* Add a new community pipeline
examples/community/latent_consistency_img2img.py
which can be called like this
import torch
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained(
"SimianLuo/LCM_Dreamshaper_v7", custom_pipeline="latent_consistency_txt2img", custom_revision="main")
# To save GPU memory, torch.float16 can be used, but it may compromise image quality.
pipe.to(torch_device="cuda", torch_dtype=torch.float32)
img2img=LatentConsistencyModelPipeline_img2img(
vae=pipe.vae,
text_encoder=pipe.text_encoder,
tokenizer=pipe.tokenizer,
unet=pipe.unet,
#scheduler=pipe.scheduler,
scheduler=None,
safety_checker=None,
feature_extractor=pipe.feature_extractor,
requires_safety_checker=False,
)
img = Image.open("thisismyimage.png")
result = img2img(prompt,img,strength,num_inference_steps=4)
* Apply suggestions from code review
Fix name formatting for scheduler
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* update readme (and run formatter on latent_consistency_img2img.py)
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Added mark_step for sdxl to run with pytorch xla. Also updated README with instructions for xla
* adding soft dependency on torch_xla
* fix some styling
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
* fix(gligen_inpaint_pipeline): 🐛 Wrap the timestep() 0-d tensor in a list to convert to 1-d tensor. This avoids the TypeError caused by trying to directly iterate over a 0-dimensional tensor in the denoising stage
* test(gligen/gligen_text_image): unit test using the EulerAncestralDiscreteScheduler
---------
Co-authored-by: zhen-hao.chu <zhen-hao.chu@vitrox.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Min-SNR Gamma: correct the fix for SNR weighted loss in v-prediction by adding 1 to SNR rather than the resulting loss weights
Co-authored-by: bghira <bghira@users.github.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Timestep bias for fine-tuning SDXL
* Adjust parameter choices to include "range" and reword the help statements
* Condition our use of weighted timesteps on the value of timestep_bias_strategy
* style
---------
Co-authored-by: bghira <bghira@users.github.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* SDXL microconditioning documentation should indicate the correct default order of parameters, so that developers know
* SDXL microconditioning documentation should indicate the correct default order of parameters, so that developers know
* empty
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Co-authored-by: bghira <bghira@users.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* min-SNR gamma for Dreambooth training
* Align the mse_loss_weights style with SDXL training example
---------
Co-authored-by: bghira <bghira@users.github.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Resolve v_prediction issue for min-SNR gamma weighted loss function
* Combine MSE loss calculation of epsilon and velocity, with a note about the application of the epsilon code to sample prediction
* style
---------
Co-authored-by: bghira <bghira@users.github.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Fixed `get_word_inds` mistake/typo in P2P community pipeline
The function `get_word_inds` was taking a string of text and either a word (str) or a word index (int) and returned the indices of token(s) the word would be encoded to.
However, there was a typo, in which in the second `if` branch the word was checked to be a `str` **again**, not `int`, which resulted in an [example code from the docs](https://github.com/huggingface/diffusers/tree/main/examples/community#prompt2prompt-pipeline) to result in an error
* Initial commit P2P
* Replaced CrossAttention, added test skeleton
* bug fixes
* Updated docstring
* Removed unused function
* Created tests
* improved tests
- made fast inference tests faster
- corrected image shape assertions
* Corrected expected output shape in tests
* small fix: test inputs
* Update tests
- used conditional unet2d
- set expected image slices
- edit_kwargs are now not popped, so pipe can be run multiple times
* Fixed bug in int tests
* Fixed tests
* Linting
* Create prompt2prompt.md
* Added to docs toc
* Ran make fix-copies
* Fixed code blocks in docs
* Using same interface as StableDiffusionPipeline
* Fixed small test bug
* Added all options SDPipeline.__call_ has
* Fixed docstring; made __call__ like in SD
* Linting
* Added test for multiple prompts
* Improved docs
* Incorporated feedback
* Reverted formatting on unrelated files
* Moved prompt2prompt to community
- Moved prompt2prompt pipeline from main to community
- Deleted tests
- Moved documentation to community and shorted it
* Update src/diffusers/utils/dummy_torch_and_transformers_objects.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
