moved images to

README.md

@@ -4,6 +4,34 @@ A browser interface based on Gradio library for Stable Diffusion.
 Original script with Gradio UI was written by a kind anonymous user. This is a modification.
 
 ![](screenshot.png)
+
+## Feature showcase
+
+[<font size="12">Detailed feature showcase with images, art by Greg Rutkowski</font>](https://github.com/AUTOMATIC1111/stable-diffusion-webui-feature-showcase)
+
+- Original txt2img and img2img modes
+- One click install and run script (but you still must install python, git and CUDA)
+- Outpainting
+- Inpainting
+- Prompt matrix
+- Stable Diffusion upscale
+- Attention
+- Loopback
+- X/Y plot
+- Textual Inversion
+- Resizing options
+- Sampling method selection
+- Interrupt processing at any time
+- 4GB videocard support
+- Option to use GFPGAN
+- Correct seeds for batches
+- Prompt length validation
+- Generation parameters added as text to PNG
+- Tab to view an existing picture's generation parameters
+- Settings page
+- Running custom code from UI
+- Mouseover hints fo most UI elements
+
 ## Installing and running
 
 You need [python](https://www.python.org/downloads/windows/) and [git](https://git-scm.com/download/win)
@@ -31,11 +59,13 @@ You optionally can use GPFGAN to improve faces, then you'll need to download the
 
 #### Troublehooting:
 
-- if your version of Python is not in PATH, edit `webui.bat`, change the line `set PYTHON=python` to say the full path to your python executable: `set PYTHON=B:\soft\Python310\python.exe`. You can do this for python, but not for git.
+- According to reports, intallation currently does not work in a directory with spaces in filenames.
+- if your version of Python is not in PATH (or if another version is), edit `webui.bat`, change the line `set PYTHON=python` to say the full path to your python executable: `set PYTHON=B:\soft\Python310\python.exe`. You can do this for python, but not for git.
 - if you get out of memory errors and your videocard has low amount of VRAM (4GB), edit `webui.bat`, change line 5 to from `set COMMANDLINE_ARGS=` to `set COMMANDLINE_ARGS=--medvram` (see below for other possible options)
 - installer creates python virtual environment, so none of installed modules will affect your system installation of python if you had one prior to installing this.
 - to prevent the creation of virtual environment and use your system python, edit `webui.bat` replacing `set VENV_DIR=venv` with `set VENV_DIR=`.
 - webui.bat installs requirements from files `requirements_versions.txt`, which lists versions for modules specifically compatible with Python 3.10.6. If you choose to install for a different version of python, editing `webui.bat` to have `set REQS_FILE=requirements.txt` instead of `set REQS_FILE=requirements_versions.txt` may help (but I still reccomend you to just use the recommended version of python).
+- if you feel you broke something and want to reinstall from scratch, delete directories: `venv`, `repositories`.
 
 ### Manual instructions
 Alternatively, if you don't want to run webui.bat, here are instructions for installing
@@ -123,216 +153,3 @@ Extra: if you get a green screen instead of generated pictures, you have a card
 precision floating point numbers. You must use `--precision full --no-half` in addition to other flags,
 and the model will take much more space in VRAM.
 
-
-## Features
-The script creates a web UI for Stable Diffusion's txt2img and img2img scripts. Following are features added
-that are not in original script.
-
-### Extras tab
-Additional neural network image improvement methods unrelated to stable diffusion.
-
-#### GFPGAN
-Lets you improve faces in pictures using the GFPGAN model. There is a checkbox in every tab to use GFPGAN at 100%, and
-also a separate tab that just allows you to use GFPGAN on any picture, with a slider that controls how strongthe effect is.
-
-![](images/GFPGAN.png)
-
-#### Real-ESRGAN
-Image upscaler. You can choose from multiple models by original author, and specify by how much the image should be upscaled.
-Requires `realesrgan` librarty:
-
-```commandline
-pip install realesrgan
-```
-
-### Sampling method selection
-Pick out of multiple sampling methods for txt2img:
-
-![](images/sampling.png)
-
-### Prompt matrix
-Separate multiple prompts using the `|` character, and the system will produce an image for every combination of them.
-For example, if you use `a busy city street in a modern city|illustration|cinematic lighting` prompt, there are four combinations possible (first part of prompt is always kept):
-
-- `a busy city street in a modern city`
-- `a busy city street in a modern city, illustration`
-- `a busy city street in a modern city, cinematic lighting`
-- `a busy city street in a modern city, illustration, cinematic lighting`
-
-Four images will be produced, in this order, all with same seed and each with corresponding prompt:
-![](images/prompt-matrix.png)
-
-Another example, this time with 5 prompts and 16 variations:
-![](images/prompt_matrix.jpg)
-
-If you use this feature, batch count will be ignored, because the number of pictures to produce
-depends on your prompts, but batch size will still work (generating multiple pictures at the
-same time for a small speed boost).
-
-### Flagging
-Click the Flag button under the output section, and generated images will be saved to `log/images` directory, and generation parameters
-will be appended to a csv file `log/log.csv` in the `/sd` directory.
-
-> but every image is saved, why would I need this?
-
-If you're like me, you experiment a lot with prompts and settings, and only few images are worth saving. You can
-just save them using right click in browser, but then you won't be able to reproduce them later because you will not
-know what exact prompt created the image. If you use the flag button, generation parameters will be written to csv file,
-and you can easily find parameters for an image by searching for its filename.
-
-### Copy-paste generation parameters
-A text output provides generation parameters in an easy to copy-paste form for easy sharing.
-
-![](images/kopipe.png)
-
-If you generate multiple pictures, the displayed seed will be the seed of the first one.
-
-### Correct seeds for batches
-If you use a seed of 1000 to generate two batches of two images each, four generated images will have seeds: `1000, 1001, 1002, 1003`.
-Previous versions of the UI would produce `1000, x, 1001, x`, where x is an image that can't be generated by any seed.
-
-### Resizing
-There are three options for resizing input images in img2img mode:
-
-- Just resize - simply resizes source image to target resolution, resulting in incorrect aspect ratio
-- Crop and resize - resize source image preserving aspect ratio so that entirety of target resolution is occupied by it, and crop parts that stick out
-- Resize and fill - resize source image preserving aspect ratio so that it entirely fits target resolution, and fill empty space by rows/columns from source image
-
-Example:
-![](images/resizing.jpg)
-
-### Loading
-Gradio's loading graphic has a very negative effect on the processing speed of the neural network. 
-My RTX 3090 makes images about 10% faster when the tab with gradio is not active. By default, the UI
-now hides loading progress animation and replaces it with static "Loading..." text, which achieves
-the same effect. Use the `--no-progressbar-hiding` commandline option to revert this and show loading animations.
-
-### Prompt validation
-Stable Diffusion has a limit for input text length. If your prompt is too long, you will get a
-warning in the text output field, showing which parts of your text were truncated and ignored by the model.
-
-### Loopback
-A checkbox for img2img allowing to automatically feed output image as input for the next batch. Equivalent to
-saving output image, and replacing input image with it. Batch count setting controls how many iterations of
-this you get.
-
-Usually, when doing this, you would choose one of many images for the next iteration yourself, so the usefulness
-of this feature may be questionable, but I've managed to get some very nice outputs with it that I wasn't abble
-to get otherwise.
-
-Example: (cherrypicked result; original picture by anon)
-
-![](images/loopback.jpg)
-
-### Png info
-Adds information about generation parameters to PNG as a text chunk. You
-can view this information later using any software that supports viewing
-PNG chunk info, for example: https://www.nayuki.io/page/png-file-chunk-inspector
-
-![](images/pnginfo.png)
-
-### Textual Inversion
-Allows you to use pretrained textual inversion embeddings.
-See original site for details: https://textual-inversion.github.io/.
-I used lstein's repo for training embdedding: https://github.com/lstein/stable-diffusion; if
-you want to train your own, I recommend following the guide on his site.
-
-No additional libraries/repositories are required to use pretrained embeddings.
-
-To make use of pretrained embeddings, create `embeddings` directory in the root dir of Stable
-Diffusion and put your embeddings into it. They must be .pt files about 5Kb in size, each with only
-one trained embedding, and the filename (without .pt) will be the term you'd use in prompt
-to get that embedding.
-
-As an example, I trained one for about 5000 steps: https://files.catbox.moe/e2ui6r.pt; it does
-not produce very good results, but it does work. Download and rename it to `Usada Pekora.pt`,
-and put it into `embeddings` dir and use Usada Pekora in prompt.
-
-![](images/inversion.png)
-
-### Settings
-A tab with settings, allowing you to use UI to edit more than half of parameters that previously
-were commandline. Settings are saved to config.js file. Settings that remain as commandline
-options are ones that are required at startup.
-
-### Attention
-Using `()` in prompt increases model's attention to enclosed words, and `[]` decreases it. You can combine
-multiple modifiers:
-
-![](images/attention-3.jpg)
-
-### SD upscale
-Upscale image using RealESRGAN and then go through tiles of the result, improving them with img2img.
-
-Original idea by: https://github.com/jquesnelle/txt2imghd. This is an independent implementation.
-
-To use this feature, tick a checkbox in the img2img interface. Original
-image will be upscaled to twice the original width and height, while width and height sliders
-will specify the size of individual tiles. At the moment this method does not support batch size.
-
-Rcommended parameters for upscaling:
- - Sampling method: Euler a
- - Denoising strength: 0.2, can go up to 0.4 if you feel adventureous
-
-![](images/sd-upscale.jpg)
-
-### User scripts
-If the program is launched with `--allow-code` option, an extra text input field for script code
-is available in txt2img interface. It allows you to input python code that will do the work with
-image. If this field is not empty, the processing that would happen normally is skipped.
-
-In code, access parameters from web UI using the `p` variable, and provide outputs for web UI
-using the `display(images, seed, info)` function. All globals from script are also accessible.
-
-As an example, here is a script that draws a chart seen below (and also saves it as `test/gnomeplot/gnome5.png`):
-
-```python
-steps = [4, 8,12,16, 20]
-cfg_scales = [5.0,10.0,15.0]
-
-def cell(x, y, p=p):
-	p.steps = x
-	p.cfg_scale = y
-	return process_images(p).images[0]
-
-images = [draw_xy_grid(
-	xs = steps,
-	ys = cfg_scales,
-	x_label = lambda x: f'Steps = {x}',
-	y_label = lambda y: f'CFG = {y}',
-	cell = cell
-)]
-
-save_image(images[0], 'test/gnomeplot', 'gnome5')
-display(images)
-```
-
-![](images/scripting.jpg)
-
-A more simple script that would just process the image and output it normally:
-
-```python
-processed = process_images(p)
-
-print("Seed was: " + str(processed.seed))
-
-display(processed.images, processed.seed, processed.info)
-```
-
-### 4GB videocard support
-Optimizations for GPUs with low VRAM. This should make it possible to generate 512x512 images on videocards with 4GB memory.
-
-`--lowvram` is a reimplementation of optimization idea from by [basujindal](https://github.com/basujindal/stable-diffusion).
-Model is separated into modules, and only one module is kept in GPU memory; when another module needs to run, the previous
-is removed from GPU memory. The nature of this optimization makes the processing run slower -- about 10 times slower
-compared to normal operation on my RTX 3090.
-
-`--medvram` is another optimization that should reduce VRAM usage significantly by not processing conditional and
-unconditional denoising in a same batch.
-
-This implementation of optimization does not require any modification to original Stable Diffusion code.
-
-### Inpainting
-In img2img tab, draw a mask over a part of image, and that part will be in-painted.
-
-![](images/inpainting.png)

scripts/custom_code.py

@@ -9,7 +9,8 @@ class Script(scripts.Script):
     def title(self):
         return "Custom code"
 
-    def enabled(self):
+
+    def show(self, is_img2img):
         return cmd_opts.allow_code
 
     def ui(self, is_img2img):
@@ -18,8 +19,7 @@ class Script(scripts.Script):
         return [code]
 
     def run(self, p, code):
-        if not cmd_opts.allow_code:
+        assert cmd_opts.allow_code, '--allow-code option must be enabled'
-            return
 
         display_result_data = [[], -1, ""]