- This sets a step switchover point at which the k-samplers stop using the
Karras noise schedule and start using the LatentDiffusion noise schedule.
The advantage of this is that the Karras schedule produces excellent
results at low step counts but starts to become unstable at high
steps.
- A new command argument --karras_max, lets the user set where the
switchover occurs. Default is 29 steps (1-29 steps Karras),
(30 or greater LDM)
- Tildebyte, sorry to do a fast forward three-way merge for this
but rebasing was just too painful due to extensive recent
changes to the diffuser code.
This was a difficult merge because both PR #1108 and #1243 made
changes to obscure parts of the diffusion code.
- prompt weighting, merging and cross-attention working
- cross-attention does not work with runwayML inpainting
model, but weighting and merging are tested and working
- CLI command parsing code rewritten in order to get embedded
quotes right
- --hires now works with runwayML inpainting
- --embiggen does not work with runwayML and will give an error
- Added an --invert option to invert masks applied to inpainting
- Updated documentation
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.
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.
- 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.
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
- !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>
- better readability in dark mode since color change
- better looking changelog
- fix images which where not loading
- also center most of the images
- fix some syntax errors like
- headlines ending with a colon
- codeblocks with wrong fences
- codeblocks without shell
- update conda prompts from ldm to invokeai
- ....
- 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
