SDXL Turbo
Images from text in under a second – perfect for a first taste in Colab.
SDXL Turbo generates an image in a single computation step. It's ideal for trying out image AI for free in Google Colab. License: non-commercial use only.
What is SDXL Turbo?
SDXL Turbo is an image AI model. You type in a short description, for example "a red fox in the snow, watercolor". The model turns it into an image.
What makes it special: it needs just a single computation step. Many other image models need far more steps. That's why results arrive so fast. The teaser says under a second.
How does it work, in simple terms?
Image AIs of this kind usually start with an image of random noise. A subject gradually emerges from it. Your text steers which subject that becomes. SDXL Turbo shortcuts this path and reaches the result in one step.
What is it especially good at?
- Speed: You see almost instantly what your text does. That makes experimenting pleasant.
- Getting started: You can test prompts without long waits. A prompt is the text instruction you give the AI.
- Free testing: According to the short description, it's well suited for trying out image AI for free in Google Colab. Colab is a Google service where you run code in your browser. The computing power comes from Google.
Where are the limits?
- License: The model is released under the Stability AI Non-Commercial license. You may only use it non-commercially. It isn't meant for client projects or sales.
- Quality: The available information says nothing about image quality. Try it yourself to see whether the results are good enough for you.
- A first taste: According to the description, the tool is mainly meant for trying things out.
- Documentation: No README was available. You'll have to look up further details and settings yourself.
What do you need?
- A graphics card with at least 8 GB VRAM. VRAM is the graphics card's memory, and it partly decides whether a model runs.
- Or the free T4 GPU in Google Colab. According to the info, that's enough. All you need is a browser and a Google account.
Ideas: What you can do with it
You write a description and see the image almost instantly. Then you change one word and see what changes. That's a quick way to learn how image AI responds to text.
You test motifs for a game night, a birthday card or a poster for personal use. You generate several variants and pick a direction. The result is a collection of ideas, not a final version.
You demonstrate live in a group how text becomes an image. Because it's so fast, everyone can shout out requests. Colab is enough for that, so you don't need your own computer.
You're writing a short story or a role-playing adventure and want to see scenes. You enter the scene as a sentence and get an image for it. For private, non-commercial purposes only.
You have a computer with 8 GB VRAM or more and want to know whether image AI runs on it. SDXL Turbo is a cheap test because it needs only one computation step per image.
How to try it
Commands from the official guides – just copy them.
Google Colab
- Download the notebook: colab.ipynb
- Open colab.research.google.com → File → Upload notebook
- Runtime → Change runtime type → GPU (T4)
- Runtime → Run all
Linux
# One-time setup: install uv (package manager for Python, no sudo needed)
curl -LsSf https://astral.sh/uv/install.sh | sh && source $HOME/.local/bin/env
uv venv --python 3.12 sd-env && source sd-env/bin/activate
uv pip install torch diffusers transformers accelerate
python -c "
import torch; from diffusers import AutoPipelineForText2Image
p = AutoPipelineForText2Image.from_pretrained('stabilityai/sdxl-turbo', torch_dtype=torch.float16, variant='fp16').to('cuda')
p(prompt='Ein Fuchs im Schnee, Fotografie', num_inference_steps=1, guidance_scale=0.0).images[0].save('bild.png')"
# Result: bild.png in the current folder (first run downloads about 7 GB)macOS
# One-time setup: install uv (package manager for Python, no sudo needed)
curl -LsSf https://astral.sh/uv/install.sh | sh && source $HOME/.local/bin/env
uv venv --python 3.12 sd-env && source sd-env/bin/activate
uv pip install torch diffusers transformers accelerate
python -c "
import torch; from diffusers import AutoPipelineForText2Image
p = AutoPipelineForText2Image.from_pretrained('stabilityai/sdxl-turbo', torch_dtype=torch.float16, variant='fp16').to('mps')
p(prompt='Ein Fuchs im Schnee, Fotografie', num_inference_steps=1, guidance_scale=0.0).images[0].save('bild.png')"
# Result: bild.png in the current folder (first run downloads about 7 GB)Windows (PowerShell)
# One-time setup: install uv (package manager for Python)
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
$env:Path = "$HOME\.local\bin;$env:Path"
uv venv --python 3.12 sd-env
uv pip install --python sd-env torch --index-url https://download.pytorch.org/whl/cu128
uv pip install --python sd-env diffusers transformers accelerate
.\sd-env\Scripts\python.exe -c "import torch; from diffusers import AutoPipelineForText2Image; p = AutoPipelineForText2Image.from_pretrained('stabilityai/sdxl-turbo', torch_dtype=torch.float16, variant='fp16').to('cuda'); p(prompt='Ein Fuchs im Schnee, Fotografie', num_inference_steps=1, guidance_scale=0.0).images[0].save('bild.png')"
# Result: bild.png in the current folder (first run downloads about 7 GB)Tips
- Keep prompts short and specific: subject, style, mood. Because images arrive so fast, you can try lots of variants.
- Change only one thing in the prompt at a time. That way you see what has which effect.
- In Colab, select a GPU runtime, or it won't run fast. The free T4 is enough.
- Use the images only privately or for learning. The license doesn't allow commercial use.
If something goes wrong
- Colab runs very slowly or not at all without a GPU: switch the runtime to GPU (T4) and restart.
- Your own computer runs out of memory: below 8 GB VRAM, it isn't supported according to the info. Switch to Colab instead.
- You want to use the images for clients or sales: the license doesn't allow that. Check the Stability AI Non-Commercial license or use a different model.
- There's no README: you won't find settings and examples in the package. Look for hints in the model description.
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