- 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.
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.
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.
- --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.
- 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
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.
- txt2img2img back to using DDIM as img2img sampler; results produced
by some k* samplers are just not reliable enough for good user
experience
- img2img progress message clarifies why img2img steps taken != steps requested
- warn of potential problems when user tries to run img2img on a small init image
- img2img confirmed working with all samplers
- inpainting working on ddim & plms. Changes to k-diffusion
module seem to be needed for inpainting support.
- switched k-diffuser noise schedule to original karras schedule,
which reduces the step number needed for good results
-if readline.set_auto_history() is not implemented, as in pyreadline3, will fall
back gracefully to automatic history saving. The only issue with this is that
-!history commands will be recorded in the history.
-!fetch on missing file no longer crashes script
-!history is now one of the autocomplete commands
-.dream_history now stored in output directory rather than ~user directory.
An important limitation of the last feature is that the history is
loaded and saved to the .dream_history file in the --outdir directory
specified at script launch time. It is not swapped around when the
--outdir is changed during the session.
Add message about interpolation size
Fix crash if sampler not set to DDIM, change parameter name to hires_fix
Hi res mode fix duplicates with img2img scaling
-if readline.set_auto_history() is not implemented, as in pyreadline3, will fall
back gracefully to automatic history saving. The only issue with this is that
-!history commands will be recorded in the history.
-!fetch on missing file no longer crashes script
-!history is now one of the autocomplete commands
-.dream_history now stored in output directory rather than ~user directory.
An important limitation of the last feature is that the history is
loaded and saved to the .dream_history file in the --outdir directory
specified at script launch time. It is not swapped around when the
--outdir is changed during the session.
