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release-ca
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invokeai-b
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239f41f3e0 | |||
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0c1a2b68bf | |||
c06dc5b85b | |||
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ed8ee8c690 | |||
31daf1f0d7 | |||
5b692f4720 | |||
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994c6b7512 | |||
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3c732500e7 | |||
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4f72cb44ad | |||
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f240e878e5 | |||
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b656d333de | |||
7136603604 | |||
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62863ac586 | |||
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817c4a26de | |||
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103b3e7965 | |||
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4185afea5c | |||
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348b4b8be5 | |||
75f633cda8 | |||
2b3acc7b87 | |||
044e1ec2a8 | |||
79ac0f3420 | |||
c41599746d | |||
7f0cc7072b | |||
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c71d8750f7 | |||
d0832bfcaa | |||
049ea02fc7 | |||
ab39bc0bac | |||
bd4fc64156 | |||
8b0d1e59fe | |||
dc500946ad | |||
a48c03e0f4 | |||
7647490617 | |||
dbc8fc7900 | |||
5b22acca6d | |||
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7ff94383ce | |||
0891910cac | |||
1a4bed2e55 | |||
70ef83ac30 | |||
b6cf8b9052 |
6
.coveragerc
Normal file
@ -0,0 +1,6 @@
|
||||
[run]
|
||||
omit='.env/*'
|
||||
source='.'
|
||||
|
||||
[report]
|
||||
show_missing = true
|
@ -5,8 +5,7 @@ SAMPLES_DIR=${OUT_DIR}
|
||||
python scripts/dream.py \
|
||||
--from_file ${PROMPT_FILE} \
|
||||
--outdir ${OUT_DIR} \
|
||||
--sampler plms \
|
||||
--full_precision
|
||||
--sampler plms
|
||||
|
||||
# original output by CompVis/stable-diffusion
|
||||
IMAGE1=".dev_scripts/images/v1_4_astronaut_rides_horse_plms_step50_seed42.png"
|
||||
|
25
.dockerignore
Normal file
@ -0,0 +1,25 @@
|
||||
# use this file as a whitelist
|
||||
*
|
||||
!invokeai
|
||||
!ldm
|
||||
!pyproject.toml
|
||||
|
||||
# Guard against pulling in any models that might exist in the directory tree
|
||||
**/*.pt*
|
||||
**/*.ckpt
|
||||
|
||||
# ignore frontend but whitelist dist
|
||||
invokeai/frontend/
|
||||
!invokeai/frontend/dist/
|
||||
|
||||
# ignore invokeai/assets but whitelist invokeai/assets/web
|
||||
invokeai/assets/
|
||||
!invokeai/assets/web/
|
||||
|
||||
# Byte-compiled / optimized / DLL files
|
||||
**/__pycache__/
|
||||
**/*.py[cod]
|
||||
|
||||
# Distribution / packaging
|
||||
*.egg-info/
|
||||
*.egg
|
30
.editorconfig
Normal file
@ -0,0 +1,30 @@
|
||||
root = true
|
||||
|
||||
# All files
|
||||
[*]
|
||||
max_line_length = 80
|
||||
charset = utf-8
|
||||
end_of_line = lf
|
||||
indent_size = 2
|
||||
indent_style = space
|
||||
insert_final_newline = true
|
||||
trim_trailing_whitespace = true
|
||||
|
||||
# Python
|
||||
[*.py]
|
||||
indent_size = 4
|
||||
max_line_length = 120
|
||||
|
||||
# css
|
||||
[*.css]
|
||||
indent_size = 4
|
||||
|
||||
# flake8
|
||||
[.flake8]
|
||||
indent_size = 4
|
||||
|
||||
# Markdown MkDocs
|
||||
[docs/**/*.md]
|
||||
max_line_length = 80
|
||||
indent_size = 4
|
||||
indent_style = unset
|
37
.flake8
Normal file
@ -0,0 +1,37 @@
|
||||
[flake8]
|
||||
max-line-length = 120
|
||||
extend-ignore =
|
||||
# See https://github.com/PyCQA/pycodestyle/issues/373
|
||||
E203,
|
||||
# use Bugbear's B950 instead
|
||||
E501,
|
||||
# from black repo https://github.com/psf/black/blob/main/.flake8
|
||||
E266, W503, B907
|
||||
extend-select =
|
||||
# Bugbear line length
|
||||
B950
|
||||
extend-exclude =
|
||||
scripts/orig_scripts/*
|
||||
ldm/models/*
|
||||
ldm/modules/*
|
||||
ldm/data/*
|
||||
ldm/generate.py
|
||||
ldm/util.py
|
||||
ldm/simplet2i.py
|
||||
per-file-ignores =
|
||||
# B950 line too long
|
||||
# W605 invalid escape sequence
|
||||
# F841 assigned to but never used
|
||||
# F401 imported but unused
|
||||
tests/test_prompt_parser.py: B950, W605, F401
|
||||
tests/test_textual_inversion.py: F841, B950
|
||||
# B023 Function definition does not bind loop variable
|
||||
scripts/legacy_api.py: F401, B950, B023, F841
|
||||
ldm/invoke/__init__.py: F401
|
||||
# B010 Do not call setattr with a constant attribute value
|
||||
ldm/invoke/server_legacy.py: B010
|
||||
# =====================
|
||||
# flake-quote settings:
|
||||
# =====================
|
||||
# Set this to match black style:
|
||||
inline-quotes = double
|
2
.gitattributes
vendored
@ -1,4 +1,4 @@
|
||||
# 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
|
||||
|
61
.github/CODEOWNERS
vendored
Normal file
@ -0,0 +1,61 @@
|
||||
# continuous integration
|
||||
/.github/workflows/ @mauwii @lstein @blessedcoolant
|
||||
|
||||
# documentation
|
||||
/docs/ @lstein @mauwii @blessedcoolant
|
||||
mkdocs.yml @mauwii @lstein
|
||||
|
||||
# installation and configuration
|
||||
/pyproject.toml @mauwii @lstein @ebr
|
||||
/docker/ @mauwii
|
||||
/scripts/ @ebr @lstein @blessedcoolant
|
||||
/installer/ @ebr @lstein
|
||||
ldm/invoke/config @lstein @ebr
|
||||
invokeai/assets @lstein @blessedcoolant
|
||||
invokeai/configs @lstein @ebr @blessedcoolant
|
||||
/ldm/invoke/_version.py @lstein @blessedcoolant
|
||||
|
||||
# web ui
|
||||
/invokeai/frontend @blessedcoolant @psychedelicious
|
||||
/invokeai/backend @blessedcoolant @psychedelicious
|
||||
|
||||
# generation and model management
|
||||
/ldm/*.py @lstein @blessedcoolant
|
||||
/ldm/generate.py @lstein @keturn
|
||||
/ldm/invoke/args.py @lstein @blessedcoolant
|
||||
/ldm/invoke/ckpt* @lstein @blessedcoolant
|
||||
/ldm/invoke/ckpt_generator @lstein @blessedcoolant
|
||||
/ldm/invoke/CLI.py @lstein @blessedcoolant
|
||||
/ldm/invoke/config @lstein @ebr @mauwii @blessedcoolant
|
||||
/ldm/invoke/generator @keturn @damian0815
|
||||
/ldm/invoke/globals.py @lstein @blessedcoolant
|
||||
/ldm/invoke/merge_diffusers.py @lstein @blessedcoolant
|
||||
/ldm/invoke/model_manager.py @lstein @blessedcoolant
|
||||
/ldm/invoke/txt2mask.py @lstein @blessedcoolant
|
||||
/ldm/invoke/patchmatch.py @Kyle0654 @lstein
|
||||
/ldm/invoke/restoration @lstein @blessedcoolant
|
||||
|
||||
# attention, textual inversion, model configuration
|
||||
/ldm/models @damian0815 @keturn @blessedcoolant
|
||||
/ldm/modules/textual_inversion_manager.py @lstein @blessedcoolant
|
||||
/ldm/modules/attention.py @damian0815 @keturn
|
||||
/ldm/modules/diffusionmodules @damian0815 @keturn
|
||||
/ldm/modules/distributions @damian0815 @keturn
|
||||
/ldm/modules/ema.py @damian0815 @keturn
|
||||
/ldm/modules/embedding_manager.py @lstein
|
||||
/ldm/modules/encoders @damian0815 @keturn
|
||||
/ldm/modules/image_degradation @damian0815 @keturn
|
||||
/ldm/modules/losses @damian0815 @keturn
|
||||
/ldm/modules/x_transformer.py @damian0815 @keturn
|
||||
|
||||
# Nodes
|
||||
apps/ @Kyle0654 @jpphoto
|
||||
|
||||
# legacy REST API
|
||||
# these are dead code
|
||||
#/ldm/invoke/pngwriter.py @CapableWeb
|
||||
#/ldm/invoke/server_legacy.py @CapableWeb
|
||||
#/scripts/legacy_api.py @CapableWeb
|
||||
#/tests/legacy_tests.sh @CapableWeb
|
||||
|
||||
|
102
.github/ISSUE_TEMPLATE/BUG_REPORT.yml
vendored
Normal file
@ -0,0 +1,102 @@
|
||||
name: 🐞 Bug Report
|
||||
|
||||
description: File a bug report
|
||||
|
||||
title: '[bug]: '
|
||||
|
||||
labels: ['bug']
|
||||
|
||||
# assignees:
|
||||
# - moderator_bot
|
||||
# - lstein
|
||||
|
||||
body:
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: |
|
||||
Thanks for taking the time to fill out this Bug Report!
|
||||
|
||||
- type: checkboxes
|
||||
attributes:
|
||||
label: Is there an existing issue for this?
|
||||
description: |
|
||||
Please use the [search function](https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen+label%3Abug)
|
||||
irst to see if an issue already exists for the bug you encountered.
|
||||
options:
|
||||
- label: I have searched the existing issues
|
||||
required: true
|
||||
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: __Describe your environment__
|
||||
|
||||
- type: dropdown
|
||||
id: os_dropdown
|
||||
attributes:
|
||||
label: OS
|
||||
description: Which operating System did you use when the bug occured
|
||||
multiple: false
|
||||
options:
|
||||
- 'Linux'
|
||||
- 'Windows'
|
||||
- 'macOS'
|
||||
validations:
|
||||
required: true
|
||||
|
||||
- type: dropdown
|
||||
id: gpu_dropdown
|
||||
attributes:
|
||||
label: GPU
|
||||
description: Which kind of Graphic-Adapter is your System using
|
||||
multiple: false
|
||||
options:
|
||||
- 'cuda'
|
||||
- 'amd'
|
||||
- 'mps'
|
||||
- 'cpu'
|
||||
validations:
|
||||
required: true
|
||||
|
||||
- type: input
|
||||
id: vram
|
||||
attributes:
|
||||
label: VRAM
|
||||
description: Size of the VRAM if known
|
||||
placeholder: 8GB
|
||||
validations:
|
||||
required: false
|
||||
|
||||
- type: textarea
|
||||
id: what-happened
|
||||
attributes:
|
||||
label: What happened?
|
||||
description: |
|
||||
Briefly describe what happened, what you expected to happen and how to reproduce this bug.
|
||||
placeholder: When using the webinterface and right-clicking on button X instead of the popup-menu there error Y appears
|
||||
validations:
|
||||
required: true
|
||||
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: Screenshots
|
||||
description: If applicable, add screenshots to help explain your problem
|
||||
placeholder: this is what the result looked like <screenshot>
|
||||
validations:
|
||||
required: false
|
||||
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: Additional context
|
||||
description: Add any other context about the problem here
|
||||
placeholder: Only happens when there is full moon and Friday the 13th on Christmas Eve 🎅🏻
|
||||
validations:
|
||||
required: false
|
||||
|
||||
- type: input
|
||||
id: contact
|
||||
attributes:
|
||||
label: Contact Details
|
||||
description: __OPTIONAL__ How can we get in touch with you if we need more info (besides this issue)?
|
||||
placeholder: ex. email@example.com, discordname, twitter, ...
|
||||
validations:
|
||||
required: false
|
56
.github/ISSUE_TEMPLATE/FEATURE_REQUEST.yml
vendored
Normal file
@ -0,0 +1,56 @@
|
||||
name: Feature Request
|
||||
description: Commit a idea or Request a new feature
|
||||
title: '[enhancement]: '
|
||||
labels: ['enhancement']
|
||||
# assignees:
|
||||
# - lstein
|
||||
# - tildebyte
|
||||
body:
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: |
|
||||
Thanks for taking the time to fill out this Feature request!
|
||||
|
||||
- type: checkboxes
|
||||
attributes:
|
||||
label: Is there an existing issue for this?
|
||||
description: |
|
||||
Please make use of the [search function](https://github.com/invoke-ai/InvokeAI/labels/enhancement)
|
||||
to see if a simmilar issue already exists for the feature you want to request
|
||||
options:
|
||||
- label: I have searched the existing issues
|
||||
required: true
|
||||
|
||||
- type: input
|
||||
id: contact
|
||||
attributes:
|
||||
label: Contact Details
|
||||
description: __OPTIONAL__ How could we get in touch with you if we need more info (besides this issue)?
|
||||
placeholder: ex. email@example.com, discordname, twitter, ...
|
||||
validations:
|
||||
required: false
|
||||
|
||||
- type: textarea
|
||||
id: whatisexpected
|
||||
attributes:
|
||||
label: What should this feature add?
|
||||
description: Please try to explain the functionality this feature should add
|
||||
placeholder: |
|
||||
Instead of one huge textfield, it would be nice to have forms for bug-reports, feature-requests, ...
|
||||
Great benefits with automatic labeling, assigning and other functionalitys not available in that form
|
||||
via old-fashioned markdown-templates. I would also love to see the use of a moderator bot 🤖 like
|
||||
https://github.com/marketplace/actions/issue-moderator-with-commands to auto close old issues and other things
|
||||
validations:
|
||||
required: true
|
||||
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: Alternatives
|
||||
description: Describe alternatives you've considered
|
||||
placeholder: A clear and concise description of any alternative solutions or features you've considered.
|
||||
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: Aditional Content
|
||||
description: Add any other context or screenshots about the feature request here.
|
||||
placeholder: This is a Mockup of the design how I imagine it <screenshot>
|
36
.github/ISSUE_TEMPLATE/bug_report.md
vendored
@ -1,36 +0,0 @@
|
||||
---
|
||||
name: Bug report
|
||||
about: Create a report to help us improve
|
||||
title: ''
|
||||
labels: ''
|
||||
assignees: ''
|
||||
|
||||
---
|
||||
|
||||
**Describe your environment**
|
||||
- GPU: [cuda/amd/mps/cpu]
|
||||
- VRAM: [if known]
|
||||
- CPU arch: [x86/arm]
|
||||
- OS: [Linux/Windows/macOS]
|
||||
- Python: [Anaconda/miniconda/miniforge/pyenv/other (explain)]
|
||||
- Branch: [if `git status` says anything other than "On branch main" paste it here]
|
||||
- Commit: [run `git show` and paste the line that starts with "Merge" here]
|
||||
|
||||
**Describe the bug**
|
||||
A clear and concise description of what the bug is.
|
||||
|
||||
**To Reproduce**
|
||||
Steps to reproduce the behavior:
|
||||
1. Go to '...'
|
||||
2. Click on '....'
|
||||
3. Scroll down to '....'
|
||||
4. See error
|
||||
|
||||
**Expected behavior**
|
||||
A clear and concise description of what you expected to happen.
|
||||
|
||||
**Screenshots**
|
||||
If applicable, add screenshots to help explain your problem.
|
||||
|
||||
**Additional context**
|
||||
Add any other context about the problem here.
|
14
.github/ISSUE_TEMPLATE/config.yml
vendored
Normal file
@ -0,0 +1,14 @@
|
||||
blank_issues_enabled: false
|
||||
contact_links:
|
||||
- name: Project-Documentation
|
||||
url: https://invoke-ai.github.io/InvokeAI/
|
||||
about: Should be your first place to go when looking for manuals/FAQs regarding our InvokeAI Toolkit
|
||||
- name: Discord
|
||||
url: https://discord.gg/ZmtBAhwWhy
|
||||
about: Our Discord Community could maybe help you out via live-chat
|
||||
- name: GitHub Community Support
|
||||
url: https://github.com/orgs/community/discussions
|
||||
about: Please ask and answer questions regarding the GitHub Platform here.
|
||||
- name: GitHub Security Bug Bounty
|
||||
url: https://bounty.github.com/
|
||||
about: Please report security vulnerabilities of the GitHub Platform here.
|
20
.github/ISSUE_TEMPLATE/feature_request.md
vendored
@ -1,20 +0,0 @@
|
||||
---
|
||||
name: Feature request
|
||||
about: Suggest an idea for this project
|
||||
title: ''
|
||||
labels: ''
|
||||
assignees: ''
|
||||
|
||||
---
|
||||
|
||||
**Is your feature request related to a problem? Please describe.**
|
||||
A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
|
||||
|
||||
**Describe the solution you'd like**
|
||||
A clear and concise description of what you want to happen.
|
||||
|
||||
**Describe alternatives you've considered**
|
||||
A clear and concise description of any alternative solutions or features you've considered.
|
||||
|
||||
**Additional context**
|
||||
Add any other context or screenshots about the feature request here.
|
111
.github/workflows/build-container.yml
vendored
Normal file
@ -0,0 +1,111 @@
|
||||
name: build container image
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- 'main'
|
||||
- 'update/ci/docker/*'
|
||||
- 'update/docker/*'
|
||||
paths:
|
||||
- 'pyproject.toml'
|
||||
- 'ldm/**'
|
||||
- 'invokeai/backend/**'
|
||||
- 'invokeai/configs/**'
|
||||
- 'invokeai/frontend/dist/**'
|
||||
- 'docker/Dockerfile'
|
||||
tags:
|
||||
- 'v*.*.*'
|
||||
workflow_dispatch:
|
||||
|
||||
|
||||
jobs:
|
||||
docker:
|
||||
if: github.event.pull_request.draft == false
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
flavor:
|
||||
- amd
|
||||
- cuda
|
||||
- cpu
|
||||
include:
|
||||
- flavor: amd
|
||||
pip-extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
|
||||
- flavor: cuda
|
||||
pip-extra-index-url: ''
|
||||
- flavor: cpu
|
||||
pip-extra-index-url: 'https://download.pytorch.org/whl/cpu'
|
||||
runs-on: ubuntu-latest
|
||||
name: ${{ matrix.flavor }}
|
||||
env:
|
||||
PLATFORMS: 'linux/amd64,linux/arm64'
|
||||
DOCKERFILE: 'docker/Dockerfile'
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v3
|
||||
|
||||
- name: Docker meta
|
||||
id: meta
|
||||
uses: docker/metadata-action@v4
|
||||
with:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
images: |
|
||||
ghcr.io/${{ github.repository }}
|
||||
${{ vars.DOCKERHUB_REPOSITORY }}
|
||||
tags: |
|
||||
type=ref,event=branch
|
||||
type=ref,event=tag
|
||||
type=semver,pattern={{version}}
|
||||
type=semver,pattern={{major}}.{{minor}}
|
||||
type=semver,pattern={{major}}
|
||||
type=sha,enable=true,prefix=sha-,format=short
|
||||
flavor: |
|
||||
latest=${{ matrix.flavor == 'cuda' && github.ref == 'refs/heads/main' }}
|
||||
suffix=-${{ matrix.flavor }},onlatest=false
|
||||
|
||||
- name: Set up QEMU
|
||||
uses: docker/setup-qemu-action@v2
|
||||
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@v2
|
||||
with:
|
||||
platforms: ${{ env.PLATFORMS }}
|
||||
|
||||
- name: Login to GitHub Container Registry
|
||||
if: github.event_name != 'pull_request'
|
||||
uses: docker/login-action@v2
|
||||
with:
|
||||
registry: ghcr.io
|
||||
username: ${{ github.repository_owner }}
|
||||
password: ${{ secrets.GITHUB_TOKEN }}
|
||||
|
||||
- name: Login to Docker Hub
|
||||
if: github.event_name != 'pull_request' && vars.DOCKERHUB_REPOSITORY != ''
|
||||
uses: docker/login-action@v2
|
||||
with:
|
||||
username: ${{ secrets.DOCKERHUB_USERNAME }}
|
||||
password: ${{ secrets.DOCKERHUB_TOKEN }}
|
||||
|
||||
- name: Build container
|
||||
id: docker_build
|
||||
uses: docker/build-push-action@v4
|
||||
with:
|
||||
context: .
|
||||
file: ${{ env.DOCKERFILE }}
|
||||
platforms: ${{ env.PLATFORMS }}
|
||||
push: ${{ github.ref == 'refs/heads/main' || github.ref == 'refs/tags/*' }}
|
||||
tags: ${{ steps.meta.outputs.tags }}
|
||||
labels: ${{ steps.meta.outputs.labels }}
|
||||
build-args: PIP_EXTRA_INDEX_URL=${{ matrix.pip-extra-index-url }}
|
||||
cache-from: |
|
||||
type=gha,scope=${{ github.ref_name }}-${{ matrix.flavor }}
|
||||
type=gha,scope=main-${{ matrix.flavor }}
|
||||
cache-to: type=gha,mode=max,scope=${{ github.ref_name }}-${{ matrix.flavor }}
|
||||
|
||||
- name: Docker Hub Description
|
||||
if: github.ref == 'refs/heads/main' || github.ref == 'refs/tags/*' && vars.DOCKERHUB_REPOSITORY != ''
|
||||
uses: peter-evans/dockerhub-description@v3
|
||||
with:
|
||||
username: ${{ secrets.DOCKERHUB_USERNAME }}
|
||||
password: ${{ secrets.DOCKERHUB_TOKEN }}
|
||||
repository: ${{ vars.DOCKERHUB_REPOSITORY }}
|
||||
short-description: ${{ github.event.repository.description }}
|
64
.github/workflows/cache-model.yml
vendored
@ -1,64 +0,0 @@
|
||||
name: Cache Model
|
||||
on:
|
||||
workflow_dispatch
|
||||
jobs:
|
||||
build:
|
||||
strategy:
|
||||
matrix:
|
||||
os: [ macos-12 ]
|
||||
name: Create Caches using ${{ matrix.os }}
|
||||
runs-on: ${{ matrix.os }}
|
||||
steps:
|
||||
- name: Checkout sources
|
||||
uses: actions/checkout@v3
|
||||
- name: Cache model
|
||||
id: cache-sd-v1-4
|
||||
uses: actions/cache@v3
|
||||
env:
|
||||
cache-name: cache-sd-v1-4
|
||||
with:
|
||||
path: models/ldm/stable-diffusion-v1/model.ckpt
|
||||
key: ${{ env.cache-name }}
|
||||
restore-keys: |
|
||||
${{ env.cache-name }}
|
||||
- name: Download Stable Diffusion v1.4 model
|
||||
if: ${{ steps.cache-sd-v1-4.outputs.cache-hit != 'true' }}
|
||||
continue-on-error: true
|
||||
run: |
|
||||
if [ ! -e models/ldm/stable-diffusion-v1 ]; then
|
||||
mkdir -p models/ldm/stable-diffusion-v1
|
||||
fi
|
||||
if [ ! -e models/ldm/stable-diffusion-v1/model.ckpt ]; then
|
||||
curl -o models/ldm/stable-diffusion-v1/model.ckpt ${{ secrets.SD_V1_4_URL }}
|
||||
fi
|
||||
# Uncomment this when we no longer make changes to environment-mac.yaml
|
||||
# - name: Cache environment
|
||||
# id: cache-conda-env-ldm
|
||||
# uses: actions/cache@v3
|
||||
# env:
|
||||
# cache-name: cache-conda-env-ldm
|
||||
# with:
|
||||
# path: ~/.conda/envs/ldm
|
||||
# key: ${{ env.cache-name }}
|
||||
# restore-keys: |
|
||||
# ${{ env.cache-name }}
|
||||
- name: Install dependencies
|
||||
# if: ${{ steps.cache-conda-env-ldm.outputs.cache-hit != 'true' }}
|
||||
run: |
|
||||
conda env create -f environment-mac.yaml
|
||||
- name: Cache hugginface and torch models
|
||||
id: cache-hugginface-torch
|
||||
uses: actions/cache@v3
|
||||
env:
|
||||
cache-name: cache-hugginface-torch
|
||||
with:
|
||||
path: ~/.cache
|
||||
key: ${{ env.cache-name }}
|
||||
restore-keys: |
|
||||
${{ env.cache-name }}
|
||||
- name: Download Huggingface and Torch models
|
||||
if: ${{ steps.cache-hugginface-torch.outputs.cache-hit != 'true' }}
|
||||
continue-on-error: true
|
||||
run: |
|
||||
export PYTHON_BIN=/usr/local/miniconda/envs/ldm/bin/python
|
||||
$PYTHON_BIN scripts/preload_models.py
|
34
.github/workflows/clean-caches.yml
vendored
Normal file
@ -0,0 +1,34 @@
|
||||
name: cleanup caches by a branch
|
||||
on:
|
||||
pull_request:
|
||||
types:
|
||||
- closed
|
||||
workflow_dispatch:
|
||||
|
||||
jobs:
|
||||
cleanup:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Check out code
|
||||
uses: actions/checkout@v3
|
||||
|
||||
- name: Cleanup
|
||||
run: |
|
||||
gh extension install actions/gh-actions-cache
|
||||
|
||||
REPO=${{ github.repository }}
|
||||
BRANCH=${{ github.ref }}
|
||||
|
||||
echo "Fetching list of cache key"
|
||||
cacheKeysForPR=$(gh actions-cache list -R $REPO -B $BRANCH | cut -f 1 )
|
||||
|
||||
## Setting this to not fail the workflow while deleting cache keys.
|
||||
set +e
|
||||
echo "Deleting caches..."
|
||||
for cacheKey in $cacheKeysForPR
|
||||
do
|
||||
gh actions-cache delete $cacheKey -R $REPO -B $BRANCH --confirm
|
||||
done
|
||||
echo "Done"
|
||||
env:
|
||||
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
29
.github/workflows/lint-frontend.yml
vendored
Normal file
@ -0,0 +1,29 @@
|
||||
name: Lint frontend
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
paths:
|
||||
- 'invokeai/frontend/**'
|
||||
push:
|
||||
paths:
|
||||
- 'invokeai/frontend/**'
|
||||
|
||||
defaults:
|
||||
run:
|
||||
working-directory: invokeai/frontend
|
||||
|
||||
jobs:
|
||||
lint-frontend:
|
||||
if: github.event.pull_request.draft == false
|
||||
runs-on: ubuntu-22.04
|
||||
steps:
|
||||
- name: Setup Node 18
|
||||
uses: actions/setup-node@v3
|
||||
with:
|
||||
node-version: '18'
|
||||
- uses: actions/checkout@v3
|
||||
- run: 'yarn install --frozen-lockfile'
|
||||
- run: 'yarn tsc'
|
||||
- run: 'yarn run madge'
|
||||
- run: 'yarn run lint --max-warnings=0'
|
||||
- run: 'yarn run prettier --check'
|
80
.github/workflows/macos12-miniconda.yml
vendored
@ -1,80 +0,0 @@
|
||||
name: Build
|
||||
on:
|
||||
push:
|
||||
branches: [ main ]
|
||||
pull_request:
|
||||
branches: [ main ]
|
||||
jobs:
|
||||
build:
|
||||
strategy:
|
||||
matrix:
|
||||
os: [ macos-12 ]
|
||||
name: Build on ${{ matrix.os }} miniconda
|
||||
runs-on: ${{ matrix.os }}
|
||||
steps:
|
||||
- name: Checkout sources
|
||||
uses: actions/checkout@v3
|
||||
- name: Cache model
|
||||
id: cache-sd-v1-4
|
||||
uses: actions/cache@v3
|
||||
env:
|
||||
cache-name: cache-sd-v1-4
|
||||
with:
|
||||
path: models/ldm/stable-diffusion-v1/model.ckpt
|
||||
key: ${{ env.cache-name }}
|
||||
restore-keys: |
|
||||
${{ env.cache-name }}
|
||||
- name: Download Stable Diffusion v1.4 model
|
||||
if: ${{ steps.cache-sd-v1-4.outputs.cache-hit != 'true' }}
|
||||
continue-on-error: true
|
||||
run: |
|
||||
if [ ! -e models/ldm/stable-diffusion-v1 ]; then
|
||||
mkdir -p models/ldm/stable-diffusion-v1
|
||||
fi
|
||||
if [ ! -e models/ldm/stable-diffusion-v1/model.ckpt ]; then
|
||||
curl -o models/ldm/stable-diffusion-v1/model.ckpt ${{ secrets.SD_V1_4_URL }}
|
||||
fi
|
||||
# Uncomment this when we no longer make changes to environment-mac.yaml
|
||||
# - name: Cache environment
|
||||
# id: cache-conda-env-ldm
|
||||
# uses: actions/cache@v3
|
||||
# env:
|
||||
# cache-name: cache-conda-env-ldm
|
||||
# with:
|
||||
# path: ~/.conda/envs/ldm
|
||||
# key: ${{ env.cache-name }}
|
||||
# restore-keys: |
|
||||
# ${{ env.cache-name }}
|
||||
- name: Install dependencies
|
||||
# if: ${{ steps.cache-conda-env-ldm.outputs.cache-hit != 'true' }}
|
||||
run: |
|
||||
conda env create -f environment-mac.yaml
|
||||
- name: Cache hugginface and torch models
|
||||
id: cache-hugginface-torch
|
||||
uses: actions/cache@v3
|
||||
env:
|
||||
cache-name: cache-hugginface-torch
|
||||
with:
|
||||
path: ~/.cache
|
||||
key: ${{ env.cache-name }}
|
||||
restore-keys: |
|
||||
${{ env.cache-name }}
|
||||
- name: Download Huggingface and Torch models
|
||||
if: ${{ steps.cache-hugginface-torch.outputs.cache-hit != 'true' }}
|
||||
continue-on-error: true
|
||||
run: |
|
||||
export PYTHON_BIN=/usr/local/miniconda/envs/ldm/bin/python
|
||||
$PYTHON_BIN scripts/preload_models.py
|
||||
- name: Run the tests
|
||||
run: |
|
||||
# Note, can't "activate" via automation, and activation is just env vars and path
|
||||
export PYTHON_BIN=/usr/local/miniconda/envs/ldm/bin/python
|
||||
export PYTORCH_ENABLE_MPS_FALLBACK=1
|
||||
$PYTHON_BIN scripts/preload_models.py
|
||||
mkdir -p outputs/img-samples
|
||||
time $PYTHON_BIN scripts/dream.py --from_file tests/prompts.txt </dev/null 2> outputs/img-samples/err.log > outputs/img-samples/out.log
|
||||
- name: Archive results
|
||||
uses: actions/upload-artifact@v3
|
||||
with:
|
||||
name: results
|
||||
path: outputs/img-samples
|
49
.github/workflows/mkdocs-material.yml
vendored
Normal file
@ -0,0 +1,49 @@
|
||||
name: mkdocs-material
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- 'main'
|
||||
- 'development'
|
||||
|
||||
jobs:
|
||||
mkdocs-material:
|
||||
if: github.event.pull_request.draft == false
|
||||
runs-on: ubuntu-latest
|
||||
env:
|
||||
REPO_URL: '${{ github.server_url }}/${{ github.repository }}'
|
||||
REPO_NAME: '${{ github.repository }}'
|
||||
SITE_URL: 'https://${{ github.repository_owner }}.github.io/InvokeAI'
|
||||
steps:
|
||||
- name: checkout sources
|
||||
uses: actions/checkout@v3
|
||||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: setup python
|
||||
uses: actions/setup-python@v4
|
||||
with:
|
||||
python-version: '3.10'
|
||||
cache: pip
|
||||
cache-dependency-path: pyproject.toml
|
||||
|
||||
- name: install requirements
|
||||
env:
|
||||
PIP_USE_PEP517: 1
|
||||
run: |
|
||||
python -m \
|
||||
pip install ".[docs]"
|
||||
|
||||
- name: confirm buildability
|
||||
run: |
|
||||
python -m \
|
||||
mkdocs build \
|
||||
--clean \
|
||||
--verbose
|
||||
|
||||
- name: deploy to gh-pages
|
||||
if: ${{ github.ref == 'refs/heads/main' }}
|
||||
run: |
|
||||
python -m \
|
||||
mkdocs gh-deploy \
|
||||
--clean \
|
||||
--force
|
20
.github/workflows/pyflakes.yml
vendored
Normal file
@ -0,0 +1,20 @@
|
||||
on:
|
||||
pull_request:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
- development
|
||||
- 'release-candidate-*'
|
||||
|
||||
jobs:
|
||||
pyflakes:
|
||||
name: runner / pyflakes
|
||||
if: github.event.pull_request.draft == false
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v2
|
||||
- name: pyflakes
|
||||
uses: reviewdog/action-pyflakes@v1
|
||||
with:
|
||||
github_token: ${{ secrets.GITHUB_TOKEN }}
|
||||
reporter: github-pr-review
|
41
.github/workflows/pypi-release.yml
vendored
Normal file
@ -0,0 +1,41 @@
|
||||
name: PyPI Release
|
||||
|
||||
on:
|
||||
push:
|
||||
paths:
|
||||
- 'ldm/invoke/_version.py'
|
||||
workflow_dispatch:
|
||||
|
||||
jobs:
|
||||
release:
|
||||
if: github.repository == 'invoke-ai/InvokeAI'
|
||||
runs-on: ubuntu-22.04
|
||||
env:
|
||||
TWINE_USERNAME: __token__
|
||||
TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
|
||||
TWINE_NON_INTERACTIVE: 1
|
||||
steps:
|
||||
- name: checkout sources
|
||||
uses: actions/checkout@v3
|
||||
|
||||
- name: install deps
|
||||
run: pip install --upgrade build twine
|
||||
|
||||
- name: build package
|
||||
run: python3 -m build
|
||||
|
||||
- name: check distribution
|
||||
run: twine check dist/*
|
||||
|
||||
- name: check PyPI versions
|
||||
if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/v2.3'
|
||||
run: |
|
||||
pip install --upgrade requests
|
||||
python -c "\
|
||||
import scripts.pypi_helper; \
|
||||
EXISTS=scripts.pypi_helper.local_on_pypi(); \
|
||||
print(f'PACKAGE_EXISTS={EXISTS}')" >> $GITHUB_ENV
|
||||
|
||||
- name: upload package
|
||||
if: env.PACKAGE_EXISTS == 'False' && env.TWINE_PASSWORD != ''
|
||||
run: twine upload dist/*
|
67
.github/workflows/test-invoke-pip-skip.yml
vendored
Normal file
@ -0,0 +1,67 @@
|
||||
name: Test invoke.py pip
|
||||
on:
|
||||
pull_request:
|
||||
paths-ignore:
|
||||
- 'pyproject.toml'
|
||||
- 'ldm/**'
|
||||
- 'invokeai/backend/**'
|
||||
- 'invokeai/configs/**'
|
||||
- 'invokeai/frontend/dist/**'
|
||||
merge_group:
|
||||
workflow_dispatch:
|
||||
|
||||
concurrency:
|
||||
group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
|
||||
cancel-in-progress: true
|
||||
|
||||
jobs:
|
||||
matrix:
|
||||
if: github.event.pull_request.draft == false
|
||||
strategy:
|
||||
matrix:
|
||||
python-version:
|
||||
# - '3.9'
|
||||
- '3.10'
|
||||
pytorch:
|
||||
# - linux-cuda-11_6
|
||||
- linux-cuda-11_7
|
||||
- linux-rocm-5_2
|
||||
- linux-cpu
|
||||
- macos-default
|
||||
- windows-cpu
|
||||
# - windows-cuda-11_6
|
||||
# - windows-cuda-11_7
|
||||
include:
|
||||
# - pytorch: linux-cuda-11_6
|
||||
# os: ubuntu-22.04
|
||||
# extra-index-url: 'https://download.pytorch.org/whl/cu116'
|
||||
# github-env: $GITHUB_ENV
|
||||
- pytorch: linux-cuda-11_7
|
||||
os: ubuntu-22.04
|
||||
github-env: $GITHUB_ENV
|
||||
- pytorch: linux-rocm-5_2
|
||||
os: ubuntu-22.04
|
||||
extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
|
||||
github-env: $GITHUB_ENV
|
||||
- pytorch: linux-cpu
|
||||
os: ubuntu-22.04
|
||||
extra-index-url: 'https://download.pytorch.org/whl/cpu'
|
||||
github-env: $GITHUB_ENV
|
||||
- pytorch: macos-default
|
||||
os: macOS-12
|
||||
github-env: $GITHUB_ENV
|
||||
- pytorch: windows-cpu
|
||||
os: windows-2022
|
||||
github-env: $env:GITHUB_ENV
|
||||
# - pytorch: windows-cuda-11_6
|
||||
# os: windows-2022
|
||||
# extra-index-url: 'https://download.pytorch.org/whl/cu116'
|
||||
# github-env: $env:GITHUB_ENV
|
||||
# - pytorch: windows-cuda-11_7
|
||||
# os: windows-2022
|
||||
# extra-index-url: 'https://download.pytorch.org/whl/cu117'
|
||||
# github-env: $env:GITHUB_ENV
|
||||
name: ${{ matrix.pytorch }} on ${{ matrix.python-version }}
|
||||
runs-on: ${{ matrix.os }}
|
||||
steps:
|
||||
- run: 'echo "No build required"'
|
148
.github/workflows/test-invoke-pip.yml
vendored
Normal file
@ -0,0 +1,148 @@
|
||||
name: Test invoke.py pip
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- 'main'
|
||||
paths:
|
||||
- 'pyproject.toml'
|
||||
- 'ldm/**'
|
||||
- 'invokeai/backend/**'
|
||||
- 'invokeai/configs/**'
|
||||
- 'invokeai/frontend/dist/**'
|
||||
pull_request:
|
||||
paths:
|
||||
- 'pyproject.toml'
|
||||
- 'ldm/**'
|
||||
- 'invokeai/backend/**'
|
||||
- 'invokeai/configs/**'
|
||||
- 'invokeai/frontend/dist/**'
|
||||
types:
|
||||
- 'ready_for_review'
|
||||
- 'opened'
|
||||
- 'synchronize'
|
||||
merge_group:
|
||||
workflow_dispatch:
|
||||
|
||||
concurrency:
|
||||
group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
|
||||
cancel-in-progress: true
|
||||
|
||||
jobs:
|
||||
matrix:
|
||||
if: github.event.pull_request.draft == false
|
||||
strategy:
|
||||
matrix:
|
||||
python-version:
|
||||
# - '3.9'
|
||||
- '3.10'
|
||||
pytorch:
|
||||
# - linux-cuda-11_6
|
||||
- linux-cuda-11_7
|
||||
- linux-rocm-5_2
|
||||
- linux-cpu
|
||||
- macos-default
|
||||
- windows-cpu
|
||||
# - windows-cuda-11_6
|
||||
# - windows-cuda-11_7
|
||||
include:
|
||||
# - pytorch: linux-cuda-11_6
|
||||
# os: ubuntu-22.04
|
||||
# extra-index-url: 'https://download.pytorch.org/whl/cu116'
|
||||
# github-env: $GITHUB_ENV
|
||||
- pytorch: linux-cuda-11_7
|
||||
os: ubuntu-22.04
|
||||
github-env: $GITHUB_ENV
|
||||
- pytorch: linux-rocm-5_2
|
||||
os: ubuntu-22.04
|
||||
extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
|
||||
github-env: $GITHUB_ENV
|
||||
- pytorch: linux-cpu
|
||||
os: ubuntu-22.04
|
||||
extra-index-url: 'https://download.pytorch.org/whl/cpu'
|
||||
github-env: $GITHUB_ENV
|
||||
- pytorch: macos-default
|
||||
os: macOS-12
|
||||
github-env: $GITHUB_ENV
|
||||
- pytorch: windows-cpu
|
||||
os: windows-2022
|
||||
github-env: $env:GITHUB_ENV
|
||||
# - pytorch: windows-cuda-11_6
|
||||
# os: windows-2022
|
||||
# extra-index-url: 'https://download.pytorch.org/whl/cu116'
|
||||
# github-env: $env:GITHUB_ENV
|
||||
# - pytorch: windows-cuda-11_7
|
||||
# os: windows-2022
|
||||
# extra-index-url: 'https://download.pytorch.org/whl/cu117'
|
||||
# github-env: $env:GITHUB_ENV
|
||||
name: ${{ matrix.pytorch }} on ${{ matrix.python-version }}
|
||||
runs-on: ${{ matrix.os }}
|
||||
env:
|
||||
PIP_USE_PEP517: '1'
|
||||
steps:
|
||||
- name: Checkout sources
|
||||
id: checkout-sources
|
||||
uses: actions/checkout@v3
|
||||
|
||||
- name: set test prompt to main branch validation
|
||||
if: ${{ github.ref == 'refs/heads/main' }}
|
||||
run: echo "TEST_PROMPTS=tests/preflight_prompts.txt" >> ${{ matrix.github-env }}
|
||||
|
||||
- name: set test prompt to Pull Request validation
|
||||
if: ${{ github.ref != 'refs/heads/main' }}
|
||||
run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> ${{ matrix.github-env }}
|
||||
|
||||
- name: setup python
|
||||
uses: actions/setup-python@v4
|
||||
with:
|
||||
python-version: ${{ matrix.python-version }}
|
||||
cache: pip
|
||||
cache-dependency-path: pyproject.toml
|
||||
|
||||
- name: install invokeai
|
||||
env:
|
||||
PIP_EXTRA_INDEX_URL: ${{ matrix.extra-index-url }}
|
||||
run: >
|
||||
pip3 install
|
||||
--editable=".[test]"
|
||||
|
||||
- name: run pytest
|
||||
id: run-pytest
|
||||
run: pytest
|
||||
|
||||
- name: set INVOKEAI_OUTDIR
|
||||
run: >
|
||||
python -c
|
||||
"import os;from ldm.invoke.globals import Globals;OUTDIR=os.path.join(Globals.root,str('outputs'));print(f'INVOKEAI_OUTDIR={OUTDIR}')"
|
||||
>> ${{ matrix.github-env }}
|
||||
|
||||
- name: run invokeai-configure
|
||||
id: run-preload-models
|
||||
env:
|
||||
HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN }}
|
||||
run: >
|
||||
invokeai-configure
|
||||
--yes
|
||||
--default_only
|
||||
--full-precision
|
||||
# can't use fp16 weights without a GPU
|
||||
|
||||
- name: run invokeai
|
||||
id: run-invokeai
|
||||
env:
|
||||
# Set offline mode to make sure configure preloaded successfully.
|
||||
HF_HUB_OFFLINE: 1
|
||||
HF_DATASETS_OFFLINE: 1
|
||||
TRANSFORMERS_OFFLINE: 1
|
||||
run: >
|
||||
invokeai
|
||||
--no-patchmatch
|
||||
--no-nsfw_checker
|
||||
--from_file ${{ env.TEST_PROMPTS }}
|
||||
--outdir ${{ env.INVOKEAI_OUTDIR }}/${{ matrix.python-version }}/${{ matrix.pytorch }}
|
||||
|
||||
- name: Archive results
|
||||
id: archive-results
|
||||
uses: actions/upload-artifact@v3
|
||||
with:
|
||||
name: results
|
||||
path: ${{ env.INVOKEAI_OUTDIR }}
|
57
.gitignore
vendored
@ -1,6 +1,17 @@
|
||||
# ignore default image save location and model symbolic link
|
||||
.idea/
|
||||
embeddings/
|
||||
outputs/
|
||||
models/ldm/stable-diffusion-v1/model.ckpt
|
||||
**/restoration/codeformer/weights
|
||||
|
||||
# ignore user models config
|
||||
configs/models.user.yaml
|
||||
config/models.user.yml
|
||||
invokeai.init
|
||||
|
||||
# ignore the Anaconda/Miniconda installer used while building Docker image
|
||||
anaconda.sh
|
||||
|
||||
# ignore a directory which serves as a place for initial images
|
||||
inputs/
|
||||
@ -57,11 +68,13 @@ htmlcov/
|
||||
.cache
|
||||
nosetests.xml
|
||||
coverage.xml
|
||||
cov.xml
|
||||
*.cover
|
||||
*.py,cover
|
||||
.hypothesis/
|
||||
.pytest_cache/
|
||||
cover/
|
||||
junit/
|
||||
|
||||
# Translations
|
||||
*.mo
|
||||
@ -77,9 +90,6 @@ db.sqlite3-journal
|
||||
instance/
|
||||
.webassets-cache
|
||||
|
||||
# WebUI temp files:
|
||||
img2img-tmp.png
|
||||
|
||||
# Scrapy stuff:
|
||||
.scrapy
|
||||
|
||||
@ -179,10 +189,49 @@ src
|
||||
**/__pycache__/
|
||||
outputs
|
||||
|
||||
# Logs and associated folders
|
||||
# Logs and associated folders
|
||||
# created from generated embeddings.
|
||||
logs
|
||||
testtube
|
||||
checkpoints
|
||||
# If it's a Mac
|
||||
.DS_Store
|
||||
|
||||
# Let the frontend manage its own gitignore
|
||||
!invokeai/frontend/*
|
||||
|
||||
# Scratch folder
|
||||
.scratch/
|
||||
.vscode/
|
||||
gfpgan/
|
||||
models/ldm/stable-diffusion-v1/*.sha256
|
||||
|
||||
|
||||
# GFPGAN model files
|
||||
gfpgan/
|
||||
|
||||
# config file (will be created by installer)
|
||||
configs/models.yaml
|
||||
|
||||
# weights (will be created by installer)
|
||||
models/ldm/stable-diffusion-v1/*.ckpt
|
||||
models/clipseg
|
||||
models/gfpgan
|
||||
|
||||
# ignore initfile
|
||||
.invokeai
|
||||
|
||||
# ignore environment.yml and requirements.txt
|
||||
# these are links to the real files in environments-and-requirements
|
||||
environment.yml
|
||||
requirements.txt
|
||||
|
||||
# source installer files
|
||||
installer/*zip
|
||||
installer/install.bat
|
||||
installer/install.sh
|
||||
installer/update.bat
|
||||
installer/update.sh
|
||||
|
||||
# no longer stored in source directory
|
||||
models
|
||||
|
41
.pre-commit-config.yaml
Normal file
@ -0,0 +1,41 @@
|
||||
# See https://pre-commit.com for more information
|
||||
# See https://pre-commit.com/hooks.html for more hooks
|
||||
repos:
|
||||
- repo: https://github.com/psf/black
|
||||
rev: 23.1.0
|
||||
hooks:
|
||||
- id: black
|
||||
|
||||
- repo: https://github.com/pycqa/isort
|
||||
rev: 5.12.0
|
||||
hooks:
|
||||
- id: isort
|
||||
|
||||
- repo: https://github.com/PyCQA/flake8
|
||||
rev: 6.0.0
|
||||
hooks:
|
||||
- id: flake8
|
||||
additional_dependencies:
|
||||
- flake8-black
|
||||
- flake8-bugbear
|
||||
- flake8-comprehensions
|
||||
- flake8-simplify
|
||||
|
||||
- repo: https://github.com/pre-commit/mirrors-prettier
|
||||
rev: 'v3.0.0-alpha.4'
|
||||
hooks:
|
||||
- id: prettier
|
||||
|
||||
- repo: https://github.com/pre-commit/pre-commit-hooks
|
||||
rev: v4.4.0
|
||||
hooks:
|
||||
- id: check-added-large-files
|
||||
- id: check-executables-have-shebangs
|
||||
- id: check-shebang-scripts-are-executable
|
||||
- id: check-merge-conflict
|
||||
- id: check-symlinks
|
||||
- id: check-toml
|
||||
- id: end-of-file-fixer
|
||||
- id: no-commit-to-branch
|
||||
args: ['--branch', 'main']
|
||||
- id: trailing-whitespace
|
14
.prettierignore
Normal file
@ -0,0 +1,14 @@
|
||||
invokeai/frontend/.husky
|
||||
invokeai/frontend/patches
|
||||
|
||||
# Ignore artifacts:
|
||||
build
|
||||
coverage
|
||||
static
|
||||
invokeai/frontend/dist
|
||||
|
||||
# Ignore all HTML files:
|
||||
*.html
|
||||
|
||||
# Ignore deprecated docs
|
||||
docs/installation/deprecated_documentation
|
19
.prettierrc.yaml
Normal file
@ -0,0 +1,19 @@
|
||||
embeddedLanguageFormatting: auto
|
||||
endOfLine: lf
|
||||
singleQuote: true
|
||||
semi: true
|
||||
trailingComma: es5
|
||||
useTabs: false
|
||||
overrides:
|
||||
- files: '*.md'
|
||||
options:
|
||||
proseWrap: always
|
||||
printWidth: 80
|
||||
parser: markdown
|
||||
cursorOffset: -1
|
||||
- files: docs/**/*.md
|
||||
options:
|
||||
tabWidth: 4
|
||||
- files: 'invokeai/frontend/public/locales/*.json'
|
||||
options:
|
||||
tabWidth: 4
|
5
.pytest.ini
Normal file
@ -0,0 +1,5 @@
|
||||
[pytest]
|
||||
DJANGO_SETTINGS_MODULE = webtas.settings
|
||||
; python_files = tests.py test_*.py *_tests.py
|
||||
|
||||
addopts = --cov=. --cov-config=.coveragerc --cov-report xml:cov.xml
|
137
CHANGELOG.md
@ -1,137 +0,0 @@
|
||||
# **Changelog**
|
||||
|
||||
## v1.13 (in process)
|
||||
|
||||
- Supports a Google Colab notebook for a standalone server running on Google hardware [Arturo Mendivil](https://github.com/artmen1516)
|
||||
- WebUI supports GFPGAN/ESRGAN facial reconstruction and upscaling [Kevin Gibbons](https://github.com/bakkot)
|
||||
- WebUI supports incremental display of in-progress images during generation [Kevin Gibbons](https://github.com/bakkot)
|
||||
- Output directory can be specified on the dream> command line.
|
||||
- The grid was displaying duplicated images when not enough images to fill the final row [Muhammad Usama](https://github.com/SMUsamaShah)
|
||||
- Can specify --grid on dream.py command line as the default.
|
||||
- Miscellaneous internal bug and stability fixes.
|
||||
|
||||
---
|
||||
|
||||
## v1.12 (28 August 2022)
|
||||
|
||||
- Improved file handling, including ability to read prompts from standard input.
|
||||
(kudos to [Yunsaki](https://github.com/yunsaki)
|
||||
- The web server is now integrated with the dream.py script. Invoke by adding --web to
|
||||
the dream.py command arguments.
|
||||
- Face restoration and upscaling via GFPGAN and Real-ESGAN are now automatically
|
||||
enabled if the GFPGAN directory is located as a sibling to Stable Diffusion.
|
||||
VRAM requirements are modestly reduced. Thanks to both [Blessedcoolant](https://github.com/blessedcoolant) and
|
||||
[Oceanswave](https://github.com/oceanswave) for their work on this.
|
||||
- You can now swap samplers on the dream> command line. [Blessedcoolant](https://github.com/blessedcoolant)
|
||||
|
||||
---
|
||||
|
||||
## v1.11 (26 August 2022)
|
||||
|
||||
- NEW FEATURE: Support upscaling and face enhancement using the GFPGAN module. (kudos to [Oceanswave](https://github.com/Oceanswave)
|
||||
- You now can specify a seed of -1 to use the previous image's seed, -2 to use the seed for the image generated before that, etc.
|
||||
Seed memory only extends back to the previous command, but will work on all images generated with the -n# switch.
|
||||
- Variant generation support temporarily disabled pending more general solution.
|
||||
- Created a feature branch named **yunsaki-morphing-dream** which adds experimental support for
|
||||
iteratively modifying the prompt and its parameters. Please see[ Pull Request #86](https://github.com/lstein/stable-diffusion/pull/86)
|
||||
for a synopsis of how this works. Note that when this feature is eventually added to the main branch, it will may be modified
|
||||
significantly.
|
||||
|
||||
---
|
||||
|
||||
## v1.10 (25 August 2022)
|
||||
|
||||
- A barebones but fully functional interactive web server for online generation of txt2img and img2img.
|
||||
|
||||
---
|
||||
|
||||
## v1.09 (24 August 2022)
|
||||
|
||||
- A new -v option allows you to generate multiple variants of an initial image
|
||||
in img2img mode. (kudos to [Oceanswave](https://github.com/Oceanswave). [
|
||||
See this discussion in the PR for examples and details on use](https://github.com/lstein/stable-diffusion/pull/71#issuecomment-1226700810))
|
||||
- Added ability to personalize text to image generation (kudos to [Oceanswave](https://github.com/Oceanswave) and [nicolai256](https://github.com/nicolai256))
|
||||
- Enabled all of the samplers from k_diffusion
|
||||
|
||||
---
|
||||
|
||||
## v1.08 (24 August 2022)
|
||||
|
||||
- Escape single quotes on the dream> command before trying to parse. This avoids
|
||||
parse errors.
|
||||
- Removed instruction to get Python3.8 as first step in Windows install.
|
||||
Anaconda3 does it for you.
|
||||
- Added bounds checks for numeric arguments that could cause crashes.
|
||||
- Cleaned up the copyright and license agreement files.
|
||||
|
||||
---
|
||||
|
||||
## v1.07 (23 August 2022)
|
||||
|
||||
- Image filenames will now never fill gaps in the sequence, but will be assigned the
|
||||
next higher name in the chosen directory. This ensures that the alphabetic and chronological
|
||||
sort orders are the same.
|
||||
|
||||
---
|
||||
|
||||
## v1.06 (23 August 2022)
|
||||
|
||||
- Added weighted prompt support contributed by [xraxra](https://github.com/xraxra)
|
||||
- Example of using weighted prompts to tweak a demonic figure contributed by [bmaltais](https://github.com/bmaltais)
|
||||
|
||||
---
|
||||
|
||||
## v1.05 (22 August 2022 - after the drop)
|
||||
|
||||
- Filenames now use the following formats:
|
||||
000010.95183149.png -- Two files produced by the same command (e.g. -n2),
|
||||
000010.26742632.png -- distinguished by a different seed.
|
||||
|
||||
000011.455191342.01.png -- Two files produced by the same command using
|
||||
000011.455191342.02.png -- a batch size>1 (e.g. -b2). They have the same seed.
|
||||
|
||||
000011.4160627868.grid#1-4.png -- a grid of four images (-g); the whole grid can
|
||||
be regenerated with the indicated key
|
||||
|
||||
- It should no longer be possible for one image to overwrite another
|
||||
- You can use the "cd" and "pwd" commands at the dream> prompt to set and retrieve
|
||||
the path of the output directory.
|
||||
|
||||
---
|
||||
|
||||
## v1.04 (22 August 2022 - after the drop)
|
||||
|
||||
- Updated README to reflect installation of the released weights.
|
||||
- Suppressed very noisy and inconsequential warning when loading the frozen CLIP
|
||||
tokenizer.
|
||||
|
||||
---
|
||||
|
||||
## v1.03 (22 August 2022)
|
||||
|
||||
- The original txt2img and img2img scripts from the CompViz repository have been moved into
|
||||
a subfolder named "orig_scripts", to reduce confusion.
|
||||
|
||||
---
|
||||
|
||||
## v1.02 (21 August 2022)
|
||||
|
||||
- A copy of the prompt and all of its switches and options is now stored in the corresponding
|
||||
image in a tEXt metadata field named "Dream". You can read the prompt using scripts/images2prompt.py,
|
||||
or an image editor that allows you to explore the full metadata.
|
||||
**Please run "conda env update -f environment.yaml" to load the k_lms dependencies!!**
|
||||
|
||||
---
|
||||
|
||||
## v1.01 (21 August 2022)
|
||||
|
||||
- added k_lms sampling.
|
||||
**Please run "conda env update -f environment.yaml" to load the k_lms dependencies!!**
|
||||
- use half precision arithmetic by default, resulting in faster execution and lower memory requirements
|
||||
Pass argument --full_precision to dream.py to get slower but more accurate image generation
|
||||
|
||||
---
|
||||
|
||||
## Links
|
||||
|
||||
- **[Read Me](readme.md)**
|
128
CODE_OF_CONDUCT.md
Normal file
@ -0,0 +1,128 @@
|
||||
# Contributor Covenant Code of Conduct
|
||||
|
||||
## Our Pledge
|
||||
|
||||
We as members, contributors, and leaders pledge to make participation in our
|
||||
community a harassment-free experience for everyone, regardless of age, body
|
||||
size, visible or invisible disability, ethnicity, sex characteristics, gender
|
||||
identity and expression, level of experience, education, socio-economic status,
|
||||
nationality, personal appearance, race, religion, or sexual identity
|
||||
and orientation.
|
||||
|
||||
We pledge to act and interact in ways that contribute to an open, welcoming,
|
||||
diverse, inclusive, and healthy community.
|
||||
|
||||
## Our Standards
|
||||
|
||||
Examples of behavior that contributes to a positive environment for our
|
||||
community include:
|
||||
|
||||
* Demonstrating empathy and kindness toward other people
|
||||
* Being respectful of differing opinions, viewpoints, and experiences
|
||||
* Giving and gracefully accepting constructive feedback
|
||||
* Accepting responsibility and apologizing to those affected by our mistakes,
|
||||
and learning from the experience
|
||||
* Focusing on what is best not just for us as individuals, but for the
|
||||
overall community
|
||||
|
||||
Examples of unacceptable behavior include:
|
||||
|
||||
* The use of sexualized language or imagery, and sexual attention or
|
||||
advances of any kind
|
||||
* Trolling, insulting or derogatory comments, and personal or political attacks
|
||||
* Public or private harassment
|
||||
* Publishing others' private information, such as a physical or email
|
||||
address, without their explicit permission
|
||||
* Other conduct which could reasonably be considered inappropriate in a
|
||||
professional setting
|
||||
|
||||
## Enforcement Responsibilities
|
||||
|
||||
Community leaders are responsible for clarifying and enforcing our standards of
|
||||
acceptable behavior and will take appropriate and fair corrective action in
|
||||
response to any behavior that they deem inappropriate, threatening, offensive,
|
||||
or harmful.
|
||||
|
||||
Community leaders have the right and responsibility to remove, edit, or reject
|
||||
comments, commits, code, wiki edits, issues, and other contributions that are
|
||||
not aligned to this Code of Conduct, and will communicate reasons for moderation
|
||||
decisions when appropriate.
|
||||
|
||||
## Scope
|
||||
|
||||
This Code of Conduct applies within all community spaces, and also applies when
|
||||
an individual is officially representing the community in public spaces.
|
||||
Examples of representing our community include using an official e-mail address,
|
||||
posting via an official social media account, or acting as an appointed
|
||||
representative at an online or offline event.
|
||||
|
||||
## Enforcement
|
||||
|
||||
Instances of abusive, harassing, or otherwise unacceptable behavior
|
||||
may be reported to the community leaders responsible for enforcement
|
||||
at https://github.com/invoke-ai/InvokeAI/issues. All complaints will
|
||||
be reviewed and investigated promptly and fairly.
|
||||
|
||||
All community leaders are obligated to respect the privacy and security of the
|
||||
reporter of any incident.
|
||||
|
||||
## Enforcement Guidelines
|
||||
|
||||
Community leaders will follow these Community Impact Guidelines in determining
|
||||
the consequences for any action they deem in violation of this Code of Conduct:
|
||||
|
||||
### 1. Correction
|
||||
|
||||
**Community Impact**: Use of inappropriate language or other behavior deemed
|
||||
unprofessional or unwelcome in the community.
|
||||
|
||||
**Consequence**: A private, written warning from community leaders, providing
|
||||
clarity around the nature of the violation and an explanation of why the
|
||||
behavior was inappropriate. A public apology may be requested.
|
||||
|
||||
### 2. Warning
|
||||
|
||||
**Community Impact**: A violation through a single incident or series
|
||||
of actions.
|
||||
|
||||
**Consequence**: A warning with consequences for continued behavior. No
|
||||
interaction with the people involved, including unsolicited interaction with
|
||||
those enforcing the Code of Conduct, for a specified period of time. This
|
||||
includes avoiding interactions in community spaces as well as external channels
|
||||
like social media. Violating these terms may lead to a temporary or
|
||||
permanent ban.
|
||||
|
||||
### 3. Temporary Ban
|
||||
|
||||
**Community Impact**: A serious violation of community standards, including
|
||||
sustained inappropriate behavior.
|
||||
|
||||
**Consequence**: A temporary ban from any sort of interaction or public
|
||||
communication with the community for a specified period of time. No public or
|
||||
private interaction with the people involved, including unsolicited interaction
|
||||
with those enforcing the Code of Conduct, is allowed during this period.
|
||||
Violating these terms may lead to a permanent ban.
|
||||
|
||||
### 4. Permanent Ban
|
||||
|
||||
**Community Impact**: Demonstrating a pattern of violation of community
|
||||
standards, including sustained inappropriate behavior, harassment of an
|
||||
individual, or aggression toward or disparagement of classes of individuals.
|
||||
|
||||
**Consequence**: A permanent ban from any sort of public interaction within
|
||||
the community.
|
||||
|
||||
## Attribution
|
||||
|
||||
This Code of Conduct is adapted from the [Contributor Covenant][homepage],
|
||||
version 2.0, available at
|
||||
https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
|
||||
|
||||
Community Impact Guidelines were inspired by [Mozilla's code of conduct
|
||||
enforcement ladder](https://github.com/mozilla/diversity).
|
||||
|
||||
[homepage]: https://www.contributor-covenant.org
|
||||
|
||||
For answers to common questions about this code of conduct, see the FAQ at
|
||||
https://www.contributor-covenant.org/faq. Translations are available at
|
||||
https://www.contributor-covenant.org/translations.
|
84
InvokeAI_Statement_of_Values.md
Normal file
@ -0,0 +1,84 @@
|
||||
<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
|
||||
software tools which support cutting edge AI text-to-image
|
||||
applications. This community is open to anyone who wishes to
|
||||
contribute to the effort and has the skill and time to do so.
|
||||
|
||||
# Our Values
|
||||
|
||||
The InvokeAI team is a diverse community which includes individuals
|
||||
from various parts of the world and many walks of life. Despite our
|
||||
differences, we share a number of core values which we ask prospective
|
||||
contributors to understand and respect. We believe:
|
||||
|
||||
1. That Open Source Software is a positive force in the world. We
|
||||
create software that can be used, reused, and redistributed, without
|
||||
restrictions, under a straightforward Open Source license (MIT). We
|
||||
believe that Open Source benefits society as a whole by increasing the
|
||||
availability of high quality software to all.
|
||||
|
||||
2. That those who create software should receive proper attribution
|
||||
for their creative work. While we support the exchange and reuse of
|
||||
Open Source Software, we feel strongly that the original authors of a
|
||||
piece of code should receive credit for their contribution, and we
|
||||
endeavor to do so whenever possible.
|
||||
|
||||
3. That there is moral ambiguity surrounding AI-assisted art. We are
|
||||
aware of the moral and ethical issues surrounding the release of the
|
||||
Stable Diffusion model and similar products. We are aware that, due to
|
||||
the composition of their training sets, current AI-generated image
|
||||
models are biased against certain ethnic groups, cultural concepts of
|
||||
beauty, ethnic stereotypes, and gender roles.
|
||||
|
||||
1. We recognize the potential for harm to these groups that these biases
|
||||
represent and trust that future AI models will take steps towards
|
||||
reducing or eliminating the biases noted above, respect and give due
|
||||
credit to the artists whose work is sourced, and call on developers
|
||||
and users to favor these models over the older ones as they become
|
||||
available.
|
||||
|
||||
4. We are deeply committed to ensuring that this technology benefits
|
||||
everyone, including artists. We see AI art not as a replacement for
|
||||
the artist, but rather as a tool to empower them. With that
|
||||
in mind, we are constantly debating how to build systems that put
|
||||
artists’ needs first: tools which can be readily integrated into an
|
||||
artist’s existing workflows and practices, enhancing their work and
|
||||
helping them to push it further. Every decision we take as a team,
|
||||
which includes several artists, aims to build towards that goal.
|
||||
|
||||
5. That artificial intelligence can be a force for good in the world,
|
||||
but must be used responsibly. Artificial intelligence technologies
|
||||
have the potential to improve society, in everything from cancer care,
|
||||
to customer service, to creative writing.
|
||||
|
||||
1. While we do not believe that software should arbitrarily limit what
|
||||
users can do with it, we recognize that when used irresponsibly, AI
|
||||
has the potential to do much harm. Our Discord server is actively
|
||||
moderated in order to minimize the potential of harm from
|
||||
user-contributed images. In addition, we ask users of our software to
|
||||
refrain from using it in any way that would cause mental, emotional or
|
||||
physical harm to individuals and vulnerable populations including (but
|
||||
not limited to) women; minors; ethnic minorities; religious groups;
|
||||
members of LGBTQIA communities; and people with disabilities or
|
||||
impairments.
|
||||
|
||||
2. Note that some of the image generation AI models which the Invoke-AI
|
||||
toolkit supports carry licensing agreements which impose restrictions
|
||||
on how the model is used. We ask that our users read and agree to
|
||||
these terms if they wish to make use of these models. These agreements
|
||||
are distinct from the MIT license which applies to the InvokeAI
|
||||
software and source code.
|
||||
|
||||
6. That mutual respect is key to a healthy software development
|
||||
community. Members of the InvokeAI community are expected to treat
|
||||
each other with respect, beneficence, and empathy. Each of us has a
|
||||
different background and a unique set of skills. We strive to help
|
||||
each other grow and gain new skills, and we apportion expectations in
|
||||
a way that balances the members' time, skillset, and interest
|
||||
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**, **keturn**, and **ebr** (Eugene Brodsky). 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.
|
13
LICENSE
@ -1,17 +1,6 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
|
||||
|
||||
This software is derived from a fork of the source code available from
|
||||
https://github.com/pesser/stable-diffusion and
|
||||
https://github.com/CompViz/stable-diffusion. They carry the following
|
||||
copyrights:
|
||||
|
||||
Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
|
||||
Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
|
||||
|
||||
Please see individual source code files for copyright and authorship
|
||||
attributions.
|
||||
Copyright (c) 2022 InvokeAI Team
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
|
@ -1,210 +0,0 @@
|
||||
# Original README from CompViz/stable-diffusion
|
||||
*Stable Diffusion was made possible thanks to a collaboration with [Stability AI](https://stability.ai/) and [Runway](https://runwayml.com/) and builds upon our previous work:*
|
||||
|
||||
[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)<br/>
|
||||
[Robin Rombach](https://github.com/rromb)\*,
|
||||
[Andreas Blattmann](https://github.com/ablattmann)\*,
|
||||
[Dominik Lorenz](https://github.com/qp-qp)\,
|
||||
[Patrick Esser](https://github.com/pesser),
|
||||
[Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/>
|
||||
|
||||
**CVPR '22 Oral**
|
||||
|
||||
which is available on [GitHub](https://github.com/CompVis/latent-diffusion). PDF at [arXiv](https://arxiv.org/abs/2112.10752). Please also visit our [Project page](https://ommer-lab.com/research/latent-diffusion-models/).
|
||||
|
||||

|
||||
[Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion
|
||||
model.
|
||||
Thanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database.
|
||||
Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487),
|
||||
this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.
|
||||
With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.
|
||||
See [this section](#stable-diffusion-v1) below and the [model card](https://huggingface.co/CompVis/stable-diffusion).
|
||||
|
||||
|
||||
## Requirements
|
||||
|
||||
A suitable [conda](https://conda.io/) environment named `ldm` can be created
|
||||
and activated with:
|
||||
|
||||
```
|
||||
conda env create -f environment.yaml
|
||||
conda activate ldm
|
||||
```
|
||||
|
||||
You can also update an existing [latent diffusion](https://github.com/CompVis/latent-diffusion) environment by running
|
||||
|
||||
```
|
||||
conda install pytorch torchvision -c pytorch
|
||||
pip install transformers==4.19.2
|
||||
pip install -e .
|
||||
```
|
||||
|
||||
## Stable Diffusion v1
|
||||
|
||||
Stable Diffusion v1 refers to a specific configuration of the model
|
||||
architecture that uses a downsampling-factor 8 autoencoder with an 860M UNet
|
||||
and CLIP ViT-L/14 text encoder for the diffusion model. The model was pretrained on 256x256 images and
|
||||
then finetuned on 512x512 images.
|
||||
|
||||
*Note: Stable Diffusion v1 is a general text-to-image diffusion model and therefore mirrors biases and (mis-)conceptions that are present
|
||||
in its training data.
|
||||
Details on the training procedure and data, as well as the intended use of the model can be found in the corresponding [model card](https://huggingface.co/CompVis/stable-diffusion).
|
||||
Research into the safe deployment of general text-to-image models is an ongoing effort. To prevent misuse and harm, we currently provide access to the checkpoints only for [academic research purposes upon request](https://stability.ai/academia-access-form).
|
||||
**This is an experiment in safe and community-driven publication of a capable and general text-to-image model. We are working on a public release with a more permissive license that also incorporates ethical considerations.***
|
||||
|
||||
[Request access to Stable Diffusion v1 checkpoints for academic research](https://stability.ai/academia-access-form)
|
||||
|
||||
### Weights
|
||||
|
||||
We currently provide three checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and `sd-v1-3.ckpt`,
|
||||
which were trained as follows,
|
||||
|
||||
- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
|
||||
194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
|
||||
- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`.
|
||||
515k steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
|
||||
filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
|
||||
- `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-improved-aesthetics" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
|
||||
|
||||
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:
|
||||

|
||||
|
||||
|
||||
|
||||
### Text-to-Image with Stable Diffusion
|
||||

|
||||

|
||||
|
||||
Stable Diffusion is a latent diffusion model conditioned on the (non-pooled) text embeddings of a CLIP ViT-L/14 text encoder.
|
||||
|
||||
|
||||
#### Sampling Script
|
||||
|
||||
After [obtaining the weights](#weights), link them
|
||||
```
|
||||
mkdir -p models/ldm/stable-diffusion-v1/
|
||||
ln -s <path/to/model.ckpt> models/ldm/stable-diffusion-v1/model.ckpt
|
||||
```
|
||||
and sample with
|
||||
```
|
||||
python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms
|
||||
```
|
||||
|
||||
By default, this uses a guidance scale of `--scale 7.5`, [Katherine Crowson's implementation](https://github.com/CompVis/latent-diffusion/pull/51) of the [PLMS](https://arxiv.org/abs/2202.09778) sampler,
|
||||
and renders images of size 512x512 (which it was trained on) in 50 steps. All supported arguments are listed below (type `python scripts/txt2img.py --help`).
|
||||
|
||||
```commandline
|
||||
usage: txt2img.py [-h] [--prompt [PROMPT]] [--outdir [OUTDIR]] [--skip_grid] [--skip_save] [--ddim_steps DDIM_STEPS] [--plms] [--laion400m] [--fixed_code] [--ddim_eta DDIM_ETA] [--n_iter N_ITER] [--H H] [--W W] [--C C] [--f F] [--n_samples N_SAMPLES] [--n_rows N_ROWS]
|
||||
[--scale SCALE] [--from-file FROM_FILE] [--config CONFIG] [--ckpt CKPT] [--seed SEED] [--precision {full,autocast}]
|
||||
|
||||
optional arguments:
|
||||
-h, --help show this help message and exit
|
||||
--prompt [PROMPT] the prompt to render
|
||||
--outdir [OUTDIR] dir to write results to
|
||||
--skip_grid do not save a grid, only individual samples. Helpful when evaluating lots of samples
|
||||
--skip_save do not save individual samples. For speed measurements.
|
||||
--ddim_steps DDIM_STEPS
|
||||
number of ddim sampling steps
|
||||
--plms use plms sampling
|
||||
--laion400m uses the LAION400M model
|
||||
--fixed_code if enabled, uses the same starting code across samples
|
||||
--ddim_eta DDIM_ETA ddim eta (eta=0.0 corresponds to deterministic sampling
|
||||
--n_iter N_ITER sample this often
|
||||
--H H image height, in pixel space
|
||||
--W W image width, in pixel space
|
||||
--C C latent channels
|
||||
--f F downsampling factor
|
||||
--n_samples N_SAMPLES
|
||||
how many samples to produce for each given prompt. A.k.a. batch size
|
||||
(note that the seeds for each image in the batch will be unavailable)
|
||||
--n_rows N_ROWS rows in the grid (default: n_samples)
|
||||
--scale SCALE unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))
|
||||
--from-file FROM_FILE
|
||||
if specified, load prompts from this file
|
||||
--config CONFIG path to config which constructs model
|
||||
--ckpt CKPT path to checkpoint of model
|
||||
--seed SEED the seed (for reproducible sampling)
|
||||
--precision {full,autocast}
|
||||
evaluate at this precision
|
||||
|
||||
```
|
||||
Note: The inference config for all v1 versions is designed to be used with EMA-only checkpoints.
|
||||
For this reason `use_ema=False` is set in the configuration, otherwise the code will try to switch from
|
||||
non-EMA to EMA weights. If you want to examine the effect of EMA vs no EMA, we provide "full" checkpoints
|
||||
which contain both types of weights. For these, `use_ema=False` will load and use the non-EMA weights.
|
||||
|
||||
|
||||
#### Diffusers Integration
|
||||
|
||||
Another way to download and sample Stable Diffusion is by using the [diffusers library](https://github.com/huggingface/diffusers/tree/main#new--stable-diffusion-is-now-fully-compatible-with-diffusers)
|
||||
```py
|
||||
# make sure you're logged in with `huggingface-cli login`
|
||||
from torch import autocast
|
||||
from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
|
||||
|
||||
pipe = StableDiffusionPipeline.from_pretrained(
|
||||
"CompVis/stable-diffusion-v1-3-diffusers",
|
||||
use_auth_token=True
|
||||
)
|
||||
|
||||
prompt = "a photo of an astronaut riding a horse on mars"
|
||||
with autocast("cuda"):
|
||||
image = pipe(prompt)["sample"][0]
|
||||
|
||||
image.save("astronaut_rides_horse.png")
|
||||
```
|
||||
|
||||
|
||||
|
||||
### Image Modification with Stable Diffusion
|
||||
|
||||
By using a diffusion-denoising mechanism as first proposed by [SDEdit](https://arxiv.org/abs/2108.01073), the model can be used for different
|
||||
tasks such as text-guided image-to-image translation and upscaling. Similar to the txt2img sampling script,
|
||||
we provide a script to perform image modification with Stable Diffusion.
|
||||
|
||||
The following describes an example where a rough sketch made in [Pinta](https://www.pinta-project.com/) is converted into a detailed artwork.
|
||||
```
|
||||
python scripts/img2img.py --prompt "A fantasy landscape, trending on artstation" --init-img <path-to-img.jpg> --strength 0.8
|
||||
```
|
||||
Here, strength is a value between 0.0 and 1.0, that controls the amount of noise that is added to the input image.
|
||||
Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input. See the following example.
|
||||
|
||||
**Input**
|
||||
|
||||

|
||||
|
||||
**Outputs**
|
||||
|
||||

|
||||

|
||||
|
||||
This procedure can, for example, also be used to upscale samples from the base model.
|
||||
|
||||
|
||||
## Comments
|
||||
|
||||
- Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion)
|
||||
and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch).
|
||||
Thanks for open-sourcing!
|
||||
|
||||
- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories).
|
||||
|
||||
|
||||
## BibTeX
|
||||
|
||||
```
|
||||
@misc{rombach2021highresolution,
|
||||
title={High-Resolution Image Synthesis with Latent Diffusion Models},
|
||||
author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
|
||||
year={2021},
|
||||
eprint={2112.10752},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CV}
|
||||
}
|
||||
|
||||
```
|
||||
|
||||
|
@ -1,360 +0,0 @@
|
||||
# macOS Instructions
|
||||
|
||||
Requirements
|
||||
|
||||
- macOS 12.3 Monterey or later
|
||||
- Python
|
||||
- Patience
|
||||
- Apple Silicon*
|
||||
|
||||
*I haven't tested any of this on Intel Macs but I have read that one person got
|
||||
it to work, so Apple Silicon might not be requried.
|
||||
|
||||
Things have moved really fast and so these instructions change often and are
|
||||
often out-of-date. One of the problems is that there are so many different ways to
|
||||
run this.
|
||||
|
||||
We are trying to build a testing setup so that when we make changes it doesn't
|
||||
always break.
|
||||
|
||||
How to (this hasn't been 100% tested yet):
|
||||
|
||||
First get the weights checkpoint download started - it's big:
|
||||
|
||||
1. Sign up at https://huggingface.co
|
||||
2. Go to the [Stable diffusion diffusion model page](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)
|
||||
3. Accept the terms and click Access Repository:
|
||||
4. Download [sd-v1-4.ckpt (4.27 GB)](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/blob/main/sd-v1-4.ckpt) and note where you have saved it (probably the Downloads folder)
|
||||
|
||||
While that is downloading, open Terminal and run the following commands one at a time.
|
||||
|
||||
```
|
||||
# install brew (and Xcode command line tools):
|
||||
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
|
||||
|
||||
#
|
||||
# Now there are two different routes to get the Python (miniconda) environment up and running:
|
||||
# 1. Alongside pyenv
|
||||
# 2. No pyenv
|
||||
#
|
||||
# If you don't know what we are talking about, choose 2.
|
||||
#
|
||||
# NOW EITHER DO
|
||||
# 1. Installing alongside pyenv
|
||||
|
||||
brew install pyenv-virtualenv # you might have this from before, no problem
|
||||
pyenv install anaconda3-latest
|
||||
pyenv virtualenv anaconda3-latest lstein-stable-diffusion
|
||||
pyenv activate lstein-stable-diffusion
|
||||
|
||||
# OR,
|
||||
# 2. Installing standalone
|
||||
# install python 3, git, cmake, protobuf:
|
||||
brew install cmake protobuf rust
|
||||
|
||||
# install miniconda (M1 arm64 version):
|
||||
curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh -o Miniconda3-latest-MacOSX-arm64.sh
|
||||
/bin/bash Miniconda3-latest-MacOSX-arm64.sh
|
||||
|
||||
|
||||
# EITHER WAY,
|
||||
# continue from here
|
||||
|
||||
# clone the repo
|
||||
git clone https://github.com/lstein/stable-diffusion.git
|
||||
cd stable-diffusion
|
||||
|
||||
#
|
||||
# wait until the checkpoint file has downloaded, then proceed
|
||||
#
|
||||
|
||||
# create symlink to checkpoint
|
||||
mkdir -p models/ldm/stable-diffusion-v1/
|
||||
|
||||
PATH_TO_CKPT="$HOME/Downloads" # or wherever you saved sd-v1-4.ckpt
|
||||
|
||||
ln -s "$PATH_TO_CKPT/sd-v1-4.ckpt" models/ldm/stable-diffusion-v1/model.ckpt
|
||||
|
||||
# install packages
|
||||
PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-arm64 conda env create -f environment-mac.yaml
|
||||
conda activate ldm
|
||||
|
||||
# only need to do this once
|
||||
python scripts/preload_models.py
|
||||
|
||||
# run SD!
|
||||
python scripts/dream.py --full_precision # half-precision requires autocast and won't work
|
||||
```
|
||||
|
||||
The original scripts should work as well.
|
||||
|
||||
```
|
||||
python scripts/orig_scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms
|
||||
```
|
||||
|
||||
Note, `export PIP_EXISTS_ACTION=w` is a precaution to fix `conda env create -f environment-mac.yaml`
|
||||
never finishing in some situations. So it isn't required but wont hurt.
|
||||
|
||||
After you follow all the instructions and run dream.py you might get several
|
||||
errors. Here's the errors I've seen and found solutions for.
|
||||
|
||||
### Is it slow?
|
||||
|
||||
Be sure to specify 1 sample and 1 iteration.
|
||||
|
||||
python ./scripts/orig_scripts/txt2img.py --prompt "ocean" --ddim_steps 5 --n_samples 1 --n_iter 1
|
||||
|
||||
### Doesn't work anymore?
|
||||
|
||||
PyTorch nightly includes support for MPS. Because of this, this setup is
|
||||
inherently unstable. One morning I woke up and it no longer worked no matter
|
||||
what I did until I switched to miniforge. However, I have another Mac that works
|
||||
just fine with Anaconda. If you can't get it to work, please search a little
|
||||
first because many of the errors will get posted and solved. If you can't find
|
||||
a solution please [create an issue](https://github.com/lstein/stable-diffusion/issues).
|
||||
|
||||
One debugging step is to update to the latest version of PyTorch nightly.
|
||||
|
||||
conda install pytorch torchvision torchaudio -c pytorch-nightly
|
||||
|
||||
If `conda env create -f environment-mac.yaml` takes forever run this.
|
||||
|
||||
git clean -f
|
||||
|
||||
And run this.
|
||||
|
||||
conda clean --yes --all
|
||||
|
||||
Or you could reset Anaconda.
|
||||
|
||||
conda update --force-reinstall -y -n base -c defaults conda
|
||||
|
||||
### "No module named cv2", torch, 'ldm', 'transformers', 'taming', etc.
|
||||
|
||||
There are several causes of these errors.
|
||||
|
||||
First, did you remember to `conda activate ldm`? If your terminal prompt
|
||||
begins with "(ldm)" then you activated it. If it begins with "(base)"
|
||||
or something else you haven't.
|
||||
|
||||
Second, you might've run `./scripts/preload_models.py` or `./scripts/dream.py`
|
||||
instead of `python ./scripts/preload_models.py` or `python ./scripts/dream.py`.
|
||||
The cause of this error is long so it's below.
|
||||
|
||||
Third, if it says you're missing taming you need to rebuild your virtual
|
||||
environment.
|
||||
|
||||
conda env remove -n ldm
|
||||
conda env create -f environment-mac.yaml
|
||||
|
||||
Fourth, If you have activated the ldm virtual environment and tried rebuilding
|
||||
it, maybe the problem could be that I have something installed that
|
||||
you don't and you'll just need to manually install it. Make sure you
|
||||
activate the virtual environment so it installs there instead of
|
||||
globally.
|
||||
|
||||
conda activate ldm
|
||||
pip install *name*
|
||||
|
||||
You might also need to install Rust (I mention this again below).
|
||||
|
||||
### How many snakes are living in your computer?
|
||||
|
||||
Here's the reason why you have to specify which python to use.
|
||||
There are several versions of python on macOS and the computer is
|
||||
picking the wrong one. More specifically, preload_models.py and dream.py says to
|
||||
find the first `python3` in the path environment variable. You can see which one
|
||||
it is picking with `which python3`. These are the mostly likely paths you'll see.
|
||||
|
||||
% which python3
|
||||
/usr/bin/python3
|
||||
|
||||
The above path is part of the OS. However, that path is a stub that asks you if
|
||||
you want to install Xcode. If you have Xcode installed already,
|
||||
/usr/bin/python3 will execute /Library/Developer/CommandLineTools/usr/bin/python3 or
|
||||
/Applications/Xcode.app/Contents/Developer/usr/bin/python3 (depending on which
|
||||
Xcode you've selected with `xcode-select`).
|
||||
|
||||
% which python3
|
||||
/opt/homebrew/bin/python3
|
||||
|
||||
If you installed python3 with Homebrew and you've modified your path to search
|
||||
for Homebrew binaries before system ones, you'll see the above path.
|
||||
|
||||
% which python
|
||||
/opt/anaconda3/bin/python
|
||||
|
||||
If you drop the "3" you get an entirely different python. Note: starting in
|
||||
macOS 12.3, /usr/bin/python no longer exists (it was python 2 anyway).
|
||||
|
||||
If you have Anaconda installed, this is what you'll see. There is a
|
||||
/opt/anaconda3/bin/python3 also.
|
||||
|
||||
(ldm) % which python
|
||||
/Users/name/miniforge3/envs/ldm/bin/python
|
||||
|
||||
This is what you'll see if you have miniforge and you've correctly activated
|
||||
the ldm environment. This is the goal.
|
||||
|
||||
It's all a mess and you should know [how to modify the path environment variable](https://support.apple.com/guide/terminal/use-environment-variables-apd382cc5fa-4f58-4449-b20a-41c53c006f8f/mac)
|
||||
if you want to fix it. Here's a brief hint of all the ways you can modify it
|
||||
(don't really have the time to explain it all here).
|
||||
|
||||
- ~/.zshrc
|
||||
- ~/.bash_profile
|
||||
- ~/.bashrc
|
||||
- /etc/paths.d
|
||||
- /etc/path
|
||||
|
||||
Which one you use will depend on what you have installed except putting a file
|
||||
in /etc/paths.d is what I prefer to do.
|
||||
|
||||
### Debugging?
|
||||
|
||||
Tired of waiting for your renders to finish before you can see if it
|
||||
works? Reduce the steps! The image quality will be horrible but at least you'll
|
||||
get quick feedback.
|
||||
|
||||
python ./scripts/txt2img.py --prompt "ocean" --ddim_steps 5 --n_samples 1 --n_iter 1
|
||||
|
||||
### OSError: Can't load tokenizer for 'openai/clip-vit-large-patch14'...
|
||||
|
||||
python scripts/preload_models.py
|
||||
|
||||
### "The operator [name] is not current implemented for the MPS device." (sic)
|
||||
|
||||
Example error.
|
||||
|
||||
```
|
||||
...
|
||||
NotImplementedError: The operator 'aten::_index_put_impl_' is not current implemented for the MPS device. If you want this op to be added in priority during the prototype phase of this feature, please comment on [https://github.com/pytorch/pytorch/issues/77764](https://github.com/pytorch/pytorch/issues/77764). As a temporary fix, you can set the environment variable `PYTORCH_ENABLE_MPS_FALLBACK=1` to use the CPU as a fallback for this op. WARNING: this will be slower than running natively on MPS.
|
||||
```
|
||||
|
||||
The lstein branch includes this fix in [environment-mac.yaml](https://github.com/lstein/stable-diffusion/blob/main/environment-mac.yaml).
|
||||
|
||||
### "Could not build wheels for tokenizers"
|
||||
|
||||
I have not seen this error because I had Rust installed on my computer before I started playing with Stable Diffusion. The fix is to install Rust.
|
||||
|
||||
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
|
||||
|
||||
### How come `--seed` doesn't work?
|
||||
|
||||
First this:
|
||||
|
||||
> Completely reproducible results are not guaranteed across PyTorch
|
||||
releases, individual commits, or different platforms. Furthermore,
|
||||
results may not be reproducible between CPU and GPU executions, even
|
||||
when using identical seeds.
|
||||
|
||||
[PyTorch docs](https://pytorch.org/docs/stable/notes/randomness.html)
|
||||
|
||||
Second, we might have a fix that at least gets a consistent seed sort of. We're
|
||||
still working on it.
|
||||
|
||||
### libiomp5.dylib error?
|
||||
|
||||
OMP: Error #15: Initializing libiomp5.dylib, but found libomp.dylib already initialized.
|
||||
|
||||
You are likely using an Intel package by mistake. Be sure to run conda with
|
||||
the environment variable `CONDA_SUBDIR=osx-arm64`, like so:
|
||||
|
||||
`CONDA_SUBDIR=osx-arm64 conda install ...`
|
||||
|
||||
This error happens with Anaconda on Macs when the Intel-only `mkl` is pulled in by
|
||||
a dependency. [nomkl](https://stackoverflow.com/questions/66224879/what-is-the-nomkl-python-package-used-for)
|
||||
is a metapackage designed to prevent this, by making it impossible to install
|
||||
`mkl`, but if your environment is already broken it may not work.
|
||||
|
||||
Do *not* use `os.environ['KMP_DUPLICATE_LIB_OK']='True'` or equivalents as this
|
||||
masks the underlying issue of using Intel packages.
|
||||
|
||||
### Not enough memory.
|
||||
|
||||
This seems to be a common problem and is probably the underlying
|
||||
problem for a lot of symptoms (listed below). The fix is to lower your
|
||||
image size or to add `model.half()` right after the model is loaded. I
|
||||
should probably test it out. I've read that the reason this fixes
|
||||
problems is because it converts the model from 32-bit to 16-bit and
|
||||
that leaves more RAM for other things. I have no idea how that would
|
||||
affect the quality of the images though.
|
||||
|
||||
See [this issue](https://github.com/CompVis/stable-diffusion/issues/71).
|
||||
|
||||
### "Error: product of dimension sizes > 2**31'"
|
||||
|
||||
This error happens with img2img, which I haven't played with too much
|
||||
yet. But I know it's because your image is too big or the resolution
|
||||
isn't a multiple of 32x32. Because the stable-diffusion model was
|
||||
trained on images that were 512 x 512, it's always best to use that
|
||||
output size (which is the default). However, if you're using that size
|
||||
and you get the above error, try 256 x 256 or 512 x 256 or something
|
||||
as the source image.
|
||||
|
||||
BTW, 2**31-1 = [2,147,483,647](https://en.wikipedia.org/wiki/2,147,483,647#In_computing), which is also 32-bit signed [LONG_MAX](https://en.wikipedia.org/wiki/C_data_types) in C.
|
||||
|
||||
### I just got Rickrolled! Do I have a virus?
|
||||
|
||||
You don't have a virus. It's part of the project. Here's
|
||||
[Rick](https://github.com/lstein/stable-diffusion/blob/main/assets/rick.jpeg)
|
||||
and here's [the
|
||||
code](https://github.com/lstein/stable-diffusion/blob/69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc/scripts/txt2img.py#L79)
|
||||
that swaps him in. It's a NSFW filter, which IMO, doesn't work very
|
||||
good (and we call this "computer vision", sheesh).
|
||||
|
||||
Actually, this could be happening because there's not enough RAM. You could try the `model.half()` suggestion or specify smaller output images.
|
||||
|
||||
### My images come out black
|
||||
|
||||
We might have this fixed, we are still testing.
|
||||
|
||||
There's a [similar issue](https://github.com/CompVis/stable-diffusion/issues/69)
|
||||
on CUDA GPU's where the images come out green. Maybe it's the same issue?
|
||||
Someone in that issue says to use "--precision full", but this fork
|
||||
actually disables that flag. I don't know why, someone else provided
|
||||
that code and I don't know what it does. Maybe the `model.half()`
|
||||
suggestion above would fix this issue too. I should probably test it.
|
||||
|
||||
### "view size is not compatible with input tensor's size and stride"
|
||||
|
||||
```
|
||||
File "/opt/anaconda3/envs/ldm/lib/python3.10/site-packages/torch/nn/functional.py", line 2511, in layer_norm
|
||||
return torch.layer_norm(input, normalized_shape, weight, bias, eps, torch.backends.cudnn.enabled)
|
||||
RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.
|
||||
```
|
||||
|
||||
Update to the latest version of lstein/stable-diffusion. We were
|
||||
patching pytorch but we found a file in stable-diffusion that we could
|
||||
change instead. This is a 32-bit vs 16-bit problem.
|
||||
|
||||
### The processor must support the Intel bla bla bla
|
||||
|
||||
What? Intel? On an Apple Silicon?
|
||||
|
||||
Intel MKL FATAL ERROR: This system does not meet the minimum requirements for use of the Intel(R) Math Kernel Library.
|
||||
The processor must support the Intel(R) Supplemental Streaming SIMD Extensions 3 (Intel(R) SSSE3) instructions.
|
||||
The processor must support the Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) instructions.
|
||||
The processor must support the Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.
|
||||
|
||||
This is due to the Intel `mkl` package getting picked up when you try to install
|
||||
something that depends on it-- Rosetta can translate some Intel instructions but
|
||||
not the specialized ones here. To avoid this, make sure to use the environment
|
||||
variable `CONDA_SUBDIR=osx-arm64`, which restricts the Conda environment to only
|
||||
use ARM packages, and use `nomkl` as described above.
|
||||
|
||||
### input types 'tensor<2x1280xf32>' and 'tensor<*xf16>' are not broadcast compatible
|
||||
|
||||
May appear when just starting to generate, e.g.:
|
||||
|
||||
```
|
||||
dream> clouds
|
||||
Generating: 0%| | 0/1 [00:00<?, ?it/s]/Users/[...]/dev/stable-diffusion/ldm/modules/embedding_manager.py:152: UserWarning: The operator 'aten::nonzero' is not currently supported on the MPS backend and will fall back to run on the CPU. This may have performance implications. (Triggered internally at /Users/runner/work/_temp/anaconda/conda-bld/pytorch_1662016319283/work/aten/src/ATen/mps/MPSFallback.mm:11.)
|
||||
placeholder_idx = torch.where(
|
||||
loc("mps_add"("(mpsFileLoc): /AppleInternal/Library/BuildRoots/20d6c351-ee94-11ec-bcaf-7247572f23b4/Library/Caches/com.apple.xbs/Sources/MetalPerformanceShadersGraph/mpsgraph/MetalPerformanceShadersGraph/Core/Files/MPSGraphUtilities.mm":219:0)): error: input types 'tensor<2x1280xf32>' and 'tensor<*xf16>' are not broadcast compatible
|
||||
LLVM ERROR: Failed to infer result type(s).
|
||||
Abort trap: 6
|
||||
/Users/[...]/opt/anaconda3/envs/ldm/lib/python3.9/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
|
||||
warnings.warn('resource_tracker: There appear to be %d '
|
||||
```
|
||||
|
||||
Macs do not support autocast/mixed-precision. Supply `--full_precision` to use float32 everywhere.
|
@ -1,265 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Easy-peasy Windows install"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Note that you will need NVIDIA drivers, Python 3.10, and Git installed\n",
|
||||
"beforehand - simplified\n",
|
||||
"[step-by-step instructions](https://github.com/lstein/stable-diffusion/wiki/Easy-peasy-Windows-install)\n",
|
||||
"are available in the wiki (you'll only need steps 1, 2, & 3 )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Run each cell in turn. In VSCode, either hit SHIFT-ENTER, or click on the little ▶️ to the left of the cell. In Jupyter/JupyterLab, you **must** hit SHIFT-ENTER"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%pip install pew"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%cmd\n",
|
||||
"git clone https://github.com/lstein/stable-diffusion.git"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%cd stable-diffusion"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile requirements.txt\n",
|
||||
"albumentations==0.4.3\n",
|
||||
"einops==0.3.0\n",
|
||||
"huggingface-hub==0.8.1\n",
|
||||
"imageio-ffmpeg==0.4.2\n",
|
||||
"imageio==2.9.0\n",
|
||||
"kornia==0.6.0\n",
|
||||
"# pip will resolve the version which matches torch\n",
|
||||
"numpy\n",
|
||||
"omegaconf==2.1.1\n",
|
||||
"opencv-python==4.6.0.66\n",
|
||||
"pillow==9.2.0\n",
|
||||
"pip>=22\n",
|
||||
"pudb==2019.2\n",
|
||||
"pytorch-lightning==1.4.2\n",
|
||||
"streamlit==1.12.0\n",
|
||||
"# \"CompVis/taming-transformers\" doesn't work\n",
|
||||
"# ldm\\models\\autoencoder.py\", line 6, in <module>\n",
|
||||
"# from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer\n",
|
||||
"# ModuleNotFoundError\n",
|
||||
"taming-transformers-rom1504==0.0.6\n",
|
||||
"test-tube>=0.7.5\n",
|
||||
"torch-fidelity==0.3.0\n",
|
||||
"torchmetrics==0.6.0\n",
|
||||
"transformers==4.19.2\n",
|
||||
"git+https://github.com/openai/CLIP.git@main#egg=clip\n",
|
||||
"git+https://github.com/lstein/k-diffusion.git@master#egg=k-diffusion\n",
|
||||
"# No CUDA in PyPi builds\n",
|
||||
"--extra-index-url https://download.pytorch.org/whl/cu113 --trusted-host https://download.pytorch.org\n",
|
||||
"torch==1.11.0\n",
|
||||
"# Same as numpy - let pip do its thing\n",
|
||||
"torchvision\n",
|
||||
"-e .\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%cmd\n",
|
||||
"pew new --python 3.10 -r requirements.txt --dont-activate ldm"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Switch the notebook kernel to the new 'ldm' environment!\n",
|
||||
"\n",
|
||||
"## VSCode: restart VSCode and come back to this cell\n",
|
||||
"\n",
|
||||
"1. Ctrl+Shift+P\n",
|
||||
"1. Type \"Select Interpreter\" and select \"Jupyter: Select Interpreter to Start Jupyter Server\"\n",
|
||||
"1. VSCode will say that it needs to install packages. Click the \"Install\" button.\n",
|
||||
"1. Once the install is finished, do 1 & 2 again\n",
|
||||
"1. Pick 'ldm'\n",
|
||||
"1. Run the following cell"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%cd stable-diffusion"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"\n",
|
||||
"## Jupyter/JupyterLab\n",
|
||||
"\n",
|
||||
"1. Run the cell below\n",
|
||||
"1. Click on the toolbar where it says \"(ipyknel)\" ↗️. You should get a pop-up asking you to \"Select Kernel\". Pick 'ldm' from the drop-down.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### DO NOT RUN THE FOLLOWING CELL IF YOU ARE USING VSCODE!!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# DO NOT RUN THIS CELL IF YOU ARE USING VSCODE!!\n",
|
||||
"%%cmd\n",
|
||||
"pew workon ldm\n",
|
||||
"pip3 install ipykernel\n",
|
||||
"python -m ipykernel install --name=ldm"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### When running the next cell, Jupyter/JupyterLab users might get a warning saying \"IProgress not found\". This can be ignored."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%run \"scripts/preload_models.py\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%cmd\n",
|
||||
"mkdir \"models/ldm/stable-diffusion-v1\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Now copy the SD model you downloaded from Hugging Face into the above new directory, and (if necessary) rename it to 'model.ckpt'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Now go create some magic!\n",
|
||||
"\n",
|
||||
"VSCode\n",
|
||||
"\n",
|
||||
"- The actual input box for the 'dream' prompt will appear at the very top of the VSCode window. Type in your commands and hit 'ENTER'.\n",
|
||||
"- To quit, hit the 'Interrupt' button in the toolbar up there ⬆️ a couple of times, then hit ENTER (you'll probably see a terrifying traceback from Python - just ignore it).\n",
|
||||
"\n",
|
||||
"Jupyter/JupyterLab\n",
|
||||
"\n",
|
||||
"- The input box for the 'dream' prompt will appear below. Type in your commands and hit 'ENTER'.\n",
|
||||
"- To quit, hit the interrupt button (⏹️) in the toolbar up there ⬆️ a couple of times, then hit ENTER (you'll probably see a terrifying traceback from Python - just ignore it)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%run \"scripts/dream.py\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Once this seems to be working well, you can try opening a terminal\n",
|
||||
"\n",
|
||||
"- VSCode: type ('CTRL+`')\n",
|
||||
"- Jupyter/JupyterLab: File|New Terminal\n",
|
||||
"- Or jump out of the notebook entirely, and open Powershell/Command Prompt\n",
|
||||
"\n",
|
||||
"Now:\n",
|
||||
"\n",
|
||||
"1. `cd` to wherever the 'stable-diffusion' directory is\n",
|
||||
"1. Run `pew workon ldm`\n",
|
||||
"1. Run `winpty python scripts\\dream.py`"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.10.6 ('ldm')",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.6"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "a05e4574567b7bc2c98f7f9aa579f9ea5b8739b54844ab610ac85881c4be2659"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
@ -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:
|
||||
|
||||

|
||||

|
||||
|
||||
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
|
||||
|
113
VARIATIONS.md
@ -1,113 +0,0 @@
|
||||
# Cheat Sheat for Generating Variations
|
||||
|
||||
Release 1.13 of SD-Dream adds support for image variations. There are two things that you can do:
|
||||
|
||||
1. Generate a series of systematic variations of an image, given a
|
||||
prompt. The amount of variation from one image to the next can be
|
||||
controlled.
|
||||
|
||||
2. Given two or more variations that you like, you can combine them in
|
||||
a weighted fashion
|
||||
|
||||
This cheat sheet provides a quick guide for how this works in
|
||||
practice, using variations to create the desired image of Xena,
|
||||
Warrior Princess.
|
||||
|
||||
## Step 1 -- find a base image that you like
|
||||
|
||||
The prompt we will use throughout is "lucy lawless as xena, warrior
|
||||
princess, character portrait, high resolution." 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:
|
||||
|
||||
~~~
|
||||
dream> lucy lawless as xena, warrior princess, character portrait, high resolution -n6
|
||||
...
|
||||
Outputs:
|
||||
./outputs/Xena/000001.1579445059.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S1579445059
|
||||
./outputs/Xena/000001.1880768722.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S1880768722
|
||||
./outputs/Xena/000001.332057179.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S332057179
|
||||
./outputs/Xena/000001.2224800325.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S2224800325
|
||||
./outputs/Xena/000001.465250761.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S465250761
|
||||
./outputs/Xena/000001.3357757885.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S3357757885
|
||||
~~~
|
||||
|
||||
The one with seed 3357757885 looks nice:
|
||||
|
||||
<img src="static/variation_walkthru/000001.3357757885.png"/>
|
||||
|
||||
Let's try to generate some variations. Using the same seed, we pass
|
||||
the argument -v0.1 (or --variant_amount), which generates a series of
|
||||
variations each differing by a variation amount of 0.2. This number
|
||||
ranges from 0 to 1.0, with higher numbers being larger amounts of
|
||||
variation.
|
||||
|
||||
~~~
|
||||
dream> "prompt" -n6 -S3357757885 -v0.2
|
||||
...
|
||||
Outputs:
|
||||
./outputs/Xena/000002.784039624.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 784039624:0.2 -S3357757885
|
||||
./outputs/Xena/000002.3647897225.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.2 -S3357757885
|
||||
./outputs/Xena/000002.917731034.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 917731034:0.2 -S3357757885
|
||||
./outputs/Xena/000002.4116285959.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 4116285959:0.2 -S3357757885
|
||||
./outputs/Xena/000002.1614299449.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 1614299449:0.2 -S3357757885
|
||||
./outputs/Xena/000002.1335553075.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 1335553075:0.2 -S3357757885
|
||||
~~~
|
||||
|
||||
Note that the output for each image has a -V option giving the
|
||||
"variant subseed" for that image, consisting of a seed followed by the
|
||||
variation amount used to generate it.
|
||||
|
||||
This gives us a series of closely-related variations, including the
|
||||
two shown here.
|
||||
|
||||
<img src="static/variation_walkthru/000002.3647897225.png">
|
||||
<img src="static/variation_walkthru/000002.1614299449.png">
|
||||
|
||||
|
||||
I like the expression on Xena's face in the first one (subseed
|
||||
3647897225), and the armor on her shoulder in the second one (subseed
|
||||
1614299449). Can we combine them to get the best of both worlds?
|
||||
|
||||
We combine the two variations using -V (--with_variations). Again, we
|
||||
must provide the seed for the originally-chosen image in order for
|
||||
this to work.
|
||||
|
||||
~~~
|
||||
dream> "prompt" -S3357757885 -V3647897225,0.1;1614299449,0.1
|
||||
Outputs:
|
||||
./outputs/Xena/000003.1614299449.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1 -S3357757885
|
||||
~~~
|
||||
|
||||
Here we are providing equal weights (0.1 and 0.1) for both the
|
||||
subseeds. The resulting image is close, but not exactly what I
|
||||
wanted:
|
||||
|
||||
<img src="static/variation_walkthru/000003.1614299449.png">
|
||||
|
||||
We could either try combining the images with different weights, or we
|
||||
can 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:
|
||||
|
||||
~~~~
|
||||
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
|
||||
./outputs/Xena/000004.2853129515.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2853129515:0.05 -S3357757885
|
||||
./outputs/Xena/000004.3747154981.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,3747154981:0.05 -S3357757885
|
||||
./outputs/Xena/000004.2664260391.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2664260391:0.05 -S3357757885
|
||||
./outputs/Xena/000004.1642517170.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,1642517170:0.05 -S3357757885
|
||||
./outputs/Xena/000004.2183375608.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2183375608:0.05 -S3357757885
|
||||
~~~~
|
||||
|
||||
This produces six images, all slight variations on the combination of
|
||||
the chosen two images. Here's the one I like best:
|
||||
|
||||
<img src="static/variation_walkthru/000004.3747154981.png">
|
||||
|
||||
As you can see, this is a very powerful tool, which when combined with
|
||||
subprompt weighting, gives you great control over the content and
|
||||
quality of your generated images.
|
@ -1,54 +0,0 @@
|
||||
model:
|
||||
base_learning_rate: 4.5e-6
|
||||
target: ldm.models.autoencoder.AutoencoderKL
|
||||
params:
|
||||
monitor: "val/rec_loss"
|
||||
embed_dim: 16
|
||||
lossconfig:
|
||||
target: ldm.modules.losses.LPIPSWithDiscriminator
|
||||
params:
|
||||
disc_start: 50001
|
||||
kl_weight: 0.000001
|
||||
disc_weight: 0.5
|
||||
|
||||
ddconfig:
|
||||
double_z: True
|
||||
z_channels: 16
|
||||
resolution: 256
|
||||
in_channels: 3
|
||||
out_ch: 3
|
||||
ch: 128
|
||||
ch_mult: [ 1,1,2,2,4] # num_down = len(ch_mult)-1
|
||||
num_res_blocks: 2
|
||||
attn_resolutions: [16]
|
||||
dropout: 0.0
|
||||
|
||||
|
||||
data:
|
||||
target: main.DataModuleFromConfig
|
||||
params:
|
||||
batch_size: 12
|
||||
wrap: True
|
||||
train:
|
||||
target: ldm.data.imagenet.ImageNetSRTrain
|
||||
params:
|
||||
size: 256
|
||||
degradation: pil_nearest
|
||||
validation:
|
||||
target: ldm.data.imagenet.ImageNetSRValidation
|
||||
params:
|
||||
size: 256
|
||||
degradation: pil_nearest
|
||||
|
||||
lightning:
|
||||
callbacks:
|
||||
image_logger:
|
||||
target: main.ImageLogger
|
||||
params:
|
||||
batch_frequency: 1000
|
||||
max_images: 8
|
||||
increase_log_steps: True
|
||||
|
||||
trainer:
|
||||
benchmark: True
|
||||
accumulate_grad_batches: 2
|
@ -1,53 +0,0 @@
|
||||
model:
|
||||
base_learning_rate: 4.5e-6
|
||||
target: ldm.models.autoencoder.AutoencoderKL
|
||||
params:
|
||||
monitor: "val/rec_loss"
|
||||
embed_dim: 4
|
||||
lossconfig:
|
||||
target: ldm.modules.losses.LPIPSWithDiscriminator
|
||||
params:
|
||||
disc_start: 50001
|
||||
kl_weight: 0.000001
|
||||
disc_weight: 0.5
|
||||
|
||||
ddconfig:
|
||||
double_z: True
|
||||
z_channels: 4
|
||||
resolution: 256
|
||||
in_channels: 3
|
||||
out_ch: 3
|
||||
ch: 128
|
||||
ch_mult: [ 1,2,4,4 ] # num_down = len(ch_mult)-1
|
||||
num_res_blocks: 2
|
||||
attn_resolutions: [ ]
|
||||
dropout: 0.0
|
||||
|
||||
data:
|
||||
target: main.DataModuleFromConfig
|
||||
params:
|
||||
batch_size: 12
|
||||
wrap: True
|
||||
train:
|
||||
target: ldm.data.imagenet.ImageNetSRTrain
|
||||
params:
|
||||
size: 256
|
||||
degradation: pil_nearest
|
||||
validation:
|
||||
target: ldm.data.imagenet.ImageNetSRValidation
|
||||
params:
|
||||
size: 256
|
||||
degradation: pil_nearest
|
||||
|
||||
lightning:
|
||||
callbacks:
|
||||
image_logger:
|
||||
target: main.ImageLogger
|
||||
params:
|
||||
batch_frequency: 1000
|
||||
max_images: 8
|
||||
increase_log_steps: True
|
||||
|
||||
trainer:
|
||||
benchmark: True
|
||||
accumulate_grad_batches: 2
|
@ -1,54 +0,0 @@
|
||||
model:
|
||||
base_learning_rate: 4.5e-6
|
||||
target: ldm.models.autoencoder.AutoencoderKL
|
||||
params:
|
||||
monitor: "val/rec_loss"
|
||||
embed_dim: 3
|
||||
lossconfig:
|
||||
target: ldm.modules.losses.LPIPSWithDiscriminator
|
||||
params:
|
||||
disc_start: 50001
|
||||
kl_weight: 0.000001
|
||||
disc_weight: 0.5
|
||||
|
||||
ddconfig:
|
||||
double_z: True
|
||||
z_channels: 3
|
||||
resolution: 256
|
||||
in_channels: 3
|
||||
out_ch: 3
|
||||
ch: 128
|
||||
ch_mult: [ 1,2,4 ] # num_down = len(ch_mult)-1
|
||||
num_res_blocks: 2
|
||||
attn_resolutions: [ ]
|
||||
dropout: 0.0
|
||||
|
||||
|
||||
data:
|
||||
target: main.DataModuleFromConfig
|
||||
params:
|
||||
batch_size: 12
|
||||
wrap: True
|
||||
train:
|
||||
target: ldm.data.imagenet.ImageNetSRTrain
|
||||
params:
|
||||
size: 256
|
||||
degradation: pil_nearest
|
||||
validation:
|
||||
target: ldm.data.imagenet.ImageNetSRValidation
|
||||
params:
|
||||
size: 256
|
||||
degradation: pil_nearest
|
||||
|
||||
lightning:
|
||||
callbacks:
|
||||
image_logger:
|
||||
target: main.ImageLogger
|
||||
params:
|
||||
batch_frequency: 1000
|
||||
max_images: 8
|
||||
increase_log_steps: True
|
||||
|
||||
trainer:
|
||||
benchmark: True
|
||||
accumulate_grad_batches: 2
|
@ -1,53 +0,0 @@
|
||||
model:
|
||||
base_learning_rate: 4.5e-6
|
||||
target: ldm.models.autoencoder.AutoencoderKL
|
||||
params:
|
||||
monitor: "val/rec_loss"
|
||||
embed_dim: 64
|
||||
lossconfig:
|
||||
target: ldm.modules.losses.LPIPSWithDiscriminator
|
||||
params:
|
||||
disc_start: 50001
|
||||
kl_weight: 0.000001
|
||||
disc_weight: 0.5
|
||||
|
||||
ddconfig:
|
||||
double_z: True
|
||||
z_channels: 64
|
||||
resolution: 256
|
||||
in_channels: 3
|
||||
out_ch: 3
|
||||
ch: 128
|
||||
ch_mult: [ 1,1,2,2,4,4] # num_down = len(ch_mult)-1
|
||||
num_res_blocks: 2
|
||||
attn_resolutions: [16,8]
|
||||
dropout: 0.0
|
||||
|
||||
data:
|
||||
target: main.DataModuleFromConfig
|
||||
params:
|
||||
batch_size: 12
|
||||
wrap: True
|
||||
train:
|
||||
target: ldm.data.imagenet.ImageNetSRTrain
|
||||
params:
|
||||
size: 256
|
||||
degradation: pil_nearest
|
||||
validation:
|
||||
target: ldm.data.imagenet.ImageNetSRValidation
|
||||
params:
|
||||
size: 256
|
||||
degradation: pil_nearest
|
||||
|
||||
lightning:
|
||||
callbacks:
|
||||
image_logger:
|
||||
target: main.ImageLogger
|
||||
params:
|
||||
batch_frequency: 1000
|
||||
max_images: 8
|
||||
increase_log_steps: True
|
||||
|
||||
trainer:
|
||||
benchmark: True
|
||||
accumulate_grad_batches: 2
|
@ -1,86 +0,0 @@
|
||||
model:
|
||||
base_learning_rate: 2.0e-06
|
||||
target: ldm.models.diffusion.ddpm.LatentDiffusion
|
||||
params:
|
||||
linear_start: 0.0015
|
||||
linear_end: 0.0195
|
||||
num_timesteps_cond: 1
|
||||
log_every_t: 200
|
||||
timesteps: 1000
|
||||
first_stage_key: image
|
||||
image_size: 64
|
||||
channels: 3
|
||||
monitor: val/loss_simple_ema
|
||||
|
||||
unet_config:
|
||||
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
|
||||
params:
|
||||
image_size: 64
|
||||
in_channels: 3
|
||||
out_channels: 3
|
||||
model_channels: 224
|
||||
attention_resolutions:
|
||||
# note: this isn\t actually the resolution but
|
||||
# the downsampling factor, i.e. this corresnponds to
|
||||
# attention on spatial resolution 8,16,32, as the
|
||||
# spatial reolution of the latents is 64 for f4
|
||||
- 8
|
||||
- 4
|
||||
- 2
|
||||
num_res_blocks: 2
|
||||
channel_mult:
|
||||
- 1
|
||||
- 2
|
||||
- 3
|
||||
- 4
|
||||
num_head_channels: 32
|
||||
first_stage_config:
|
||||
target: ldm.models.autoencoder.VQModelInterface
|
||||
params:
|
||||
embed_dim: 3
|
||||
n_embed: 8192
|
||||
ckpt_path: models/first_stage_models/vq-f4/model.ckpt
|
||||
ddconfig:
|
||||
double_z: false
|
||||
z_channels: 3
|
||||
resolution: 256
|
||||
in_channels: 3
|
||||
out_ch: 3
|
||||
ch: 128
|
||||
ch_mult:
|
||||
- 1
|
||||
- 2
|
||||
- 4
|
||||
num_res_blocks: 2
|
||||
attn_resolutions: []
|
||||
dropout: 0.0
|
||||
lossconfig:
|
||||
target: torch.nn.Identity
|
||||
cond_stage_config: __is_unconditional__
|
||||
data:
|
||||
target: main.DataModuleFromConfig
|
||||
params:
|
||||
batch_size: 48
|
||||
num_workers: 5
|
||||
wrap: false
|
||||
train:
|
||||
target: taming.data.faceshq.CelebAHQTrain
|
||||
params:
|
||||
size: 256
|
||||
validation:
|
||||
target: taming.data.faceshq.CelebAHQValidation
|
||||
params:
|
||||
size: 256
|
||||
|
||||
|
||||
lightning:
|
||||
callbacks:
|
||||
image_logger:
|
||||
target: main.ImageLogger
|
||||
params:
|
||||
batch_frequency: 5000
|
||||
max_images: 8
|
||||
increase_log_steps: False
|
||||
|
||||
trainer:
|
||||
benchmark: True
|
@ -1,98 +0,0 @@
|
||||
model:
|
||||
base_learning_rate: 1.0e-06
|
||||
target: ldm.models.diffusion.ddpm.LatentDiffusion
|
||||
params:
|
||||
linear_start: 0.0015
|
||||
linear_end: 0.0195
|
||||
num_timesteps_cond: 1
|
||||
log_every_t: 200
|
||||
timesteps: 1000
|
||||
first_stage_key: image
|
||||
cond_stage_key: class_label
|
||||
image_size: 32
|
||||
channels: 4
|
||||
cond_stage_trainable: true
|
||||
conditioning_key: crossattn
|
||||
monitor: val/loss_simple_ema
|
||||
unet_config:
|
||||
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
|
||||
params:
|
||||
image_size: 32
|
||||
in_channels: 4
|
||||
out_channels: 4
|
||||
model_channels: 256
|
||||
attention_resolutions:
|
||||
#note: this isn\t actually the resolution but
|
||||
# the downsampling factor, i.e. this corresnponds to
|
||||
# attention on spatial resolution 8,16,32, as the
|
||||
# spatial reolution of the latents is 32 for f8
|
||||
- 4
|
||||
- 2
|
||||
- 1
|
||||
num_res_blocks: 2
|
||||
channel_mult:
|
||||
- 1
|
||||
- 2
|
||||
- 4
|
||||
num_head_channels: 32
|
||||
use_spatial_transformer: true
|
||||
transformer_depth: 1
|
||||
context_dim: 512
|
||||
first_stage_config:
|
||||
target: ldm.models.autoencoder.VQModelInterface
|
||||
params:
|
||||
embed_dim: 4
|
||||
n_embed: 16384
|
||||
ckpt_path: configs/first_stage_models/vq-f8/model.yaml
|
||||
ddconfig:
|
||||
double_z: false
|
||||
z_channels: 4
|
||||
resolution: 256
|
||||
in_channels: 3
|
||||
out_ch: 3
|
||||
ch: 128
|
||||
ch_mult:
|
||||
- 1
|
||||
- 2
|
||||
- 2
|
||||
- 4
|
||||
num_res_blocks: 2
|
||||
attn_resolutions:
|
||||
- 32
|
||||
dropout: 0.0
|
||||
lossconfig:
|
||||
target: torch.nn.Identity
|
||||
cond_stage_config:
|
||||
target: ldm.modules.encoders.modules.ClassEmbedder
|
||||
params:
|
||||
embed_dim: 512
|
||||
key: class_label
|
||||
data:
|
||||
target: main.DataModuleFromConfig
|
||||
params:
|
||||
batch_size: 64
|
||||
num_workers: 12
|
||||
wrap: false
|
||||
train:
|
||||
target: ldm.data.imagenet.ImageNetTrain
|
||||
params:
|
||||
config:
|
||||
size: 256
|
||||
validation:
|
||||
target: ldm.data.imagenet.ImageNetValidation
|
||||
params:
|
||||
config:
|
||||
size: 256
|
||||
|
||||
|
||||
lightning:
|
||||
callbacks:
|
||||
image_logger:
|
||||
target: main.ImageLogger
|
||||
params:
|
||||
batch_frequency: 5000
|
||||
max_images: 8
|
||||
increase_log_steps: False
|
||||
|
||||
trainer:
|
||||
benchmark: True
|
@ -1,68 +0,0 @@
|
||||
model:
|
||||
base_learning_rate: 0.0001
|
||||
target: ldm.models.diffusion.ddpm.LatentDiffusion
|
||||
params:
|
||||
linear_start: 0.0015
|
||||
linear_end: 0.0195
|
||||
num_timesteps_cond: 1
|
||||
log_every_t: 200
|
||||
timesteps: 1000
|
||||
first_stage_key: image
|
||||
cond_stage_key: class_label
|
||||
image_size: 64
|
||||
channels: 3
|
||||
cond_stage_trainable: true
|
||||
conditioning_key: crossattn
|
||||
monitor: val/loss
|
||||
use_ema: False
|
||||
|
||||
unet_config:
|
||||
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
|
||||
params:
|
||||
image_size: 64
|
||||
in_channels: 3
|
||||
out_channels: 3
|
||||
model_channels: 192
|
||||
attention_resolutions:
|
||||
- 8
|
||||
- 4
|
||||
- 2
|
||||
num_res_blocks: 2
|
||||
channel_mult:
|
||||
- 1
|
||||
- 2
|
||||
- 3
|
||||
- 5
|
||||
num_heads: 1
|
||||
use_spatial_transformer: true
|
||||
transformer_depth: 1
|
||||
context_dim: 512
|
||||
|
||||
first_stage_config:
|
||||
target: ldm.models.autoencoder.VQModelInterface
|
||||
params:
|
||||
embed_dim: 3
|
||||
n_embed: 8192
|
||||
ddconfig:
|
||||
double_z: false
|
||||
z_channels: 3
|
||||
resolution: 256
|
||||
in_channels: 3
|
||||
out_ch: 3
|
||||
ch: 128
|
||||
ch_mult:
|
||||
- 1
|
||||
- 2
|
||||
- 4
|
||||
num_res_blocks: 2
|
||||
attn_resolutions: []
|
||||
dropout: 0.0
|
||||
lossconfig:
|
||||
target: torch.nn.Identity
|
||||
|
||||
cond_stage_config:
|
||||
target: ldm.modules.encoders.modules.ClassEmbedder
|
||||
params:
|
||||
n_classes: 1001
|
||||
embed_dim: 512
|
||||
key: class_label
|
@ -1,85 +0,0 @@
|
||||
model:
|
||||
base_learning_rate: 2.0e-06
|
||||
target: ldm.models.diffusion.ddpm.LatentDiffusion
|
||||
params:
|
||||
linear_start: 0.0015
|
||||
linear_end: 0.0195
|
||||
num_timesteps_cond: 1
|
||||
log_every_t: 200
|
||||
timesteps: 1000
|
||||
first_stage_key: image
|
||||
image_size: 64
|
||||
channels: 3
|
||||
monitor: val/loss_simple_ema
|
||||
unet_config:
|
||||
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
|
||||
params:
|
||||
image_size: 64
|
||||
in_channels: 3
|
||||
out_channels: 3
|
||||
model_channels: 224
|
||||
attention_resolutions:
|
||||
# note: this isn\t actually the resolution but
|
||||
# the downsampling factor, i.e. this corresnponds to
|
||||
# attention on spatial resolution 8,16,32, as the
|
||||
# spatial reolution of the latents is 64 for f4
|
||||
- 8
|
||||
- 4
|
||||
- 2
|
||||
num_res_blocks: 2
|
||||
channel_mult:
|
||||
- 1
|
||||
- 2
|
||||
- 3
|
||||
- 4
|
||||
num_head_channels: 32
|
||||
first_stage_config:
|
||||
target: ldm.models.autoencoder.VQModelInterface
|
||||
params:
|
||||
embed_dim: 3
|
||||
n_embed: 8192
|
||||
ckpt_path: configs/first_stage_models/vq-f4/model.yaml
|
||||
ddconfig:
|
||||
double_z: false
|
||||
z_channels: 3
|
||||
resolution: 256
|
||||
in_channels: 3
|
||||
out_ch: 3
|
||||
ch: 128
|
||||
ch_mult:
|
||||
- 1
|
||||
- 2
|
||||
- 4
|
||||
num_res_blocks: 2
|
||||
attn_resolutions: []
|
||||
dropout: 0.0
|
||||
lossconfig:
|
||||
target: torch.nn.Identity
|
||||
cond_stage_config: __is_unconditional__
|
||||
data:
|
||||
target: main.DataModuleFromConfig
|
||||
params:
|
||||
batch_size: 42
|
||||
num_workers: 5
|
||||
wrap: false
|
||||
train:
|
||||
target: taming.data.faceshq.FFHQTrain
|
||||
params:
|
||||
size: 256
|
||||
validation:
|
||||
target: taming.data.faceshq.FFHQValidation
|
||||
params:
|
||||
size: 256
|
||||
|
||||
|
||||
lightning:
|
||||
callbacks:
|
||||
image_logger:
|
||||
target: main.ImageLogger
|
||||
params:
|
||||
batch_frequency: 5000
|
||||
max_images: 8
|
||||
increase_log_steps: False
|
||||
|
||||
trainer:
|
||||
benchmark: True
|
@ -1,85 +0,0 @@
|
||||
model:
|
||||
base_learning_rate: 2.0e-06
|
||||
target: ldm.models.diffusion.ddpm.LatentDiffusion
|
||||
params:
|
||||
linear_start: 0.0015
|
||||
linear_end: 0.0195
|
||||
num_timesteps_cond: 1
|
||||
log_every_t: 200
|
||||
timesteps: 1000
|
||||
first_stage_key: image
|
||||
image_size: 64
|
||||
channels: 3
|
||||
monitor: val/loss_simple_ema
|
||||
unet_config:
|
||||
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
|
||||
params:
|
||||
image_size: 64
|
||||
in_channels: 3
|
||||
out_channels: 3
|
||||
model_channels: 224
|
||||
attention_resolutions:
|
||||
# note: this isn\t actually the resolution but
|
||||
# the downsampling factor, i.e. this corresnponds to
|
||||
# attention on spatial resolution 8,16,32, as the
|
||||
# spatial reolution of the latents is 64 for f4
|
||||
- 8
|
||||
- 4
|
||||
- 2
|
||||
num_res_blocks: 2
|
||||
channel_mult:
|
||||
- 1
|
||||
- 2
|
||||
- 3
|
||||
- 4
|
||||
num_head_channels: 32
|
||||
first_stage_config:
|
||||
target: ldm.models.autoencoder.VQModelInterface
|
||||
params:
|
||||
ckpt_path: configs/first_stage_models/vq-f4/model.yaml
|
||||
embed_dim: 3
|
||||
n_embed: 8192
|
||||
ddconfig:
|
||||
double_z: false
|
||||
z_channels: 3
|
||||
resolution: 256
|
||||
in_channels: 3
|
||||
out_ch: 3
|
||||
ch: 128
|
||||
ch_mult:
|
||||
- 1
|
||||
- 2
|
||||
- 4
|
||||
num_res_blocks: 2
|
||||
attn_resolutions: []
|
||||
dropout: 0.0
|
||||
lossconfig:
|
||||
target: torch.nn.Identity
|
||||
cond_stage_config: __is_unconditional__
|
||||
data:
|
||||
target: main.DataModuleFromConfig
|
||||
params:
|
||||
batch_size: 48
|
||||
num_workers: 5
|
||||
wrap: false
|
||||
train:
|
||||
target: ldm.data.lsun.LSUNBedroomsTrain
|
||||
params:
|
||||
size: 256
|
||||
validation:
|
||||
target: ldm.data.lsun.LSUNBedroomsValidation
|
||||
params:
|
||||
size: 256
|
||||
|
||||
|
||||
lightning:
|
||||
callbacks:
|
||||
image_logger:
|
||||
target: main.ImageLogger
|
||||
params:
|
||||
batch_frequency: 5000
|
||||
max_images: 8
|
||||
increase_log_steps: False
|
||||
|
||||
trainer:
|
||||
benchmark: True
|
@ -1,91 +0,0 @@
|
||||
model:
|
||||
base_learning_rate: 5.0e-5 # set to target_lr by starting main.py with '--scale_lr False'
|
||||
target: ldm.models.diffusion.ddpm.LatentDiffusion
|
||||
params:
|
||||
linear_start: 0.0015
|
||||
linear_end: 0.0155
|
||||
num_timesteps_cond: 1
|
||||
log_every_t: 200
|
||||
timesteps: 1000
|
||||
loss_type: l1
|
||||
first_stage_key: "image"
|
||||
cond_stage_key: "image"
|
||||
image_size: 32
|
||||
channels: 4
|
||||
cond_stage_trainable: False
|
||||
concat_mode: False
|
||||
scale_by_std: True
|
||||
monitor: 'val/loss_simple_ema'
|
||||
|
||||
scheduler_config: # 10000 warmup steps
|
||||
target: ldm.lr_scheduler.LambdaLinearScheduler
|
||||
params:
|
||||
warm_up_steps: [10000]
|
||||
cycle_lengths: [10000000000000]
|
||||
f_start: [1.e-6]
|
||||
f_max: [1.]
|
||||
f_min: [ 1.]
|
||||
|
||||
unet_config:
|
||||
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
|
||||
params:
|
||||
image_size: 32
|
||||
in_channels: 4
|
||||
out_channels: 4
|
||||
model_channels: 192
|
||||
attention_resolutions: [ 1, 2, 4, 8 ] # 32, 16, 8, 4
|
||||
num_res_blocks: 2
|
||||
channel_mult: [ 1,2,2,4,4 ] # 32, 16, 8, 4, 2
|
||||
num_heads: 8
|
||||
use_scale_shift_norm: True
|
||||
resblock_updown: True
|
||||
|
||||
first_stage_config:
|
||||
target: ldm.models.autoencoder.AutoencoderKL
|
||||
params:
|
||||
embed_dim: 4
|
||||
monitor: "val/rec_loss"
|
||||
ckpt_path: "models/first_stage_models/kl-f8/model.ckpt"
|
||||
ddconfig:
|
||||
double_z: True
|
||||
z_channels: 4
|
||||
resolution: 256
|
||||
in_channels: 3
|
||||
out_ch: 3
|
||||
ch: 128
|
||||
ch_mult: [ 1,2,4,4 ] # num_down = len(ch_mult)-1
|
||||
num_res_blocks: 2
|
||||
attn_resolutions: [ ]
|
||||
dropout: 0.0
|
||||
lossconfig:
|
||||
target: torch.nn.Identity
|
||||
|
||||
cond_stage_config: "__is_unconditional__"
|
||||
|
||||
data:
|
||||
target: main.DataModuleFromConfig
|
||||
params:
|
||||
batch_size: 96
|
||||
num_workers: 5
|
||||
wrap: False
|
||||
train:
|
||||
target: ldm.data.lsun.LSUNChurchesTrain
|
||||
params:
|
||||
size: 256
|
||||
validation:
|
||||
target: ldm.data.lsun.LSUNChurchesValidation
|
||||
params:
|
||||
size: 256
|
||||
|
||||
lightning:
|
||||
callbacks:
|
||||
image_logger:
|
||||
target: main.ImageLogger
|
||||
params:
|
||||
batch_frequency: 5000
|
||||
max_images: 8
|
||||
increase_log_steps: False
|
||||
|
||||
|
||||
trainer:
|
||||
benchmark: True
|
@ -1,18 +0,0 @@
|
||||
# This file describes the alternative machine learning models
|
||||
# available to the dream script.
|
||||
#
|
||||
# To add a new model, follow the examples below. Each
|
||||
# model requires a model config file, a weights file,
|
||||
# and the width and height of the images it
|
||||
# was trained on.
|
||||
|
||||
laion400m:
|
||||
config: configs/latent-diffusion/txt2img-1p4B-eval.yaml
|
||||
weights: models/ldm/text2img-large/model.ckpt
|
||||
width: 256
|
||||
height: 256
|
||||
stable-diffusion-1.4:
|
||||
config: configs/stable-diffusion/v1-inference.yaml
|
||||
weights: models/ldm/stable-diffusion-v1/model.ckpt
|
||||
width: 512
|
||||
height: 512
|
@ -1,68 +0,0 @@
|
||||
model:
|
||||
base_learning_rate: 0.0001
|
||||
target: ldm.models.diffusion.ddpm.LatentDiffusion
|
||||
params:
|
||||
linear_start: 0.0015
|
||||
linear_end: 0.015
|
||||
num_timesteps_cond: 1
|
||||
log_every_t: 200
|
||||
timesteps: 1000
|
||||
first_stage_key: jpg
|
||||
cond_stage_key: nix
|
||||
image_size: 48
|
||||
channels: 16
|
||||
cond_stage_trainable: false
|
||||
conditioning_key: crossattn
|
||||
monitor: val/loss_simple_ema
|
||||
scale_by_std: false
|
||||
scale_factor: 0.22765929
|
||||
unet_config:
|
||||
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
|
||||
params:
|
||||
image_size: 48
|
||||
in_channels: 16
|
||||
out_channels: 16
|
||||
model_channels: 448
|
||||
attention_resolutions:
|
||||
- 4
|
||||
- 2
|
||||
- 1
|
||||
num_res_blocks: 2
|
||||
channel_mult:
|
||||
- 1
|
||||
- 2
|
||||
- 3
|
||||
- 4
|
||||
use_scale_shift_norm: false
|
||||
resblock_updown: false
|
||||
num_head_channels: 32
|
||||
use_spatial_transformer: true
|
||||
transformer_depth: 1
|
||||
context_dim: 768
|
||||
use_checkpoint: true
|
||||
first_stage_config:
|
||||
target: ldm.models.autoencoder.AutoencoderKL
|
||||
params:
|
||||
monitor: val/rec_loss
|
||||
embed_dim: 16
|
||||
ddconfig:
|
||||
double_z: true
|
||||
z_channels: 16
|
||||
resolution: 256
|
||||
in_channels: 3
|
||||
out_ch: 3
|
||||
ch: 128
|
||||
ch_mult:
|
||||
- 1
|
||||
- 1
|
||||
- 2
|
||||
- 2
|
||||
- 4
|
||||
num_res_blocks: 2
|
||||
attn_resolutions:
|
||||
- 16
|
||||
dropout: 0.0
|
||||
lossconfig:
|
||||
target: torch.nn.Identity
|
||||
cond_stage_config:
|
||||
target: torch.nn.Identity
|
103
docker/Dockerfile
Normal file
@ -0,0 +1,103 @@
|
||||
# syntax=docker/dockerfile:1
|
||||
|
||||
ARG PYTHON_VERSION=3.9
|
||||
##################
|
||||
## base image ##
|
||||
##################
|
||||
FROM python:${PYTHON_VERSION}-slim AS python-base
|
||||
|
||||
LABEL org.opencontainers.image.authors="mauwii@outlook.de"
|
||||
|
||||
# prepare for buildkit cache
|
||||
RUN rm -f /etc/apt/apt.conf.d/docker-clean \
|
||||
&& echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' >/etc/apt/apt.conf.d/keep-cache
|
||||
|
||||
# Install necessary packages
|
||||
RUN \
|
||||
--mount=type=cache,target=/var/cache/apt,sharing=locked \
|
||||
--mount=type=cache,target=/var/lib/apt,sharing=locked \
|
||||
apt-get update \
|
||||
&& apt-get install -y \
|
||||
--no-install-recommends \
|
||||
libgl1-mesa-glx=20.3.* \
|
||||
libglib2.0-0=2.66.* \
|
||||
libopencv-dev=4.5.*
|
||||
|
||||
# set working directory and env
|
||||
ARG APPDIR=/usr/src
|
||||
ARG APPNAME=InvokeAI
|
||||
WORKDIR ${APPDIR}
|
||||
ENV PATH ${APPDIR}/${APPNAME}/bin:$PATH
|
||||
# Keeps Python from generating .pyc files in the container
|
||||
ENV PYTHONDONTWRITEBYTECODE 1
|
||||
# Turns off buffering for easier container logging
|
||||
ENV PYTHONUNBUFFERED 1
|
||||
# don't fall back to legacy build system
|
||||
ENV PIP_USE_PEP517=1
|
||||
|
||||
#######################
|
||||
## build pyproject ##
|
||||
#######################
|
||||
FROM python-base AS pyproject-builder
|
||||
|
||||
# Install dependencies
|
||||
RUN \
|
||||
--mount=type=cache,target=/var/cache/apt,sharing=locked \
|
||||
--mount=type=cache,target=/var/lib/apt,sharing=locked \
|
||||
apt-get update \
|
||||
&& apt-get install -y \
|
||||
--no-install-recommends \
|
||||
build-essential=12.9 \
|
||||
gcc=4:10.2.* \
|
||||
python3-dev=3.9.*
|
||||
|
||||
# prepare pip for buildkit cache
|
||||
ARG PIP_CACHE_DIR=/var/cache/buildkit/pip
|
||||
ENV PIP_CACHE_DIR ${PIP_CACHE_DIR}
|
||||
RUN mkdir -p ${PIP_CACHE_DIR}
|
||||
|
||||
# create virtual environment
|
||||
RUN --mount=type=cache,target=${PIP_CACHE_DIR},sharing=locked \
|
||||
python3 -m venv "${APPNAME}" \
|
||||
--upgrade-deps
|
||||
|
||||
# copy sources
|
||||
COPY --link . .
|
||||
|
||||
# install pyproject.toml
|
||||
ARG PIP_EXTRA_INDEX_URL
|
||||
ENV PIP_EXTRA_INDEX_URL ${PIP_EXTRA_INDEX_URL}
|
||||
RUN --mount=type=cache,target=${PIP_CACHE_DIR},sharing=locked \
|
||||
"${APPNAME}/bin/pip" install .
|
||||
|
||||
# build patchmatch
|
||||
RUN python3 -c "from patchmatch import patch_match"
|
||||
|
||||
#####################
|
||||
## runtime image ##
|
||||
#####################
|
||||
FROM python-base AS runtime
|
||||
|
||||
# Create a new user
|
||||
ARG UNAME=appuser
|
||||
RUN useradd \
|
||||
--no-log-init \
|
||||
-m \
|
||||
-U \
|
||||
"${UNAME}"
|
||||
|
||||
# create volume directory
|
||||
ARG VOLUME_DIR=/data
|
||||
RUN mkdir -p "${VOLUME_DIR}" \
|
||||
&& chown -R "${UNAME}" "${VOLUME_DIR}"
|
||||
|
||||
# setup runtime environment
|
||||
USER ${UNAME}
|
||||
COPY --chown=${UNAME} --from=pyproject-builder ${APPDIR}/${APPNAME} ${APPNAME}
|
||||
ENV INVOKEAI_ROOT ${VOLUME_DIR}
|
||||
ENV TRANSFORMERS_CACHE ${VOLUME_DIR}/.cache
|
||||
ENV INVOKE_MODEL_RECONFIGURE "--yes --default_only"
|
||||
EXPOSE 9090
|
||||
ENTRYPOINT [ "invokeai" ]
|
||||
CMD [ "--web", "--host", "0.0.0.0", "--port", "9090" ]
|
||||
VOLUME [ "${VOLUME_DIR}" ]
|
51
docker/build.sh
Executable file
@ -0,0 +1,51 @@
|
||||
#!/usr/bin/env bash
|
||||
set -e
|
||||
|
||||
# If you want to build a specific flavor, set the CONTAINER_FLAVOR environment variable
|
||||
# e.g. CONTAINER_FLAVOR=cpu ./build.sh
|
||||
# Possible Values are:
|
||||
# - cpu
|
||||
# - cuda
|
||||
# - rocm
|
||||
# Don't forget to also set it when executing run.sh
|
||||
# if it is not set, the script will try to detect the flavor by itself.
|
||||
#
|
||||
# Doc can be found here:
|
||||
# https://invoke-ai.github.io/InvokeAI/installation/040_INSTALL_DOCKER/
|
||||
|
||||
SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}")
|
||||
cd "$SCRIPTDIR" || exit 1
|
||||
|
||||
source ./env.sh
|
||||
|
||||
DOCKERFILE=${INVOKE_DOCKERFILE:-./Dockerfile}
|
||||
|
||||
# print the settings
|
||||
echo -e "You are using these values:\n"
|
||||
echo -e "Dockerfile:\t\t${DOCKERFILE}"
|
||||
echo -e "index-url:\t\t${PIP_EXTRA_INDEX_URL:-none}"
|
||||
echo -e "Volumename:\t\t${VOLUMENAME}"
|
||||
echo -e "Platform:\t\t${PLATFORM}"
|
||||
echo -e "Container Registry:\t${CONTAINER_REGISTRY}"
|
||||
echo -e "Container Repository:\t${CONTAINER_REPOSITORY}"
|
||||
echo -e "Container Tag:\t\t${CONTAINER_TAG}"
|
||||
echo -e "Container Flavor:\t${CONTAINER_FLAVOR}"
|
||||
echo -e "Container Image:\t${CONTAINER_IMAGE}\n"
|
||||
|
||||
# Create docker volume
|
||||
if [[ -n "$(docker volume ls -f name="${VOLUMENAME}" -q)" ]]; then
|
||||
echo -e "Volume already exists\n"
|
||||
else
|
||||
echo -n "creating docker volume "
|
||||
docker volume create "${VOLUMENAME}"
|
||||
fi
|
||||
|
||||
# Build Container
|
||||
DOCKER_BUILDKIT=1 docker build \
|
||||
--platform="${PLATFORM:-linux/amd64}" \
|
||||
--tag="${CONTAINER_IMAGE:-invokeai}" \
|
||||
${CONTAINER_FLAVOR:+--build-arg="CONTAINER_FLAVOR=${CONTAINER_FLAVOR}"} \
|
||||
${PIP_EXTRA_INDEX_URL:+--build-arg="PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}"} \
|
||||
${PIP_PACKAGE:+--build-arg="PIP_PACKAGE=${PIP_PACKAGE}"} \
|
||||
--file="${DOCKERFILE}" \
|
||||
..
|
51
docker/env.sh
Normal file
@ -0,0 +1,51 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
# This file is used to set environment variables for the build.sh and run.sh scripts.
|
||||
|
||||
# Try to detect the container flavor if no PIP_EXTRA_INDEX_URL got specified
|
||||
if [[ -z "$PIP_EXTRA_INDEX_URL" ]]; then
|
||||
|
||||
# Activate virtual environment if not already activated and exists
|
||||
if [[ -z $VIRTUAL_ENV ]]; then
|
||||
[[ -e "$(dirname "${BASH_SOURCE[0]}")/../.venv/bin/activate" ]] \
|
||||
&& source "$(dirname "${BASH_SOURCE[0]}")/../.venv/bin/activate" \
|
||||
&& echo "Activated virtual environment: $VIRTUAL_ENV"
|
||||
fi
|
||||
|
||||
# Decide which container flavor to build if not specified
|
||||
if [[ -z "$CONTAINER_FLAVOR" ]] && python -c "import torch" &>/dev/null; then
|
||||
# Check for CUDA and ROCm
|
||||
CUDA_AVAILABLE=$(python -c "import torch;print(torch.cuda.is_available())")
|
||||
ROCM_AVAILABLE=$(python -c "import torch;print(torch.version.hip is not None)")
|
||||
if [[ "${CUDA_AVAILABLE}" == "True" ]]; then
|
||||
CONTAINER_FLAVOR="cuda"
|
||||
elif [[ "${ROCM_AVAILABLE}" == "True" ]]; then
|
||||
CONTAINER_FLAVOR="rocm"
|
||||
else
|
||||
CONTAINER_FLAVOR="cpu"
|
||||
fi
|
||||
fi
|
||||
|
||||
# Set PIP_EXTRA_INDEX_URL based on container flavor
|
||||
if [[ "$CONTAINER_FLAVOR" == "rocm" ]]; then
|
||||
PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/rocm"
|
||||
elif [[ "$CONTAINER_FLAVOR" == "cpu" ]]; then
|
||||
PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu"
|
||||
# elif [[ -z "$CONTAINER_FLAVOR" || "$CONTAINER_FLAVOR" == "cuda" ]]; then
|
||||
# PIP_PACKAGE=${PIP_PACKAGE-".[xformers]"}
|
||||
fi
|
||||
fi
|
||||
|
||||
# Variables shared by build.sh and run.sh
|
||||
REPOSITORY_NAME="${REPOSITORY_NAME-$(basename "$(git rev-parse --show-toplevel)")}"
|
||||
REPOSITORY_NAME="${REPOSITORY_NAME,,}"
|
||||
VOLUMENAME="${VOLUMENAME-"${REPOSITORY_NAME}_data"}"
|
||||
ARCH="${ARCH-$(uname -m)}"
|
||||
PLATFORM="${PLATFORM-linux/${ARCH}}"
|
||||
INVOKEAI_BRANCH="${INVOKEAI_BRANCH-$(git branch --show)}"
|
||||
CONTAINER_REGISTRY="${CONTAINER_REGISTRY-"ghcr.io"}"
|
||||
CONTAINER_REPOSITORY="${CONTAINER_REPOSITORY-"$(whoami)/${REPOSITORY_NAME}"}"
|
||||
CONTAINER_FLAVOR="${CONTAINER_FLAVOR-cuda}"
|
||||
CONTAINER_TAG="${CONTAINER_TAG-"${INVOKEAI_BRANCH##*/}-${CONTAINER_FLAVOR}"}"
|
||||
CONTAINER_IMAGE="${CONTAINER_REGISTRY}/${CONTAINER_REPOSITORY}:${CONTAINER_TAG}"
|
||||
CONTAINER_IMAGE="${CONTAINER_IMAGE,,}"
|
41
docker/run.sh
Executable file
@ -0,0 +1,41 @@
|
||||
#!/usr/bin/env bash
|
||||
set -e
|
||||
|
||||
# How to use: https://invoke-ai.github.io/InvokeAI/installation/040_INSTALL_DOCKER/
|
||||
|
||||
SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}")
|
||||
cd "$SCRIPTDIR" || exit 1
|
||||
|
||||
source ./env.sh
|
||||
|
||||
# Create outputs directory if it does not exist
|
||||
[[ -d ./outputs ]] || mkdir ./outputs
|
||||
|
||||
echo -e "You are using these values:\n"
|
||||
echo -e "Volumename:\t${VOLUMENAME}"
|
||||
echo -e "Invokeai_tag:\t${CONTAINER_IMAGE}"
|
||||
echo -e "local Models:\t${MODELSPATH:-unset}\n"
|
||||
|
||||
docker run \
|
||||
--interactive \
|
||||
--tty \
|
||||
--rm \
|
||||
--platform="${PLATFORM}" \
|
||||
--name="${REPOSITORY_NAME,,}" \
|
||||
--hostname="${REPOSITORY_NAME,,}" \
|
||||
--mount=source="${VOLUMENAME}",target=/data \
|
||||
--mount type=bind,source="$(pwd)"/outputs,target=/data/outputs \
|
||||
${MODELSPATH:+--mount="type=bind,source=${MODELSPATH},target=/data/models"} \
|
||||
${HUGGING_FACE_HUB_TOKEN:+--env="HUGGING_FACE_HUB_TOKEN=${HUGGING_FACE_HUB_TOKEN}"} \
|
||||
--publish=9090:9090 \
|
||||
--cap-add=sys_nice \
|
||||
${GPU_FLAGS:+--gpus="${GPU_FLAGS}"} \
|
||||
"${CONTAINER_IMAGE}" ${@:+$@}
|
||||
|
||||
# Remove Trash folder
|
||||
for f in outputs/.Trash*; do
|
||||
if [ -e "$f" ]; then
|
||||
rm -Rf "$f"
|
||||
break
|
||||
fi
|
||||
done
|
5
docs/.markdownlint.jsonc
Normal file
@ -0,0 +1,5 @@
|
||||
{
|
||||
"MD046": false,
|
||||
"MD007": false,
|
||||
"MD030": false
|
||||
}
|
587
docs/CHANGELOG.md
Normal file
@ -0,0 +1,587 @@
|
||||
---
|
||||
title: Changelog
|
||||
---
|
||||
|
||||
# :octicons-log-16: **Changelog**
|
||||
|
||||
## v2.3.0 <small>(15 January 2023)</small>
|
||||
|
||||
**Transition to diffusers
|
||||
|
||||
Version 2.3 provides support for both the traditional `.ckpt` weight
|
||||
checkpoint files as well as the HuggingFace `diffusers` format. This
|
||||
introduces several changes you should know about.
|
||||
|
||||
1. The models.yaml format has been updated. There are now two
|
||||
different type of configuration stanza. The traditional ckpt
|
||||
one will look like this, with a `format` of `ckpt` and a
|
||||
`weights` field that points to the absolute or ROOTDIR-relative
|
||||
location of the ckpt file.
|
||||
|
||||
```
|
||||
inpainting-1.5:
|
||||
description: RunwayML SD 1.5 model optimized for inpainting (4.27 GB)
|
||||
repo_id: runwayml/stable-diffusion-inpainting
|
||||
format: ckpt
|
||||
width: 512
|
||||
height: 512
|
||||
weights: models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt
|
||||
config: configs/stable-diffusion/v1-inpainting-inference.yaml
|
||||
vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
|
||||
```
|
||||
|
||||
A configuration stanza for a diffusers model hosted at HuggingFace will look like this,
|
||||
with a `format` of `diffusers` and a `repo_id` that points to the
|
||||
repository ID of the model on HuggingFace:
|
||||
|
||||
```
|
||||
stable-diffusion-2.1:
|
||||
description: Stable Diffusion version 2.1 diffusers model (5.21 GB)
|
||||
repo_id: stabilityai/stable-diffusion-2-1
|
||||
format: diffusers
|
||||
```
|
||||
|
||||
A configuration stanza for a diffuers model stored locally should
|
||||
look like this, with a `format` of `diffusers`, but a `path` field
|
||||
that points at the directory that contains `model_index.json`:
|
||||
|
||||
```
|
||||
waifu-diffusion:
|
||||
description: Latest waifu diffusion 1.4
|
||||
format: diffusers
|
||||
path: models/diffusers/hakurei-haifu-diffusion-1.4
|
||||
```
|
||||
|
||||
2. In order of precedence, InvokeAI will now use HF_HOME, then
|
||||
XDG_CACHE_HOME, then finally default to `ROOTDIR/models` to
|
||||
store HuggingFace diffusers models.
|
||||
|
||||
Consequently, the format of the models directory has changed to
|
||||
mimic the HuggingFace cache directory. When HF_HOME and XDG_HOME
|
||||
are not set, diffusers models are now automatically downloaded
|
||||
and retrieved from the directory `ROOTDIR/models/diffusers`,
|
||||
while other models are stored in the directory
|
||||
`ROOTDIR/models/hub`. This organization is the same as that used
|
||||
by HuggingFace for its cache management.
|
||||
|
||||
This allows you to share diffusers and ckpt model files easily with
|
||||
other machine learning applications that use the HuggingFace
|
||||
libraries. To do this, set the environment variable HF_HOME
|
||||
before starting up InvokeAI to tell it what directory to
|
||||
cache models in. To tell InvokeAI to use the standard HuggingFace
|
||||
cache directory, you would set HF_HOME like this (Linux/Mac):
|
||||
|
||||
`export HF_HOME=~/.cache/huggingface`
|
||||
|
||||
Both HuggingFace and InvokeAI will fall back to the XDG_CACHE_HOME
|
||||
environment variable if HF_HOME is not set; this path
|
||||
takes precedence over `ROOTDIR/models` to allow for the same sharing
|
||||
with other machine learning applications that use HuggingFace
|
||||
libraries.
|
||||
|
||||
3. If you upgrade to InvokeAI 2.3.* from an earlier version, there
|
||||
will be a one-time migration from the old models directory format
|
||||
to the new one. You will see a message about this the first time
|
||||
you start `invoke.py`.
|
||||
|
||||
4. Both the front end back ends of the model manager have been
|
||||
rewritten to accommodate diffusers. You can import models using
|
||||
their local file path, using their URLs, or their HuggingFace
|
||||
repo_ids. On the command line, all these syntaxes work:
|
||||
|
||||
```
|
||||
!import_model stabilityai/stable-diffusion-2-1-base
|
||||
!import_model /opt/sd-models/sd-1.4.ckpt
|
||||
!import_model https://huggingface.co/Fictiverse/Stable_Diffusion_PaperCut_Model/blob/main/PaperCut_v1.ckpt
|
||||
```
|
||||
|
||||
**KNOWN BUGS (15 January 2023)
|
||||
|
||||
1. On CUDA systems, the 768 pixel stable-diffusion-2.0 and
|
||||
stable-diffusion-2.1 models can only be run as `diffusers` models
|
||||
when the `xformer` library is installed and configured. Without
|
||||
`xformers`, InvokeAI returns black images.
|
||||
|
||||
2. Inpainting and outpainting have regressed in quality.
|
||||
|
||||
Both these issues are being actively worked on.
|
||||
|
||||
## v2.2.4 <small>(11 December 2022)</small>
|
||||
|
||||
**the `invokeai` directory**
|
||||
|
||||
Previously there were two directories to worry about, the directory that
|
||||
contained the InvokeAI source code and the launcher scripts, and the `invokeai`
|
||||
directory that contained the models files, embeddings, configuration and
|
||||
outputs. With the 2.2.4 release, this dual system is done away with, and
|
||||
everything, including the `invoke.bat` and `invoke.sh` launcher scripts, now
|
||||
live in a directory named `invokeai`. By default this directory is located in
|
||||
your home directory (e.g. `\Users\yourname` on Windows), but you can select
|
||||
where it goes at install time.
|
||||
|
||||
After installation, you can delete the install directory (the one that the zip
|
||||
file creates when it unpacks). Do **not** delete or move the `invokeai`
|
||||
directory!
|
||||
|
||||
**Initialization file `invokeai/invokeai.init`**
|
||||
|
||||
You can place frequently-used startup options in this file, such as the default
|
||||
number of steps or your preferred sampler. To keep everything in one place, this
|
||||
file has now been moved into the `invokeai` directory and is named
|
||||
`invokeai.init`.
|
||||
|
||||
**To update from Version 2.2.3**
|
||||
|
||||
The easiest route is to download and unpack one of the 2.2.4 installer files.
|
||||
When it asks you for the location of the `invokeai` runtime directory, respond
|
||||
with the path to the directory that contains your 2.2.3 `invokeai`. That is, if
|
||||
`invokeai` lives at `C:\Users\fred\invokeai`, then answer with `C:\Users\fred`
|
||||
and answer "Y" when asked if you want to reuse the directory.
|
||||
|
||||
The `update.sh` (`update.bat`) script that came with the 2.2.3 source installer
|
||||
does not know about the new directory layout and won't be fully functional.
|
||||
|
||||
**To update to 2.2.5 (and beyond) there's now an update path**
|
||||
|
||||
As they become available, you can update to more recent versions of InvokeAI
|
||||
using an `update.sh` (`update.bat`) script located in the `invokeai` directory.
|
||||
Running it without any arguments will install the most recent version of
|
||||
InvokeAI. Alternatively, you can get set releases by running the `update.sh`
|
||||
script with an argument in the command shell. This syntax accepts the path to
|
||||
the desired release's zip file, which you can find by clicking on the green
|
||||
"Code" button on this repository's home page.
|
||||
|
||||
**Other 2.2.4 Improvements**
|
||||
|
||||
- Fix InvokeAI GUI initialization by @addianto in #1687
|
||||
- fix link in documentation by @lstein in #1728
|
||||
- Fix broken link by @ShawnZhong in #1736
|
||||
- Remove reference to binary installer by @lstein in #1731
|
||||
- documentation fixes for 2.2.3 by @lstein in #1740
|
||||
- Modify installer links to point closer to the source installer by @ebr in
|
||||
#1745
|
||||
- add documentation warning about 1650/60 cards by @lstein in #1753
|
||||
- Fix Linux source URL in installation docs by @andybearman in #1756
|
||||
- Make install instructions discoverable in readme by @damian0815 in #1752
|
||||
- typo fix by @ofirkris in #1755
|
||||
- Non-interactive model download (support HUGGINGFACE_TOKEN) by @ebr in #1578
|
||||
- fix(srcinstall): shell installer - cp scripts instead of linking by @tildebyte
|
||||
in #1765
|
||||
- stability and usage improvements to binary & source installers by @lstein in
|
||||
#1760
|
||||
- fix off-by-one bug in cross-attention-control by @damian0815 in #1774
|
||||
- Eventually update APP_VERSION to 2.2.3 by @spezialspezial in #1768
|
||||
- invoke script cds to its location before running by @lstein in #1805
|
||||
- Make PaperCut and VoxelArt models load again by @lstein in #1730
|
||||
- Fix --embedding_directory / --embedding_path not working by @blessedcoolant in
|
||||
#1817
|
||||
- Clean up readme by @hipsterusername in #1820
|
||||
- Optimized Docker build with support for external working directory by @ebr in
|
||||
#1544
|
||||
- disable pushing the cloud container by @mauwii in #1831
|
||||
- Fix docker push github action and expand with additional metadata by @ebr in
|
||||
#1837
|
||||
- Fix Broken Link To Notebook by @VedantMadane in #1821
|
||||
- Account for flat models by @spezialspezial in #1766
|
||||
- Update invoke.bat.in isolate environment variables by @lynnewu in #1833
|
||||
- Arch Linux Specific PatchMatch Instructions & fixing conda install on linux by
|
||||
@SammCheese in #1848
|
||||
- Make force free GPU memory work in img2img by @addianto in #1844
|
||||
- New installer by @lstein
|
||||
|
||||
## v2.2.3 <small>(2 December 2022)</small>
|
||||
|
||||
!!! Note
|
||||
|
||||
This point release removes references to the binary installer from the
|
||||
installation guide. The binary installer is not stable at the current
|
||||
time. First time users are encouraged to use the "source" installer as
|
||||
described in [Installing InvokeAI with the Source Installer](installation/deprecated_documentation/INSTALL_SOURCE.md)
|
||||
|
||||
With InvokeAI 2.2, this project now provides enthusiasts and professionals a
|
||||
robust workflow solution for creating AI-generated and human facilitated
|
||||
compositions. Additional enhancements have been made as well, improving safety,
|
||||
ease of use, and installation.
|
||||
|
||||
Optimized for efficiency, InvokeAI needs only ~3.5GB of VRAM to generate a
|
||||
512x768 image (and less for smaller images), and is compatible with
|
||||
Windows/Linux/Mac (M1 & M2).
|
||||
|
||||
You can see the [release video](https://youtu.be/hIYBfDtKaus) here, which
|
||||
introduces the main WebUI enhancement for version 2.2 -
|
||||
[The Unified Canvas](features/UNIFIED_CANVAS.md). This new workflow is the
|
||||
biggest enhancement added to the WebUI to date, and unlocks a stunning amount of
|
||||
potential for users to create and iterate on their creations. The following
|
||||
sections describe what's new for InvokeAI.
|
||||
|
||||
## v2.2.2 <small>(30 November 2022)</small>
|
||||
|
||||
!!! note
|
||||
|
||||
The binary installer is not ready for prime time. First time users are recommended to install via the "source" installer accessible through the links at the bottom of this page.****
|
||||
|
||||
With InvokeAI 2.2, this project now provides enthusiasts and professionals a
|
||||
robust workflow solution for creating AI-generated and human facilitated
|
||||
compositions. Additional enhancements have been made as well, improving safety,
|
||||
ease of use, and installation.
|
||||
|
||||
Optimized for efficiency, InvokeAI needs only ~3.5GB of VRAM to generate a
|
||||
512x768 image (and less for smaller images), and is compatible with
|
||||
Windows/Linux/Mac (M1 & M2).
|
||||
|
||||
You can see the [release video](https://youtu.be/hIYBfDtKaus) here, which
|
||||
introduces the main WebUI enhancement for version 2.2 -
|
||||
[The Unified Canvas](https://invoke-ai.github.io/InvokeAI/features/UNIFIED_CANVAS/).
|
||||
This new workflow is the biggest enhancement added to the WebUI to date, and
|
||||
unlocks a stunning amount of potential for users to create and iterate on their
|
||||
creations. The following sections describe what's new for InvokeAI.
|
||||
|
||||
## v2.2.0 <small>(2 December 2022)</small>
|
||||
|
||||
With InvokeAI 2.2, this project now provides enthusiasts and professionals a
|
||||
robust workflow solution for creating AI-generated and human facilitated
|
||||
compositions. Additional enhancements have been made as well, improving safety,
|
||||
ease of use, and installation.
|
||||
|
||||
Optimized for efficiency, InvokeAI needs only ~3.5GB of VRAM to generate a
|
||||
512x768 image (and less for smaller images), and is compatible with
|
||||
Windows/Linux/Mac (M1 & M2).
|
||||
|
||||
You can see the [release video](https://youtu.be/hIYBfDtKaus) here, which
|
||||
introduces the main WebUI enhancement for version 2.2 -
|
||||
[The Unified Canvas](features/UNIFIED_CANVAS.md). This new workflow is the
|
||||
biggest enhancement added to the WebUI to date, and unlocks a stunning amount of
|
||||
potential for users to create and iterate on their creations. The following
|
||||
sections describe what's new for InvokeAI.
|
||||
|
||||
## v2.1.3 <small>(13 November 2022)</small>
|
||||
|
||||
- A choice of installer scripts that automate installation and configuration.
|
||||
See
|
||||
[Installation](installation/index.md).
|
||||
- A streamlined manual installation process that works for both Conda and
|
||||
PIP-only installs. See
|
||||
[Manual Installation](installation/020_INSTALL_MANUAL.md).
|
||||
- The ability to save frequently-used startup options (model to load, steps,
|
||||
sampler, etc) in a `.invokeai` file. See
|
||||
[Client](features/CLI.md)
|
||||
- Support for AMD GPU cards (non-CUDA) on Linux machines.
|
||||
- Multiple bugs and edge cases squashed.
|
||||
|
||||
## v2.1.0 <small>(2 November 2022)</small>
|
||||
|
||||
- update mac instructions to use invokeai for env name by @willwillems in #1030
|
||||
- Update .gitignore by @blessedcoolant in #1040
|
||||
- reintroduce fix for m1 from #579 missing after merge by @skurovec in #1056
|
||||
- Update Stable_Diffusion_AI_Notebook.ipynb (Take 2) by @ChloeL19 in #1060
|
||||
- Print out the device type which is used by @manzke in #1073
|
||||
- Hires Addition by @hipsterusername in #1063
|
||||
- fix for "1 leaked semaphore objects to clean up at shutdown" on M1 by
|
||||
@skurovec in #1081
|
||||
- Forward dream.py to invoke.py using the same interpreter, add deprecation
|
||||
warning by @db3000 in #1077
|
||||
- fix noisy images at high step counts by @lstein in #1086
|
||||
- Generalize facetool strength argument by @db3000 in #1078
|
||||
- Enable fast switching among models at the invoke> command line by @lstein in
|
||||
#1066
|
||||
- Fix Typo, committed changing ldm environment to invokeai by @jdries3 in #1095
|
||||
- Update generate.py by @unreleased in #1109
|
||||
- Update 'ldm' env to 'invokeai' in troubleshooting steps by @19wolf in #1125
|
||||
- Fixed documentation typos and resolved merge conflicts by @rupeshs in #1123
|
||||
- Fix broken doc links, fix malaprop in the project subtitle by @majick in #1131
|
||||
- Only output facetool parameters if enhancing faces by @db3000 in #1119
|
||||
- Update gitignore to ignore codeformer weights at new location by
|
||||
@spezialspezial in #1136
|
||||
- fix links to point to invoke-ai.github.io #1117 by @mauwii in #1143
|
||||
- Rework-mkdocs by @mauwii in #1144
|
||||
- add option to CLI and pngwriter that allows user to set PNG compression level
|
||||
by @lstein in #1127
|
||||
- Fix img2img DDIM index out of bound by @wfng92 in #1137
|
||||
- Fix gh actions by @mauwii in #1128
|
||||
- update mac instructions to use invokeai for env name by @willwillems in #1030
|
||||
- Update .gitignore by @blessedcoolant in #1040
|
||||
- reintroduce fix for m1 from #579 missing after merge by @skurovec in #1056
|
||||
- Update Stable_Diffusion_AI_Notebook.ipynb (Take 2) by @ChloeL19 in #1060
|
||||
- Print out the device type which is used by @manzke in #1073
|
||||
- Hires Addition by @hipsterusername in #1063
|
||||
- fix for "1 leaked semaphore objects to clean up at shutdown" on M1 by
|
||||
@skurovec in #1081
|
||||
- Forward dream.py to invoke.py using the same interpreter, add deprecation
|
||||
warning by @db3000 in #1077
|
||||
- fix noisy images at high step counts by @lstein in #1086
|
||||
- Generalize facetool strength argument by @db3000 in #1078
|
||||
- Enable fast switching among models at the invoke> command line by @lstein in
|
||||
#1066
|
||||
- Fix Typo, committed changing ldm environment to invokeai by @jdries3 in #1095
|
||||
- Fixed documentation typos and resolved merge conflicts by @rupeshs in #1123
|
||||
- Only output facetool parameters if enhancing faces by @db3000 in #1119
|
||||
- add option to CLI and pngwriter that allows user to set PNG compression level
|
||||
by @lstein in #1127
|
||||
- Fix img2img DDIM index out of bound by @wfng92 in #1137
|
||||
- Add text prompt to inpaint mask support by @lstein in #1133
|
||||
- Respect http[s] protocol when making socket.io middleware by @damian0815 in
|
||||
#976
|
||||
- WebUI: Adds Codeformer support by @psychedelicious in #1151
|
||||
- Skips normalizing prompts for web UI metadata by @psychedelicious in #1165
|
||||
- Add Asymmetric Tiling by @carson-katri in #1132
|
||||
- Web UI: Increases max CFG Scale to 200 by @psychedelicious in #1172
|
||||
- Corrects color channels in face restoration; Fixes #1167 by @psychedelicious
|
||||
in #1175
|
||||
- Flips channels using array slicing instead of using OpenCV by @psychedelicious
|
||||
in #1178
|
||||
- Fix typo in docs: s/Formally/Formerly by @noodlebox in #1176
|
||||
- fix clipseg loading problems by @lstein in #1177
|
||||
- Correct color channels in upscale using array slicing by @wfng92 in #1181
|
||||
- Web UI: Filters existing images when adding new images; Fixes #1085 by
|
||||
@psychedelicious in #1171
|
||||
- fix a number of bugs in textual inversion by @lstein in #1190
|
||||
- Improve !fetch, add !replay command by @ArDiouscuros in #882
|
||||
- Fix generation of image with s>1000 by @holstvoogd in #951
|
||||
- Web UI: Gallery improvements by @psychedelicious in #1198
|
||||
- Update CLI.md by @krummrey in #1211
|
||||
- outcropping improvements by @lstein in #1207
|
||||
- add support for loading VAE autoencoders by @lstein in #1216
|
||||
- remove duplicate fix_func for MPS by @wfng92 in #1210
|
||||
- Metadata storage and retrieval fixes by @lstein in #1204
|
||||
- nix: add shell.nix file by @Cloudef in #1170
|
||||
- Web UI: Changes vite dist asset paths to relative by @psychedelicious in #1185
|
||||
- Web UI: Removes isDisabled from PromptInput by @psychedelicious in #1187
|
||||
- Allow user to generate images with initial noise as on M1 / mps system by
|
||||
@ArDiouscuros in #981
|
||||
- feat: adding filename format template by @plucked in #968
|
||||
- Web UI: Fixes broken bundle by @psychedelicious in #1242
|
||||
- Support runwayML custom inpainting model by @lstein in #1243
|
||||
- Update IMG2IMG.md by @talitore in #1262
|
||||
- New dockerfile - including a build- and a run- script as well as a GH-Action
|
||||
by @mauwii in #1233
|
||||
- cut over from karras to model noise schedule for higher steps by @lstein in
|
||||
#1222
|
||||
- Prompt tweaks by @lstein in #1268
|
||||
- Outpainting implementation by @Kyle0654 in #1251
|
||||
- fixing aspect ratio on hires by @tjennings in #1249
|
||||
- Fix-build-container-action by @mauwii in #1274
|
||||
- handle all unicode characters by @damian0815 in #1276
|
||||
- adds models.user.yml to .gitignore by @JakeHL in #1281
|
||||
- remove debug branch, set fail-fast to false by @mauwii in #1284
|
||||
- Protect-secrets-on-pr by @mauwii in #1285
|
||||
- Web UI: Adds initial inpainting implementation by @psychedelicious in #1225
|
||||
- fix environment-mac.yml - tested on x64 and arm64 by @mauwii in #1289
|
||||
- Use proper authentication to download model by @mauwii in #1287
|
||||
- Prevent indexing error for mode RGB by @spezialspezial in #1294
|
||||
- Integrate sd-v1-5 model into test matrix (easily expandable), remove
|
||||
unecesarry caches by @mauwii in #1293
|
||||
- add --no-interactive to configure_invokeai step by @mauwii in #1302
|
||||
- 1-click installer and updater. Uses micromamba to install git and conda into a
|
||||
contained environment (if necessary) before running the normal installation
|
||||
script by @cmdr2 in #1253
|
||||
- configure_invokeai.py script downloads the weight files by @lstein in #1290
|
||||
|
||||
## v2.0.1 <small>(13 October 2022)</small>
|
||||
|
||||
- fix noisy images at high step count when using k\* samplers
|
||||
- dream.py script now calls invoke.py module directly rather than via a new
|
||||
python process (which could break the environment)
|
||||
|
||||
## v2.0.0 <small>(9 October 2022)</small>
|
||||
|
||||
- `dream.py` script renamed `invoke.py`. A `dream.py` script wrapper remains for
|
||||
backward compatibility.
|
||||
- Completely new WebGUI - launch with `python3 scripts/invoke.py --web`
|
||||
- Support for [inpainting](features/INPAINTING.md) and
|
||||
[outpainting](features/OUTPAINTING.md)
|
||||
- img2img runs on all k\* samplers
|
||||
- Support for
|
||||
[negative prompts](features/PROMPTS.md#negative-and-unconditioned-prompts)
|
||||
- Support for CodeFormer face reconstruction
|
||||
- Support for Textual Inversion on Macintoshes
|
||||
- Support in both WebGUI and CLI for
|
||||
[post-processing of previously-generated images](features/POSTPROCESS.md)
|
||||
using facial reconstruction, ESRGAN upscaling, outcropping (similar to DALL-E
|
||||
infinite canvas), and "embiggen" upscaling. See the `!fix` command.
|
||||
- New `--hires` option on `invoke>` line allows
|
||||
[larger images to be created without duplicating elements](features/CLI.md#this-is-an-example-of-txt2img),
|
||||
at the cost of some performance.
|
||||
- New `--perlin` and `--threshold` options allow you to add and control
|
||||
variation during image generation (see
|
||||
[Thresholding and Perlin Noise Initialization](features/OTHER.md#thresholding-and-perlin-noise-initialization-options))
|
||||
- Extensive metadata now written into PNG files, allowing reliable regeneration
|
||||
of images and tweaking of previous settings.
|
||||
- Command-line completion in `invoke.py` now works on Windows, Linux and Mac
|
||||
platforms.
|
||||
- Improved [command-line completion behavior](features/CLI.md) New commands
|
||||
added:
|
||||
- List command-line history with `!history`
|
||||
- Search command-line history with `!search`
|
||||
- Clear history with `!clear`
|
||||
- Deprecated `--full_precision` / `-F`. Simply omit it and `invoke.py` will auto
|
||||
configure. To switch away from auto use the new flag like
|
||||
`--precision=float32`.
|
||||
|
||||
## v1.14 <small>(11 September 2022)</small>
|
||||
|
||||
- Memory optimizations for small-RAM cards. 512x512 now possible on 4 GB GPUs.
|
||||
- Full support for Apple hardware with M1 or M2 chips.
|
||||
- Add "seamless mode" for circular tiling of image. Generates beautiful effects.
|
||||
([prixt](https://github.com/prixt)).
|
||||
- Inpainting support.
|
||||
- Improved web server GUI.
|
||||
- Lots of code and documentation cleanups.
|
||||
|
||||
## v1.13 <small>(3 September 2022)</small>
|
||||
|
||||
- Support image variations (see [VARIATIONS](features/VARIATIONS.md)
|
||||
([Kevin Gibbons](https://github.com/bakkot) and many contributors and
|
||||
reviewers)
|
||||
- Supports a Google Colab notebook for a standalone server running on Google
|
||||
hardware [Arturo Mendivil](https://github.com/artmen1516)
|
||||
- WebUI supports GFPGAN/ESRGAN facial reconstruction and upscaling
|
||||
[Kevin Gibbons](https://github.com/bakkot)
|
||||
- WebUI supports incremental display of in-progress images during generation
|
||||
[Kevin Gibbons](https://github.com/bakkot)
|
||||
- A new configuration file scheme that allows new models (including upcoming
|
||||
stable-diffusion-v1.5) to be added without altering the code.
|
||||
([David Wager](https://github.com/maddavid12))
|
||||
- Can specify --grid on invoke.py command line as the default.
|
||||
- Miscellaneous internal bug and stability fixes.
|
||||
- Works on M1 Apple hardware.
|
||||
- Multiple bug fixes.
|
||||
|
||||
---
|
||||
|
||||
## v1.12 <small>(28 August 2022)</small>
|
||||
|
||||
- Improved file handling, including ability to read prompts from standard input.
|
||||
(kudos to [Yunsaki](https://github.com/yunsaki)
|
||||
- The web server is now integrated with the invoke.py script. Invoke by adding
|
||||
--web to the invoke.py command arguments.
|
||||
- Face restoration and upscaling via GFPGAN and Real-ESGAN are now automatically
|
||||
enabled if the GFPGAN directory is located as a sibling to Stable Diffusion.
|
||||
VRAM requirements are modestly reduced. Thanks to both
|
||||
[Blessedcoolant](https://github.com/blessedcoolant) and
|
||||
[Oceanswave](https://github.com/oceanswave) for their work on this.
|
||||
- You can now swap samplers on the invoke> command line.
|
||||
[Blessedcoolant](https://github.com/blessedcoolant)
|
||||
|
||||
---
|
||||
|
||||
## v1.11 <small>(26 August 2022)</small>
|
||||
|
||||
- NEW FEATURE: Support upscaling and face enhancement using the GFPGAN module.
|
||||
(kudos to [Oceanswave](https://github.com/Oceanswave)
|
||||
- You now can specify a seed of -1 to use the previous image's seed, -2 to use
|
||||
the seed for the image generated before that, etc. Seed memory only extends
|
||||
back to the previous command, but will work on all images generated with the
|
||||
-n# switch.
|
||||
- Variant generation support temporarily disabled pending more general solution.
|
||||
- Created a feature branch named **yunsaki-morphing-invoke** which adds
|
||||
experimental support for iteratively modifying the prompt and its parameters.
|
||||
Please
|
||||
see[Pull Request #86](https://github.com/lstein/stable-diffusion/pull/86) for
|
||||
a synopsis of how this works. Note that when this feature is eventually added
|
||||
to the main branch, it will may be modified significantly.
|
||||
|
||||
---
|
||||
|
||||
## v1.10 <small>(25 August 2022)</small>
|
||||
|
||||
- A barebones but fully functional interactive web server for online generation
|
||||
of txt2img and img2img.
|
||||
|
||||
---
|
||||
|
||||
## v1.09 <small>(24 August 2022)</small>
|
||||
|
||||
- A new -v option allows you to generate multiple variants of an initial image
|
||||
in img2img mode. (kudos to [Oceanswave](https://github.com/Oceanswave).
|
||||
[ See this discussion in the PR for examples and details on use](https://github.com/lstein/stable-diffusion/pull/71#issuecomment-1226700810))
|
||||
- Added ability to personalize text to image generation (kudos to
|
||||
[Oceanswave](https://github.com/Oceanswave) and
|
||||
[nicolai256](https://github.com/nicolai256))
|
||||
- Enabled all of the samplers from k_diffusion
|
||||
|
||||
---
|
||||
|
||||
## v1.08 <small>(24 August 2022)</small>
|
||||
|
||||
- Escape single quotes on the invoke> command before trying to parse. This
|
||||
avoids parse errors.
|
||||
- Removed instruction to get Python3.8 as first step in Windows install.
|
||||
Anaconda3 does it for you.
|
||||
- Added bounds checks for numeric arguments that could cause crashes.
|
||||
- Cleaned up the copyright and license agreement files.
|
||||
|
||||
---
|
||||
|
||||
## v1.07 <small>(23 August 2022)</small>
|
||||
|
||||
- Image filenames will now never fill gaps in the sequence, but will be assigned
|
||||
the next higher name in the chosen directory. This ensures that the alphabetic
|
||||
and chronological sort orders are the same.
|
||||
|
||||
---
|
||||
|
||||
## v1.06 <small>(23 August 2022)</small>
|
||||
|
||||
- Added weighted prompt support contributed by
|
||||
[xraxra](https://github.com/xraxra)
|
||||
- Example of using weighted prompts to tweak a demonic figure contributed by
|
||||
[bmaltais](https://github.com/bmaltais)
|
||||
|
||||
---
|
||||
|
||||
## v1.05 <small>(22 August 2022 - after the drop)</small>
|
||||
|
||||
- Filenames now use the following formats: 000010.95183149.png -- Two files
|
||||
produced by the same command (e.g. -n2), 000010.26742632.png -- distinguished
|
||||
by a different seed.
|
||||
|
||||
000011.455191342.01.png -- Two files produced by the same command using
|
||||
000011.455191342.02.png -- a batch size>1 (e.g. -b2). They have the same seed.
|
||||
|
||||
000011.4160627868.grid#1-4.png -- a grid of four images (-g); the whole grid
|
||||
can be regenerated with the indicated key
|
||||
|
||||
- It should no longer be possible for one image to overwrite another
|
||||
- You can use the "cd" and "pwd" commands at the invoke> prompt to set and
|
||||
retrieve the path of the output directory.
|
||||
|
||||
---
|
||||
|
||||
## v1.04 <small>(22 August 2022 - after the drop)</small>
|
||||
|
||||
- Updated README to reflect installation of the released weights.
|
||||
- Suppressed very noisy and inconsequential warning when loading the frozen CLIP
|
||||
tokenizer.
|
||||
|
||||
---
|
||||
|
||||
## v1.03 <small>(22 August 2022)</small>
|
||||
|
||||
- The original txt2img and img2img scripts from the CompViz repository have been
|
||||
moved into a subfolder named "orig_scripts", to reduce confusion.
|
||||
|
||||
---
|
||||
|
||||
## v1.02 <small>(21 August 2022)</small>
|
||||
|
||||
- A copy of the prompt and all of its switches and options is now stored in the
|
||||
corresponding image in a tEXt metadata field named "Dream". You can read the
|
||||
prompt using scripts/images2prompt.py, or an image editor that allows you to
|
||||
explore the full metadata. **Please run "conda env update" to load the k_lms
|
||||
dependencies!!**
|
||||
|
||||
---
|
||||
|
||||
## v1.01 <small>(21 August 2022)</small>
|
||||
|
||||
- added k_lms sampling. **Please run "conda env update" to load the k_lms
|
||||
dependencies!!**
|
||||
- use half precision arithmetic by default, resulting in faster execution and
|
||||
lower memory requirements Pass argument --full_precision to invoke.py to get
|
||||
slower but more accurate image generation
|
||||
|
||||
---
|
||||
|
||||
## Links
|
||||
|
||||
- **[Read Me](index.md)**
|
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