- ldm.generate.Generator() now takes an argument named `max_load_models`.
This is an integer that limits the model cache size. When the cache
reaches the limit, it will start purging older models from cache.
- CLI takes an argument --max_load_models, default to 2. This will keep
one model in GPU and the other in CPU and switch back and forth
quickly.
- To not cache models at all, pass --max_load_models=1
- user can select which weight files to download using huggingface cache
- user must log in to huggingface, generate an access token, and accept
license terms the very first time this is run. After that, everything
works automatically.
- added placeholder for docs for installing models
- also got rid of unused config files. hopefully they weren't needed
for textual inversion, but I don't think so.
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!
- 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
- 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.
- !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 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
