* removed unnecessary parameters from get_up_block and get_down_block functions
* adding resnet_skip_time_act, resnet_out_scale_factor and cross_attention_norm to get_up_block and get_down_block functions
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* add
* clean
* up
* clean up more
* fix more tests
* Improve docs further
* improve
* more fixes docs
* Improve docs more
* Update src/diffusers/models/unet_2d_condition.py
* fix
* up
* update doc links
* make fix-copies
* add safety checker and watermarker to stage 3 doc page code snippets
* speed optimizations docs
* memory optimization docs
* make style
* add watermarking snippets to doc string examples
* make style
* use pt_to_pil helper functions in doc strings
* skip mps tests
* Improve safety
* make style
* new logic
* fix
* fix bad onnx design
* make new stable diffusion upscale pipeline model arguments optional
* define has_nsfw_concept when non-pil output type
* lowercase linked to notebook name
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Co-authored-by: William Berman <WLBberman@gmail.com>
When the token used for textual inversion does not have any special symbols (e.g. it is not surrounded by <>), the tokenizer does not properly split the replacement tokens. Adding a space for the padding tokens fixes this.
* Add karras pattern to discrete heun scheduler
* Add integration test
* Fix failing CI on pytorch test on M1 (mps)
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* add: LoRA text encoder support for DreamBooth example.
* fix initialization.
* fix: modification call.
* add: entry in the readme.
* use dog dataset from hub.
* fix: params to clip.
* add entry to the LoRA doc.
* add: tests for lora.
* remove unnecessary list comprehension./
* Update Pix2PixZero Auto-correlation Loss
* Add fast inversion tests
* Clarify purpose and mark as deprecated
Fix inversion prompt broadcasting
* Register modules set to `None` in config for `test_save_load_optional_components`
* Update new tests to coordinate with #2953
* Added distillation for quantization example on textual inversion.
Signed-off-by: Ye, Xinyu <xinyu.ye@intel.com>
* refined readme and code style.
Signed-off-by: Ye, Xinyu <xinyu.ye@intel.com>
* Update text2images.py
* refined code of model load and added compatibility check.
Signed-off-by: Ye, Xinyu <xinyu.ye@intel.com>
* fixed code style.
Signed-off-by: Ye, Xinyu <xinyu.ye@intel.com>
* fix C403 [*] Unnecessary `list` comprehension (rewrite as a `set` comprehension)
Signed-off-by: Ye, Xinyu <xinyu.ye@intel.com>
---------
Signed-off-by: Ye, Xinyu <xinyu.ye@intel.com>
controlnet training center crop input images to multiple of 8
The pipeline code resizes inputs to multiples of 8.
Not doing this resizing in the training script is causing
the encoded image to have different height/width dimensions
than the encoded conditioning image (which uses a separate
encoder that's part of the controlnet model).
We resize and center crop the inputs to make sure they're the
same size (as well as all other images in the batch). We also
check that the initial resolution is a multiple of 8.
