Now you can activate the Hugging Face `diffusers` library safety check
for NSFW and other potentially disturbing imagery.
To turn on the safety check, pass --safety_checker at the command
line. For developers, the flag is `safety_checker=True` passed to
ldm.generate.Generate(). Once the safety checker is turned on, it
cannot be turned off unless you reinitialize a new Generate object.
When the safety checker is active, suspect images will be blurred and
a warning icon is added. There is also a warning message printed in
the CLI, but it can be a little hard to see because of its positioning
in the output stream.
There is a slight but noticeable delay when the safety checker runs.
Note that invisible watermarking is *not* currently implemented. The
watermark code distributed by the CompViz distribution uses a library
that does not seem to be able to retrieve the watermarks it creates,
and it does not appear that Hugging Face `diffusers` or other SD
distributions are doing any watermarking.
1. If tensors are passed to inpaint as init_image and/or init_mask, then
the post-generation image fixup code will be skipped.
2. Post-generation image fixup will work with either a black and white "L"
or "RGB" mask, or an "RGBA" mask.
- pass a PIL.Image to img2img and inpaint rather than tensor
- To support clipseg, inpaint needs to accept an "L" or "1" format
mask. Made the appropriate change.
To add a VAE autoencoder to an existing model:
1. Download the appropriate autoencoder and put it into
models/ldm/stable-diffusion
Note that you MUST use a VAE that was written for the
original CompViz Stable Diffusion codebase. For v1.4,
that would be the file named vae-ft-mse-840000-ema-pruned.ckpt
that you can download from https://huggingface.co/stabilityai/sd-vae-ft-mse-original
2. Edit config/models.yaml to contain the following stanza, modifying `weights`
and `vae` as required to match the weights and vae model file names. There is
no requirement to rename the VAE file.
~~~
stable-diffusion-1.4:
weights: models/ldm/stable-diffusion-v1/sd-v1-4.ckpt
description: Stable Diffusion v1.4
config: configs/stable-diffusion/v1-inference.yaml
vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
width: 512
height: 512
~~~
3. Alternatively from within the `invoke.py` CLI, you may use the command
`!editmodel stable-diffusion-1.4` to bring up a simple editor that will
allow you to add the path to the VAE.
4. If you are just installing InvokeAI for the first time, you can also
use `!import_model models/ldm/stable-diffusion/sd-v1.4.ckpt` instead
to create the configuration from scratch.
5. That's it!
* Removed duplicate fix_func for MPS
* add support for loading VAE autoencoders
To add a VAE autoencoder to an existing model:
1. Download the appropriate autoencoder and put it into
models/ldm/stable-diffusion
Note that you MUST use a VAE that was written for the
original CompViz Stable Diffusion codebase. For v1.4,
that would be the file named vae-ft-mse-840000-ema-pruned.ckpt
that you can download from https://huggingface.co/stabilityai/sd-vae-ft-mse-original
2. Edit config/models.yaml to contain the following stanza, modifying `weights`
and `vae` as required to match the weights and vae model file names. There is
no requirement to rename the VAE file.
~~~
stable-diffusion-1.4:
weights: models/ldm/stable-diffusion-v1/sd-v1-4.ckpt
description: Stable Diffusion v1.4
config: configs/stable-diffusion/v1-inference.yaml
vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
width: 512
height: 512
~~~
3. Alternatively from within the `invoke.py` CLI, you may use the command
`!editmodel stable-diffusion-1.4` to bring up a simple editor that will
allow you to add the path to the VAE.
4. If you are just installing InvokeAI for the first time, you can also
use `!import_model models/ldm/stable-diffusion/sd-v1.4.ckpt` instead
to create the configuration from scratch.
5. That's it!
* ported code refactor changes from PR #1221
- pass a PIL.Image to img2img and inpaint rather than tensor
- To support clipseg, inpaint needs to accept an "L" or "1" format
mask. Made the appropriate change.
* minor fixes to inpaint code
1. If tensors are passed to inpaint as init_image and/or init_mask, then
the post-generation image fixup code will be skipped.
2. Post-generation image fixup will work with either a black and white "L"
or "RGB" mask, or an "RGBA" mask.
Co-authored-by: wfng92 <43742196+wfng92@users.noreply.github.com>
The k_samplers come with a "karras" noise schedule which performs
very well at low step counts but becomes noisy at higher ones.
This commit introduces a threshold (currently 30 steps) at which the
k samplers will switch over from using karras to the older model
noise schedule.
To add a VAE autoencoder to an existing model:
1. Download the appropriate autoencoder and put it into
models/ldm/stable-diffusion
Note that you MUST use a VAE that was written for the
original CompViz Stable Diffusion codebase. For v1.4,
that would be the file named vae-ft-mse-840000-ema-pruned.ckpt
that you can download from https://huggingface.co/stabilityai/sd-vae-ft-mse-original
2. Edit config/models.yaml to contain the following stanza, modifying `weights`
and `vae` as required to match the weights and vae model file names. There is
no requirement to rename the VAE file.
~~~
stable-diffusion-1.4:
weights: models/ldm/stable-diffusion-v1/sd-v1-4.ckpt
description: Stable Diffusion v1.4
config: configs/stable-diffusion/v1-inference.yaml
vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
width: 512
height: 512
~~~
3. Alternatively from within the `invoke.py` CLI, you may use the command
`!editmodel stable-diffusion-1.4` to bring up a simple editor that will
allow you to add the path to the VAE.
4. If you are just installing InvokeAI for the first time, you can also
use `!import_model models/ldm/stable-diffusion/sd-v1.4.ckpt` instead
to create the configuration from scratch.
5. That's it!
Ironically, the black and white mask file generated by the
`invoke> !mask` command could not be passed as the mask to
`img2img`. This is now fixed and the documentation updated.
- code for committing config changes to models.yaml now in module
rather than in invoke script
- model marked "default" is now loaded if model not specified on
command line
- uncache changed models when edited, so that they reload properly
- removed liaon from models.yaml and added stable-diffusion-1.5
- The !mask command takes an image path, a text prompt, and
(optionally) a masking threshold. It creates a mask over the region
indicated by the prompt, and outputs several files that show which
regions will be masked by the chosen prompt and threshold.
- The mask images should not be passed directly to img2img because
they are designed for visualization only. Instead, use the
--text_mask option to pass the selected prompt and threshold.
- See docs/features/INPAINTING.md for details.
- The directory "models" in the main InvokeAI directory was conflicting
with loading "models.clipseg". To fix this issue, I have renamed the
models.clipseg to clipseg_models.clipseg, and applied this change to
the 'models-rename' branch of invoke-ai's fork of clipseg.
attention is parsed but ignored, blends old syntax doesn't work,
conjunctions are parsed but ignored, the only part that's used
here is the new .blend() syntax and cross-attention control
using .swap()
commit 9bb0b5d0036c4dffbb72ce11e097fae4ab63defd
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Sat Oct 15 23:43:41 2022 +0200
undo local_files_only stuff
commit eed93f5d30c34cfccaf7497618ae9af17a5ecfbb
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Sat Oct 15 23:40:37 2022 +0200
Revert "Merge branch 'development-invoke' into fix-prompts"
This reverts commit 7c40892a9f184f7e216f14d14feb0411c5a90e24, reversing
changes made to e3f2dd62b0548ca6988818ef058093a4f5b022f2.
commit f06d6024e345c69e6d5a91ab5423925a68ee95a7
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Thu Oct 13 23:30:16 2022 +0200
more efficiently handle multiple conditioning
commit 5efdfcbcd980ce6202ab74e7f90e7415ce7260da
Merge: b9c0dc5 ac08bb6
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Thu Oct 13 14:51:01 2022 +0200
Merge branch 'optional-disable-karras-schedule' into fix-prompts
commit ac08bb6fd25e19a9d35cf6c199e66500fb604af1
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Thu Oct 13 14:50:43 2022 +0200
append '*use_model_sigmas*' to prompt string to use model sigmas
commit 70d8c05a3ff329409f76204f4af94e55d468ab8b
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Thu Oct 13 12:12:17 2022 +0200
make karras scheduling switchable
commit d60df54f69 replaced the model's
own scheduling with karras scheduling. this has changed image generation
(seems worse now?)
this commit wraps the change in a bool.
commit b9c0dc5f1a658a0e6c3936000e9ae559e1c7a1db
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Wed Oct 12 20:16:00 2022 +0200
add test of more complex conjunction
commit 9ac0c15cc0d7b5f6df3289d3ad474260972a17be
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Wed Oct 12 17:18:25 2022 +0200
improve comments
commit ad33bce60590b87b2a93e90f16dc9d3e935d04a5
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Wed Oct 12 17:04:46 2022 +0200
put back thresholding stuff
commit 4852c698a325049834ba0d4b358f07210bc7171a
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Wed Oct 12 14:25:02 2022 +0200
notes on improving conjunction efficiency
commit a53bb1e5b68025d09642b935ae6a9a015cfaf2d6
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Wed Oct 12 14:14:33 2022 +0200
optional weights support for Conjunction
commit fec79ab15e4f0c84dd61cb1b45a5e6a72ae4aaeb
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Wed Oct 12 12:07:27 2022 +0200
fix blend error and log parsing output
commit 1f751c2a039f9c97af57b18e0f019512631d5a25
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Wed Oct 12 10:33:33 2022 +0200
fix broken euler sampler
commit 02f8148d17efe4b6bde8d29b827092a0626363ee
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Wed Oct 12 10:24:20 2022 +0200
cleanup prompt parser
commit 8028d49ae6c16c0d6ec9c9de9c12d56c32201421
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Wed Oct 12 10:14:18 2022 +0200
explicit conjunction, improve flattening logic
commit 8a1710892185f07eb77483f7edae0fc4d6bbb250
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Tue Oct 11 22:59:30 2022 +0200
adapt multi-conditioning to also work with ddim
commit 53802a839850d0d1ff017c6bafe457c4bed750b0
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Tue Oct 11 22:31:42 2022 +0200
unconditioning is also fancy-prompt-syntaxable
commit 7c40892a9f184f7e216f14d14feb0411c5a90e24
Merge: e3f2dd6 dbe0da4
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Tue Oct 11 21:39:54 2022 +0200
Merge branch 'development-invoke' into fix-prompts
commit e3f2dd62b0548ca6988818ef058093a4f5b022f2
Merge: eef0e48 06f542e
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Tue Oct 11 21:38:09 2022 +0200
Merge remote-tracking branch 'upstream/development' into fix-prompts
commit eef0e484c2eaa1bd4e0e0b1d3f8d7bba38478144
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Tue Oct 11 21:26:25 2022 +0200
fix run-on paren-less attention, add some comments
commit fd29afdf0e9f5e0cdc60239e22480c36ca0aaeca
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Tue Oct 11 21:03:02 2022 +0200
python 3.9 compatibility
commit 26f7646eef7f39bc8f7ce805e747df0f723464da
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Tue Oct 11 20:58:42 2022 +0200
first pass connecting PromptParser to conditioning
commit ae53dff3796d7b9a5e7ed30fa1edb0374af6cd8d
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Tue Oct 11 20:51:15 2022 +0200
update frontend dist
commit 9be4a59a2d76f49e635474b5984bfca826a5dab4
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Tue Oct 11 19:01:39 2022 +0200
fix issues with correctness checking FlattenedPrompt
commit 3be212323eab68e72a363a654124edd9809e4cf0
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Tue Oct 11 18:43:16 2022 +0200
parsing nested seems to work pretty ok
commit acd73eb08cf67c27cac8a22934754321256f56a9
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Tue Oct 11 18:26:17 2022 +0200
wip introducing FlattenedPrompt class
commit 71698d5c7c2ac855b690d8ef67e8830148c59eda
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Tue Oct 11 15:59:42 2022 +0200
recursive attention weighting seems to actually work
commit a4e1ec6b20deb7cc0cd12737bdbd266e56144709
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Tue Oct 11 15:06:24 2022 +0200
now apparently almost supported nested attention
commit da76fd1ddf22a3888cdc08fd4fed38d8b178e524
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Tue Oct 11 13:23:37 2022 +0200
wip prompt parsing
commit dbe0da4572c2ac22f26a7afd722349a5680a9e47
Author: Kyle Schouviller <kyle0654@hotmail.com>
Date: Mon Oct 10 22:32:35 2022 -0700
Adding node-based invocation apps
commit 8f2a2ffc083366de74d7dae471b50b6f98a7c5f8
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Mon Oct 10 19:03:18 2022 +0200
fix merge issues
commit 73118dee2a8f4891700756e014caf1c9ca629267
Merge: fd00844 12413b0
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Mon Oct 10 12:42:48 2022 +0200
Merge remote-tracking branch 'upstream/development' into fix-prompts
commit fd0084413541013c2cf71e006af0392719bef53d
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Mon Oct 10 12:39:38 2022 +0200
wip prompt parsing
commit 0be9363db9307859d2b65cffc6af01f57d7873a4
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Mon Oct 10 03:20:06 2022 +0200
better +/- attention parsing
commit 5383f691874a58ab01cda1e4fac6cf330146526a
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Mon Oct 10 02:27:47 2022 +0200
prompt parser seems to work
commit 591d098a33ce35462428d8c169501d8ed73615ab
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Sun Oct 9 20:25:37 2022 +0200
supports weighting unconditioning, cross-attention with |
commit 7a7220563aa05a2980235b5b908362f66b728309
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Sun Oct 9 18:15:56 2022 +0200
i think cross attention might be working?
commit 951ed391e7126bff228c18b2db304ad28d59644a
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Sun Oct 9 16:04:54 2022 +0200
weighted CFG denoiser working with a single item
commit ee532a0c2827368c9e45a6a5f3975666402873da
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Sun Oct 9 06:33:40 2022 +0200
wip probably doesn't work or compile
commit 14654bcbd207b9ca28a6cbd37dbd967d699b062d
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Fri Oct 7 18:11:48 2022 +0200
use tan() to calculate embedding weight for <1 attentions
commit 1a8e76b31aa5abf5150419ebf3b29d4658d07f2b
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Fri Oct 7 16:14:54 2022 +0200
fix bad math.max reference
commit f697ff896875876ccaa1e5527405bdaa7ed27cde
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Fri Oct 7 15:55:57 2022 +0200
respect http[s]x protocol when making socket.io middleware
commit 41d3dd4eeae8d4efb05dfb44fc6d8aac5dc468ab
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Fri Oct 7 13:29:54 2022 +0200
fractional weighting works, by blending with prompts excluding the word
commit 087fb6dfb3e8f5e84de8c911f75faa3e3fa3553c
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Fri Oct 7 10:52:03 2022 +0200
wip doing weights <1 by averaging with conditioning absent the lower-weighted fragment
commit 3c49e3f3ec7c18dc60f3e18ed2f7f0d97aad3a47
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Fri Oct 7 10:36:15 2022 +0200
notate CFGDenoiser, perhaps
commit d2bcf1bb522026ebf209ad0103f6b370383e5070
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Thu Oct 6 05:04:47 2022 +0200
hack blending syntax to test attention weighting more extensively
commit 94904ef2cf917f74ec23ef7a570e12ff8255b048
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Thu Oct 6 04:56:37 2022 +0200
conditioning works, apparently
commit 7c6663ddd70f665fd1308b6dd74f92ca393a8df5
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Thu Oct 6 02:20:24 2022 +0200
attention weighting, definitely works in positive direction
commit 5856d453a9b020bc1a28ff643ae1f58c12c9be73
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Tue Oct 4 19:02:14 2022 +0200
wip bubbling weights down
commit a2ed14fd9b7d3cb36b6c5348018b364c76d1e892
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date: Tue Oct 4 17:35:39 2022 +0200
bring in changes from PC
test prompt:
"a cat sitting on a car {a dog sitting on a car}" -W 384 -H 256 -s 10 -S 12346 -A k_euler
note that substition of dog for cat is currently hard-coded (ksampler.py
line 43-44)
On the command line, the new option is --text_mask or -tm.
Example:
```
invoke> a baseball -I /path/to/still_life.png -tm orange
```
This will find the orange fruit in the still life painting and replace
it with an image of a baseball.
- In CLI: the argument is --png_compression <0..9> (-z<0..9>)
- In API, pass `compress_level` to PngWriter.save_image_and_prompt_to_png()
Compression ranges from 0 (no compression) to 9 (maximum compression).
Default value is 6 (as specified by Pillow package).
This addresses an issue first raised in #652.
- --inpaint_replace 0.X will cause inpainting to ignore what is under
the masked region with a strength ranging from 0 (don't ignore at all)
to 1.0 (ignore completely)
- sync with upstream development
- update docs
- add a `--inpaint_replace` option that fills masked regions with
latent noise. This allows radical changes to inpainted regions
at the cost of losing context.
- fix up readline, arg processing and metadata writing to accommodate
this change
- fixed bug in storage and retrieval of variations, discovered incidentally
during testing
- update documentation
- Error checks for invalid model
- Add !del_model command to invoke.py
- Add del_model() method to model_cache
- Autocompleter kept in sync with model addition/subtraction.
At step counts greater than ~75, the ksamplers start producing noisy
images when using the Karras noise schedule. This PR reverts to using
the model's own noise schedule, which eliminates the problem at the
cost of slowing convergence at lower step counts.
This PR also introduces a new CLI `--save_intermediates <n>' argument,
which will save every nth intermediate image into a subdirectory
named `intermediates/<image_prefix>'.
Addresses issue #1083.
At step counts greater than ~75, the ksamplers start producing noisy
images when using the Karras noise schedule. This PR reverts to using
the model's own noise schedule, which eliminates the problem at the
cost of slowing convergence at lower step counts.
This PR also introduces a new CLI `--save_intermediates <n>' argument,
which will save every nth intermediate image into a subdirectory
named `intermediates/<image_prefix>'.
Addresses issue #1083.
- !import_model <path/to/model/weights> will import a new model,
prompt the user for its name and description, write it to the
models.yaml file, and load it.
- !edit_model <model_name> will bring up a previously-defined model
and prompt the user to edit its descriptive fields.
Example of !import_model
<pre>
invoke> <b>!import_model models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt</b>
>> Model import in process. Please enter the values needed to configure this model:
Name for this model: <b>waifu-diffusion</b>
Description of this model: <b>Waifu Diffusion v1.3</b>
Configuration file for this model: <b>configs/stable-diffusion/v1-inference.yaml</b>
Default image width: <b>512</b>
Default image height: <b>512</b>
>> New configuration:
waifu-diffusion:
config: configs/stable-diffusion/v1-inference.yaml
description: Waifu Diffusion v1.3
height: 512
weights: models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt
width: 512
OK to import [n]? <b>y</b>
>> Caching model stable-diffusion-1.4 in system RAM
>> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt
| LatentDiffusion: Running in eps-prediction mode
| DiffusionWrapper has 859.52 M params.
| Making attention of type 'vanilla' with 512 in_channels
| Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
| Making attention of type 'vanilla' with 512 in_channels
| Using faster float16 precision
</pre>
Example of !edit_model
<pre>
invoke> <b>!edit_model waifu-diffusion</b>
>> Editing model waifu-diffusion from configuration file ./configs/models.yaml
description: <b>Waifu diffusion v1.4beta</b>
weights: models/ldm/stable-diffusion-v1/<b>model-epoch10-float16.ckpt</b>
config: configs/stable-diffusion/v1-inference.yaml
width: 512
height: 512
>> New configuration:
waifu-diffusion:
config: configs/stable-diffusion/v1-inference.yaml
description: Waifu diffusion v1.4beta
weights: models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt
height: 512
width: 512
OK to import [n]? y
>> Caching model stable-diffusion-1.4 in system RAM
>> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt
...
</pre>
This commit "reverts" the new API changes by extracting the old
functionality into new files.
The work is based on the commit `803a51d5adca7e6e28491fc414fd3937bee7cb79`
PngWriter regained PromptFormatter as old server used that.
`server_legacy.py` is the old server that `dream.py` used.
Finally `legacy_api.py` is what `dream.py` used to be at the mentioned
commit.
One manually run test has been added in order to be able to test
compatibility with the old API, currently just testing that the API
endpoint works the same way + the image hash is the same as it used to
be before.
- This PR enables two new commands in the invoke.py script
!models -- list the available models and their cache status
!switch <model> -- switch to the indicated model
Example:
invoke> !models
laion400m not loaded Latent Diffusion LAION400M model
stable-diffusion-1.4 active Stable Diffusion inference model version 1.4
waifu-1.3 cached Waifu anime model version 1.3
invoke> !switch waifu-1.3
>> Caching model stable-diffusion-1.4 in system RAM
>> Retrieving model waifu-1.3 from system RAM cache
The name and descriptions of the models are taken from
`config/models.yaml`. A future enhancement to `model_cache.py` will be
to enable new model stanzas to be added to the file
programmatically. This will be useful for the WebGUI.
More details:
- Use fast switching algorithm described in PR #948
- Models are selected using their configuration stanza name
given in models.yaml.
- To avoid filling up CPU RAM with cached models, this PR
implements an LRU cache that monitors available CPU RAM.
- The caching code allows the minimum value of available RAM
to be adjusted, but invoke.py does not currently have a
command-line argument that allows you to set it. The
minimum free RAM is arbitrarily set to 2 GB.
- Add optional description field to configs/models.yaml
Unrelated fixes:
- Added ">>" to CompViz model loading messages in order to make user experience
more consistent.
- When generating an image greater than defaults, will only warn about possible
VRAM filling the first time.
- Fixed bug that was causing help message to be printed twice. This involved
moving the import line for the web backend into the section where it is
called.
Coauthored by: @ArDiouscuros
- the prompt argument comes before the optional arguments
- usage statement shows 'invoke>' rather than 'invoke.py'
- use pydoc pager to help display long help message
- the prompt argument comes before the optional arguments
- usage statement shows 'invoke>' rather than 'invoke.py'
- use pydoc pager to help display long help message
- rename dream.py to invoke.py
- create a compatibility script named dream.py that execs() invoke.py
- redo documentation
- change help message in args
- this does **not** rename the libraries, which are still ldm.dream.util, etc
This reverts commit 5f42d08945.
This fix was intended to solve issue #939, in which ESRGAN generates
dark images when upscaling 4X on certain GTX cards. However, the fix
apparently causes conflicts with some versions of the ESRGAN library,
and this fix will have to wait until after release of 2.0.
