Merge branch 'main' into lora-load-adapter

Author: sayakpaul

.github/workflows/ssh-runner.yml

@@ -4,12 +4,13 @@ on:
   workflow_dispatch:
     inputs:
       runner_type:
-        description: 'Type of runner to test (aws-g6-4xlarge-plus: a10 or aws-g4dn-2xlarge: t4)'
+        description: 'Type of runner to test (aws-g6-4xlarge-plus: a10, aws-g4dn-2xlarge: t4, aws-g6e-xlarge-plus: L40)'
         type: choice
         required: true
         options:
           - aws-g6-4xlarge-plus
           - aws-g4dn-2xlarge
+          - aws-g6e-xlarge-plus
       docker_image:
         description: 'Name of the Docker image'
         required: true

docs/source/en/_toctree.yml

@@ -188,6 +188,8 @@
       title: Metal Performance Shaders (MPS)
     - local: optimization/habana
       title: Habana Gaudi
+    - local: optimization/neuron
+      title: AWS Neuron
     title: Optimized hardware
   title: Accelerate inference and reduce memory
 - sections:
@@ -250,6 +252,8 @@
         title: SparseControlNetModel
       title: ControlNets
     - sections:
+      - local: api/models/allegro_transformer3d
+        title: AllegroTransformer3DModel
       - local: api/models/aura_flow_transformer2d
         title: AuraFlowTransformer2DModel
       - local: api/models/cogvideox_transformer3d
@@ -298,6 +302,8 @@
     - sections:
       - local: api/models/autoencoderkl
         title: AutoencoderKL
+      - local: api/models/autoencoderkl_allegro
+        title: AutoencoderKLAllegro
       - local: api/models/autoencoderkl_cogvideox
         title: AutoencoderKLCogVideoX
       - local: api/models/asymmetricautoencoderkl
@@ -316,6 +322,8 @@
     sections:
     - local: api/pipelines/overview
       title: Overview
+    - local: api/pipelines/allegro
+      title: Allegro
     - local: api/pipelines/amused
       title: aMUSEd
     - local: api/pipelines/animatediff

docs/source/en/api/models/allegro_transformer3d.md

@@ -0,0 +1,30 @@
+<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License. -->
+
+# AllegroTransformer3DModel
+
+A Diffusion Transformer model for 3D data from [Allegro](https://github.com/rhymes-ai/Allegro) was introduced in [Allegro: Open the Black Box of Commercial-Level Video Generation Model](https://huggingface.co/papers/2410.15458) by RhymesAI.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import AllegroTransformer3DModel
+
+vae = AllegroTransformer3DModel.from_pretrained("rhymes-ai/Allegro", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
+```
+
+## AllegroTransformer3DModel
+
+[[autodoc]] AllegroTransformer3DModel
+
+## Transformer2DModelOutput
+
+[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

docs/source/en/api/models/autoencoderkl_allegro.md

@@ -0,0 +1,37 @@
+<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License. -->
+
+# AutoencoderKLAllegro
+
+The 3D variational autoencoder (VAE) model with KL loss used in [Allegro](https://github.com/rhymes-ai/Allegro) was introduced in [Allegro: Open the Black Box of Commercial-Level Video Generation Model](https://huggingface.co/papers/2410.15458) by RhymesAI.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import AutoencoderKLAllegro
+
+vae = AutoencoderKLCogVideoX.from_pretrained("rhymes-ai/Allegro", subfolder="vae", torch_dtype=torch.float32).to("cuda")
+```
+
+## AutoencoderKLAllegro
+
+[[autodoc]] AutoencoderKLAllegro
+    - decode
+    - encode
+    - all
+
+## AutoencoderKLOutput
+
+[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
+
+## DecoderOutput
+
+[[autodoc]] models.autoencoders.vae.DecoderOutput

docs/source/en/api/pipelines/allegro.md

@@ -0,0 +1,34 @@
+<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License. -->
+
+# Allegro
+
+[Allegro: Open the Black Box of Commercial-Level Video Generation Model](https://huggingface.co/papers/2410.15458) from RhymesAI, by Yuan Zhou, Qiuyue Wang, Yuxuan Cai, Huan Yang.
+
+The abstract from the paper is:
+
+*Significant advancements have been made in the field of video generation, with the open-source community contributing a wealth of research papers and tools for training high-quality models. However, despite these efforts, the available information and resources remain insufficient for achieving commercial-level performance. In this report, we open the black box and introduce Allegro, an advanced video generation model that excels in both quality and temporal consistency. We also highlight the current limitations in the field and present a comprehensive methodology for training high-performance, commercial-level video generation models, addressing key aspects such as data, model architecture, training pipeline, and evaluation. Our user study shows that Allegro surpasses existing open-source models and most commercial models, ranking just behind Hailuo and Kling. Code: https://github.com/rhymes-ai/Allegro , Model: https://huggingface.co/rhymes-ai/Allegro , Gallery: https://rhymes.ai/allegro_gallery .*
+
+<Tip>
+
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers.md) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+
+</Tip>
+
+## AllegroPipeline
+
+[[autodoc]] AllegroPipeline
+  - all
+  - __call__
+
+## AllegroPipelineOutput
+
+[[autodoc]] pipelines.allegro.pipeline_output.AllegroPipelineOutput

docs/source/en/optimization/neuron.md

@@ -0,0 +1,61 @@
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# AWS Neuron
+
+Diffusers functionalities are available on [AWS Inf2 instances](https://aws.amazon.com/ec2/instance-types/inf2/), which are EC2 instances powered by [Neuron machine learning accelerators](https://aws.amazon.com/machine-learning/inferentia/). These instances aim to provide better compute performance (higher throughput, lower latency) with good cost-efficiency, making them good candidates for AWS users to deploy diffusion models to production.
+
+[Optimum Neuron](https://huggingface.co/docs/optimum-neuron/en/index) is the interface between Hugging Face libraries and AWS Accelerators, including AWS [Trainium](https://aws.amazon.com/machine-learning/trainium/) and AWS [Inferentia](https://aws.amazon.com/machine-learning/inferentia/). It supports many of the features in Diffusers with similar APIs, so it is easier to learn if you're already familiar with Diffusers. Once you have created an AWS Inf2 instance, install Optimum Neuron.
+
+```bash
+python -m pip install --upgrade-strategy eager optimum[neuronx]
+```
+
+<Tip>
+
+We provide pre-built [Hugging Face Neuron Deep Learning AMI](https://aws.amazon.com/marketplace/pp/prodview-gr3e6yiscria2) (DLAMI) and Optimum Neuron containers for Amazon SageMaker. It's recommended to correctly set up your environment.
+
+</Tip>
+
+The example below demonstrates how to generate images with the Stable Diffusion XL model on an inf2.8xlarge instance (you can switch to cheaper inf2.xlarge instances once the model is compiled). To generate some images, use the [`~optimum.neuron.NeuronStableDiffusionXLPipeline`] class, which is similar to the [`StableDiffusionXLPipeline`] class in Diffusers.
+
+Unlike Diffusers, you need to compile models in the pipeline to the Neuron format, `.neuron`. Launch the following command to export the model to the `.neuron` format.
+
+```bash
+optimum-cli export neuron --model stabilityai/stable-diffusion-xl-base-1.0 \
+  --batch_size 1 \
+  --height 1024 `# height in pixels of generated image, eg. 768, 1024` \
+  --width 1024 `# width in pixels of generated image, eg. 768, 1024` \
+  --num_images_per_prompt 1 `# number of images to generate per prompt, defaults to 1` \
+  --auto_cast matmul `# cast only matrix multiplication operations` \
+  --auto_cast_type bf16 `# cast operations from FP32 to BF16` \
+  sd_neuron_xl/
+```
+
+Now generate some images with the pre-compiled SDXL model.
+
+```python
+>>> from optimum.neuron import NeuronStableDiffusionXLPipeline
+
+>>> stable_diffusion_xl = NeuronStableDiffusionXLPipeline.from_pretrained("sd_neuron_xl/")
+>>> prompt = "a pig with wings flying in floating US dollar banknotes in the air, skyscrapers behind, warm color palette, muted colors, detailed, 8k"
+>>> image = stable_diffusion_xl(prompt).images[0]
+```
+
+<img
+  src="https://huggingface.co/datasets/Jingya/document_images/resolve/main/optimum/neuron/sdxl_pig.png"
+  width="256"
+  height="256"
+  alt="peggy generated by sdxl on inf2"
+/>
+
+Feel free to check out more guides and examples on different use cases from the Optimum Neuron [documentation](https://huggingface.co/docs/optimum-neuron/en/inference_tutorials/stable_diffusion#generate-images-with-stable-diffusion-models-on-aws-inferentia)!

examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py

@@ -1650,6 +1650,8 @@ def save_model_hook(models, weights, output_dir):
                 elif isinstance(model, type(unwrap_model(text_encoder_one))):
                     if args.train_text_encoder:  # when --train_text_encoder_ti we don't save the layers
                         text_encoder_one_lora_layers_to_save = get_peft_model_state_dict(model)
+                elif isinstance(model, type(unwrap_model(text_encoder_two))):
+                    pass  # when --train_text_encoder_ti and --enable_t5_ti we don't save the layers
                 else:
                     raise ValueError(f"unexpected save model: {model.__class__}")
 
@@ -2198,8 +2200,8 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
 
                 latent_image_ids = FluxPipeline._prepare_latent_image_ids(
                     model_input.shape[0],
-                    model_input.shape[2],
-                    model_input.shape[3],
+                    model_input.shape[2] // 2,
+                    model_input.shape[3] // 2,
                     accelerator.device,
                     weight_dtype,
                 )
@@ -2253,8 +2255,8 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                 )[0]
                 model_pred = FluxPipeline._unpack_latents(
                     model_pred,
-                    height=int(model_input.shape[2] * vae_scale_factor / 2),
-                    width=int(model_input.shape[3] * vae_scale_factor / 2),
+                    height=model_input.shape[2] * vae_scale_factor,
+                    width=model_input.shape[3] * vae_scale_factor,
                     vae_scale_factor=vae_scale_factor,
                 )
 

examples/community/README.md

@@ -73,6 +73,7 @@ Please also check out our [Community Scripts](https://github.com/huggingface/dif
 | Stable Diffusion BoxDiff Pipeline | Training-free controlled generation with bounding boxes using [BoxDiff](https://github.com/showlab/BoxDiff) | [Stable Diffusion BoxDiff Pipeline](#stable-diffusion-boxdiff) | - | [Jingyang Zhang](https://github.com/zjysteven/) |
 |   FRESCO V2V Pipeline                                                                                                    | Implementation of [[CVPR 2024] FRESCO: Spatial-Temporal Correspondence for Zero-Shot Video Translation](https://arxiv.org/abs/2403.12962)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [FRESCO V2V Pipeline](#fresco)      | - |              [Yifan Zhou](https://github.com/SingleZombie) |
 | AnimateDiff IPEX Pipeline | Accelerate AnimateDiff inference pipeline with BF16/FP32 precision on Intel Xeon CPUs with [IPEX](https://github.com/intel/intel-extension-for-pytorch) | [AnimateDiff on IPEX](#animatediff-on-ipex) | - | [Dan Li](https://github.com/ustcuna/) |
+PIXART-α Controlnet pipeline | Implementation of the controlnet model for pixart alpha and its diffusers pipeline | [PIXART-α Controlnet pipeline](#pixart-α-controlnet-pipeline) | - | [Raul Ciotescu](https://github.com/raulc0399/) |
 | HunyuanDiT Differential Diffusion Pipeline | Applies [Differential Diffusion](https://github.com/exx8/differential-diffusion) to [HunyuanDiT](https://github.com/huggingface/diffusers/pull/8240). | [HunyuanDiT with Differential Diffusion](#hunyuandit-with-differential-diffusion) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1v44a5fpzyr4Ffr4v2XBQ7BajzG874N4P?usp=sharing) | [Monjoy Choudhury](https://github.com/MnCSSJ4x) |
 | [🪆Matryoshka Diffusion Models](https://huggingface.co/papers/2310.15111) | A diffusion process that denoises inputs at multiple resolutions jointly and uses a NestedUNet architecture where features and parameters for small scale inputs are nested within those of the large scales. See [original codebase](https://github.com/apple/ml-mdm). | [🪆Matryoshka Diffusion Models](#matryoshka-diffusion-models) | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/pcuenq/mdm) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/tolgacangoz/1f54875fc7aeaabcf284ebde64820966/matryoshka_hf.ipynb) | [M. Tolga Cangöz](https://github.com/tolgacangoz) |
 
@@ -4445,3 +4446,94 @@ grid_image.save(grid_dir + "sample.png")
 `pag_scale` : guidance scale of PAG (ex: 5.0)
 
 `pag_applied_layers_index` : index of the layer to apply perturbation (ex: ['m0'])
+
+# PIXART-α Controlnet pipeline
+
+[Project](https://pixart-alpha.github.io/) / [GitHub](https://github.com/PixArt-alpha/PixArt-alpha/blob/master/asset/docs/pixart_controlnet.md)
+
+This the implementation of the controlnet model and the pipelne for the Pixart-alpha model, adapted to use the HuggingFace Diffusers.
+
+## Example Usage
+
+This example uses the Pixart HED Controlnet model, converted from the control net model as trained by the authors of the paper.
+
+```py
+import sys
+import os
+import torch
+import torchvision.transforms as T
+import torchvision.transforms.functional as TF
+
+from pipeline_pixart_alpha_controlnet import PixArtAlphaControlnetPipeline
+from diffusers.utils import load_image
+
+from diffusers.image_processor import PixArtImageProcessor
+
+from controlnet_aux import HEDdetector
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from pixart.controlnet_pixart_alpha import PixArtControlNetAdapterModel
+
+controlnet_repo_id = "raulc0399/pixart-alpha-hed-controlnet"
+
+weight_dtype = torch.float16
+image_size = 1024
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+torch.manual_seed(0)
+
+# load controlnet
+controlnet = PixArtControlNetAdapterModel.from_pretrained(
+    controlnet_repo_id,
+    torch_dtype=weight_dtype,
+    use_safetensors=True,
+).to(device)
+
+pipe = PixArtAlphaControlnetPipeline.from_pretrained(
+    "PixArt-alpha/PixArt-XL-2-1024-MS",
+    controlnet=controlnet,
+    torch_dtype=weight_dtype,
+    use_safetensors=True,
+).to(device)
+
+images_path = "images"
+control_image_file = "0_7.jpg"
+
+prompt = "battleship in space, galaxy in background"
+
+control_image_name = control_image_file.split('.')[0]
+
+control_image = load_image(f"{images_path}/{control_image_file}")
+print(control_image.size)
+height, width = control_image.size
+
+hed = HEDdetector.from_pretrained("lllyasviel/Annotators")
+
+condition_transform = T.Compose([
+    T.Lambda(lambda img: img.convert('RGB')),
+    T.CenterCrop([image_size, image_size]),
+])
+
+control_image = condition_transform(control_image)
+hed_edge = hed(control_image, detect_resolution=image_size, image_resolution=image_size)
+
+hed_edge.save(f"{images_path}/{control_image_name}_hed.jpg")
+
+# run pipeline
+with torch.no_grad():
+    out = pipe(
+        prompt=prompt,
+        image=hed_edge,
+        num_inference_steps=14,
+        guidance_scale=4.5,
+        height=image_size,
+        width=image_size,
+    )
+
+    out.images[0].save(f"{images_path}//{control_image_name}_output.jpg")
+    
+```
+
+In the folder examples/pixart there is also a script that can be used to train new models.
+Please check the script `train_controlnet_hf_diffusers.sh` on how to start the training.

examples/controlnet/train_controlnet_flux.py

@@ -1256,8 +1256,8 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
 
                 latent_image_ids = FluxControlNetPipeline._prepare_latent_image_ids(
                     batch_size=pixel_latents_tmp.shape[0],
-                    height=pixel_latents_tmp.shape[2],
-                    width=pixel_latents_tmp.shape[3],
+                    height=pixel_latents_tmp.shape[2] // 2,
+                    width=pixel_latents_tmp.shape[3] // 2,
                     device=pixel_values.device,
                     dtype=pixel_values.dtype,
                 )