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Google Colab
Stable Diffusion AI Notebook:
Open and follow instructions to use an isolated environment running
Dream.
Output Example: 
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:
dream> "pond garden with lotus by claude monet" --seamless -s100 -n4
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):
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
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:
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.
(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