GitLab

* Add from_pt flag to enable model from PT

* Format the file

* Reformat the file

* 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>

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

Right now, only "main" branch has this community pipeline code. So, adding it manually into pipeline

* wip

* wip

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Add latent consistency

* Update examples/community/README.md

* Add latent consistency

* make fix copies

* Apply suggestions from code review

* update training examples to use HFAPI.

* update training example.

* reflect the changes in the korean version too.

* Empty-Commit

* chore: fix typos

* Update src/diffusers/pipelines/shap_e/renderer.py
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>

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Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>

* initial script

* formatting

* prior trainer wip

* add efficient_net_encoder

* add CLIPTextModel

* add prior ema support

* optimizer

* fix typo

* add dataloader

* prompt_embeds and image_embeds

* intial training loop

* fix output_dir

* fix add_noise

* accelerator check

* make effnet_transforms dynamic

* fix training loop

* add validation logging

* use loaded text_encoder

* use PreTrainedTokenizerFast

* load weigth from pickle

* save_model_card

* remove unused file

* fix typos

* save prior pipeilne in its own folder

* fix imports

* fix pipe_t2i

* scale image_embeds

* remove snr_gamma

* format

* initial lora prior training

* log_validation and save

* initial gradient working

* remove save/load hooks

* set set_attn_processor on prior_prior

* add lora script

* typos

* use LoraLoaderMixin for prior pipeline

* fix usage

* make fix-copies

* yse repo_id

* write_lora_layers is a staitcmethod

* use defualts

* fix defaults

* undo

* Update src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/diffusers/loaders.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/diffusers/loaders.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_prior.py

* Update src/diffusers/loaders.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/diffusers/loaders.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* add graident checkpoint support to prior

* gradient_checkpointing

* formatting

* Update examples/wuerstchen/text_to_image/README.md
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update examples/wuerstchen/text_to_image/README.md
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update examples/wuerstchen/text_to_image/README.md
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update examples/wuerstchen/text_to_image/README.md
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update examples/wuerstchen/text_to_image/README.md
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update examples/wuerstchen/text_to_image/train_text_to_image_lora_prior.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update src/diffusers/loaders.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update examples/wuerstchen/text_to_image/train_text_to_image_prior.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* use default unet and text_encoder

* fix test

---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* fix: unconditional generation example

* fix: float in loss.

* apply styling.

* use loralinear instead of depecrecated lora attn procs.

* fix parameters()

* fix saving

* add back support for add kv proj.

* fix: param accumul,ation.

* propagate the changes.

* 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 problem of 'accelerator.is_main_process' to run in mutiple GPUs or NPUs
Co-authored-by: jiaqiw <wangjiaqi50@huawei.com>

Update requirements_sdxl.txt

Add missing 'datasets'
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Update train_custom_diffusion.py

* make style

* Empty-Commit

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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

Fix [core/GLIGEN]: TypeError when iterating over 0-d tensor with In-painting mode when EulerAncestralDiscreteScheduler is used (#5305)

* 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>

* [SDXL Flax] Add research folder

* Add co-author
Co-authored-by: Juan Acevedo <jfacevedo@google.com>

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Co-authored-by: Juan Acevedo <jfacevedo@google.com>

* fix all

* make fix copies

* make fix copies

* fix: how print training resume logs.

* propagate changes to text-to-image scripts.

* propagate changes to instructpix2pix.

* propagate changes to dreambooth

* propagate changes to custom diffusion and instructpix2pix

* propagate changes to kandinsky

* propagate changes to textual inv.

* debug

* fix: checkpointing.

* debug

* debug

* debug

* back to the square

* debug

* debug

* change condition order.

* debug

* debug

* debug

* debug

* revert to original

* clean

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Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

Update README_sdxl.md

* Update run_onnx_controlnet.py

* Update run_tensorrt_controlnet.py

add compute_snr() to training utils.

* merge with main

* fix flax example

* fix onnx example

---------
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 (#5155)

* 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

---------
Co-authored-by: bghira <bghira@users.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Fix type annotation on Scheduler.from_pretrained

* Fix type annotation on PIL.Image

* 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>

Co-authored-by: bghira <bghira@users.github.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>

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

* Implement `CustomDiffusionAttnProcessor2_0`

* Doc-strings and type annotations for `CustomDiffusionAttnProcessor2_0`. (#1)

* Update attnprocessor.md

* Update attention_processor.py

* Interops for `CustomDiffusionAttnProcessor2_0`.

* Formatted `attention_processor.py`.

* Formatted doc-string in `attention_processor.py`

* Conditional CustomDiffusion2_0 for training example.

* Remove unnecessary reference impl in comments.

* Fix `save_attn_procs`.