InvokeAI/docs/features/OTHER.md

---
title: Others
---

## **Google Colab**

Stable Diffusion AI Notebook: <a
href="https://colab.research.google.com/github/lstein/stable-diffusion/blob/main/notebooks/Stable_Diffusion_AI_Notebook.ipynb"
target="_parent">
<img
src="https://colab.research.google.com/assets/colab-badge.svg"
alt="Open In Colab"/></a> <br> Open and follow instructions to use an isolated environment running
Dream.<br>

Output Example: ![Colab Notebook](../assets/colab_notebook.png)

---

## **Seamless Tiling**

The seamless tiling mode causes generated images to seamlessly tile with itself. To use it, add the
`--seamless` option when starting the script which will result in all generated images to tile, or
for each `dream>` prompt as shown here:

```python
dream> "pond garden with lotus by claude monet" --seamless -s100 -n4
```

---

## **Reading Prompts from a File**

You can automate `dream.py` by providing a text file with the prompts you want to run, one line per
prompt. The text file must be composed with a text editor (e.g. Notepad) and not a word processor.
Each line should look like what you would type at the dream> prompt:

```bash
a beautiful sunny day in the park, children playing -n4 -C10
stormy weather on a mountain top, goats grazing     -s100
innovative packaging for a squid's dinner           -S137038382
```

Then pass this file's name to `dream.py` when you invoke it:

```bash
(ldm) ~/stable-diffusion$ python3 scripts/dream.py --from_file "path/to/prompts.txt"
```

You may read a series of prompts from standard input by providing a filename of `-`:

```bash
(ldm) ~/stable-diffusion$ echo "a beautiful day" | python3 scripts/dream.py --from_file -
```

---

## **Shortcuts: Reusing Seeds**

Since it is so common to reuse seeds while refining a prompt, there is now a shortcut as of version
1.11. Provide a `**-S**` (or `**--seed**`) switch of `-1` to use the seed of the most recent image
generated. If you produced multiple images with the `**-n**` switch, then you can go back further
using -2, -3, etc. up to the first image generated by the previous command. Sorry, but you can't go
back further than one command.

Here's an example of using this to do a quick refinement. It also illustrates using the new `**-G**`
switch to turn on upscaling and face enhancement (see previous section):

```bash
dream> a cute child playing hopscotch -G0.5
[...]
outputs/img-samples/000039.3498014304.png: "a cute child playing hopscotch" -s50 -W512 -H512 -C7.5 -mk_lms -S3498014304

# I wonder what it will look like if I bump up the steps and set facial enhancement to full strength?
dream> a cute child playing hopscotch -G1.0 -s100 -S -1
reusing previous seed 3498014304
[...]
outputs/img-samples/000040.3498014304.png: "a cute child playing hopscotch" -G1.0 -s100 -W512 -H512 -C7.5 -mk_lms -S3498014304
```

---

## **Weighted Prompts**

You may weight different sections of the prompt to tell the sampler to attach different levels of
priority to them, by adding `:(number)` to the end of the section you wish to up- or downweight. For
example consider this prompt:

```bash
tabby cat:0.25 white duck:0.75 hybrid
```

This will tell the sampler to invest 25% of its effort on the tabby cat aspect of the image and 75%
on the white duck aspect (surprisingly, this example actually works). The prompt weights can use any
combination of integers and floating point numbers, and they do not need to add up to 1.

---

## **Simplified API**

For programmers who wish to incorporate stable-diffusion into other products, this repository
includes a simplified API for text to image generation, which lets you create images from a prompt
in just three lines of code:

```bash
from ldm.generate import Generate
g       = Generate()
outputs = g.txt2img("a unicorn in manhattan")
```

Outputs is a list of lists in the format [filename1,seed1],[filename2,seed2]...].

Please see ldm/generate.py for more information. A set of example scripts is coming RSN.

---

## **Preload Models**

In situations where you have limited internet connectivity or are blocked behind a firewall, you can
use the preload script to preload the required files for Stable Diffusion to run.

The preload script `scripts/preload_models.py` needs to be run once at least while connected to the
internet. In the following runs, it will load up the cached versions of the required files from the
`.cache` directory of the system.

```bash
(ldm) ~/stable-diffusion$ python3 ./scripts/preload_models.py
preloading bert tokenizer...
Downloading: 100%|██████████████████████████████████| 28.0/28.0 [00:00<00:00, 49.3kB/s]
Downloading: 100%|██████████████████████████████████| 226k/226k [00:00<00:00, 2.79MB/s]
Downloading: 100%|██████████████████████████████████| 455k/455k [00:00<00:00, 4.36MB/s]
Downloading: 100%|██████████████████████████████████| 570/570 [00:00<00:00, 477kB/s]
...success
preloading kornia requirements...
Downloading: "https://github.com/DagnyT/hardnet/raw/master/pretrained/train_liberty_with_aug/checkpoint_liberty_with_aug.pth" to /u/lstein/.cache/torch/hub/checkpoints/checkpoint_liberty_with_aug.pth
100%|███████████████████████████████████████████████| 5.10M/5.10M [00:00<00:00, 101MB/s]
...success
```