mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
134 lines
5.4 KiB
Markdown
134 lines
5.4 KiB
Markdown
## **Google Colab**
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Stable Diffusion AI Notebook: <a
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href="https://colab.research.google.com/github/lstein/stable-diffusion/blob/main/notebooks/Stable_Diffusion_AI_Notebook.ipynb"
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target="_parent"><img
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src="https://colab.research.google.com/assets/colab-badge.svg"
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alt="Open In Colab"/></a> <br> Open and follow instructions to use an
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isolated environment running Dream.<br>
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Output Example:
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---
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## **Seamless Tiling**
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The seamless tiling mode causes generated images to seamlessly tile
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with itself. To use it, add the `--seamless` option when starting the
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script which will result in all generated images to tile, or for each
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`dream>` prompt as shown here:
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```
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dream> "pond garden with lotus by claude monet" --seamless -s100 -n4
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```
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---
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## **Reading Prompts from a File**
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You can automate `dream.py` by providing a text file with the prompts
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you want to run, one line per prompt. The text file must be composed
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with a text editor (e.g. Notepad) and not a word processor. Each line
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should look like what you would type at the dream> prompt:
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```
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a beautiful sunny day in the park, children playing -n4 -C10
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stormy weather on a mountain top, goats grazing -s100
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innovative packaging for a squid's dinner -S137038382
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```
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Then pass this file's name to `dream.py` when you invoke it:
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```
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(ldm) ~/stable-diffusion$ python3 scripts/dream.py --from_file "path/to/prompts.txt"
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```
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You may read a series of prompts from standard input by providing a filename of `-`:
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```
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(ldm) ~/stable-diffusion$ echo "a beautiful day" | python3 scripts/dream.py --from_file -
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```
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---
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## **Shortcuts: Reusing Seeds**
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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**`)
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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.
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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):
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```
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dream> a cute child playing hopscotch -G0.5
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[...]
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outputs/img-samples/000039.3498014304.png: "a cute child playing hopscotch" -s50 -W512 -H512 -C7.5 -mk_lms -S3498014304
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# I wonder what it will look like if I bump up the steps and set facial enhancement to full strength?
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dream> a cute child playing hopscotch -G1.0 -s100 -S -1
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reusing previous seed 3498014304
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[...]
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outputs/img-samples/000040.3498014304.png: "a cute child playing hopscotch" -G1.0 -s100 -W512 -H512 -C7.5 -mk_lms -S3498014304
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```
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---
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## **Weighted Prompts**
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You may weight different sections of the prompt to tell the sampler to attach different levels of
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priority to them, by adding `:(number)` to the end of the section you wish to up- or downweight.
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For example consider this prompt:
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```
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tabby cat:0.25 white duck:0.75 hybrid
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```
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This will tell the sampler to invest 25% of its effort on the tabby
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cat aspect of the image and 75% on the white duck aspect
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(surprisingly, this example actually works). The prompt weights can
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use any combination of integers and floating point numbers, and they
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do not need to add up to 1.
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---
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## **Simplified API**
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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:
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```
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from ldm.generate import Generate
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g = Generate()
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outputs = g.txt2img("a unicorn in manhattan")
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```
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Outputs is a list of lists in the format [filename1,seed1],[filename2,seed2]...].
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Please see ldm/generate.py for more information. A set of example scripts is coming RSN.
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---
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## **Preload Models**
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In situations where you have limited internet connectivity or are
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blocked behind a firewall, you can use the preload script to preload
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the required files for Stable Diffusion to run.
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The preload script `scripts/preload_models.py` needs to be run once at
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least while connected to the internet. In the following runs, it will
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load up the cached versions of the required files from the `.cache`
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directory of the system.
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```
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(ldm) ~/stable-diffusion$ python3 ./scripts/preload_models.py
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preloading bert tokenizer...
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Downloading: 100%|██████████████████████████████████| 28.0/28.0 [00:00<00:00, 49.3kB/s]
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Downloading: 100%|██████████████████████████████████| 226k/226k [00:00<00:00, 2.79MB/s]
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Downloading: 100%|██████████████████████████████████| 455k/455k [00:00<00:00, 4.36MB/s]
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Downloading: 100%|██████████████████████████████████| 570/570 [00:00<00:00, 477kB/s]
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...success
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preloading kornia requirements...
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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
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100%|███████████████████████████████████████████████| 5.10M/5.10M [00:00<00:00, 101MB/s]
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...success
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```
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