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dev/pytorc
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e33ed45cfc | |||
a1813fd23c | |||
7a6587d3dd | |||
cc0cf147c8 | |||
4cf4853ae4 | |||
90d8f0af73 | |||
c0e1fb5f71 | |||
e8e6be0ebe | |||
7830fd8ca1 | |||
4efee2a1ec | |||
e902b50bfc | |||
c08eedf264 | |||
1ee3023cdd | |||
3e8a861fc0 | |||
cae0579ba9 | |||
f06f69a81a | |||
b970ec4ce9 | |||
a22ae23e9e | |||
bb75174f4a | |||
27b238999f | |||
893bdca0a8 | |||
de47f68b61 | |||
6af9f2716e | |||
60b83ff07e | |||
38c9001e8e | |||
7335f908af | |||
96b90be5c3 | |||
06ad4387a2 | |||
a637c2418a | |||
5f8f2e63eb | |||
c6e4352c3f | |||
8c72da3643 | |||
23af057e5c | |||
bde9d6d33b | |||
c14bdcb8fd | |||
f816526d0d | |||
50d607ffea | |||
57577401bd | |||
58c63fe339 | |||
7b0cbb34d6 | |||
37c44ced1d | |||
e59307d284 | |||
2a6999d500 | |||
5ab7c68cc7 | |||
e92122f2c2 | |||
ead0e92bac | |||
682d74754c | |||
082df27ecd | |||
dc024845cf | |||
94ca13c494 | |||
1f29cb1dc1 | |||
f404c692ad | |||
6bf19cd897 | |||
2743e17588 | |||
f0b500fba8 | |||
aaec6baeca | |||
61611d7d0d | |||
73154a25d4 | |||
f4a275d1b5 | |||
c3712b013f | |||
3692f223e1 | |||
fccf809e3a | |||
23e62efdc5 | |||
6ea0a7699e | |||
1e8e5245eb | |||
4f926fc470 | |||
a0a9b12daf | |||
f3292a6953 | |||
062f3e8f31 | |||
20ffd4082c | |||
578638c258 | |||
cdc78cc6a1 | |||
c98ade9b25 | |||
fe0f5bcc11 | |||
df98178018 | |||
0b0cde2351 | |||
5b4c37e043 | |||
3c4c4d71c9 | |||
ea2b0828d8 | |||
045aa7a9a3 | |||
d478a241a8 | |||
0a4397094e | |||
0b786f61cc | |||
b68cb521ba | |||
e1f0ee819d | |||
f2c3fba28d | |||
676c772f11 | |||
016fd65f6a | |||
09bf6dd7c1 | |||
6e927acd58 | |||
383b870499 | |||
98f189cc69 | |||
dbc9134630 | |||
746162b578 | |||
0071f43b2c | |||
6d09f8c6b2 | |||
66e9fd4771 | |||
ef6609abcb | |||
2f93418095 | |||
9bcb0dff96 | |||
f84372efd8 | |||
334045b27d | |||
071f65a892 | |||
e30827e19b | |||
af98524179 | |||
e994073b5b | |||
ad292b095d | |||
d8685ad66b | |||
239f41f3e0 | |||
e0951f28cf | |||
100f2e8f57 | |||
7ade11c4f3 | |||
2faa116238 | |||
c94b8cd959 | |||
0c1a2b68bf | |||
c06dc5b85b | |||
34fa6e38e7 | |||
7b9958e59d | |||
f8775f2f2d | |||
b74354795d | |||
9461c8127d | |||
b5ed668eff | |||
c6c19f1b3c | |||
20ba51ce7d | |||
e45f46d673 | |||
b3e026aa4e | |||
89540f293b | |||
ed8ee8c690 | |||
31daf1f0d7 | |||
5b692f4720 | |||
b89aadb3c9 | |||
b9183b00a0 | |||
7b28b5c9a1 | |||
994c6b7512 | |||
42072fc15c | |||
103b30f915 | |||
1799bf5e42 | |||
17e755e062 | |||
ae963fcfdc | |||
3c732500e7 | |||
cd494c2f6c | |||
443fcd030f | |||
fefcdffb55 | |||
fa7fe382b7 | |||
d8d30ab4cb | |||
61f46cac31 | |||
df4c80f177 | |||
df95a7ddf2 | |||
fb7a9f37e4 | |||
1e3200801f | |||
b4debcc4ad | |||
622db491b2 | |||
0db8d6943c | |||
37e2418ee0 | |||
d81bc46218 | |||
40b61870f6 | |||
6cab2e0ca0 | |||
ba4892e03f | |||
2b9f8e7218 | |||
6cb6c4a911 | |||
693bed5514 | |||
fe12c6c099 | |||
67fbaa7c31 | |||
ddc68b01f7 | |||
f9feaac8c7 | |||
d1de1e357a | |||
cbac95b02a | |||
00d2d0e90e | |||
d1a2c4cd8c | |||
403d02d94f | |||
9a8fecb2cb | |||
45af30f3a4 | |||
58baf9533b | |||
f59b399f52 | |||
10f4c0c6b3 | |||
f9b272a7b9 | |||
96d7639d2a | |||
e6011631a1 | |||
54b9cb49c1 | |||
60b731e7ab | |||
ec2dc24ad7 | |||
357e1ad35f | |||
340189fa0d | |||
8d2afefe6a | |||
9faf7025c6 | |||
511924c9ab | |||
4d997145b4 | |||
9df743e2bf | |||
ccb2b7c2fb | |||
30e69f8b32 | |||
df4d1162b5 | |||
81bb44319a | |||
bb05a43787 | |||
66ff890b85 | |||
dd3fff1d3e | |||
d8d2043467 | |||
94a7b3cc07 | |||
b02ea331df | |||
9208bfd151 | |||
80579a30e5 | |||
5818528aa6 | |||
6ec7eab85a | |||
e6179af46a | |||
d15c75ecae | |||
2e438542e9 | |||
54c5665635 | |||
8a8c093795 | |||
7fa45b0540 | |||
89da371f48 | |||
10c51b4f35 | |||
ecb84ecc10 | |||
0d1aad53ef | |||
d0a71dc361 | |||
f31aa32e4d | |||
e1a6d0c138 | |||
0aa3dfbc35 | |||
5ad080f056 | |||
d4941ca833 | |||
00b002f731 | |||
82a223c5f6 | |||
654ec17000 | |||
e1f6ea2be7 | |||
5941ee620c | |||
a18d0b9ef1 | |||
eeecc33aaa | |||
dfad1dccf4 | |||
d016017b6d | |||
9b28c65e4b | |||
0a6c98e47d | |||
dedf8a3692 | |||
993158fc6a | |||
5e15f1e017 | |||
b9592ff2dc | |||
0bc6779361 | |||
2a292d5b82 | |||
4a5a228fd8 | |||
6665f4494f | |||
dbf2c63c90 | |||
bf1beaa607 | |||
7dee9efb24 | |||
9d6d728b51 | |||
1c649e4663 | |||
ea60d036d1 | |||
4d197f699e | |||
a3e07fb84a | |||
9fa1f31bf2 | |||
77db46f99e | |||
190ba78960 | |||
012c0dfdeb | |||
25d9ccc509 | |||
9cdf3aca7d | |||
49a96b90d8 | |||
aba94b85e8 | |||
aac5102cf3 | |||
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95f4ae4c1e | |||
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3f8a289e9b | |||
0ab5a36464 | |||
443a4ad87c | |||
585b47fdd1 | |||
5e433728b5 | |||
7708f4fb98 | |||
b86a1deb00 | |||
4951e66103 | |||
79b445b0ca | |||
a323070a4d | |||
f7662c1808 | |||
93c242c9fb | |||
c7c6cd7735 | |||
77ca83e103 | |||
0ea145d188 | |||
162285ae86 | |||
37c921dfe2 | |||
4f72cb44ad | |||
878ef2e9e0 | |||
4923118610 | |||
defafc0e8e | |||
16f6a6731d | |||
19fb66f3d5 | |||
0881d429f2 | |||
9a29d442b4 | |||
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da95729d90 | |||
70aa674e9e | |||
737a97c898 | |||
8748370f44 | |||
839e30e4b8 | |||
e21938c12d | |||
eeff8e9033 | |||
336e16ef85 | |||
eceb7d2b54 | |||
9775a3502c | |||
f240e878e5 | |||
529fc57f2b | |||
0ca9d1f228 | |||
b656d333de | |||
7136603604 | |||
5cbea51f31 | |||
2cf8de9234 | |||
f9239af7dc | |||
97c0c4bfe8 | |||
c6be8f320d | |||
bfb2781279 | |||
5c43988862 | |||
62863ac586 | |||
99122708ca | |||
817c4a26de | |||
ecc6b75a3e | |||
bf707d9e75 | |||
db52991b9d | |||
a34d8813b6 | |||
103b3e7965 | |||
f74e52079b | |||
e3be28ecca | |||
dbfc35ece2 | |||
4185afea5c | |||
723d074442 | |||
6d2084e030 | |||
4a0354c604 | |||
424f4fe244 | |||
348b4b8be5 | |||
75f633cda8 | |||
2b3acc7b87 | |||
044e1ec2a8 | |||
79ac0f3420 | |||
c41599746d | |||
7f0cc7072b | |||
bd1715ff5c | |||
c71d8750f7 | |||
d0832bfcaa | |||
049ea02fc7 | |||
ab39bc0bac | |||
bd4fc64156 | |||
8b0d1e59fe | |||
dc500946ad | |||
a48c03e0f4 | |||
7647490617 | |||
dbc8fc7900 | |||
5b22acca6d | |||
8c8b34a889 | |||
7ff94383ce | |||
0891910cac | |||
1a4bed2e55 | |||
70ef83ac30 | |||
b6cf8b9052 |
@ -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
|
||||
|
||||
# ignore frontend/web but whitelist dist
|
||||
invokeai/frontend/web/
|
||||
!invokeai/frontend/web/dist/
|
||||
|
||||
# ignore invokeai/assets but whitelist invokeai/assets/web
|
||||
invokeai/assets/
|
||||
!invokeai/assets/web/
|
||||
|
||||
# Guard against pulling in any models that might exist in the directory tree
|
||||
**/*.pt*
|
||||
**/*.ckpt
|
||||
|
||||
# Byte-compiled / optimized / DLL files
|
||||
**/__pycache__/
|
||||
**/*.py[cod]
|
||||
|
||||
# Distribution / packaging
|
||||
**/*.egg-info/
|
||||
**/*.egg
|
12
.editorconfig
Normal file
@ -0,0 +1,12 @@
|
||||
# All files
|
||||
[*]
|
||||
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
|
1
.git-blame-ignore-revs
Normal file
@ -0,0 +1 @@
|
||||
b3dccfaeb636599c02effc377cdd8a87d658256c
|
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
|
||||
|
34
.github/CODEOWNERS
vendored
Normal file
@ -0,0 +1,34 @@
|
||||
# continuous integration
|
||||
/.github/workflows/ @mauwii @lstein @blessedcoolant
|
||||
|
||||
# documentation
|
||||
/docs/ @lstein @mauwii @tildebyte @blessedcoolant
|
||||
/mkdocs.yml @lstein @mauwii @blessedcoolant
|
||||
|
||||
# nodes
|
||||
/invokeai/app/ @Kyle0654 @blessedcoolant
|
||||
|
||||
# installation and configuration
|
||||
/pyproject.toml @mauwii @lstein @blessedcoolant
|
||||
/docker/ @mauwii @lstein @blessedcoolant
|
||||
/scripts/ @ebr @lstein
|
||||
/installer/ @lstein @ebr
|
||||
/invokeai/assets @lstein @ebr
|
||||
/invokeai/configs @lstein
|
||||
/invokeai/version @lstein @blessedcoolant
|
||||
|
||||
# web ui
|
||||
/invokeai/frontend @blessedcoolant @psychedelicious @lstein
|
||||
/invokeai/backend @blessedcoolant @psychedelicious @lstein
|
||||
|
||||
# generation, model management, postprocessing
|
||||
/invokeai/backend @keturn @damian0815 @lstein @blessedcoolant @jpphoto
|
||||
|
||||
# front ends
|
||||
/invokeai/frontend/CLI @lstein
|
||||
/invokeai/frontend/install @lstein @ebr @mauwii
|
||||
/invokeai/frontend/merge @lstein @blessedcoolant @hipsterusername
|
||||
/invokeai/frontend/training @lstein @blessedcoolant @hipsterusername
|
||||
/invokeai/frontend/web @psychedelicious @blessedcoolant
|
||||
|
||||
|
112
.github/ISSUE_TEMPLATE/BUG_REPORT.yml
vendored
Normal file
@ -0,0 +1,112 @@
|
||||
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: input
|
||||
id: version-number
|
||||
attributes:
|
||||
label: What version did you experience this issue on?
|
||||
description: |
|
||||
Please share the version of Invoke AI that you experienced the issue on. If this is not the latest version, please update first to confirm the issue still exists. If you are testing main, please include the commit hash instead.
|
||||
placeholder: X.X.X
|
||||
validations:
|
||||
required: true
|
||||
|
||||
- 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.
|
114
.github/workflows/build-container.yml
vendored
Normal file
@ -0,0 +1,114 @@
|
||||
name: build container image
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- 'main'
|
||||
- 'update/ci/docker/*'
|
||||
- 'update/docker/*'
|
||||
- 'dev/ci/docker/*'
|
||||
- 'dev/docker/*'
|
||||
paths:
|
||||
- 'pyproject.toml'
|
||||
- '.dockerignore'
|
||||
- 'invokeai/**'
|
||||
- 'docker/Dockerfile'
|
||||
tags:
|
||||
- 'v*.*.*'
|
||||
workflow_dispatch:
|
||||
|
||||
permissions:
|
||||
contents: write
|
||||
packages: write
|
||||
|
||||
jobs:
|
||||
docker:
|
||||
if: github.event.pull_request.draft == false
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
flavor:
|
||||
- rocm
|
||||
- cuda
|
||||
- cpu
|
||||
include:
|
||||
- flavor: rocm
|
||||
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 }}
|
||||
${{ vars.DOCKERHUB_REPOSITORY }}
|
||||
tags: |
|
||||
type=ref,event=branch
|
||||
type=ref,event=tag
|
||||
type=pep440,pattern={{version}}
|
||||
type=pep440,pattern={{major}}.{{minor}}
|
||||
type=pep440,pattern={{major}}
|
||||
type=sha,enable=true,prefix=sha-,format=short
|
||||
flavor: |
|
||||
latest=${{ 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_type == 'tag' }}
|
||||
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 }}
|
27
.github/workflows/close-inactive-issues.yml
vendored
Normal file
@ -0,0 +1,27 @@
|
||||
name: Close inactive issues
|
||||
on:
|
||||
schedule:
|
||||
- cron: "00 6 * * *"
|
||||
|
||||
env:
|
||||
DAYS_BEFORE_ISSUE_STALE: 14
|
||||
DAYS_BEFORE_ISSUE_CLOSE: 28
|
||||
|
||||
jobs:
|
||||
close-issues:
|
||||
runs-on: ubuntu-latest
|
||||
permissions:
|
||||
issues: write
|
||||
pull-requests: write
|
||||
steps:
|
||||
- uses: actions/stale@v5
|
||||
with:
|
||||
days-before-issue-stale: ${{ env.DAYS_BEFORE_ISSUE_STALE }}
|
||||
days-before-issue-close: ${{ env.DAYS_BEFORE_ISSUE_CLOSE }}
|
||||
stale-issue-label: "Inactive Issue"
|
||||
stale-issue-message: "There has been no activity in this issue for ${{ env.DAYS_BEFORE_ISSUE_STALE }} days. If this issue is still being experienced, please reply with an updated confirmation that the issue is still being experienced with the latest release."
|
||||
close-issue-message: "Due to inactivity, this issue was automatically closed. If you are still experiencing the issue, please recreate the issue."
|
||||
days-before-pr-stale: -1
|
||||
days-before-pr-close: -1
|
||||
repo-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
operations-per-run: 500
|
37
.github/workflows/lint-frontend.yml
vendored
Normal file
@ -0,0 +1,37 @@
|
||||
name: Lint frontend
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
paths:
|
||||
- 'invokeai/frontend/web/**'
|
||||
types:
|
||||
- 'ready_for_review'
|
||||
- 'opened'
|
||||
- 'synchronize'
|
||||
push:
|
||||
branches:
|
||||
- 'main'
|
||||
paths:
|
||||
- 'invokeai/frontend/web/**'
|
||||
merge_group:
|
||||
workflow_dispatch:
|
||||
|
||||
defaults:
|
||||
run:
|
||||
working-directory: invokeai/frontend/web
|
||||
|
||||
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 run lint:tsc'
|
||||
- run: 'yarn run lint:madge'
|
||||
- run: 'yarn run lint:eslint'
|
||||
- run: 'yarn run lint:prettier'
|
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
|
44
.github/workflows/mkdocs-material.yml
vendored
Normal file
@ -0,0 +1,44 @@
|
||||
name: mkdocs-material
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- 'main'
|
||||
- 'development'
|
||||
|
||||
permissions:
|
||||
contents: write
|
||||
|
||||
jobs:
|
||||
mkdocs-material:
|
||||
if: github.event.pull_request.draft == false
|
||||
runs-on: ubuntu-latest
|
||||
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'
|
||||
|
||||
- name: install requirements
|
||||
run: |
|
||||
python -m \
|
||||
pip install -r docs/requirements-mkdocs.txt
|
||||
|
||||
- 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:
|
||||
- 'invokeai/version/invokeai_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/*
|
66
.github/workflows/test-invoke-pip-skip.yml
vendored
Normal file
@ -0,0 +1,66 @@
|
||||
name: Test invoke.py pip
|
||||
on:
|
||||
pull_request:
|
||||
paths:
|
||||
- '**'
|
||||
- '!pyproject.toml'
|
||||
- '!invokeai/**'
|
||||
- 'invokeai/frontend/web/**'
|
||||
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"'
|
144
.github/workflows/test-invoke-pip.yml
vendored
Normal file
@ -0,0 +1,144 @@
|
||||
name: Test invoke.py pip
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- 'main'
|
||||
paths:
|
||||
- 'pyproject.toml'
|
||||
- 'invokeai/**'
|
||||
- '!invokeai/frontend/web/**'
|
||||
pull_request:
|
||||
paths:
|
||||
- 'pyproject.toml'
|
||||
- 'invokeai/**'
|
||||
- '!invokeai/frontend/web/**'
|
||||
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 invokeai.backend.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 }}
|
51
.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/
|
||||
@ -52,16 +63,20 @@ pip-delete-this-directory.txt
|
||||
htmlcov/
|
||||
.tox/
|
||||
.nox/
|
||||
.coveragerc
|
||||
.coverage
|
||||
.coverage.*
|
||||
.cache
|
||||
nosetests.xml
|
||||
coverage.xml
|
||||
cov.xml
|
||||
*.cover
|
||||
*.py,cover
|
||||
.hypothesis/
|
||||
.pytest_cache/
|
||||
.pytest.ini
|
||||
cover/
|
||||
junit/
|
||||
|
||||
# Translations
|
||||
*.mo
|
||||
@ -77,9 +92,6 @@ db.sqlite3-journal
|
||||
instance/
|
||||
.webassets-cache
|
||||
|
||||
# WebUI temp files:
|
||||
img2img-tmp.png
|
||||
|
||||
# Scrapy stuff:
|
||||
.scrapy
|
||||
|
||||
@ -179,10 +191,41 @@ 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/web/*
|
||||
|
||||
# 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
|
||||
|
||||
# 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
|
||||
|
13
.prettierrc.yaml
Normal file
@ -0,0 +1,13 @@
|
||||
endOfLine: lf
|
||||
tabWidth: 2
|
||||
useTabs: false
|
||||
singleQuote: true
|
||||
quoteProps: as-needed
|
||||
embeddedLanguageFormatting: auto
|
||||
overrides:
|
||||
- files: '*.md'
|
||||
options:
|
||||
proseWrap: always
|
||||
printWidth: 80
|
||||
parser: markdown
|
||||
cursorOffset: -1
|
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.
|
BIN
binary_installer/WinLongPathsEnabled.reg
Normal file
164
binary_installer/install.bat.in
Normal file
@ -0,0 +1,164 @@
|
||||
@echo off
|
||||
|
||||
@rem This script will install git (if not found on the PATH variable)
|
||||
@rem using micromamba (an 8mb static-linked single-file binary, conda replacement).
|
||||
@rem For users who already have git, this step will be skipped.
|
||||
|
||||
@rem Next, it'll download the project's source code.
|
||||
@rem Then it will download a self-contained, standalone Python and unpack it.
|
||||
@rem Finally, it'll create the Python virtual environment and preload the models.
|
||||
|
||||
@rem This enables a user to install this project without manually installing git or Python
|
||||
|
||||
@rem change to the script's directory
|
||||
PUSHD "%~dp0"
|
||||
|
||||
set "no_cache_dir=--no-cache-dir"
|
||||
if "%1" == "use-cache" (
|
||||
set "no_cache_dir="
|
||||
)
|
||||
|
||||
echo ***** Installing InvokeAI.. *****
|
||||
@rem Config
|
||||
set INSTALL_ENV_DIR=%cd%\installer_files\env
|
||||
@rem https://mamba.readthedocs.io/en/latest/installation.html
|
||||
set MICROMAMBA_DOWNLOAD_URL=https://github.com/cmdr2/stable-diffusion-ui/releases/download/v1.1/micromamba.exe
|
||||
set RELEASE_URL=https://github.com/invoke-ai/InvokeAI
|
||||
set RELEASE_SOURCEBALL=/archive/refs/heads/main.tar.gz
|
||||
set PYTHON_BUILD_STANDALONE_URL=https://github.com/indygreg/python-build-standalone/releases/download
|
||||
set PYTHON_BUILD_STANDALONE=20221002/cpython-3.10.7+20221002-x86_64-pc-windows-msvc-shared-install_only.tar.gz
|
||||
|
||||
set PACKAGES_TO_INSTALL=
|
||||
|
||||
call git --version >.tmp1 2>.tmp2
|
||||
if "%ERRORLEVEL%" NEQ "0" set PACKAGES_TO_INSTALL=%PACKAGES_TO_INSTALL% git
|
||||
|
||||
@rem Cleanup
|
||||
del /q .tmp1 .tmp2
|
||||
|
||||
@rem (if necessary) install git into a contained environment
|
||||
if "%PACKAGES_TO_INSTALL%" NEQ "" (
|
||||
@rem download micromamba
|
||||
echo ***** Downloading micromamba from %MICROMAMBA_DOWNLOAD_URL% to micromamba.exe *****
|
||||
|
||||
call curl -L "%MICROMAMBA_DOWNLOAD_URL%" > micromamba.exe
|
||||
|
||||
@rem test the mamba binary
|
||||
echo ***** Micromamba version: *****
|
||||
call micromamba.exe --version
|
||||
|
||||
@rem create the installer env
|
||||
if not exist "%INSTALL_ENV_DIR%" (
|
||||
call micromamba.exe create -y --prefix "%INSTALL_ENV_DIR%"
|
||||
)
|
||||
|
||||
echo ***** Packages to install:%PACKAGES_TO_INSTALL% *****
|
||||
|
||||
call micromamba.exe install -y --prefix "%INSTALL_ENV_DIR%" -c conda-forge %PACKAGES_TO_INSTALL%
|
||||
|
||||
if not exist "%INSTALL_ENV_DIR%" (
|
||||
echo ----- There was a problem while installing "%PACKAGES_TO_INSTALL%" using micromamba. Cannot continue. -----
|
||||
pause
|
||||
exit /b
|
||||
)
|
||||
)
|
||||
|
||||
del /q micromamba.exe
|
||||
|
||||
@rem For 'git' only
|
||||
set PATH=%INSTALL_ENV_DIR%\Library\bin;%PATH%
|
||||
|
||||
@rem Download/unpack/clean up InvokeAI release sourceball
|
||||
set err_msg=----- InvokeAI source download failed -----
|
||||
echo Trying to download "%RELEASE_URL%%RELEASE_SOURCEBALL%"
|
||||
curl -L %RELEASE_URL%%RELEASE_SOURCEBALL% --output InvokeAI.tgz
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
|
||||
set err_msg=----- InvokeAI source unpack failed -----
|
||||
tar -zxf InvokeAI.tgz
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
|
||||
del /q InvokeAI.tgz
|
||||
|
||||
set err_msg=----- InvokeAI source copy failed -----
|
||||
cd InvokeAI-*
|
||||
xcopy . .. /e /h
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
cd ..
|
||||
|
||||
@rem cleanup
|
||||
for /f %%i in ('dir /b InvokeAI-*') do rd /s /q %%i
|
||||
rd /s /q .dev_scripts .github docker-build tests
|
||||
del /q requirements.in requirements-mkdocs.txt shell.nix
|
||||
|
||||
echo ***** Unpacked InvokeAI source *****
|
||||
|
||||
@rem Download/unpack/clean up python-build-standalone
|
||||
set err_msg=----- Python download failed -----
|
||||
curl -L %PYTHON_BUILD_STANDALONE_URL%/%PYTHON_BUILD_STANDALONE% --output python.tgz
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
|
||||
set err_msg=----- Python unpack failed -----
|
||||
tar -zxf python.tgz
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
|
||||
del /q python.tgz
|
||||
|
||||
echo ***** Unpacked python-build-standalone *****
|
||||
|
||||
@rem create venv
|
||||
set err_msg=----- problem creating venv -----
|
||||
.\python\python -E -s -m venv .venv
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
call .venv\Scripts\activate.bat
|
||||
|
||||
echo ***** Created Python virtual environment *****
|
||||
|
||||
@rem Print venv's Python version
|
||||
set err_msg=----- problem calling venv's python -----
|
||||
echo We're running under
|
||||
.venv\Scripts\python --version
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
|
||||
set err_msg=----- pip update failed -----
|
||||
.venv\Scripts\python -m pip install %no_cache_dir% --no-warn-script-location --upgrade pip wheel
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
|
||||
echo ***** Updated pip and wheel *****
|
||||
|
||||
set err_msg=----- requirements file copy failed -----
|
||||
copy binary_installer\py3.10-windows-x86_64-cuda-reqs.txt requirements.txt
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
|
||||
set err_msg=----- main pip install failed -----
|
||||
.venv\Scripts\python -m pip install %no_cache_dir% --no-warn-script-location -r requirements.txt
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
|
||||
echo ***** Installed Python dependencies *****
|
||||
|
||||
set err_msg=----- InvokeAI setup failed -----
|
||||
.venv\Scripts\python -m pip install %no_cache_dir% --no-warn-script-location -e .
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
|
||||
copy binary_installer\invoke.bat.in .\invoke.bat
|
||||
echo ***** Installed invoke launcher script ******
|
||||
|
||||
@rem more cleanup
|
||||
rd /s /q binary_installer installer_files
|
||||
|
||||
@rem preload the models
|
||||
call .venv\Scripts\python ldm\invoke\config\invokeai_configure.py
|
||||
set err_msg=----- model download clone failed -----
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
deactivate
|
||||
|
||||
echo ***** Finished downloading models *****
|
||||
|
||||
echo All done! Execute the file invoke.bat in this directory to start InvokeAI
|
||||
pause
|
||||
exit
|
||||
|
||||
:err_exit
|
||||
echo %err_msg%
|
||||
pause
|
||||
exit
|
235
binary_installer/install.sh.in
Normal file
@ -0,0 +1,235 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
# ensure we're in the correct folder in case user's CWD is somewhere else
|
||||
scriptdir=$(dirname "$0")
|
||||
cd "$scriptdir"
|
||||
|
||||
set -euo pipefail
|
||||
IFS=$'\n\t'
|
||||
|
||||
function _err_exit {
|
||||
if test "$1" -ne 0
|
||||
then
|
||||
echo -e "Error code $1; Error caught was '$2'"
|
||||
read -p "Press any key to exit..."
|
||||
exit
|
||||
fi
|
||||
}
|
||||
|
||||
# This script will install git (if not found on the PATH variable)
|
||||
# using micromamba (an 8mb static-linked single-file binary, conda replacement).
|
||||
# For users who already have git, this step will be skipped.
|
||||
|
||||
# Next, it'll download the project's source code.
|
||||
# Then it will download a self-contained, standalone Python and unpack it.
|
||||
# Finally, it'll create the Python virtual environment and preload the models.
|
||||
|
||||
# This enables a user to install this project without manually installing git or Python
|
||||
|
||||
echo -e "\n***** Installing InvokeAI into $(pwd)... *****\n"
|
||||
|
||||
export no_cache_dir="--no-cache-dir"
|
||||
if [ $# -ge 1 ]; then
|
||||
if [ "$1" = "use-cache" ]; then
|
||||
export no_cache_dir=""
|
||||
fi
|
||||
fi
|
||||
|
||||
|
||||
OS_NAME=$(uname -s)
|
||||
case "${OS_NAME}" in
|
||||
Linux*) OS_NAME="linux";;
|
||||
Darwin*) OS_NAME="darwin";;
|
||||
*) echo -e "\n----- Unknown OS: $OS_NAME! This script runs only on Linux or macOS -----\n" && exit
|
||||
esac
|
||||
|
||||
OS_ARCH=$(uname -m)
|
||||
case "${OS_ARCH}" in
|
||||
x86_64*) ;;
|
||||
arm64*) ;;
|
||||
*) echo -e "\n----- Unknown system architecture: $OS_ARCH! This script runs only on x86_64 or arm64 -----\n" && exit
|
||||
esac
|
||||
|
||||
# https://mamba.readthedocs.io/en/latest/installation.html
|
||||
MAMBA_OS_NAME=$OS_NAME
|
||||
MAMBA_ARCH=$OS_ARCH
|
||||
if [ "$OS_NAME" == "darwin" ]; then
|
||||
MAMBA_OS_NAME="osx"
|
||||
fi
|
||||
|
||||
if [ "$OS_ARCH" == "linux" ]; then
|
||||
MAMBA_ARCH="aarch64"
|
||||
fi
|
||||
|
||||
if [ "$OS_ARCH" == "x86_64" ]; then
|
||||
MAMBA_ARCH="64"
|
||||
fi
|
||||
|
||||
PY_ARCH=$OS_ARCH
|
||||
if [ "$OS_ARCH" == "arm64" ]; then
|
||||
PY_ARCH="aarch64"
|
||||
fi
|
||||
|
||||
# Compute device ('cd' segment of reqs files) detect goes here
|
||||
# This needs a ton of work
|
||||
# Suggestions:
|
||||
# - lspci
|
||||
# - check $PATH for nvidia-smi, gtt CUDA/GPU version from output
|
||||
# - Surely there's a similar utility for AMD?
|
||||
CD="cuda"
|
||||
if [ "$OS_NAME" == "darwin" ] && [ "$OS_ARCH" == "arm64" ]; then
|
||||
CD="mps"
|
||||
fi
|
||||
|
||||
# config
|
||||
INSTALL_ENV_DIR="$(pwd)/installer_files/env"
|
||||
MICROMAMBA_DOWNLOAD_URL="https://micro.mamba.pm/api/micromamba/${MAMBA_OS_NAME}-${MAMBA_ARCH}/latest"
|
||||
RELEASE_URL=https://github.com/invoke-ai/InvokeAI
|
||||
RELEASE_SOURCEBALL=/archive/refs/heads/main.tar.gz
|
||||
PYTHON_BUILD_STANDALONE_URL=https://github.com/indygreg/python-build-standalone/releases/download
|
||||
if [ "$OS_NAME" == "darwin" ]; then
|
||||
PYTHON_BUILD_STANDALONE=20221002/cpython-3.10.7+20221002-${PY_ARCH}-apple-darwin-install_only.tar.gz
|
||||
elif [ "$OS_NAME" == "linux" ]; then
|
||||
PYTHON_BUILD_STANDALONE=20221002/cpython-3.10.7+20221002-${PY_ARCH}-unknown-linux-gnu-install_only.tar.gz
|
||||
fi
|
||||
echo "INSTALLING $RELEASE_SOURCEBALL FROM $RELEASE_URL"
|
||||
|
||||
PACKAGES_TO_INSTALL=""
|
||||
|
||||
if ! hash "git" &>/dev/null; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL git"; fi
|
||||
|
||||
# (if necessary) install git and conda into a contained environment
|
||||
if [ "$PACKAGES_TO_INSTALL" != "" ]; then
|
||||
# download micromamba
|
||||
echo -e "\n***** Downloading micromamba from $MICROMAMBA_DOWNLOAD_URL to micromamba *****\n"
|
||||
|
||||
curl -L "$MICROMAMBA_DOWNLOAD_URL" | tar -xvjO bin/micromamba > micromamba
|
||||
|
||||
chmod u+x ./micromamba
|
||||
|
||||
# test the mamba binary
|
||||
echo -e "\n***** Micromamba version: *****\n"
|
||||
./micromamba --version
|
||||
|
||||
# create the installer env
|
||||
if [ ! -e "$INSTALL_ENV_DIR" ]; then
|
||||
./micromamba create -y --prefix "$INSTALL_ENV_DIR"
|
||||
fi
|
||||
|
||||
echo -e "\n***** Packages to install:$PACKAGES_TO_INSTALL *****\n"
|
||||
|
||||
./micromamba install -y --prefix "$INSTALL_ENV_DIR" -c conda-forge "$PACKAGES_TO_INSTALL"
|
||||
|
||||
if [ ! -e "$INSTALL_ENV_DIR" ]; then
|
||||
echo -e "\n----- There was a problem while initializing micromamba. Cannot continue. -----\n"
|
||||
exit
|
||||
fi
|
||||
fi
|
||||
|
||||
rm -f micromamba.exe
|
||||
|
||||
export PATH="$INSTALL_ENV_DIR/bin:$PATH"
|
||||
|
||||
# Download/unpack/clean up InvokeAI release sourceball
|
||||
_err_msg="\n----- InvokeAI source download failed -----\n"
|
||||
curl -L $RELEASE_URL/$RELEASE_SOURCEBALL --output InvokeAI.tgz
|
||||
_err_exit $? _err_msg
|
||||
_err_msg="\n----- InvokeAI source unpack failed -----\n"
|
||||
tar -zxf InvokeAI.tgz
|
||||
_err_exit $? _err_msg
|
||||
|
||||
rm -f InvokeAI.tgz
|
||||
|
||||
_err_msg="\n----- InvokeAI source copy failed -----\n"
|
||||
cd InvokeAI-*
|
||||
cp -r . ..
|
||||
_err_exit $? _err_msg
|
||||
cd ..
|
||||
|
||||
# cleanup
|
||||
rm -rf InvokeAI-*/
|
||||
rm -rf .dev_scripts/ .github/ docker-build/ tests/ requirements.in requirements-mkdocs.txt shell.nix
|
||||
|
||||
echo -e "\n***** Unpacked InvokeAI source *****\n"
|
||||
|
||||
# Download/unpack/clean up python-build-standalone
|
||||
_err_msg="\n----- Python download failed -----\n"
|
||||
curl -L $PYTHON_BUILD_STANDALONE_URL/$PYTHON_BUILD_STANDALONE --output python.tgz
|
||||
_err_exit $? _err_msg
|
||||
_err_msg="\n----- Python unpack failed -----\n"
|
||||
tar -zxf python.tgz
|
||||
_err_exit $? _err_msg
|
||||
|
||||
rm -f python.tgz
|
||||
|
||||
echo -e "\n***** Unpacked python-build-standalone *****\n"
|
||||
|
||||
# create venv
|
||||
_err_msg="\n----- problem creating venv -----\n"
|
||||
|
||||
if [ "$OS_NAME" == "darwin" ]; then
|
||||
# patch sysconfig so that extensions can build properly
|
||||
# adapted from https://github.com/cashapp/hermit-packages/commit/fcba384663892f4d9cfb35e8639ff7a28166ee43
|
||||
PYTHON_INSTALL_DIR="$(pwd)/python"
|
||||
SYSCONFIG="$(echo python/lib/python*/_sysconfigdata_*.py)"
|
||||
TMPFILE="$(mktemp)"
|
||||
chmod +w "${SYSCONFIG}"
|
||||
cp "${SYSCONFIG}" "${TMPFILE}"
|
||||
sed "s,'/install,'${PYTHON_INSTALL_DIR},g" "${TMPFILE}" > "${SYSCONFIG}"
|
||||
rm -f "${TMPFILE}"
|
||||
fi
|
||||
|
||||
./python/bin/python3 -E -s -m venv .venv
|
||||
_err_exit $? _err_msg
|
||||
source .venv/bin/activate
|
||||
|
||||
echo -e "\n***** Created Python virtual environment *****\n"
|
||||
|
||||
# Print venv's Python version
|
||||
_err_msg="\n----- problem calling venv's python -----\n"
|
||||
echo -e "We're running under"
|
||||
.venv/bin/python3 --version
|
||||
_err_exit $? _err_msg
|
||||
|
||||
_err_msg="\n----- pip update failed -----\n"
|
||||
.venv/bin/python3 -m pip install $no_cache_dir --no-warn-script-location --upgrade pip
|
||||
_err_exit $? _err_msg
|
||||
|
||||
echo -e "\n***** Updated pip *****\n"
|
||||
|
||||
_err_msg="\n----- requirements file copy failed -----\n"
|
||||
cp binary_installer/py3.10-${OS_NAME}-"${OS_ARCH}"-${CD}-reqs.txt requirements.txt
|
||||
_err_exit $? _err_msg
|
||||
|
||||
_err_msg="\n----- main pip install failed -----\n"
|
||||
.venv/bin/python3 -m pip install $no_cache_dir --no-warn-script-location -r requirements.txt
|
||||
_err_exit $? _err_msg
|
||||
|
||||
echo -e "\n***** Installed Python dependencies *****\n"
|
||||
|
||||
_err_msg="\n----- InvokeAI setup failed -----\n"
|
||||
.venv/bin/python3 -m pip install $no_cache_dir --no-warn-script-location -e .
|
||||
_err_exit $? _err_msg
|
||||
|
||||
echo -e "\n***** Installed InvokeAI *****\n"
|
||||
|
||||
cp binary_installer/invoke.sh.in ./invoke.sh
|
||||
chmod a+rx ./invoke.sh
|
||||
echo -e "\n***** Installed invoke launcher script ******\n"
|
||||
|
||||
# more cleanup
|
||||
rm -rf binary_installer/ installer_files/
|
||||
|
||||
# preload the models
|
||||
.venv/bin/python3 scripts/configure_invokeai.py
|
||||
_err_msg="\n----- model download clone failed -----\n"
|
||||
_err_exit $? _err_msg
|
||||
deactivate
|
||||
|
||||
echo -e "\n***** Finished downloading models *****\n"
|
||||
|
||||
echo "All done! Run the command"
|
||||
echo " $scriptdir/invoke.sh"
|
||||
echo "to start InvokeAI."
|
||||
read -p "Press any key to exit..."
|
||||
exit
|
36
binary_installer/invoke.bat.in
Normal file
@ -0,0 +1,36 @@
|
||||
@echo off
|
||||
|
||||
PUSHD "%~dp0"
|
||||
call .venv\Scripts\activate.bat
|
||||
|
||||
echo Do you want to generate images using the
|
||||
echo 1. command-line
|
||||
echo 2. browser-based UI
|
||||
echo OR
|
||||
echo 3. open the developer console
|
||||
set /p choice="Please enter 1, 2 or 3: "
|
||||
if /i "%choice%" == "1" (
|
||||
echo Starting the InvokeAI command-line.
|
||||
.venv\Scripts\python scripts\invoke.py %*
|
||||
) else if /i "%choice%" == "2" (
|
||||
echo Starting the InvokeAI browser-based UI.
|
||||
.venv\Scripts\python scripts\invoke.py --web %*
|
||||
) else if /i "%choice%" == "3" (
|
||||
echo Developer Console
|
||||
echo Python command is:
|
||||
where python
|
||||
echo Python version is:
|
||||
python --version
|
||||
echo *************************
|
||||
echo You are now in the system shell, with the local InvokeAI Python virtual environment activated,
|
||||
echo so that you can troubleshoot this InvokeAI installation as necessary.
|
||||
echo *************************
|
||||
echo *** Type `exit` to quit this shell and deactivate the Python virtual environment ***
|
||||
call cmd /k
|
||||
) else (
|
||||
echo Invalid selection
|
||||
pause
|
||||
exit /b
|
||||
)
|
||||
|
||||
deactivate
|
46
binary_installer/invoke.sh.in
Normal file
@ -0,0 +1,46 @@
|
||||
#!/usr/bin/env sh
|
||||
|
||||
set -eu
|
||||
|
||||
. .venv/bin/activate
|
||||
|
||||
# set required env var for torch on mac MPS
|
||||
if [ "$(uname -s)" == "Darwin" ]; then
|
||||
export PYTORCH_ENABLE_MPS_FALLBACK=1
|
||||
fi
|
||||
|
||||
echo "Do you want to generate images using the"
|
||||
echo "1. command-line"
|
||||
echo "2. browser-based UI"
|
||||
echo "OR"
|
||||
echo "3. open the developer console"
|
||||
echo "Please enter 1, 2, or 3:"
|
||||
read choice
|
||||
|
||||
case $choice in
|
||||
1)
|
||||
printf "\nStarting the InvokeAI command-line..\n";
|
||||
.venv/bin/python scripts/invoke.py $*;
|
||||
;;
|
||||
2)
|
||||
printf "\nStarting the InvokeAI browser-based UI..\n";
|
||||
.venv/bin/python scripts/invoke.py --web $*;
|
||||
;;
|
||||
3)
|
||||
printf "\nDeveloper Console:\n";
|
||||
printf "Python command is:\n\t";
|
||||
which python;
|
||||
printf "Python version is:\n\t";
|
||||
python --version;
|
||||
echo "*************************"
|
||||
echo "You are now in your user shell ($SHELL) with the local InvokeAI Python virtual environment activated,";
|
||||
echo "so that you can troubleshoot this InvokeAI installation as necessary.";
|
||||
printf "*************************\n"
|
||||
echo "*** Type \`exit\` to quit this shell and deactivate the Python virtual environment *** ";
|
||||
/usr/bin/env "$SHELL";
|
||||
;;
|
||||
*)
|
||||
echo "Invalid selection";
|
||||
exit
|
||||
;;
|
||||
esac
|
2097
binary_installer/py3.10-darwin-arm64-mps-reqs.txt
Normal file
2077
binary_installer/py3.10-darwin-x86_64-cpu-reqs.txt
Normal file
2103
binary_installer/py3.10-linux-x86_64-cuda-reqs.txt
Normal file
2109
binary_installer/py3.10-windows-x86_64-cuda-reqs.txt
Normal file
17
binary_installer/readme.txt
Normal file
@ -0,0 +1,17 @@
|
||||
InvokeAI
|
||||
|
||||
Project homepage: https://github.com/invoke-ai/InvokeAI
|
||||
|
||||
Installation on Windows:
|
||||
NOTE: You might need to enable Windows Long Paths. If you're not sure,
|
||||
then you almost certainly need to. Simply double-click the 'WinLongPathsEnabled.reg'
|
||||
file. Note that you will need to have admin privileges in order to
|
||||
do this.
|
||||
|
||||
Please double-click the 'install.bat' file (while keeping it inside the invokeAI folder).
|
||||
|
||||
Installation on Linux and Mac:
|
||||
Please open the terminal, and run './install.sh' (while keeping it inside the invokeAI folder).
|
||||
|
||||
After installation, please run the 'invoke.bat' file (on Windows) or 'invoke.sh'
|
||||
file (on Linux/Mac) to start InvokeAI.
|
33
binary_installer/requirements.in
Normal file
@ -0,0 +1,33 @@
|
||||
--prefer-binary
|
||||
--extra-index-url https://download.pytorch.org/whl/torch_stable.html
|
||||
--extra-index-url https://download.pytorch.org/whl/cu116
|
||||
--trusted-host https://download.pytorch.org
|
||||
accelerate~=0.15
|
||||
albumentations
|
||||
diffusers[torch]~=0.11
|
||||
einops
|
||||
eventlet
|
||||
flask_cors
|
||||
flask_socketio
|
||||
flaskwebgui==1.0.3
|
||||
getpass_asterisk
|
||||
imageio-ffmpeg
|
||||
pyreadline3
|
||||
realesrgan
|
||||
send2trash
|
||||
streamlit
|
||||
taming-transformers-rom1504
|
||||
test-tube
|
||||
torch-fidelity
|
||||
torch==1.12.1 ; platform_system == 'Darwin'
|
||||
torch==1.12.0+cu116 ; platform_system == 'Linux' or platform_system == 'Windows'
|
||||
torchvision==0.13.1 ; platform_system == 'Darwin'
|
||||
torchvision==0.13.0+cu116 ; platform_system == 'Linux' or platform_system == 'Windows'
|
||||
transformers
|
||||
picklescan
|
||||
https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip
|
||||
https://github.com/invoke-ai/clipseg/archive/1f754751c85d7d4255fa681f4491ff5711c1c288.zip
|
||||
https://github.com/invoke-ai/GFPGAN/archive/3f5d2397361199bc4a91c08bb7d80f04d7805615.zip ; platform_system=='Windows'
|
||||
https://github.com/invoke-ai/GFPGAN/archive/c796277a1cf77954e5fc0b288d7062d162894248.zip ; platform_system=='Linux' or platform_system=='Darwin'
|
||||
https://github.com/Birch-san/k-diffusion/archive/363386981fee88620709cf8f6f2eea167bd6cd74.zip
|
||||
https://github.com/invoke-ai/PyPatchMatch/archive/129863937a8ab37f6bbcec327c994c0f932abdbc.zip
|
@ -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
|
@ -1,71 +0,0 @@
|
||||
model:
|
||||
base_learning_rate: 5.0e-05
|
||||
target: ldm.models.diffusion.ddpm.LatentDiffusion
|
||||
params:
|
||||
linear_start: 0.00085
|
||||
linear_end: 0.012
|
||||
num_timesteps_cond: 1
|
||||
log_every_t: 200
|
||||
timesteps: 1000
|
||||
first_stage_key: image
|
||||
cond_stage_key: caption
|
||||
image_size: 32
|
||||
channels: 4
|
||||
cond_stage_trainable: true
|
||||
conditioning_key: crossattn
|
||||
monitor: val/loss_simple_ema
|
||||
scale_factor: 0.18215
|
||||
use_ema: False
|
||||
|
||||
unet_config:
|
||||
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
|
||||
params:
|
||||
image_size: 32
|
||||
in_channels: 4
|
||||
out_channels: 4
|
||||
model_channels: 320
|
||||
attention_resolutions:
|
||||
- 4
|
||||
- 2
|
||||
- 1
|
||||
num_res_blocks: 2
|
||||
channel_mult:
|
||||
- 1
|
||||
- 2
|
||||
- 4
|
||||
- 4
|
||||
num_heads: 8
|
||||
use_spatial_transformer: true
|
||||
transformer_depth: 1
|
||||
context_dim: 1280
|
||||
use_checkpoint: true
|
||||
legacy: False
|
||||
|
||||
first_stage_config:
|
||||
target: ldm.models.autoencoder.AutoencoderKL
|
||||
params:
|
||||
embed_dim: 4
|
||||
monitor: val/rec_loss
|
||||
ddconfig:
|
||||
double_z: true
|
||||
z_channels: 4
|
||||
resolution: 256
|
||||
in_channels: 3
|
||||
out_ch: 3
|
||||
ch: 128
|
||||
ch_mult:
|
||||
- 1
|
||||
- 2
|
||||
- 4
|
||||
- 4
|
||||
num_res_blocks: 2
|
||||
attn_resolutions: []
|
||||
dropout: 0.0
|
||||
lossconfig:
|
||||
target: torch.nn.Identity
|
||||
|
||||
cond_stage_config:
|
||||
target: ldm.modules.encoders.modules.BERTEmbedder
|
||||
params:
|
||||
n_embed: 1280
|
||||
n_layer: 32
|
@ -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
|
4
coverage/.gitignore
vendored
Normal file
@ -0,0 +1,4 @@
|
||||
# Ignore everything in this directory
|
||||
*
|
||||
# Except this file
|
||||
!.gitignore
|
107
docker/Dockerfile
Normal file
@ -0,0 +1,107 @@
|
||||
# syntax=docker/dockerfile:1
|
||||
|
||||
ARG PYTHON_VERSION=3.9
|
||||
##################
|
||||
## base image ##
|
||||
##################
|
||||
FROM --platform=${TARGETPLATFORM} python:${PYTHON_VERSION}-slim AS python-base
|
||||
|
||||
LABEL org.opencontainers.image.authors="mauwii@outlook.de"
|
||||
|
||||
# Prepare apt 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 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 \
|
||||
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 build 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} \
|
||||
python3 -m venv "${APPNAME}" \
|
||||
--upgrade-deps
|
||||
|
||||
# Install requirements
|
||||
COPY --link pyproject.toml .
|
||||
COPY --link invokeai/version/invokeai_version.py invokeai/version/__init__.py invokeai/version/
|
||||
ARG PIP_EXTRA_INDEX_URL
|
||||
ENV PIP_EXTRA_INDEX_URL ${PIP_EXTRA_INDEX_URL}
|
||||
RUN --mount=type=cache,target=${PIP_CACHE_DIR} \
|
||||
"${APPNAME}"/bin/pip install .
|
||||
|
||||
# Install pyproject.toml
|
||||
COPY --link . .
|
||||
RUN --mount=type=cache,target=${PIP_CACHE_DIR} \
|
||||
"${APPNAME}/bin/pip" install .
|
||||
|
||||
# Build patchmatch
|
||||
RUN python3 -c "from patchmatch import patch_match"
|
||||
|
||||
#####################
|
||||
## runtime image ##
|
||||
#####################
|
||||
FROM python-base AS runtime
|
||||
|
||||
# Create a new user
|
||||
ARG UNAME=appuser
|
||||
RUN useradd \
|
||||
--no-log-init \
|
||||
-m \
|
||||
-U \
|
||||
"${UNAME}"
|
||||
|
||||
# Create volume directory
|
||||
ARG VOLUME_DIR=/data
|
||||
RUN mkdir -p "${VOLUME_DIR}" \
|
||||
&& chown -hR "${UNAME}:${UNAME}" "${VOLUME_DIR}"
|
||||
|
||||
# Setup runtime environment
|
||||
USER ${UNAME}:${UNAME}
|
||||
COPY --chown=${UNAME}:${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 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="${DOCKERFILE}" \
|
||||
..
|
54
docker/env.sh
Normal file
@ -0,0 +1,54 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
# This file is used to set environment variables for the build.sh and run.sh scripts.
|
||||
|
||||
# Try to detect the container flavor if no PIP_EXTRA_INDEX_URL got specified
|
||||
if [[ -z "$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="${CONTAINER_IMAGE,,}"
|
||||
|
||||
# enable docker buildkit
|
||||
export DOCKER_BUILDKIT=1
|
41
docker/run.sh
Executable file
@ -0,0 +1,41 @@
|
||||
#!/usr/bin/env bash
|
||||
set -e
|
||||
|
||||
# How to use: https://invoke-ai.github.io/InvokeAI/installation/040_INSTALL_DOCKER/
|
||||
|
||||
SCRIPTDIR=$(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 type=volume,volume-driver=local,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}" ${@:+$@}
|
||||
|
||||
echo -e "\nCleaning trash folder ..."
|
||||
for f in outputs/.Trash*; do
|
||||
if [ -e "$f" ]; then
|
||||
rm -Rf "$f"
|
||||
break
|
||||
fi
|
||||
done
|
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>
|
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|
||||
- 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
|
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|
||||
---
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## Links
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|
||||
- **[Read Me](index.md)**
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