# Summary of the change
- new Scene-to-Image tab
- new scn2img function
- functions for loading and running monocular_depth_estimation with
tensorflow
# Description
(relevant motivation, which issue is fixed)
Related to discussion #925
> Would it be possible to have a layers system where we could do have
foreground, mid, and background objects which relate to one another and
share the style? So we could say generate a landscape, one another layer
generate a castle, and on another layer generate a crowd of people.
To make this work I made a prompt-based layering system in a new
"Scene-to-Image" tab.
You write a a multi-line prompt that looks like markdown, where each
section declares one layer.
It is hierarchical, so each layer can have their own child layers.
Examples: https://imgur.com/a/eUxd5qn
In the frontend you can find a brief documentation for the syntax,
examples and reference for the various arguments.
Here a short summary:
Sections with "prompt" and child layers are img2img, without child
layers they are txt2img.
Without "prompt" they are just images, useful for mask selection, image
composition, etc.
Images can be initialized with "color", resized with "resize" and their
position specified with "pos".
Rotation and rotation center are "rotation" and "center".
Mask can automatically be selected by color or by estimated depth based
on https://huggingface.co/spaces/atsantiago/Monocular_Depth_Filter.
# Additional dependencies that are required for this change
For mask selection by monocular depth estimation tensorflow is required
and the model must be cloned to ./src/monocular_depth_estimation/
Changes in environment.yaml:
- einops>=0.3.0
- tensorflow>=2.10.0
Einops must be allowed to be newer for tensorflow to work.
# Checklist:
- [x] I have changed the base branch to `dev`
- [x] I have performed a self-review of my own code
- [x] I have commented my code in hard-to-understand areas
- [x] I have made corresponding changes to the documentation
Co-authored-by: hlky <106811348+hlky@users.noreply.github.com>
Bumps [numpy](https://github.com/numpy/numpy) from 1.21.5 to 1.22.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/numpy/numpy/releases">numpy's
releases</a>.</em></p>
<blockquote>
<h2>v1.22.0</h2>
<h1>NumPy 1.22.0 Release Notes</h1>
<p>NumPy 1.22.0 is a big release featuring the work of 153 contributors
spread over 609 pull requests. There have been many improvements,
highlights are:</p>
<ul>
<li>Annotations of the main namespace are essentially complete. Upstream
is a moving target, so there will likely be further improvements,
but the major work is done. This is probably the most user visible
enhancement in this release.</li>
<li>A preliminary version of the proposed Array-API is provided. This is
a step in creating a standard collection of functions that can be
used across application such as CuPy and JAX.</li>
<li>NumPy now has a DLPack backend. DLPack provides a common interchange
format for array (tensor) data.</li>
<li>New methods for <code>quantile</code>, <code>percentile</code>, and
related functions. The
new methods provide a complete set of the methods commonly found in
the literature.</li>
<li>A new configurable allocator for use by downstream projects.</li>
</ul>
<p>These are in addition to the ongoing work to provide SIMD support for
commonly used functions, improvements to F2PY, and better
documentation.</p>
<p>The Python versions supported in this release are 3.8-3.10, Python
3.7
has been dropped. Note that 32 bit wheels are only provided for Python
3.8 and 3.9 on Windows, all other wheels are 64 bits on account of
Ubuntu, Fedora, and other Linux distributions dropping 32 bit support.
All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which
should fix
the occasional problems encountered by folks using truly huge
arrays.</p>
<h2>Expired deprecations</h2>
<h3>Deprecated numeric style dtype strings have been removed</h3>
<p>Using the strings <code>"Bytes0"</code>,
<code>"Datetime64"</code>, <code>"Str0"</code>,
<code>"Uint32"</code>,
and <code>"Uint64"</code> as a dtype will now raise a
<code>TypeError</code>.</p>
<p>(<a
href="https://github-redirect.dependabot.com/numpy/numpy/pull/19539">gh-19539</a>)</p>
<h3>Expired deprecations for <code>loads</code>, <code>ndfromtxt</code>,
and <code>mafromtxt</code> in npyio</h3>
<p><code>numpy.loads</code> was deprecated in v1.15, with the
recommendation that
users use <code>pickle.loads</code> instead. <code>ndfromtxt</code> and
<code>mafromtxt</code> were both
deprecated in v1.17 - users should use <code>numpy.genfromtxt</code>
instead with
the appropriate value for the <code>usemask</code> parameter.</p>
<p>(<a
href="https://github-redirect.dependabot.com/numpy/numpy/pull/19615">gh-19615</a>)</p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="4adc87dff1"><code>4adc87d</code></a>
Merge pull request <a
href="https://github-redirect.dependabot.com/numpy/numpy/issues/20685">#20685</a>
from charris/prepare-for-1.22.0-release</li>
<li><a
href="fd66547557"><code>fd66547</code></a>
REL: Prepare for the NumPy 1.22.0 release.</li>
<li><a
href="125304b035"><code>125304b</code></a>
wip</li>
<li><a
href="c283859128"><code>c283859</code></a>
Merge pull request <a
href="https://github-redirect.dependabot.com/numpy/numpy/issues/20682">#20682</a>
from charris/backport-20416</li>
<li><a
href="5399c03d4a"><code>5399c03</code></a>
Merge pull request <a
href="https://github-redirect.dependabot.com/numpy/numpy/issues/20681">#20681</a>
from charris/backport-20954</li>
<li><a
href="f9c45f8ebf"><code>f9c45f8</code></a>
Merge pull request <a
href="https://github-redirect.dependabot.com/numpy/numpy/issues/20680">#20680</a>
from charris/backport-20663</li>
<li><a
href="794b36f7e1"><code>794b36f</code></a>
Update armccompiler.py</li>
<li><a
href="d93b14e3d7"><code>d93b14e</code></a>
Update test_public_api.py</li>
<li><a
href="7662c0789c"><code>7662c07</code></a>
Update <strong>init</strong>.py</li>
<li><a
href="311ab52488"><code>311ab52</code></a>
Update armccompiler.py</li>
<li>Additional commits viewable in <a
href="https://github.com/numpy/numpy/compare/v1.21.5...v1.22.0">compare
view</a></li>
</ul>
</details>
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# Description
AMD support can be built by prefixing your compose commands with
`docker compose -f docker-compose.yml -f docker-compose.amd.yml ...`
or, by setting
```
export COMPOSE_FILE=docker-compose.yml:docker-compose.amd.yml
```
in your `.profile` or through a tool like `direnv`
Closes: Discord #installation-packing:AMD, at least for Linux hosts
# Checklist:
- [x] I have changed the base branch to `dev`
- [x] I have performed a self-review of my own code
- [x] I have commented my code in hard-to-understand areas
- [x] I have made corresponding changes to the documentation
# Description
Docker package sync
Closes: N/A
# Checklist:
- [x] I have changed the base branch to `dev`
- [x] I have performed a self-review of my own code
- [x] I have commented my code in hard-to-understand areas
- [x] I have made corresponding changes to the documentation
# Checklist:
- [x] I have changed the base branch to `dev`
- [x] I have performed a self-review of my own code
- [x] I have commented my code in hard-to-understand areas
- [x] I have made corresponding changes to the documentation
Signed-off-by: dependabot[bot] <support@github.com>
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1. The update to Streamlit 1.13 has bee mirrored
2. Streamlit needs to be in Headless mode so it doesn't pester about
wanting an email on startup, which crashes in docker since there's no
stdin
3. Fixed an issue where opencv-python from the base image was not
allowing us to override it with opencv-python-headless, which is
required because of its lack of runtime DLLs (+ it's smaller)
