By supplying --model (defaulting to stable-diffusion-1.4) a user can specify which model to load.
Width/Height/Config Location/Weights Location are referenced from configs/models.yaml
models.yaml can serve as a base for expanding our support for other versions of Latent/Stable Diffusion.
Contained are parameters for default width/height, as well as where to find the config and weights for this model.
Adding a new model is as simple as adding to this file.
I'm using stable-diffusion on a 2022 Macbook M2 Air with 24 GB unified memory.
I see this taking about 2.0s/it.
I've moved many deps from pip to conda-forge, to take advantage of the
precompiled binaries. Some notes for Mac users, since I've seen a lot of
confusion about this:
One doesn't need the `apple` channel to run this on a Mac-- that's only
used by `tensorflow-deps`, required for running tensorflow-metal. For
that, I have an example environment.yml here:
https://developer.apple.com/forums/thread/711792?answerId=723276022#723276022
However, the `CONDA_ENV=osx-arm64` environment variable *is* needed to
ensure that you do not run any Intel-specific packages such as `mkl`,
which will fail with [cryptic errors](https://github.com/CompVis/stable-diffusion/issues/25#issuecomment-1226702274)
on the ARM architecture and cause the environment to break.
I've also added a comment in the env file about 3.10 not working yet.
When it becomes possible to update, those commands run on an osx-arm64
machine should work to determine the new version set.
Here's what a successful run of dream.py should look like:
```
$ python scripts/dream.py --full_precision SIGABRT(6) ↵ 08:42:59
* Initializing, be patient...
Loading model from models/ldm/stable-diffusion-v1/model.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 slower but more accurate full-precision math (--full_precision)
>> Setting Sampler to k_lms
model loaded in 6.12s
* Initialization done! Awaiting your command (-h for help, 'q' to quit)
dream> "an astronaut riding a horse"
Generating: 0%| | 0/1 [00:00<?, ?it/s]/Users/corajr/Documents/lstein/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(
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [01:37<00:00, 1.95s/it]
Generating: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [01:38<00:00, 98.55s/it]
Usage stats:
1 image(s) generated in 98.60s
Max VRAM used for this generation: 0.00G
Outputs:
outputs/img-samples/000001.1525943180.png: "an astronaut riding a horse" -s50 -W512 -H512 -C7.5 -Ak_lms -F -S1525943180
```
- move all device init logic to T2I.__init__
- handle m1 specific edge case with autocast device type
- check torch.cuda.is_available before using cuda
* This functionality is triggered by the --fit option in the CLI (default
false), and by the "fit" checkbox in the WebGUI (default True)
* In addition, this commit contains a number of whitespace changes to
make the code more readable, as well as an attempt to unify the visual
appearance of info and warning messages.
* fix AttributeError crash when running on non-CUDA systems; closes issue #234 and issue #250
* although this prevents dream.py script from crashing immediately on MPS systems, MPS support still very much a work in progress.
* Allow configuration of which SD model to use
Closes https://github.com/lstein/stable-diffusion/issues/49 The syntax isn't quite the same (opting for --weights over --model), although --weights is more in-line with the existing naming convention.
This method also locks us into models in the models/ldm/stable-diffusion-v1/ directory. Personally, I'm not averse to this, although a secondary solution may be necessary if we wish to supply weights from an external directory.
* Fix typo
* Allow either filename OR filepath input for arg
This approach allows both
--weights SD13
--weights C:/StableDiffusion/models/ldm/stable-diffusion-v1/SD13.ckpt
Fixed merging embeddings based on the changes made in textual inversion. Tested and working. Inverted their logic to prioritize Stable Diffusion implementation over alternatives, but left the option for alternatives to still be used.
* Optimizations to the training model
Based on the changes made in
textual_inversion I carried over the relevant changes that improve model training. These changes reduce the amount of memory used, significantly improve the speed at which training runs, and improves the quality of the results.
It also fixes the problem where the model trainer wouldn't automatically stop when it hit the set number of steps.
* Update main.py
Cleaned up whitespace
