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Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
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.gitattributes vendored
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# Auto normalizes line endings on commit so devs don't need to change local settings.
# Only affects text files and ignores other file types.
# Only affects text files and ignores other file types.
# For more info see: https://www.aleksandrhovhannisyan.com/blog/crlf-vs-lf-normalizing-line-endings-in-git/
* text=auto

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InvokeAI_Statement_of_Values.md
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@ -1,4 +1,4 @@
<img src="docs/assets/invoke_ai_banner.png" align="center">
<img src="docs/assets/invoke_ai_banner.png" align="center">
Invoke-AI is a community of software developers, researchers, and user
interface experts who have come together on a voluntary basis to build
@ -81,5 +81,5 @@ area. Disputes are resolved by open and honest communication.
## Signature
This document has been collectively crafted and approved by the current InvokeAI team members, as of 28 Nov 2022: **lstein** (Lincoln Stein), **blessedcoolant**, **hipsterusername** (Kent Keirsey), **Kyle0654** (Kyle Schouviller), **damian0815**, **mauwii** (Matthias Wild), **Netsvetaev** (Artur Netsvetaev), **psychedelicious**, **tildebyte**, and **keturn**. Although individuals within the group may hold differing views on particular details and/or their implications, we are all in agreement about its fundamental statements, as well as their significance and importance to this project moving forward.
This document has been collectively crafted and approved by the current InvokeAI team members, as of 28 Nov 2022: **lstein** (Lincoln Stein), **blessedcoolant**, **hipsterusername** (Kent Keirsey), **Kyle0654** (Kyle Schouviller), **damian0815**, **mauwii** (Matthias Wild), **Netsvetaev** (Artur Netsvetaev), **psychedelicious**, **tildebyte**, and **keturn**. Although individuals within the group may hold differing views on particular details and/or their implications, we are all in agreement about its fundamental statements, as well as their significance and importance to this project moving forward.

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README.md
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@ -53,11 +53,11 @@ For full installation and upgrade instructions, please see:
1. Go to the bottom of the [Latest Release Page](https://github.com/invoke-ai/InvokeAI/releases/latest)
2. Download the .zip file for your OS (Windows/macOS/Linux).
3. Unzip the file.
3. Unzip the file.
4. If you are on Windows, double-click on the `install.bat` script. On macOS, open a Terminal window, drag the file `install.sh` from Finder into the Terminal, and press return. On Linux, run `install.sh`.
5. Wait a while, until it is done.
5. Wait a while, until it is done.
6. The folder where you ran the installer from will now be filled with lots of files. If you are on Windows, double-click on the `invoke.bat` file. On macOS, open a Terminal window, drag `invoke.sh` from the folder into the Terminal, and press return. On Linux, run `invoke.sh`
7. Press 2 to open the "browser-based UI", press enter/return, wait a minute or two for Stable Diffusion to start up, then open your browser and go to http://localhost:9090.
7. Press 2 to open the "browser-based UI", press enter/return, wait a minute or two for Stable Diffusion to start up, then open your browser and go to http://localhost:9090.
8. Type `banana sushi` in the box on the top left and click `Invoke`:
<div align="center"><img src="docs/assets/invoke-web-server-1.png" width=640></div>
@ -161,9 +161,9 @@ problems and other issues.
# Contributing
Anyone who wishes to contribute to this project, whether documentation, features, bug fixes, code
cleanup, testing, or code reviews, is very much encouraged to do so.
cleanup, testing, or code reviews, is very much encouraged to do so.
To join, just raise your hand on the InvokeAI Discord server (#dev-chat) or the GitHub discussion board.
To join, just raise your hand on the InvokeAI Discord server (#dev-chat) or the GitHub discussion board.
If you are unfamiliar with how
to contribute to GitHub projects, here is a

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Stable_Diffusion_v1_Model_Card.md
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@ -21,7 +21,7 @@ This model card focuses on the model associated with the Stable Diffusion model,
# Uses
## Direct Use
## Direct Use
The model is intended for research purposes only. Possible research areas and
tasks include
@ -68,11 +68,11 @@ Using the model to generate content that is cruel to individuals is a misuse of
considerations.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are primarily limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are primarily limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
@ -84,7 +84,7 @@ The model developers used the following dataset for training the model:
- LAION-2B (en) and subsets thereof (see next section)
**Training Procedure**
Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through a ViT-L/14 text-encoder.
@ -108,12 +108,12 @@ filtered to images with an original size `>= 512x512`, estimated aesthetics scor
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
## Evaluation Results
## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling
steps show the relative improvements of the checkpoints:
![pareto](assets/v1-variants-scores.jpg)
![pareto](assets/v1-variants-scores.jpg)
Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
## Environmental Impact

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backend/modules/get_canvas_generation_mode.py
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@ -43,7 +43,7 @@ def get_canvas_generation_mode(
)
"""
Mask images are white in areas where no change should be made, black where changes
Mask images are white in areas where no change should be made, black where changes
should be made.
"""

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configs/INITIAL_MODELS.yaml
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@ -31,7 +31,7 @@ stable-diffusion-1.4:
width: 512
height: 512
waifu-diffusion-1.3:
description: Stable Diffusion 1.4 fine tuned on anime-styled images (4.27)
description: Stable Diffusion 1.4 fine tuned on anime-styled images (4.27 GB)
repo_id: hakurei/waifu-diffusion-v1-3
config: v1-inference.yaml
file: model-epoch09-float32.ckpt

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configs/stable-diffusion/v1-finetune.yaml
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@ -107,4 +107,4 @@ lightning:
benchmark: True
max_steps: 4000000
# max_steps: 4000

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configs/stable-diffusion/v1-m1-finetune.yaml
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@ -107,4 +107,4 @@ lightning:
benchmark: False
max_steps: 6200
# max_steps: 4000

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docs/features/UNIFIED_CANVAS.md
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@ -1,4 +1,4 @@
The Unified Canvas is a tool designed to streamline and simplify the process of composing an image using Stable Diffusion. It offers artists all of the available Stable Diffusion generation modes (Text To Image, Image To Image, Inpainting, and Outpainting) as a single unified workflow. The flexibility of the tool allows you to tweak and edit image generations, extend images beyond their initial size, and to create new content in a freeform way both inside and outside of existing images.
The Unified Canvas is a tool designed to streamline and simplify the process of composing an image using Stable Diffusion. It offers artists all of the available Stable Diffusion generation modes (Text To Image, Image To Image, Inpainting, and Outpainting) as a single unified workflow. The flexibility of the tool allows you to tweak and edit image generations, extend images beyond their initial size, and to create new content in a freeform way both inside and outside of existing images.
This document explains the basics of using the Unified Canvas, introducing you to its features and tools one by one. It also describes some of the more advanced tools available to power users of the Canvas.
@ -21,7 +21,7 @@ Accepting generations will commit the new generation to the **Base Layer**. You
The **Mask Layer** consists of any masked sections that have been created to inform Inpainting generations. You can paint a new mask, or edit an existing mask, using the Brush tool and the Eraser with the Mask layer set as your Active layer. Any masked areas will only affect generation inside of the current bounding box.
### Bounding Box
When generating a new image, Invoke will process and apply new images within the area denoted by the **Bounding Box**. The Width & Height settings of the Bounding Box, as well as its location within the Unified Canvas and pixels or empty space that it encloses, determine how new invocations are generated - see [Inpainting & Outpainting](#inpainting-and-outpainting) below. The Bounding Box can be moved and resized using the Move (V) tool. It can also be resized using the Bounding Box options in the Options Panel. By using these controls you can generate larger or smaller images, control which sections of the image are being processed, as well as control Bounding Box tools like the Bounding Box fill/erase.
When generating a new image, Invoke will process and apply new images within the area denoted by the **Bounding Box**. The Width & Height settings of the Bounding Box, as well as its location within the Unified Canvas and pixels or empty space that it encloses, determine how new invocations are generated - see [Inpainting & Outpainting](#inpainting-and-outpainting) below. The Bounding Box can be moved and resized using the Move (V) tool. It can also be resized using the Bounding Box options in the Options Panel. By using these controls you can generate larger or smaller images, control which sections of the image are being processed, as well as control Bounding Box tools like the Bounding Box fill/erase.
### <a name="inpainting-and-outpainting"></a> Inpainting & Outpainting
"Inpainting" means asking the AI to refine part of an image while leaving the rest alone. For example, updating a portrait of your grandmother to have her wear a biker's jacket.
@ -48,9 +48,9 @@ To get started with the Unified Canvas, you will want to generate a new base lay
From there, you can consider the following techniques to augment your image:
* **New Images**: Move the bounding box to an empty area of the Canvas, type in your prompt, and Invoke, to generate a new image using the Text to Image function.
* **Image Correction**: Use the color picker and brush tool to paint corrections on the image, switch to the Mask layer, and brush a mask over your painted area to use **Inpainting**. You can also use the **ImageToImage** generation method to invoke new interpretations of the image.
* **Image Correction**: Use the color picker and brush tool to paint corrections on the image, switch to the Mask layer, and brush a mask over your painted area to use **Inpainting**. You can also use the **ImageToImage** generation method to invoke new interpretations of the image.
* **Image Expansion**: Move the bounding box to include a portion of your initial image, and a portion of transparent/empty pixels, then Invoke using a prompt that describes what you'd like to see in that area. This will Outpaint the image. You'll typically find more coherent results if you keep about 50-60% of the original image in the bounding box. Make sure that the Image To Image Strength slider is set to a high value - you may need to set it higher than you are used to.
* **New Content on Existing Images**: If you want to add new details or objects into your image, use the brush tool to paint a sketch of what you'd like to see on the image, switch to the Mask layer, and brush a mask over your painted area to use **Inpainting**. If the masked area is small, consider using a smaller bounding box to take advantage of Invoke's automatic Scaling features, which can help to produce better details.
* **New Content on Existing Images**: If you want to add new details or objects into your image, use the brush tool to paint a sketch of what you'd like to see on the image, switch to the Mask layer, and brush a mask over your painted area to use **Inpainting**. If the masked area is small, consider using a smaller bounding box to take advantage of Invoke's automatic Scaling features, which can help to produce better details.
* **And more**: There are a number of creative ways to use the Canvas, and the above are just starting points. We're excited to see what you come up with!
@ -82,27 +82,27 @@ Features with non-obvious behavior are detailed below, in order to provide clari
## Toolbar
### Mask Options
* **Enable Mask** - This flag can be used to Enable or Disable the currently painted mask. If you have painted a mask, but you don't want it affect the next invocation, but you *also* don't want to delete it, then you can set this option to Disable. When you want the mask back, set this back to Enable.
* **Enable Mask** - This flag can be used to Enable or Disable the currently painted mask. If you have painted a mask, but you don't want it affect the next invocation, but you *also* don't want to delete it, then you can set this option to Disable. When you want the mask back, set this back to Enable.
* **Preserve Masked Area** - When enabled, Preserve Masked Area inverts the effect of the Mask on the Inpainting process. Pixels in masked areas will be kept unchanged, and unmasked areas will be regenerated.
### Creative Tools
* **Brush - Base/Mask Modes** - The Brush tool switches automatically between different modes of operation for the Base and Mask layers respectively.
* On the Base layer, the brush will directly paint on the Canvas using the color selected on the Brush Options menu.
* **Brush - Base/Mask Modes** - The Brush tool switches automatically between different modes of operation for the Base and Mask layers respectively.
* On the Base layer, the brush will directly paint on the Canvas using the color selected on the Brush Options menu.
* On the Mask layer, the brush will create a new mask. If you're finding the mask difficult to see over the existing content of the Unified Canvas, you can change the color it is drawn with using the color selector on the Mask Options dropdown.
* **Erase Bounding Box** - On the Base layer, erases all pixels within the Bounding Box.
* **Fill Bounding Box** - On the Base layer, fills all pixels within the Bounding Box with the currently selected color.
### Canvas Tools
* **Move Tool** - Allows for manipulation of the Canvas view (by dragging on the Canvas, outside the bounding box), the Bounding Box (by dragging the edges of the box), or the Width/Height of the Bounding Box (by dragging one of the 9 directional handles).
* **Reset View** - Click to re-orients the view to the center of the Bounding Box.
* **Reset View** - Click to re-orients the view to the center of the Bounding Box.
* **Merge Visible** - If your browser is having performance problems drawing the image in the Unified Canvas, click this to consolidate all of the information currently being rendered by your browser into a merged copy of the image. This lowers the resource requirements and should improve performance.
## Seam Correction
When doing Inpainting or Outpainting, Invoke needs to merge the pixels generated by Stable Diffusion into your existing image. To do this, the area around the `seam` at the boundary between your image and the new generation is automatically blended to produce a seamless output. In a fully automatic process, a mask is generated to cover the seam, and then the area of the seam is Inpainted.
When doing Inpainting or Outpainting, Invoke needs to merge the pixels generated by Stable Diffusion into your existing image. To do this, the area around the `seam` at the boundary between your image and the new generation is automatically blended to produce a seamless output. In a fully automatic process, a mask is generated to cover the seam, and then the area of the seam is Inpainted.
Although the default options should work well most of the time, sometimes it can help to alter the parameters that control the seam Inpainting. A wider seam and a blur setting of about 1/3 of the seam have been noted as producing consistently strong results (e.g. 96 wide and 16 blur - adds up to 32 blur with both sides). Seam strength of 0.7 is best for reducing hard seams.
* **Seam Size** - The size of the seam masked area. Set higher to make a larger mask around the seam.
* **Seam Blur** - The size of the blur that is applied on *each* side of the masked area.
* **Seam Blur** - The size of the blur that is applied on *each* side of the masked area.
* **Seam Strength** - The Image To Image Strength parameter used for the Inpainting generation that is applied to the seam area.
* **Seam Steps** - The number of generation steps that should be used to Inpaint the seam.

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@ -39,7 +39,7 @@ Looking for a short version? Here's a TL;DR in 3 tables.
!!! tip "suggestions"
For most use cases, `K_LMS`, `K_HEUN` and `K_DPM_2` are the best choices (the latter 2 run 0.5x as quick, but tend to converge 2x as quick as `K_LMS`). At very low steps (≤ `-s8`), `K_HEUN` and `K_DPM_2` are not recommended. Use `K_LMS` instead.
For variability, use `K_EULER_A` (runs 2x as quick as `K_DPM_2_A`).
---

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docs/installation/050_INSTALLING_MODELS.md
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@ -100,7 +100,7 @@ directory
The original Stable Diffusion version 1.4 weight file (4.27 GB)
Download? [n] n
[4] waifu-diffusion-1.3:
Stable Diffusion 1.4 fine tuned on anime-styled images (4.27)
Stable Diffusion 1.4 fine tuned on anime-styled images (4.27 GB)
Download? [n] y
[5] ft-mse-improved-autoencoder-840000:
StabilityAI improved autoencoder fine-tuned for human faces (recommended; 335 MB) (recommended)

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docs/installation/060_INSTALL_PATCHMATCH.md
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@ -64,7 +64,7 @@ steps:
It should look like the follwing:
```
Python 3.9.5 (default, Nov 23 2021, 15:27:38)
Python 3.9.5 (default, Nov 23 2021, 15:27:38)
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from patchmatch import patch_match

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020_INSTALL_MANUAL.md

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@ -0,0 +1,429 @@
---
title: Manual Installation
---
<figure markdown>
# :fontawesome-brands-linux: Linux | :fontawesome-brands-apple: macOS | :fontawesome-brands-windows: Windows
</figure>
!!! warning "This is for advanced Users"
who are already experienced with using conda or pip
## Introduction
You have two choices for manual installation, the [first one](#Conda_method)
based on the Anaconda3 package manager (`conda`), and
[a second one](#PIP_method) which uses basic Python virtual environment (`venv`)
commands and the PIP package manager. Both methods require you to enter commands
on the terminal, also known as the "console".
On Windows systems you are encouraged to install and use the
[Powershell](https://learn.microsoft.com/en-us/powershell/scripting/install/installing-powershell-on-windows?view=powershell-7.3),
which provides compatibility with Linux and Mac shells and nice features such as
command-line completion.
### Conda method
1. Check that your system meets the
[hardware requirements](index.md#Hardware_Requirements) and has the
appropriate GPU drivers installed. In particular, if you are a Linux user
with an AMD GPU installed, you may need to install the
[ROCm driver](https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html).
InvokeAI does not yet support Windows machines with AMD GPUs due to the lack
of ROCm driver support on this platform.
To confirm that the appropriate drivers are installed, run `nvidia-smi` on
NVIDIA/CUDA systems, and `rocm-smi` on AMD systems. These should return
information about the installed video card.
Macintosh users with MPS acceleration, or anybody with a CPU-only system,
can skip this step.
2. You will need to install Anaconda3 and Git if they are not already
available. Use your operating system's preferred package manager, or
download the installers manually. You can find them here:
- [Anaconda3](https://www.anaconda.com/)
- [git](https://git-scm.com/downloads)
3. Clone the [InvokeAI](https://github.com/invoke-ai/InvokeAI) source code from
GitHub:
```bash
git clone https://github.com/invoke-ai/InvokeAI.git
```
This will create InvokeAI folder where you will follow the rest of the
steps.
4. Enter the newly-created InvokeAI folder:
```bash
cd InvokeAI
```
From this step forward make sure that you are working in the InvokeAI
directory!
5. Select the appropriate environment file:
We have created a series of environment files suited for different operating
systems and GPU hardware. They are located in the
`environments-and-requirements` directory:
<figure markdown>
| filename | OS |
| :----------------------: | :----------------------------: |
| environment-lin-amd.yml | Linux with an AMD (ROCm) GPU |
| environment-lin-cuda.yml | Linux with an NVIDIA CUDA GPU |
| environment-mac.yml | Macintosh |
| environment-win-cuda.yml | Windows with an NVIDA CUDA GPU |
</figure>
Choose the appropriate environment file for your system and link or copy it
to `environment.yml` in InvokeAI's top-level directory. To do so, run
following command from the repository-root:
!!! Example ""
=== "Macintosh and Linux"
!!! todo "Replace `xxx` and `yyy` with the appropriate OS and GPU codes as seen in the table above"
```bash
ln -sf environments-and-requirements/environment-xxx-yyy.yml environment.yml
```
When this is done, confirm that a file `environment.yml` has been linked in
the InvokeAI root directory and that it points to the correct file in the
`environments-and-requirements`.
```bash
ls -la
```
=== "Windows"
!!! todo " Since it requires admin privileges to create links, we will use the copy command to create your `environment.yml`"
```cmd
copy environments-and-requirements\environment-win-cuda.yml environment.yml
```
Afterwards verify that the file `environment.yml` has been created, either via the
explorer or by using the command `dir` from the terminal
```cmd
dir
```
!!! warning "Do not try to run conda on directly on the subdirectory environments file. This won't work. Instead, copy or link it to the top-level directory as shown."
6. Create the conda environment:
```bash
conda env update
```
This will create a new environment named `invokeai` and install all InvokeAI
dependencies into it. If something goes wrong you should take a look at
[troubleshooting](#troubleshooting).
7. Activate the `invokeai` environment:
In order to use the newly created environment you will first need to
activate it
```bash
conda activate invokeai
```
Your command-line prompt should change to indicate that `invokeai` is active
by prepending `(invokeai)`.
8. Pre-Load the model weights files:
!!! tip
If you have already downloaded the weights file(s) for another Stable
Diffusion distribution, you may skip this step (by selecting "skip" when
prompted) and configure InvokeAI to use the previously-downloaded files. The
process for this is described in [here](INSTALLING_MODELS.md).
```bash
python scripts/configure_invokeai.py
```
The script `configure_invokeai.py` will interactively guide you through the
process of downloading and installing the weights files needed for InvokeAI.
Note that the main Stable Diffusion weights file is protected by a license
agreement that you have to agree to. The script will list the steps you need
to take to create an account on the site that hosts the weights files,
accept the agreement, and provide an access token that allows InvokeAI to
legally download and install the weights files.
If you get an error message about a module not being installed, check that
the `invokeai` environment is active and if not, repeat step 5.
9. Run the command-line- or the web- interface:
!!! example ""
!!! warning "Make sure that the conda environment is activated, which should create `(invokeai)` in front of your prompt!"
=== "CLI"
```bash
python scripts/invoke.py
```
=== "local Webserver"
```bash
python scripts/invoke.py --web
```
=== "Public Webserver"
```bash
python scripts/invoke.py --web --host 0.0.0.0
```
If you choose the run the web interface, point your browser at
http://localhost:9090 in order to load the GUI.
10. Render away!
Browse the [features](../features/CLI.md) section to learn about all the things you
can do with InvokeAI.
Note that some GPUs are slow to warm up. In particular, when using an AMD
card with the ROCm driver, you may have to wait for over a minute the first
time you try to generate an image. Fortunately, after the warm up period
rendering will be fast.
11. Subsequently, to relaunch the script, be sure to run "conda activate
invokeai", enter the `InvokeAI` directory, and then launch the invoke
script. If you forget to activate the 'invokeai' environment, the script
will fail with multiple `ModuleNotFound` errors.
## Updating to newer versions of the script
This distribution is changing rapidly. If you used the `git clone` method
(step 5) to download the InvokeAI directory, then to update to the latest and
greatest version, launch the Anaconda window, enter `InvokeAI` and type:
```bash
git pull
conda env update
python scripts/configure_invokeai.py --no-interactive #optional
```
This will bring your local copy into sync with the remote one. The last step may
be needed to take advantage of new features or released models. The
`--no-interactive` flag will prevent the script from prompting you to download
the big Stable Diffusion weights files.
## pip Install
To install InvokeAI with only the PIP package manager, please follow these
steps:
1. Make sure you are using Python 3.9 or higher. The rest of the install
procedure depends on this:
```bash
python -V
```
2. Install the `virtualenv` tool if you don't have it already:
```bash
pip install virtualenv
```
3. From within the InvokeAI top-level directory, create and activate a virtual
environment named `invokeai`:
```bash
virtualenv invokeai
source invokeai/bin/activate
```
4. Pick the correct `requirements*.txt` file for your hardware and operating
system.
We have created a series of environment files suited for different operating
systems and GPU hardware. They are located in the
`environments-and-requirements` directory:
<figure markdown>
| filename | OS |
| :---------------------------------: | :-------------------------------------------------------------: |
| requirements-lin-amd.txt | Linux with an AMD (ROCm) GPU |
| requirements-lin-arm64.txt | Linux running on arm64 systems |
| requirements-lin-cuda.txt | Linux with an NVIDIA (CUDA) GPU |
| requirements-mac-mps-cpu.txt | Macintoshes with MPS acceleration |
| requirements-lin-win-colab-cuda.txt | Windows with an NVIDA (CUDA) GPU<br>(supports Google Colab too) |
</figure>
Select the appropriate requirements file, and make a link to it from
`requirements.txt` in the top-level InvokeAI directory. The command to do
this from the top-level directory is:
!!! example ""
=== "Macintosh and Linux"
!!! info "Replace `xxx` and `yyy` with the appropriate OS and GPU codes."
```bash
ln -sf environments-and-requirements/requirements-xxx-yyy.txt requirements.txt
```
=== "Windows"
!!! info "on Windows, admin privileges are required to make links, so we use the copy command instead"
```cmd
copy environments-and-requirements\requirements-lin-win-colab-cuda.txt requirements.txt
```
!!! warning
Please do not link or copy `environments-and-requirements/requirements-base.txt`.
This is a base requirements file that does not have the platform-specific
libraries. Also, be sure to link or copy the platform-specific file to
a top-level file named `requirements.txt` as shown here. Running pip on
a requirements file in a subdirectory will not work as expected.
When this is done, confirm that a file named `requirements.txt` has been
created in the InvokeAI root directory and that it points to the correct
file in `environments-and-requirements`.
5. Run PIP
Be sure that the `invokeai` environment is active before doing this:
```bash
pip install --prefer-binary -r requirements.txt
```
---
## Troubleshooting
Here are some common issues and their suggested solutions.
### Conda
#### Conda fails before completing `conda update`
The usual source of these errors is a package incompatibility. While we have
tried to minimize these, over time packages get updated and sometimes introduce
incompatibilities.
We suggest that you search
[Issues](https://github.com/invoke-ai/InvokeAI/issues) or the "bugs-and-support"
channel of the [InvokeAI Discord](https://discord.gg/ZmtBAhwWhy).
You may also try to install the broken packages manually using PIP. To do this,
activate the `invokeai` environment, and run `pip install` with the name and
version of the package that is causing the incompatibility. For example:
```bash
pip install test-tube==0.7.5
```
You can keep doing this until all requirements are satisfied and the `invoke.py`
script runs without errors. Please report to
[Issues](https://github.com/invoke-ai/InvokeAI/issues) what you were able to do
to work around the problem so that others can benefit from your investigation.
### Create Conda Environment fails on MacOS
If conda create environment fails with lmdb error, this is most likely caused by Clang.
Run brew config to see which Clang is installed on your Mac. If Clang isn't installed, that's causing the error.
Start by installing additional XCode command line tools, followed by brew install llvm.
```bash
xcode-select --install
brew install llvm
```
If brew config has Clang installed, update to the latest llvm and try creating the environment again.
#### `configure_invokeai.py` or `invoke.py` crashes at an early stage
This is usually due to an incomplete or corrupted Conda install. Make sure you
have linked to the correct environment file and run `conda update` again.
If the problem persists, a more extreme measure is to clear Conda's caches and
remove the `invokeai` environment:
```bash
conda deactivate
conda env remove -n invokeai
conda clean -a
conda update
```
This removes all cached library files, including ones that may have been
corrupted somehow. (This is not supposed to happen, but does anyway).
#### `invoke.py` crashes at a later stage
If the CLI or web site had been working ok, but something unexpected happens
later on during the session, you've encountered a code bug that is probably
unrelated to an install issue. Please search
[Issues](https://github.com/invoke-ai/InvokeAI/issues), file a bug report, or
ask for help on [Discord](https://discord.gg/ZmtBAhwWhy)
#### My renders are running very slowly
You may have installed the wrong torch (machine learning) package, and the
system is running on CPU rather than the GPU. To check, look at the log messages
that appear when `invoke.py` is first starting up. One of the earlier lines
should say `Using device type cuda`. On AMD systems, it will also say "cuda",
and on Macintoshes, it should say "mps". If instead the message says it is
running on "cpu", then you may need to install the correct torch library.
You may be able to fix this by installing a different torch library. Here are
the magic incantations for Conda and PIP.
!!! todo "For CUDA systems"
- conda
```bash
conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia
```
- pip
```bash
pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116
```
!!! todo "For AMD systems"
- conda
```bash
conda activate invokeai
pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/rocm5.2/
```
- pip
```bash
pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/rocm5.2/
```
More information and troubleshooting tips can be found at https://pytorch.org.

8
docs/openapi3_0.yaml
View File

@ -3,10 +3,10 @@ info:
title: Stable Diffusion
description: |-
TODO: Description Here
Some useful links:
- [Stable Diffusion Dream Server](https://github.com/lstein/stable-diffusion)
license:
name: MIT License
url: https://github.com/lstein/stable-diffusion/blob/main/LICENSE
@ -36,7 +36,7 @@ paths:
description: successful operation
content:
image/png:
schema:
schema:
type: string
format: binary
'404':
@ -66,7 +66,7 @@ paths:
description: successful operation
content:
image/png:
schema:
schema:
type: string
format: binary
'404':

1
frontend/dist/index.html vendored
View File

@ -15,7 +15,6 @@
<body>
<div id="root"></div>
<script nomodule>!function(){var e=document,t=e.createElement("script");if(!("noModule"in t)&&"onbeforeload"in t){var n=!1;e.addEventListener("beforeload",(function(e){if(e.target===t)n=!0;else if(!e.target.hasAttribute("nomodule")||!n)return;e.preventDefault()}),!0),t.type="module",t.src=".",e.head.appendChild(t),t.remove()}}();</script>
<script nomodule crossorigin id="vite-legacy-polyfill" src="./assets/polyfills-legacy-dde3a68a.js"></script>
<script nomodule crossorigin id="vite-legacy-entry" data-src="./assets/index-legacy-b98e060c.js">System.import(document.getElementById('vite-legacy-entry').getAttribute('data-src'))</script>

10
ldm/invoke/args.py
View File

@ -341,7 +341,7 @@ class Args(object):
if not hasattr(cmd_switches,name) and not hasattr(arg_switches,name):
raise AttributeError
value_arg,value_cmd = (None,None)
try:
value_cmd = getattr(cmd_switches,name)
@ -397,7 +397,7 @@ class Args(object):
description=
"""
Generate images using Stable Diffusion.
Use --web to launch the web interface.
Use --web to launch the web interface.
Use --from_file to load prompts from a file path or standard input ("-").
Otherwise you will be dropped into an interactive command prompt (type -h for help.)
Other command-line arguments are defaults that can usually be overridden
@ -1052,7 +1052,7 @@ def metadata_dumps(opt,
Given an Args object, returns a dict containing the keys and
structure of the proposed stable diffusion metadata standard
https://github.com/lstein/stable-diffusion/discussions/392
This is intended to be turned into JSON and stored in the
This is intended to be turned into JSON and stored in the
"sd
'''
@ -1135,7 +1135,7 @@ def args_from_png(png_file_path) -> list[Args]:
meta = ldm.invoke.pngwriter.retrieve_metadata(png_file_path)
except AttributeError:
return [legacy_metadata_load({},png_file_path)]
try:
return metadata_loads(meta)
except:
@ -1234,4 +1234,4 @@ def legacy_metadata_load(meta,pathname) -> Args:
opt.prompt = ''
opt.seed = 0
return opt

6
ldm/invoke/concepts_lib.py
View File

@ -119,11 +119,11 @@ class Concepts(object):
self.download_concept(concept_name)
path = os.path.join(self._concept_path(concept_name), file_name)
return path if os.path.exists(path) else None
def concept_is_downloaded(self, concept_name)->bool:
concept_directory = self._concept_path(concept_name)
return os.path.exists(concept_directory)
def download_concept(self,concept_name)->bool:
repo_id = self._concept_id(concept_name)
dest = self._concept_path(concept_name)
@ -136,7 +136,7 @@ class Concepts(object):
os.makedirs(dest, exist_ok=True)
succeeded = True
bytes = 0
def tally_download_size(chunk, size, total):
nonlocal bytes

8
ldm/invoke/generator/embiggen.py
View File

@ -21,7 +21,7 @@ class Embiggen(Generator):
def generate(self,prompt,iterations=1,seed=None,
image_callback=None, step_callback=None,
**kwargs):
scope = choose_autocast(self.precision)
make_image = self.get_make_image(
prompt,
@ -39,7 +39,7 @@ class Embiggen(Generator):
results.append([image, seed])
if image_callback is not None:
image_callback(image, seed, prompt_in=prompt)
seed = self.new_seed()
seed = self.new_seed()
return results
@torch.no_grad()
@ -179,9 +179,9 @@ class Embiggen(Generator):
# Clamp values to max 255
if distanceToLR > 255:
distanceToLR = 255
#Place the pixel as invert of distance
#Place the pixel as invert of distance
agradientC.putpixel((x, y), round(255 - distanceToLR))
# Create alternative asymmetric diagonal corner to use on "tailing" intersections to prevent hard edges
# Fits for a left-fading gradient on the bottom side and full opacity on the right side.
agradientAsymC = Image.new('L', (256, 256))

8
ldm/invoke/generator/omnibus.py
View File

@ -62,7 +62,7 @@ class Omnibus(Img2Img,Txt2Img):
if init_image is not None and mask_image is not None: # inpainting
masked_image = init_image * (1 - mask_image) # masked image is the image masked by mask - masked regions zero
elif init_image is not None: # img2img
scope = choose_autocast(self.precision)
@ -99,7 +99,7 @@ class Omnibus(Img2Img,Txt2Img):
device=model.device,
num_samples=num_samples,
)
c = model.cond_stage_model.encode(batch["txt"])
c_cat = list()
for ck in model.concat_keys:
@ -164,10 +164,10 @@ class Omnibus(Img2Img,Txt2Img):
def sample_to_image(self, samples)->Image.Image:
gen_result = super().sample_to_image(samples).convert('RGB')
if self.pil_image is None or self.pil_mask is None:
return gen_result
corrected_result = super(Img2Img, self).repaste_and_color_correct(gen_result, self.pil_image, self.pil_mask, self.mask_blur_radius)
return corrected_result

8
ldm/invoke/image_util.py
View File

@ -9,8 +9,8 @@ class InitImageResizer():
def resize(self,width=None,height=None) -> Image:
"""
Return a copy of the image resized to fit within
a box width x height. The aspect ratio is
maintained. If neither width nor height are provided,
a box width x height. The aspect ratio is
maintained. If neither width nor height are provided,
then returns a copy of the original image. If one or the other is
provided, then the other will be calculated from the
aspect ratio.
@ -19,7 +19,7 @@ class InitImageResizer():
that it can be passed to img2img()
"""
im = self.image
ar = im.width/float(im.height)
# Infer missing values from aspect ratio
@ -44,7 +44,7 @@ class InitImageResizer():
# no resize necessary, but return a copy
if im.width == width and im.height == height:
return im.copy()
# otherwise resize the original image so that it fits inside the bounding box
resized_image = self.image.resize((rw,rh),resample=Image.Resampling.LANCZOS)
return resized_image

24
ldm/invoke/model_cache.py
View File

@ -1,5 +1,5 @@
'''
Manage a cache of Stable Diffusion model files for fast switching.
Manage a cache of Stable Diffusion model files for fast switching.
They are moved between GPU and CPU as necessary. If CPU memory falls
below a preset minimum, the least recently used model will be
cleared and loaded from disk when next needed.
@ -51,7 +51,7 @@ class ModelCache(object):
identifier.
'''
return model_name in self.config
def get_model(self, model_name:str):
'''
Given a model named identified in models.yaml, return
@ -66,7 +66,7 @@ class ModelCache(object):
if model_name not in self.models: # make room for a new one
self._make_cache_room()
self.offload_model(self.current_model)
if model_name in self.models:
requested_model = self.models[model_name]['model']
print(f'>> Retrieving model {model_name} from system RAM cache')
@ -92,7 +92,7 @@ class ModelCache(object):
print(f'** restoring {self.current_model}')
self.get_model(self.current_model)
return
self.current_model = model_name
self._push_newest_model(model_name)
return {
@ -191,7 +191,7 @@ class ModelCache(object):
omega[model_name] = config
if clobber:
self._invalidate_cached_model(model_name)
def _load_model(self, model_name:str):
"""Load and initialize the model from configuration variables passed at object creation time"""
if model_name not in self.config:
@ -254,7 +254,7 @@ class ModelCache(object):
model.to(self.device)
# model.to doesn't change the cond_stage_model.device used to move the tokenizer output, so set it here
model.cond_stage_model.device = self.device
model.eval()
for module in model.modules():
@ -274,7 +274,7 @@ class ModelCache(object):
)
return model, width, height, model_hash
def offload_model(self, model_name:str) -> None:
'''
Offload the indicated model to CPU. Will call
@ -290,7 +290,7 @@ class ModelCache(object):
gc.collect()
if self._has_cuda():
torch.cuda.empty_cache()
def scan_model(self, model_name, checkpoint):
# scan model
print(f'>> Scanning Model: {model_name}')
@ -320,7 +320,7 @@ class ModelCache(object):
if least_recent_model is not None:
del self.models[least_recent_model]
gc.collect()
def print_vram_usage(self) -> None:
if self._has_cuda:
print('>> Current VRAM usage: ','%4.2fG' % (torch.cuda.memory_allocated() / 1e9))
@ -355,12 +355,12 @@ class ModelCache(object):
if model_name in self.stack:
self.stack.remove(model_name)
self.models.pop(model_name,None)
def _model_to_cpu(self,model):
if self.device != 'cpu':
model.cond_stage_model.device = 'cpu'
model.first_stage_model.to('cpu')
model.cond_stage_model.to('cpu')
model.cond_stage_model.to('cpu')
model.model.to('cpu')
return model.to('cpu')
else:
@ -390,7 +390,7 @@ class ModelCache(object):
with contextlib.suppress(ValueError):
self.stack.remove(model_name)
self.stack.append(model_name)
def _has_cuda(self) -> bool:
return self.device.type == 'cuda'

4
ldm/invoke/restoration/base.py
View File

@ -10,7 +10,7 @@ class Restoration():
else:
print('>> GFPGAN Disabled')
gfpgan = None
# Load CodeFormer
codeformer = self.load_codeformer()
if codeformer.codeformer_model_exists:
@ -18,7 +18,7 @@ class Restoration():
else:
print('>> CodeFormer Disabled')
codeformer = None
return gfpgan, codeformer
# Face Restore Models

6
ldm/invoke/restoration/codeformer.py
View File

@ -14,7 +14,7 @@ class CodeFormerRestoration():
if not os.path.isabs(codeformer_dir):
codeformer_dir = os.path.join(Globals.root, codeformer_dir)
self.model_path = os.path.join(codeformer_dir, codeformer_model_path)
self.codeformer_model_exists = os.path.isfile(self.model_path)
@ -35,9 +35,9 @@ class CodeFormerRestoration():
from ldm.invoke.restoration.codeformer_arch import CodeFormer
from torchvision.transforms.functional import normalize
from PIL import Image
cf_class = CodeFormer
cf = cf_class(
dim_embd=512,
codebook_size=1024,

12
ldm/invoke/restoration/codeformer_arch.py
View File

@ -119,7 +119,7 @@ class TransformerSALayer(nn.Module):
tgt_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
query_pos: Optional[Tensor] = None):
# self attention
tgt2 = self.norm1(tgt)
q = k = self.with_pos_embed(tgt2, query_pos)
@ -159,7 +159,7 @@ class Fuse_sft_block(nn.Module):
@ARCH_REGISTRY.register()
class CodeFormer(VQAutoEncoder):
def __init__(self, dim_embd=512, n_head=8, n_layers=9,
def __init__(self, dim_embd=512, n_head=8, n_layers=9,
codebook_size=1024, latent_size=256,
connect_list=['32', '64', '128', '256'],
fix_modules=['quantize','generator']):
@ -179,14 +179,14 @@ class CodeFormer(VQAutoEncoder):
self.feat_emb = nn.Linear(256, self.dim_embd)
# transformer
self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0)
self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0)
for _ in range(self.n_layers)])
# logits_predict head
self.idx_pred_layer = nn.Sequential(
nn.LayerNorm(dim_embd),
nn.Linear(dim_embd, codebook_size, bias=False))
self.channels = {
'16': 512,
'32': 256,
@ -221,7 +221,7 @@ class CodeFormer(VQAutoEncoder):
enc_feat_dict = {}
out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]
for i, block in enumerate(self.encoder.blocks):
x = block(x)
x = block(x)
if i in out_list:
enc_feat_dict[str(x.shape[-1])] = x.clone()
@ -266,7 +266,7 @@ class CodeFormer(VQAutoEncoder):
fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list]
for i, block in enumerate(self.generator.blocks):
x = block(x)
x = block(x)
if i in fuse_list: # fuse after i-th block
f_size = str(x.shape[-1])
if w>0:

8
ldm/invoke/restoration/gfpgan.py
View File

@ -13,19 +13,19 @@ class GFPGAN():
self,
gfpgan_model_path='models/gfpgan/GFPGANv1.4.pth'
) -> None:
if not os.path.isabs(gfpgan_model_path):
gfpgan_model_path=os.path.abspath(os.path.join(Globals.root,gfpgan_model_path))
self.model_path = gfpgan_model_path
self.gfpgan_model_exists = os.path.isfile(self.model_path)
if not self.gfpgan_model_exists:
print('## NOT FOUND: GFPGAN model not found at ' + self.model_path)
return None
def model_exists(self):
return os.path.isfile(self.model_path)
def process(self, image, strength: float, seed: str = None):
if seed is not None:
print(f'>> GFPGAN - Restoring Faces for image seed:{seed}')

2
ldm/invoke/restoration/outcrop.py
View File

@ -51,7 +51,7 @@ class Outcrop(object):
color_match = True,
force_outpaint = True, # this just stops the warning about erased regions
)
# swap sampler back
self.generate.sampler = curr_sampler
return result

2
ldm/invoke/restoration/outpaint.py
View File

@ -16,7 +16,7 @@ class Outpaint(object):
def wrapped_callback(img,seed,**kwargs):
image_callback(img,seed,use_prefix=prefix,**kwargs)
return self.generate.prompt2image(
prompt,
seed = seed,

2
ldm/invoke/restoration/realesrgan.py
View File

@ -67,7 +67,7 @@ class ESRGAN():
# REALSRGAN expects a BGR np array; make array and flip channels
bgr_image_array = np.array(image, dtype=np.uint8)[...,::-1]
output, _ = upsampler.enhance(
bgr_image_array,
outscale=upsampler_scale,

38
ldm/invoke/restoration/vqgan_arch.py
View File

@ -13,7 +13,7 @@ from basicsr.utils.registry import ARCH_REGISTRY
def normalize(in_channels):
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
@torch.jit.script
def swish(x):
@ -210,15 +210,15 @@ class AttnBlock(nn.Module):
# compute attention
b, c, h, w = q.shape
q = q.reshape(b, c, h*w)
q = q.permute(0, 2, 1)
q = q.permute(0, 2, 1)
k = k.reshape(b, c, h*w)
w_ = torch.bmm(q, k)
w_ = torch.bmm(q, k)
w_ = w_ * (int(c)**(-0.5))
w_ = F.softmax(w_, dim=2)
# attend to values
v = v.reshape(b, c, h*w)
w_ = w_.permute(0, 2, 1)
w_ = w_.permute(0, 2, 1)
h_ = torch.bmm(v, w_)
h_ = h_.reshape(b, c, h, w)
@ -270,18 +270,18 @@ class Encoder(nn.Module):
def forward(self, x):
for block in self.blocks:
x = block(x)
return x
class Generator(nn.Module):
def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions):
super().__init__()
self.nf = nf
self.ch_mult = ch_mult
self.nf = nf
self.ch_mult = ch_mult
self.num_resolutions = len(self.ch_mult)
self.num_res_blocks = res_blocks
self.resolution = img_size
self.resolution = img_size
self.attn_resolutions = attn_resolutions
self.in_channels = emb_dim
self.out_channels = 3
@ -315,24 +315,24 @@ class Generator(nn.Module):
blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1))
self.blocks = nn.ModuleList(blocks)
def forward(self, x):
for block in self.blocks:
x = block(x)
return x
@ARCH_REGISTRY.register()
class VQAutoEncoder(nn.Module):
def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=[16], codebook_size=1024, emb_dim=256,
beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None):
super().__init__()
logger = get_root_logger()
self.in_channels = 3
self.nf = nf
self.n_blocks = res_blocks
self.in_channels = 3
self.nf = nf
self.n_blocks = res_blocks
self.codebook_size = codebook_size
self.embed_dim = emb_dim
self.ch_mult = ch_mult
@ -363,11 +363,11 @@ class VQAutoEncoder(nn.Module):
self.kl_weight
)
self.generator = Generator(
self.nf,
self.nf,
self.embed_dim,
self.ch_mult,
self.n_blocks,
self.resolution,
self.ch_mult,
self.n_blocks,
self.resolution,
self.attn_resolutions
)
@ -432,4 +432,4 @@ class VQGANDiscriminator(nn.Module):
raise ValueError(f'Wrong params!')
def forward(self, x):
return self.main(x)
return self.main(x)

4
ldm/invoke/seamless.py
View File

@ -1,5 +1,5 @@
import torch.nn as nn
def _conv_forward_asymmetric(self, input, weight, bias):
"""
Patch for Conv2d._conv_forward that supports asymmetric padding
@ -27,4 +27,4 @@ def configure_model_padding(model, seamless, seamless_axes):
if hasattr(m, 'asymmetric_padding_mode'):
del m.asymmetric_padding_mode
if hasattr(m, 'asymmetric_padding'):
del m.asymmetric_padding
del m.asymmetric_padding

4
ldm/invoke/server.py
View File

@ -61,7 +61,7 @@ def build_opt(post_data, seed, gfpgan_model_exists):
broken = True
break
opt.with_variations.append([seed, weight])
if broken:
raise CanceledException
@ -99,7 +99,7 @@ class DreamServer(BaseHTTPRequestHandler):
self.send_header("Content-type", "application/json")
self.end_headers()
output = []
log_file = os.path.join(self.outdir, "legacy_web_log.txt")
if os.path.exists(log_file):
with open(log_file, "r") as log:

4
ldm/invoke/server_legacy.py
View File

@ -45,7 +45,7 @@ def build_opt(post_data, seed, gfpgan_model_exists):
broken = True
break
opt.with_variations.append([seed, weight])
if broken:
raise CanceledException
@ -84,7 +84,7 @@ class DreamServer(BaseHTTPRequestHandler):
self.send_header("Content-type", "application/json")
self.end_headers()
output = []
log_file = os.path.join(self.outdir, "dream_web_log.txt")
if os.path.exists(log_file):
with open(log_file, "r") as log:

6
ldm/invoke/txt2mask.py
View File

@ -2,13 +2,13 @@
assignment of masks via text prompt using clipseg.
Here is typical usage:
from ldm.invoke.txt2mask import Txt2Mask, SegmentedGrayscale
from PIL import Image
txt2mask = Txt2Mask(self.device)
segmented = txt2mask.segment(Image.open('/path/to/img.png'),'a bagel')
# this will return a grayscale Image of the segmented data
grayscale = segmented.to_grayscale()
@ -45,7 +45,7 @@ class SegmentedGrayscale(object):
def __init__(self, image:Image, heatmap:torch.Tensor):
self.heatmap = heatmap
self.image = image
def to_grayscale(self,invert:bool=False)->Image:
return self._rescale(Image.fromarray(np.uint8(255 - self.heatmap * 255 if invert else self.heatmap * 255)))

12
ldm/models/diffusion/sampler.py
View File

@ -113,7 +113,7 @@ class Sampler(object):
'ddim_sigmas_for_original_num_steps',
sigmas_for_original_sampling_steps,
)
@torch.no_grad()
def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
# fast, but does not allow for exact reconstruction
@ -340,7 +340,7 @@ class Sampler(object):
x_dec = x_latent
x0 = init_latent
self.prepare_to_sample(t_enc=total_steps, all_timesteps_count=all_timesteps_count, **kwargs)
for i, step in enumerate(iterator):
index = total_steps - i - 1
ts = torch.full(
@ -373,7 +373,7 @@ class Sampler(object):
t_next = ts_next,
step_count=len(self.ddim_timesteps)
)
x_dec, pred_x0, e_t = outs
if img_callback:
img_callback(x_dec,i)
@ -385,7 +385,7 @@ class Sampler(object):
return torch.randn(shape, device=self.device)
else:
return x_T
def p_sample(
self,
img,
@ -423,10 +423,10 @@ class Sampler(object):
timesteps that will be used for sampling, depending on the t_enc in img2img.
'''
return self.ddim_timesteps[:ddim_steps]
def q_sample(self,x0,ts):
'''
Returns self.model.q_sample(x0,ts). Is overridden in the k* samplers to
Returns self.model.q_sample(x0,ts). Is overridden in the k* samplers to
return self.model.inner_model.q_sample(x0,ts)
'''
return self.model.q_sample(x0,ts)

4
ldm/modules/diffusionmodules/model.py
View File

@ -220,7 +220,7 @@ class AttnBlock(nn.Module):
if mem_required > mem_free_total:
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
else:
@ -228,7 +228,7 @@ class AttnBlock(nn.Module):
slice_size = 1
else:
slice_size = min(q.shape[1], math.floor(2**30 / (q.shape[0] * q.shape[1])))
for i in range(0, q.shape[1], slice_size):
end = i + slice_size

2
ldm/modules/embedding_manager.py
View File

@ -241,7 +241,7 @@ class EmbeddingManager(nn.Module):
# both will be stored in this dictionary
for term in self.string_to_param_dict.keys():
term = term.strip('<').strip('>')
self.concepts_loaded[term] = True
self.concepts_loaded[term] = True
print(f'>> Current embedding manager terms: {", ".join(self.string_to_param_dict.keys())}')
def _expand_directories(self, paths:list[str]):

2
ldm/modules/encoders/modules.py
View File

@ -548,7 +548,7 @@ class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder):
#print(f"assembled tokens for '{fragments}' into tensor of shape {lerped_embeddings.shape}")
# append to batch
# append to batch
batch_z = lerped_embeddings.unsqueeze(0) if batch_z is None else torch.cat([batch_z, lerped_embeddings.unsqueeze(0)], dim=1)
batch_tokens = tokens.unsqueeze(0) if batch_tokens is None else torch.cat([batch_tokens, tokens.unsqueeze(0)], dim=1)

2
ldm/modules/image_degradation/utils_image.py
View File

@ -98,7 +98,7 @@ def _get_paths_from_images(path):
"""
# --------------------------------------------
# split large images into small images
# split large images into small images
# --------------------------------------------
"""

6
ldm/util.py
View File

@ -221,7 +221,7 @@ def rand_perlin_2d(shape, res, device, fade = lambda t: 6*t**5 - 15*t**4 + 10*t*
grid = torch.stack(torch.meshgrid(torch.arange(0, res[0], delta[0]), torch.arange(0, res[1], delta[1]), indexing='ij'), dim = -1).to(device) % 1
rand_val = torch.rand(res[0]+1, res[1]+1)
angles = 2*math.pi*rand_val
gradients = torch.stack((torch.cos(angles), torch.sin(angles)), dim = -1).to(device)
@ -249,7 +249,7 @@ def ask_user(question: str, answers: list):
def debug_image(debug_image, debug_text, debug_show=True, debug_result=False, debug_status=False ):
if not debug_status:
return
image_copy = debug_image.copy()
ImageDraw.Draw(image_copy).text(
(5, 5),
@ -261,4 +261,4 @@ def debug_image(debug_image, debug_text, debug_show=True, debug_result=False, de
image_copy.show()
if debug_result:
return image_copy
return image_copy

4
main.py
View File

@ -474,7 +474,7 @@ class ImageLogger(Callback):
self.check_frequency(check_idx)
and hasattr( # batch_idx % self.batch_freq == 0
pl_module, 'log_images'
)
)
and callable(pl_module.log_images)
and self.max_images > 0
):
@ -868,7 +868,7 @@ if __name__ == '__main__':
if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
trainer_opt.accelerator = 'mps'
trainer_opt.detect_anomaly = False
trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs)
trainer.logdir = logdir ###

2
scripts/images2prompt.py
View File

@ -24,7 +24,7 @@ for f in filenames:
except PermissionError:
sys.stderr.write(f'{f} could not be opened due to inadequate permissions\n')
continue

2
scripts/legacy_api.py
View File

@ -398,7 +398,7 @@ SAMPLER_CHOICES = [
def create_argv_parser():
parser = argparse.ArgumentParser(
description="""Generate images using Stable Diffusion.
Use --web to launch the web interface.
Use --web to launch the web interface.
Use --from_file to load prompts from a file path or standard input ("-").
Otherwise you will be dropped into an interactive command prompt (type -h for help.)
Other command-line arguments are defaults that can usually be overridden

20
scripts/merge_embeddings.py
View File

@ -8,9 +8,9 @@ from functools import partial
import torch
def get_placeholder_loop(placeholder_string, embedder, use_bert):
new_placeholder = None
while True:
if new_placeholder is None:
new_placeholder = input(f"Placeholder string {placeholder_string} was already used. Please enter a replacement string: ")
@ -21,7 +21,7 @@ def get_placeholder_loop(placeholder_string, embedder, use_bert):
if token is not None:
return new_placeholder, token
def get_clip_token_for_string(tokenizer, string):
batch_encoding = tokenizer(
string,
@ -37,7 +37,7 @@ def get_clip_token_for_string(tokenizer, string):
if torch.count_nonzero(tokens - 49407) == 2:
return tokens[0, 1]
return None
def get_bert_token_for_string(tokenizer, string):
@ -53,16 +53,16 @@ if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--root_dir",
type=str,
"--root_dir",
type=str,
default='.',
help="Path to the InvokeAI install directory containing 'models', 'outputs' and 'configs'."
)
parser.add_argument(
"--manager_ckpts",
type=str,
nargs="+",
"--manager_ckpts",
type=str,
nargs="+",
required=True,
help="Paths to a set of embedding managers to be merged."
)
@ -90,7 +90,7 @@ if __name__ == "__main__":
EmbeddingManager = partial(EmbeddingManager, embedder, ["*"])
string_to_token_dict = {}
string_to_token_dict = {}
string_to_param_dict = torch.nn.ParameterDict()
placeholder_to_src = {}

2
static/dream_web/index.js
View File

@ -54,7 +54,7 @@ function loadPriorResults() {
appendOutput(src, seed, metadata, true);
});
});
// Load until page is full
if (!priorResultsLoadState.initialized) {
if (document.body.scrollHeight <= window.innerHeight) {

2
static/legacy_web/index.js
View File

@ -9,7 +9,7 @@ function toBase64(file) {
function appendOutput(src, seed, config) {
let outputNode = document.createElement("figure");
let variations = config.with_variations;
if (config.variation_amount > 0) {
variations = (variations ? variations + ',' : '') + seed + ':' + config.variation_amount;