mirror of
https://github.com/invoke-ai/InvokeAI
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145 lines
6.6 KiB
Markdown
145 lines
6.6 KiB
Markdown
---
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title: Others
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---
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# :fontawesome-regular-share-from-square: Others
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## **Google Colab**
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[{ align="right" }](https://colab.research.google.com/github/lstein/stable-diffusion/blob/main/notebooks/Stable_Diffusion_AI_Notebook.ipynb)
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Open and follow instructions to use an isolated environment running Dream.
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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 with itself. To use it, add the
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`--seamless` option when starting the script which will result in all generated images to tile, or
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for each `invoke>` prompt as shown here:
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```python
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invoke> "pond garden with lotus by claude monet" --seamless -s100 -n4
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```
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By default this will tile on both the X and Y axes. However, you can also specify specific axes to tile on with `--seamless_axes`.
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Possible values are `x`, `y`, and `x,y`:
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```python
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invoke> "pond garden with lotus by claude monet" --seamless --seamless_axes=x -s100 -n4
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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
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1.11. Provide a `-S` (or `--seed`) switch of `-1` to use the seed of the most recent image
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generated. If you produced multiple images with the `-n` switch, then you can go back further
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using `-2`, `-3`, etc. up to the first image generated by the previous command. Sorry, but you can't go
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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`
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switch to turn on upscaling and face enhancement (see previous section):
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```bash
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invoke> 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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invoke> 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 `:<percent>` to the end of the section you wish to up- or downweight. For
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example consider this prompt:
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```bash
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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 cat aspect of the image and 75%
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on the white duck aspect (surprisingly, this example actually works). The prompt weights can use any
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combination of integers and floating point numbers, and they do not need to add up to 1.
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---
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## **Thresholding and Perlin Noise Initialization Options**
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Two new options are the thresholding (`--threshold`) and the perlin noise initialization (`--perlin`) options. Thresholding limits the range of the latent values during optimization, which helps combat oversaturation with higher CFG scale values. Perlin noise initialization starts with a percentage (a value ranging from 0 to 1) of perlin noise mixed into the initial noise. Both features allow for more variations and options in the course of generating images.
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For better intuition into what these options do in practice:
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In generating this graphic, perlin noise at initialization was programmatically varied going across on the diagram by values 0.0, 0.1, 0.2, 0.4, 0.5, 0.6, 0.8, 0.9, 1.0; and the threshold was varied going down from
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0, 1, 2, 3, 4, 5, 10, 20, 100. The other options are fixed, so the initial prompt is as follows (no thresholding or perlin noise):
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```bash
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invoke> "a portrait of a beautiful young lady" -S 1950357039 -s 100 -C 20 -A k_euler_a --threshold 0 --perlin 0
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```
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Here's an example of another prompt used when setting the threshold to 5 and perlin noise to 0.2:
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```bash
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invoke> "a portrait of a beautiful young lady" -S 1950357039 -s 100 -C 20 -A k_euler_a --threshold 5 --perlin 0.2
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```
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!!! note
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currently the thresholding feature is only implemented for the k-diffusion style samplers, and empirically appears to work best with `k_euler_a` and `k_dpm_2_a`. Using 0 disables thresholding. Using 0 for perlin noise disables using perlin noise for initialization. Finally, using 1 for perlin noise uses only perlin noise for initialization.
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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
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includes a simplified API for text to image generation, which lets you create images from a prompt
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in just three lines of code:
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```bash
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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 blocked behind a firewall, you can
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use the preload script to preload 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 least while connected to the
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internet. In the following runs, it will load up the cached versions of the required files from the
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`.cache` directory of the system.
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```bash
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(invokeai) ~/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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