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docs/features/CLI.md

@@ -22,8 +22,10 @@ be retrieved using scripts/images2prompt.py
 
 The script is confirmed to work on Linux, Windows and Mac systems.
 
-_Note:_ This script runs from the command-line or can be used as a Web application. The Web GUI is
+!!! note
-currently rudimentary, but a much better replacement is on its way.
+
+    This script runs from the command-line or can be used as a Web application. The Web GUI is
+    currently rudimentary, but a much better replacement is on its way.
 
 ```bash
 (ldm) ~/stable-diffusion$ python3 ./scripts/dream.py
@@ -99,12 +101,12 @@ These arguments are deprecated but still work:
 | `--weights <path>` |          | `None`  | Pth to weights file; use `--model stable-diffusion-1.4` instead |
 | `--laion400m`      | `-l`     | `False` | Use older LAION400m weights; use `--model=laion400m` instead   |
 
-### **A note on path names:**
+!!! note
 
-On Windows systems, you may run into problems when passing the dream script standard backslashed
+    On Windows systems, you may run into problems when passing the dream script standard backslashed
-path names because the Python interpreter treats "\" as an escape. You can either double your
+    path names because the Python interpreter treats `\` as an escape. You can either double your
-slashes (ick): `C:\\\\path\\\\to\\\\my\\\\file`, or use Linux/Mac style forward slashes (better):
+    slashes (ick): `C:\\\\path\\\\to\\\\my\\\\file`, or use Linux/Mac style forward slashes (better):
-`C:/path/to/my/file`.
+    `C:/path/to/my/file`.
 
 ### List of prompt arguments
 
@@ -144,8 +146,10 @@ Those are the `dream` commands that apply to txt2img:
 | `--variation <float>`          | `-v<float>`        | `0.0`                 | Add a bit of noise (0.0=none, 1.0=high) to the image<br>in order to generate a series of variations. Usually<br>used in combination with `-S<seed>` and `-n<int>`<br>to generate a series a riffs on a starting image.<br>See [Variations](./VARIATIONS.md). |
 | `--with_variations <pattern>` | `-V<pattern>`     | `None`                | Combine two or more variations. See [Variations](./VARIATIONS.md)<br>for now to use this.                                                                                                                                                                    |
 
-Note that the width and height of the image must be multiples of 64. You can provide different
+!!! note
-values, but they will be rounded down to the nearest multiple of 64.
+
+    The width and height of the image must be multiples of 64. You can provide different
+    values, but they will be rounded down to the nearest multiple of 64.
 
 ### This is an example of img2img
 

docs/features/EMBIGGEN.md

@@ -59,7 +59,6 @@ and it can also be less than one if the init_img is too big.
 Esrgan_strength defaults to 0.75, and the overlap_ratio defaults to
 0.25, both are optional.
 
-
 Unlike Img2Img, the `--width` (`-W`) and `--height` (`-H`) arguments
 do not control the size of the image as a whole, but the size of the
 tiles used to Embiggen the image.
@@ -120,19 +119,19 @@ tiles:
 dream> a photo of puffy clouds over a forest at sunset -s 100 -W 512 -H 512 -I outputs/000002.seed.png -f 0.5 -embiggen_tiles 1 2 3
 ```
 
-## Note
+!!! note
 
-Because the same prompt is used on all the tiled images, and the model
+    Because the same prompt is used on all the tiled images, and the model
-doesn't have the context of anything outside the tile being run - it
+    doesn't have the context of anything outside the tile being run - it
-can end up creating repeated pattern (also called 'motifs') across all
+    can end up creating repeated pattern (also called 'motifs') across all
-the tiles based on that prompt. The best way to combat this is
+    the tiles based on that prompt. The best way to combat this is
-lowering the `--strength` (`-f`) to stay more true to the init image,
+    lowering the `--strength` (`-f`) to stay more true to the init image,
-and increasing the number of steps so there is more compute-time to
+    and increasing the number of steps so there is more compute-time to
-create the detail.  Anecdotally `--strength` 0.35-0.45 works pretty
+    create the detail.  Anecdotally `--strength` 0.35-0.45 works pretty
-well on most things. It may also work great in some examples even with
+    well on most things. It may also work great in some examples even with
-the `--strength` set high for patterns, landscapes, or subjects that
+    the `--strength` set high for patterns, landscapes, or subjects that
-are more abstract. Because this is (relatively) fast, you can also
+    are more abstract. Because this is (relatively) fast, you can also
-always create a few Embiggen'ed images and manually composite them to
+    always create a few Embiggen'ed images and manually composite them to
-preserve the best parts from each.
+    preserve the best parts from each.
 
 Author: [Travco](https://github.com/travco)

docs/features/TEXTUAL_INVERSION.md

@@ -39,7 +39,7 @@ and one with the init word provided.
 
 On a RTX3090, the process for SD will take ~1h @1.6 iterations/sec.
 
-!!! Info _Note_
+!!! note
 
     According to the associated paper, the optimal number of
     images is 3-5. Your model may not converge if you use more images than

docs/features/UPSCALE.md

@@ -30,11 +30,13 @@ this package which asked you to install GFPGAN in a sibling directory, you may u
 `--gfpgan_dir` argument with `dream.py` to set a custom path to your GFPGAN directory. _There are
 other GFPGAN related boot arguments if you wish to customize further._
 
-**Note: Internet connection needed:** Users whose GPU machines are isolated from the Internet (e.g.
+!!! warning "Internet connection needed"
-on a University cluster) should be aware that the first time you run dream.py with GFPGAN and
+
-Real-ESRGAN turned on, it will try to download model files from the Internet. To rectify this, you
+    Users whose GPU machines are isolated from the Internet (e.g.
-may run `python3 scripts/preload_models.py` after you have installed GFPGAN and all its
+    on a University cluster) should be aware that the first time you run dream.py with GFPGAN and
-dependencies.
+    Real-ESRGAN turned on, it will try to download model files from the Internet. To rectify this, you
+    may run `python3 scripts/preload_models.py` after you have installed GFPGAN and all its
+    dependencies.
 
 ## **Usage**
 
@@ -83,17 +85,17 @@ This also works with img2img:
 dream> a man wearing a pineapple hat -I path/to/your/file.png -U 2 0.5 -G 0.6
 ```
 
-### **Note**
+!!! note
 
-GFPGAN and Real-ESRGAN are both memory intensive. In order to avoid crashes and memory overloads
+    GFPGAN and Real-ESRGAN are both memory intensive. In order to avoid crashes and memory overloads
-during the Stable Diffusion process, these effects are applied after Stable Diffusion has completed
+    during the Stable Diffusion process, these effects are applied after Stable Diffusion has completed
-its work.
+    its work.
 
-In single image generations, you will see the output right away but when you are using multiple
+    In single image generations, you will see the output right away but when you are using multiple
-iterations, the images will first be generated and then upscaled and face restored after that
+    iterations, the images will first be generated and then upscaled and face restored after that
-process is complete. While the image generation is taking place, you will still be able to preview
+    process is complete. While the image generation is taking place, you will still be able to preview
-the base images.
+    the base images.
 
-If you wish to stop during the image generation but want to upscale or face restore a particular
+    If you wish to stop during the image generation but want to upscale or face restore a particular
-generated image, pass it again with the same prompt and generated seed along with the `-U` and `-G`
+    generated image, pass it again with the same prompt and generated seed along with the `-U` and `-G`
-prompt arguments to perform those actions.
+    prompt arguments to perform those actions.

docs/features/VARIATIONS.md

@@ -29,7 +29,7 @@ This will be indicated as `prompt` in the examples below.
 First we let SD create a series of images in the usual way, in this case
 requesting six iterations:
 
-```
+```bash
 dream> lucy lawless as xena, warrior princess, character portrait, high resolution -n6
 ...
 Outputs:
@@ -102,6 +102,7 @@ generate more variations around the almost-but-not-quite image. We do the
 latter, using both the `-V` (combining) and `-v` (variation strength) options.
 Note that we use `-n6` to generate 6 variations:
 
+```bash
 dream> "prompt" -S3357757885 -V3647897225,0.1,1614299449,0.1 -v0.05 -n6
 Outputs:
 ./outputs/Xena/000004.3279757577.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,3279757577:0.05 -S3357757885

docs/index.md

@@ -34,9 +34,11 @@ source text-to-image generator. It provides a streamlined process with various n
 options to aid the image generation process. It runs on Windows, Mac and Linux machines, and runs on
 GPU cards with as little as 4 GB or RAM.
 
-_Note: This fork is rapidly evolving. Please use the
+!!! note
-[Issues](https://github.com/lstein/stable-diffusion/issues) tab to report bugs and make feature
+
-requests. Be sure to use the provided templates. They will help aid diagnose issues faster._
+    This fork is rapidly evolving. Please use the
+    [Issues](https://github.com/lstein/stable-diffusion/issues) tab to report bugs and make feature
+    requests. Be sure to use the provided templates. They will help aid diagnose issues faster.
 
 ## Installation
 
@@ -64,18 +66,18 @@ You wil need one of the following:
 
 - At least 6 GB of free disk space for the machine learning model, Python, and all its dependencies.
 
-### Note
+!!! note
 
-If you are have a Nvidia 10xx series card (e.g. the 1080ti), please run the dream script in
+    If you are have a Nvidia 10xx series card (e.g. the 1080ti), please run the dream script in
-full-precision mode as shown below.
+    full-precision mode as shown below.
 
-Similarly, specify full-precision mode on Apple M1 hardware.
+    Similarly, specify full-precision mode on Apple M1 hardware.
 
-To run in full-precision mode, start `dream.py` with the `--full_precision` flag:
+    To run in full-precision mode, start `dream.py` with the `--full_precision` flag:
 
-```bash
+    ```bash
-(ldm) ~/stable-diffusion$ python scripts/dream.py --full_precision
+    (ldm) ~/stable-diffusion$ python scripts/dream.py --full_precision
-```
+    ```
 
 ## Features
 

docs/installation/INSTALL_LINUX.md

@@ -57,7 +57,9 @@ title: Linux
     (ldm) ~/stable-diffusion$ python3 scripts/preload_models.py
     ```
 
-    Note that this step is necessary because I modified the original just-in-time
+    !!! note
+
+        This step is necessary because I modified the original just-in-time
         model loading scheme to allow the script to work on GPU machines that are not
         internet connected. See [Preload Models](../features/OTHER.md#preload-models)