Add `dump_path` arg to the converter function & save the model to disk inside the conversion function. This is the same pattern as in the other conversion functions.
* pass model config to _load_model
* make conversion work again
* do not write diffusers to disk when convert_cache set to 0
* adding same model to cache twice is a no-op, not an assertion error
* fix issues identified by psychedelicious during pr review
* following conversion, avoid redundant read of cached submodels
* fix error introduced while merging
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
"Normal" models have 4 in-channels, while "Depth" models have 5 and "Inpaint" models have 9.
We need to explicitly tell diffusers the channel count when converting models.
Closes #6058
It's possible for a model's state dict to have integer keys, though we do not actually support such models.
As part of probing, we call `key.startswith(...)` on the state dict keys. This raises an `AttributeError` for integer keys.
This logic is in `invokeai/backend/model_manager/probe.py:get_model_type_from_checkpoint`
To fix this, we can cast the keys to strings first. The models w/ integer keys will still fail to be probed, but we'll get a `InvalidModelConfigException` instead of `AttributeError`.
Closes#6044
Add `extra="forbid"` to the default settings models.
Closes#6035.
Pydantic has some quirks related to unions. This affected how the union of default settings was evaluated. See https://github.com/pydantic/pydantic/issues/9095 for a detailed description of the behaviour that this change addresses.
- Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own.
- Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
For SSDs, `blake3` is about 10x faster than `blake3_single` - 3 files/second vs 30 files/second.
For spinning HDDs, `blake3` is about 100x slower than `blake3_single` - 300 seconds/file vs 3 seconds/file.
For external drives, `blake3` is always worse, but the difference is highly variable. For external spinning drives, it's probably way worse than internal.
The least offensive algorithm is `blake3_single`, and it's still _much_ faster than any other algorithm.
* add probe for SDXL controlnet models
* Update invokeai/backend/model_management/model_probe.py
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* Update invokeai/backend/model_manager/probe.py
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
The models from INITIAL_MODELS.yaml have been recreated as a structured python object. This data is served on a new route. The model sources are compared against currently-installed models to determine if they are already installed or not.
- Move base of t2i and clip_vision config models to DiffusersBase, which contains
a field to record the model variant (e.g. "fp16")
- This restore the ability to load fp16 t2i and clip_vision models
- Also add defensive coding to load the vanilla model when the fp16 model
has been replaced (or more likely, user's preferences changed since installation)
Co-authored-by: Lincoln Stein <lstein@gmail.com>
- No longer install core conversion models. Use the HuggingFace cache to load
them if and when needed.
- Call directly into the diffusers library to perform conversions with only shallow
wrappers around them to massage arguments, etc.
- At root configuration time, do not create all the possible model subdirectories,
but let them be created and populated at model install time.
- Remove checks for missing core conversion files, since they are no
longer installed.
- This adds additional logic to the safetensors->diffusers conversion script
to check for and install missing core conversion models at runtime.
- Fixes#5934
BLAKE3 has poor performance on spinning disks when parallelized. See https://github.com/BLAKE3-team/BLAKE3/issues/31
- Replace `skip_model_hash` setting with `hashing_algorithm`. Any algorithm we support is accepted.
- Add `random` algorithm: hashes a UUID with BLAKE3 to create a random "hash". Equivalent to the previous skip functionality.
- Add `blake3_single` algorithm: hashes on a single thread using BLAKE3, fixes the aforementioned performance issue
- Update model probe to accept the algorithm to hash with as an optional arg, defaulting to `blake3`
- Update all calls of the probe to use the app's configured hashing algorithm
- Update an external script that probes models
- Update tests
- Move ModelHash into its own module to avoid circuclar import issues
- No need for it to by a pydantic model. Just a class now.
- Remove ABC, it made it hard to understand what was going on as attributes were spread across the ABC and implementation. Also, there is no other implementation.
- Add tests
There is a breaking change in python 3.11 related to how enums with `str` as a mixin are formatted. This appears to have not caused any grief for us until now.
Re-jigger the discriminator setup to use `.value` so everything works on both python 3.10 and 3.11.
- Metadata is merged with the config. We can simplify the MM substantially and remove the handling for metadata.
- Per discussion, we don't have an ETA for frontend implementation of tags, and with the realization that the tags from CivitAI are largely useless, there's no reason to keep tags in the MM right now. When we are ready to implement tags on the frontend, we can refer back to the implementation here and use it if it supports the design.
- Fix all tests.
Sometimes, diffusers model components (tokenizer, unet, etc.) have multiple weights files in the same directory.
In this situation, we assume the files are different versions of the same weights. For example, we may have multiple
formats (`.bin`, `.safetensors`) with different precisions. When downloading model files, we want to select only
the best of these files for the requested format and precision/variant.
The previous logic assumed that each model weights file would have the same base filename, but this assumption was
not always true. The logic is revised score each file and choose the best scoring file, resulting in only a single
file being downloaded for each submodel/subdirectory.
