free & Open-Source Text to Image AI (2026) — tasarim.ai](https://tasarim.ai/en/models/deepfloyd-if)
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*Topic: Encyclopedia › Technology and the built world › Computing and digital systems › Modern AI: foundation models, generative AI, and the AI industry › Model families and named models › Image generation models*
*Initially written Sep 17, 2026 · Edited: Oct 11, 2026; Oct 15, 2026; Oct 25, 2026*
*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*
License: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license