update

docs/source/en/_toctree.yml

@@ -239,8 +239,6 @@
       title: VQModel
     - local: api/models/autoencoderkl
       title: AutoencoderKL
-    - local: api/models/autoencoderkl_cogvideox
-      title: AutoencoderKLCogVideoX
     - local: api/models/asymmetricautoencoderkl
       title: AsymmetricAutoencoderKL
     - local: api/models/stable_cascade_unet
@@ -265,8 +263,6 @@
       title: FluxTransformer2DModel
     - local: api/models/latte_transformer3d
       title: LatteTransformer3DModel
-    - local: api/models/cogvideox_transformer3d
-      title: CogVideoXTransformer3DModel
     - local: api/models/lumina_nextdit2d
       title: LuminaNextDiT2DModel
     - local: api/models/transformer_temporal
@@ -306,8 +302,6 @@
       title: AutoPipeline
     - local: api/pipelines/blip_diffusion
       title: BLIP-Diffusion
-    - local: api/pipelines/cogvideox
-      title: CogVideoX
     - local: api/pipelines/consistency_models
       title: Consistency Models
     - local: api/pipelines/controlnet

docs/source/en/api/loaders/single_file.md

@@ -22,7 +22,6 @@ The [`~loaders.FromSingleFileMixin.from_single_file`] method allows you to load:
 
 ## Supported pipelines
 
-- [`CogVideoXPipeline`]
 - [`StableDiffusionPipeline`]
 - [`StableDiffusionImg2ImgPipeline`]
 - [`StableDiffusionInpaintPipeline`]
@@ -50,7 +49,6 @@ The [`~loaders.FromSingleFileMixin.from_single_file`] method allows you to load:
 - [`UNet2DConditionModel`]
 - [`StableCascadeUNet`]
 - [`AutoencoderKL`]
-- [`AutoencoderKLCogVideoX`]
 - [`ControlNetModel`]
 - [`SD3Transformer2DModel`]
 - [`FluxTransformer2DModel`]

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

@@ -1,37 +0,0 @@
-<!--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. -->
-
-# AutoencoderKLCogVideoX
-
-The 3D variational autoencoder (VAE) model with KL loss used in [CogVideoX](https://github.com/THUDM/CogVideo) was introduced in [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://github.com/THUDM/CogVideo/blob/main/resources/CogVideoX.pdf) by Tsinghua University & ZhipuAI.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLCogVideoX
-
-vae = AutoencoderKLCogVideoX.from_pretrained("THUDM/CogVideoX-2b", subfolder="vae", torch_dtype=torch.float16).to("cuda")
-```
-
-## AutoencoderKLCogVideoX
-
-[[autodoc]] AutoencoderKLCogVideoX
-    - decode
-    - encode
-    - all
-
-## AutoencoderKLOutput
-
-[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput

docs/source/en/api/models/cogvideox_transformer3d.md

@@ -1,30 +0,0 @@
-<!--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. -->
-
-# CogVideoXTransformer3DModel
-
-A Diffusion Transformer model for 3D data from [CogVideoX](https://github.com/THUDM/CogVideo) was introduced in [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://github.com/THUDM/CogVideo/blob/main/resources/CogVideoX.pdf) by Tsinghua University & ZhipuAI.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import CogVideoXTransformer3DModel
-
-vae = CogVideoXTransformer3DModel.from_pretrained("THUDM/CogVideoX-2b", subfolder="transformer", torch_dtype=torch.float16).to("cuda")
-```
-
-## CogVideoXTransformer3DModel
-
-[[autodoc]] CogVideoXTransformer3DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

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

@@ -1,92 +0,0 @@
-<!--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.
--->
-
-# CogVideoX
-
-[CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://arxiv.org/abs/2408.06072) from Tsinghua University & ZhipuAI, by Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, Da Yin, Xiaotao Gu, Yuxuan Zhang, Weihan Wang, Yean Cheng, Ting Liu, Bin Xu, Yuxiao Dong, Jie Tang.
-
-The abstract from the paper is:
-
-*We introduce CogVideoX, a large-scale diffusion transformer model designed for generating videos based on text prompts. To efficently model video data, we propose to levearge a 3D Variational Autoencoder (VAE) to compresses videos along both spatial and temporal dimensions. To improve the text-video alignment, we propose an expert transformer with the expert adaptive LayerNorm to facilitate the deep fusion between the two modalities. By employing a progressive training technique, CogVideoX is adept at producing coherent, long-duration videos characterized by significant motion. In addition, we develop an effectively text-video data processing pipeline that includes various data preprocessing strategies and a video captioning method. It significantly helps enhance the performance of CogVideoX, improving both generation quality and semantic alignment. Results show that CogVideoX demonstrates state-of-the-art performance across both multiple machine metrics and human evaluations. The model weight of CogVideoX-2B is publicly available at https://github.com/THUDM/CogVideo.*
-
-<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>
-
-This pipeline was contributed by [zRzRzRzRzRzRzR](https://github.com/zRzRzRzRzRzRzR). The original codebase can be found [here](https://huggingface.co/THUDM). The original weights can be found under [hf.co/THUDM](https://huggingface.co/THUDM).
-
-There are two models available that can be used with the CogVideoX pipeline:
-- [`THUDM/CogVideoX-2b`](https://huggingface.co/THUDM/CogVideoX-2b)
-- [`THUDM/CogVideoX-5b`](https://huggingface.co/THUDM/CogVideoX-5b)
-
-## Inference
-
-Use [`torch.compile`](https://huggingface.co/docs/diffusers/main/en/tutorials/fast_diffusion#torchcompile) to reduce the inference latency.
-
-First, load the pipeline:
-
-```python
-import torch
-from diffusers import CogVideoXPipeline
-from diffusers.utils import export_to_video
-
-pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-2b").to("cuda")
-```
-
-Then change the memory layout of the pipelines `transformer` component to `torch.channels_last`:
-
-```python
-pipe.transformer.to(memory_format=torch.channels_last)
-```
-
-Finally, compile the components and run inference:
-
-```python
-pipe.transformer = torch.compile(pipeline.transformer, mode="max-autotune", fullgraph=True)
-
-# CogVideoX works well with long and well-described prompts
-prompt = "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance."
-video = pipe(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]
-```
-
-The [benchmark](https://gist.github.com/a-r-r-o-w/5183d75e452a368fd17448fcc810bd3f) results on an 80GB A100 machine are:
-
-```
-Without torch.compile(): Average inference time: 96.89 seconds.
-With torch.compile(): Average inference time: 76.27 seconds.
-```
-
-### Memory optimization
-
-CogVideoX-2b requires about 19 GB of GPU memory to decode 49 frames (6 seconds of video at 8 FPS) with output resolution 720x480 (W x H), which makes it not possible to run on consumer GPUs or free-tier T4 Colab. The following memory optimizations could be used to reduce the memory footprint. For replication, you can refer to [this](https://gist.github.com/a-r-r-o-w/3959a03f15be5c9bd1fe545b09dfcc93) script.
-
-- `pipe.enable_model_cpu_offload()`:
-  - Without enabling cpu offloading, memory usage is `33 GB`
-  - With enabling cpu offloading, memory usage is `19 GB`
-- `pipe.vae.enable_tiling()`:
-  - With enabling cpu offloading and tiling, memory usage is `11 GB`
-- `pipe.vae.enable_slicing()`
-
-## CogVideoXPipeline
-
-[[autodoc]] CogVideoXPipeline
-  - all
-  - __call__
-
-## CogVideoXPipelineOutput
-
-[[autodoc]] pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput

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

@@ -37,7 +37,7 @@ Both checkpoints have slightly difference usage which we detail below.
 
 ```python
 import torch
-from diffusers import FluxPipeline
+from diffusers import  FluxPipeline
 
 pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
 pipe.enable_model_cpu_offload()
@@ -61,7 +61,7 @@ out.save("image.png")
 
 ```python
 import torch
-from diffusers import FluxPipeline
+from diffusers import  FluxPipeline
 
 pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
 pipe.enable_model_cpu_offload()
@@ -77,34 +77,6 @@ out = pipe(
 out.save("image.png")
 ```
 
-## Running FP16 inference
-Flux can generate high-quality images with FP16 (i.e. to accelerate inference on Turing/Volta GPUs) but produces different outputs compared to FP32/BF16. The issue is that some activations in the text encoders have to be clipped when running in FP16, which affects the overall image. Forcing text encoders to run with FP32 inference thus removes this output difference. See [here](https://github.com/huggingface/diffusers/pull/9097#issuecomment-2272292516) for details.
-
-FP16 inference code:
-```python
-import torch
-from diffusers import FluxPipeline
-
-pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16) # can replace schnell with dev
-# to run on low vram GPUs (i.e. between 4 and 32 GB VRAM)
-pipe.enable_sequential_cpu_offload()
-pipe.vae.enable_slicing()
-pipe.vae.enable_tiling()
-
-pipe.to(torch.float16) # casting here instead of in the pipeline constructor because doing so in the constructor loads all models into CPU memory at once
-
-prompt = "A cat holding a sign that says hello world"
-out = pipe(
-    prompt=prompt,
-    guidance_scale=0.,
-    height=768,
-    width=1360,
-    num_inference_steps=4,
-    max_sequence_length=256,
-).images[0]
-out.save("image.png")
-```
-
 ## Single File Loading for the `FluxTransformer2DModel`
 
 The `FluxTransformer2DModel` supports loading checkpoints in the original format shipped by Black Forest Labs. This is also useful when trying to load finetunes or quantized versions of the models that have been published by the community.
@@ -162,4 +134,4 @@ image.save("flux-fp8-dev.png")
 
 [[autodoc]] FluxPipeline
 	- all
-	- __call__
+	- __call__

examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py

@@ -71,7 +71,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__)
 

examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py

@@ -79,7 +79,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__)
 

examples/community/marigold_depth_estimation.py

@@ -43,7 +43,8 @@ from diffusers.utils import BaseOutput, check_min_version
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
+
 
 class MarigoldDepthOutput(BaseOutput):
     """

examples/consistency_distillation/train_lcm_distill_lora_sd_wds.py

@@ -73,7 +73,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__)
 

examples/consistency_distillation/train_lcm_distill_lora_sdxl.py

@@ -66,7 +66,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__)
 

examples/consistency_distillation/train_lcm_distill_lora_sdxl_wds.py

@@ -79,7 +79,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__)
 

examples/consistency_distillation/train_lcm_distill_sd_wds.py

@@ -72,7 +72,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__)
 

examples/consistency_distillation/train_lcm_distill_sdxl_wds.py

@@ -78,7 +78,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__)
 

examples/controlnet/train_controlnet.py

@@ -60,7 +60,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__)
 

examples/controlnet/train_controlnet_flax.py

@@ -60,7 +60,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = logging.getLogger(__name__)
 

examples/controlnet/train_controlnet_sdxl.py

@@ -61,7 +61,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__)
 if is_torch_npu_available():

examples/custom_diffusion/train_custom_diffusion.py

@@ -63,7 +63,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__)
 

examples/dreambooth/train_dreambooth.py

@@ -63,7 +63,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__)
 

examples/dreambooth/train_dreambooth_flax.py

@@ -35,7 +35,7 @@ from diffusers.utils import check_min_version
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 # Cache compiled models across invocations of this script.
 cc.initialize_cache(os.path.expanduser("~/.cache/jax/compilation_cache"))

examples/dreambooth/train_dreambooth_lora.py

@@ -70,7 +70,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__)
 

examples/dreambooth/train_dreambooth_lora_sd3.py

@@ -72,7 +72,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__)
 

examples/dreambooth/train_dreambooth_lora_sdxl.py

@@ -78,7 +78,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__)
 

examples/dreambooth/train_dreambooth_sd3.py

@@ -64,7 +64,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__)
 

examples/instruct_pix2pix/train_instruct_pix2pix.py

@@ -57,7 +57,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py

@@ -60,7 +60,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/kandinsky2_2/text_to_image/train_text_to_image_decoder.py

@@ -52,7 +52,7 @@ if is_wandb_available():
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/kandinsky2_2/text_to_image/train_text_to_image_lora_decoder.py

@@ -46,7 +46,7 @@ from diffusers.utils import check_min_version, is_wandb_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/kandinsky2_2/text_to_image/train_text_to_image_lora_prior.py

@@ -46,7 +46,7 @@ from diffusers.utils import check_min_version, is_wandb_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/kandinsky2_2/text_to_image/train_text_to_image_prior.py

@@ -51,7 +51,7 @@ if is_wandb_available():
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/t2i_adapter/train_t2i_adapter_sdxl.py

@@ -60,7 +60,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__)
 

examples/text_to_image/train_text_to_image.py

@@ -57,7 +57,7 @@ if is_wandb_available():
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/text_to_image/train_text_to_image_flax.py

@@ -49,7 +49,7 @@ from diffusers.utils import check_min_version
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = logging.getLogger(__name__)
 

examples/text_to_image/train_text_to_image_lora.py

@@ -56,7 +56,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/text_to_image/train_text_to_image_lora_sdxl.py

@@ -68,7 +68,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__)
 if is_torch_npu_available():

examples/text_to_image/train_text_to_image_sdxl.py

@@ -55,7 +55,7 @@ from diffusers.utils.torch_utils import is_compiled_module
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__)
 if is_torch_npu_available():

examples/textual_inversion/textual_inversion.py

@@ -81,7 +81,7 @@ else:
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__)
 

examples/textual_inversion/textual_inversion_flax.py

@@ -56,7 +56,7 @@ else:
 # ------------------------------------------------------------------------------
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = logging.getLogger(__name__)
 

examples/textual_inversion/textual_inversion_sdxl.py

@@ -76,7 +76,7 @@ else:
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__)
 

examples/unconditional_image_generation/train_unconditional.py

@@ -29,7 +29,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/vqgan/train_vqgan.py

@@ -50,7 +50,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/wuerstchen/text_to_image/train_text_to_image_lora_prior.py

@@ -50,7 +50,7 @@ if is_wandb_available():
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/wuerstchen/text_to_image/train_text_to_image_prior.py

@@ -51,7 +51,7 @@ if is_wandb_available():
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.30.0")
+check_min_version("0.30.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

scripts/convert_cogvideox_to_diffusers.py

@@ -1,268 +0,0 @@
-import argparse
-from typing import Any, Dict
-
-import torch
-from transformers import T5EncoderModel, T5Tokenizer
-
-from diffusers import AutoencoderKLCogVideoX, CogVideoXDDIMScheduler, CogVideoXPipeline, CogVideoXTransformer3DModel
-
-
-def reassign_query_key_value_inplace(key: str, state_dict: Dict[str, Any]):
-    to_q_key = key.replace("query_key_value", "to_q")
-    to_k_key = key.replace("query_key_value", "to_k")
-    to_v_key = key.replace("query_key_value", "to_v")
-    to_q, to_k, to_v = torch.chunk(state_dict[key], chunks=3, dim=0)
-    state_dict[to_q_key] = to_q
-    state_dict[to_k_key] = to_k
-    state_dict[to_v_key] = to_v
-    state_dict.pop(key)
-
-
-def reassign_query_key_layernorm_inplace(key: str, state_dict: Dict[str, Any]):
-    layer_id, weight_or_bias = key.split(".")[-2:]
-
-    if "query" in key:
-        new_key = f"transformer_blocks.{layer_id}.attn1.norm_q.{weight_or_bias}"
-    elif "key" in key:
-        new_key = f"transformer_blocks.{layer_id}.attn1.norm_k.{weight_or_bias}"
-
-    state_dict[new_key] = state_dict.pop(key)
-
-
-def reassign_adaln_norm_inplace(key: str, state_dict: Dict[str, Any]):
-    layer_id, _, weight_or_bias = key.split(".")[-3:]
-
-    weights_or_biases = state_dict[key].chunk(12, dim=0)
-    norm1_weights_or_biases = torch.cat(weights_or_biases[0:3] + weights_or_biases[6:9])
-    norm2_weights_or_biases = torch.cat(weights_or_biases[3:6] + weights_or_biases[9:12])
-
-    norm1_key = f"transformer_blocks.{layer_id}.norm1.linear.{weight_or_bias}"
-    state_dict[norm1_key] = norm1_weights_or_biases
-
-    norm2_key = f"transformer_blocks.{layer_id}.norm2.linear.{weight_or_bias}"
-    state_dict[norm2_key] = norm2_weights_or_biases
-
-    state_dict.pop(key)
-
-
-def remove_keys_inplace(key: str, state_dict: Dict[str, Any]):
-    state_dict.pop(key)
-
-
-def replace_up_keys_inplace(key: str, state_dict: Dict[str, Any]):
-    key_split = key.split(".")
-    layer_index = int(key_split[2])
-    replace_layer_index = 4 - 1 - layer_index
-
-    key_split[1] = "up_blocks"
-    key_split[2] = str(replace_layer_index)
-    new_key = ".".join(key_split)
-
-    state_dict[new_key] = state_dict.pop(key)
-
-
-TRANSFORMER_KEYS_RENAME_DICT = {
-    "transformer.final_layernorm": "norm_final",
-    "transformer": "transformer_blocks",
-    "attention": "attn1",
-    "mlp": "ff.net",
-    "dense_h_to_4h": "0.proj",
-    "dense_4h_to_h": "2",
-    ".layers": "",
-    "dense": "to_out.0",
-    "input_layernorm": "norm1.norm",
-    "post_attn1_layernorm": "norm2.norm",
-    "time_embed.0": "time_embedding.linear_1",
-    "time_embed.2": "time_embedding.linear_2",
-    "mixins.patch_embed": "patch_embed",
-    "mixins.final_layer.norm_final": "norm_out.norm",
-    "mixins.final_layer.linear": "proj_out",
-    "mixins.final_layer.adaLN_modulation.1": "norm_out.linear",
-}
-
-TRANSFORMER_SPECIAL_KEYS_REMAP = {
-    "query_key_value": reassign_query_key_value_inplace,
-    "query_layernorm_list": reassign_query_key_layernorm_inplace,
-    "key_layernorm_list": reassign_query_key_layernorm_inplace,
-    "adaln_layer.adaLN_modulations": reassign_adaln_norm_inplace,
-    "embed_tokens": remove_keys_inplace,
-    "freqs_sin": remove_keys_inplace,
-    "freqs_cos": remove_keys_inplace,
-    "position_embedding": remove_keys_inplace,
-}
-
-VAE_KEYS_RENAME_DICT = {
-    "block.": "resnets.",
-    "down.": "down_blocks.",
-    "downsample": "downsamplers.0",
-    "upsample": "upsamplers.0",
-    "nin_shortcut": "conv_shortcut",
-    "encoder.mid.block_1": "encoder.mid_block.resnets.0",
-    "encoder.mid.block_2": "encoder.mid_block.resnets.1",
-    "decoder.mid.block_1": "decoder.mid_block.resnets.0",
-    "decoder.mid.block_2": "decoder.mid_block.resnets.1",
-}
-
-VAE_SPECIAL_KEYS_REMAP = {
-    "loss": remove_keys_inplace,
-    "up.": replace_up_keys_inplace,
-}
-
-TOKENIZER_MAX_LENGTH = 226
-
-
-def get_state_dict(saved_dict: Dict[str, Any]) -> Dict[str, Any]:
-    state_dict = saved_dict
-    if "model" in saved_dict.keys():
-        state_dict = state_dict["model"]
-    if "module" in saved_dict.keys():
-        state_dict = state_dict["module"]
-    if "state_dict" in saved_dict.keys():
-        state_dict = state_dict["state_dict"]
-    return state_dict
-
-
-def update_state_dict_inplace(state_dict: Dict[str, Any], old_key: str, new_key: str) -> Dict[str, Any]:
-    state_dict[new_key] = state_dict.pop(old_key)
-
-
-def convert_transformer(
-    ckpt_path: str,
-    num_layers: int,
-    num_attention_heads: int,
-    use_rotary_positional_embeddings: bool,
-    dtype: torch.dtype,
-):
-    PREFIX_KEY = "model.diffusion_model."
-
-    original_state_dict = get_state_dict(torch.load(ckpt_path, map_location="cpu", mmap=True))
-    transformer = CogVideoXTransformer3DModel(
-        num_layers=num_layers,
-        num_attention_heads=num_attention_heads,
-        use_rotary_positional_embeddings=use_rotary_positional_embeddings,
-    ).to(dtype=dtype)
-
-    for key in list(original_state_dict.keys()):
-        new_key = key[len(PREFIX_KEY) :]
-        for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items():
-            new_key = new_key.replace(replace_key, rename_key)
-        update_state_dict_inplace(original_state_dict, key, new_key)
-
-    for key in list(original_state_dict.keys()):
-        for special_key, handler_fn_inplace in TRANSFORMER_SPECIAL_KEYS_REMAP.items():
-            if special_key not in key:
-                continue
-            handler_fn_inplace(key, original_state_dict)
-
-    transformer.load_state_dict(original_state_dict, strict=True)
-    return transformer
-
-
-def convert_vae(ckpt_path: str, scaling_factor: float, dtype: torch.dtype):
-    original_state_dict = get_state_dict(torch.load(ckpt_path, map_location="cpu", mmap=True))
-    vae = AutoencoderKLCogVideoX(scaling_factor=scaling_factor).to(dtype=dtype)
-
-    for key in list(original_state_dict.keys()):
-        new_key = key[:]
-        for replace_key, rename_key in VAE_KEYS_RENAME_DICT.items():
-            new_key = new_key.replace(replace_key, rename_key)
-        update_state_dict_inplace(original_state_dict, key, new_key)
-
-    for key in list(original_state_dict.keys()):
-        for special_key, handler_fn_inplace in VAE_SPECIAL_KEYS_REMAP.items():
-            if special_key not in key:
-                continue
-            handler_fn_inplace(key, original_state_dict)
-
-    vae.load_state_dict(original_state_dict, strict=True)
-    return vae
-
-
-def get_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--transformer_ckpt_path", type=str, default=None, help="Path to original transformer checkpoint"
-    )
-    parser.add_argument("--vae_ckpt_path", type=str, default=None, help="Path to original vae checkpoint")
-    parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved")
-    parser.add_argument("--fp16", action="store_true", default=False, help="Whether to save the model weights in fp16")
-    parser.add_argument("--bf16", action="store_true", default=False, help="Whether to save the model weights in bf16")
-    parser.add_argument(
-        "--push_to_hub", action="store_true", default=False, help="Whether to push to HF Hub after saving"
-    )
-    parser.add_argument(
-        "--text_encoder_cache_dir", type=str, default=None, help="Path to text encoder cache directory"
-    )
-    # For CogVideoX-2B, num_layers is 30. For 5B, it is 42
-    parser.add_argument("--num_layers", type=int, default=30, help="Number of transformer blocks")
-    # For CogVideoX-2B, num_attention_heads is 30. For 5B, it is 48
-    parser.add_argument("--num_attention_heads", type=int, default=30, help="Number of attention heads")
-    # For CogVideoX-2B, use_rotary_positional_embeddings is False. For 5B, it is True
-    parser.add_argument(
-        "--use_rotary_positional_embeddings", action="store_true", default=False, help="Whether to use RoPE or not"
-    )
-    # For CogVideoX-2B, scaling_factor is 1.15258426. For 5B, it is 0.7
-    parser.add_argument("--scaling_factor", type=float, default=1.15258426, help="Scaling factor in the VAE")
-    # For CogVideoX-2B, snr_shift_scale is 3.0. For 5B, it is 1.0
-    parser.add_argument("--snr_shift_scale", type=float, default=3.0, help="Scaling factor in the VAE")
-    return parser.parse_args()
-
-
-if __name__ == "__main__":
-    args = get_args()
-
-    transformer = None
-    vae = None
-
-    if args.fp16 and args.bf16:
-        raise ValueError("You cannot pass both --fp16 and --bf16 at the same time.")
-
-    dtype = torch.float16 if args.fp16 else torch.bfloat16 if args.bf16 else torch.float32
-
-    if args.transformer_ckpt_path is not None:
-        transformer = convert_transformer(
-            args.transformer_ckpt_path,
-            args.num_layers,
-            args.num_attention_heads,
-            args.use_rotary_positional_embeddings,
-            dtype,
-        )
-    if args.vae_ckpt_path is not None:
-        vae = convert_vae(args.vae_ckpt_path, args.scaling_factor, dtype)
-
-    text_encoder_id = "google/t5-v1_1-xxl"
-    tokenizer = T5Tokenizer.from_pretrained(text_encoder_id, model_max_length=TOKENIZER_MAX_LENGTH)
-    text_encoder = T5EncoderModel.from_pretrained(text_encoder_id, cache_dir=args.text_encoder_cache_dir)
-
-    # Apparently, the conversion does not work any more without this :shrug:
-    for param in text_encoder.parameters():
-        param.data = param.data.contiguous()
-
-    scheduler = CogVideoXDDIMScheduler.from_config(
-        {
-            "snr_shift_scale": args.snr_shift_scale,
-            "beta_end": 0.012,
-            "beta_schedule": "scaled_linear",
-            "beta_start": 0.00085,
-            "clip_sample": False,
-            "num_train_timesteps": 1000,
-            "prediction_type": "v_prediction",
-            "rescale_betas_zero_snr": True,
-            "set_alpha_to_one": True,
-            "timestep_spacing": "trailing",
-        }
-    )
-
-    pipe = CogVideoXPipeline(
-        tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler
-    )
-
-    if args.fp16:
-        pipe = pipe.to(dtype=torch.float16)
-    if args.bf16:
-        pipe = pipe.to(dtype=torch.bfloat16)
-
-    # We don't use variant here because the model must be run in fp16 (2B) or bf16 (5B). It would be weird
-    # for users to specify variant when the default is not fp32 and they want to run with the correct default (which
-    # is either fp16/bf16 here).
-    pipe.save_pretrained(args.output_path, safe_serialization=True, push_to_hub=args.push_to_hub)

setup.py

@@ -254,7 +254,7 @@ version_range_max = max(sys.version_info[1], 10) + 1
 
 setup(
     name="diffusers",
-    version="0.30.2",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
+    version="0.30.0.dev0",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
     description="State-of-the-art diffusion in PyTorch and JAX.",
     long_description=open("README.md", "r", encoding="utf-8").read(),
     long_description_content_type="text/markdown",

src/diffusers/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.30.2"
+__version__ = "0.30.0.dev0"
 
 from typing import TYPE_CHECKING
 
@@ -79,11 +79,9 @@ else:
             "AsymmetricAutoencoderKL",
             "AuraFlowTransformer2DModel",
             "AutoencoderKL",
-            "AutoencoderKLCogVideoX",
             "AutoencoderKLTemporalDecoder",
             "AutoencoderOobleck",
             "AutoencoderTiny",
-            "CogVideoXTransformer3DModel",
             "ConsistencyDecoderVAE",
             "ControlNetModel",
             "ControlNetXSAdapter",
@@ -157,8 +155,6 @@ else:
         [
             "AmusedScheduler",
             "CMStochasticIterativeScheduler",
-            "CogVideoXDDIMScheduler",
-            "CogVideoXDPMScheduler",
             "DDIMInverseScheduler",
             "DDIMParallelScheduler",
             "DDIMScheduler",
@@ -252,7 +248,6 @@ else:
             "BlipDiffusionControlNetPipeline",
             "BlipDiffusionPipeline",
             "CLIPImageProjection",
-            "CogVideoXPipeline",
             "CycleDiffusionPipeline",
             "FluxPipeline",
             "HunyuanDiTControlNetPipeline",
@@ -540,11 +535,9 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AsymmetricAutoencoderKL,
             AuraFlowTransformer2DModel,
             AutoencoderKL,
-            AutoencoderKLCogVideoX,
             AutoencoderKLTemporalDecoder,
             AutoencoderOobleck,
             AutoencoderTiny,
-            CogVideoXTransformer3DModel,
             ConsistencyDecoderVAE,
             ControlNetModel,
             ControlNetXSAdapter,
@@ -615,8 +608,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .schedulers import (
             AmusedScheduler,
             CMStochasticIterativeScheduler,
-            CogVideoXDDIMScheduler,
-            CogVideoXDPMScheduler,
             DDIMInverseScheduler,
             DDIMParallelScheduler,
             DDIMScheduler,
@@ -691,7 +682,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AudioLDMPipeline,
             AuraFlowPipeline,
             CLIPImageProjection,
-            CogVideoXPipeline,
             CycleDiffusionPipeline,
             FluxPipeline,
             HunyuanDiTControlNetPipeline,

src/diffusers/loaders/ip_adapter.py

@@ -208,8 +208,6 @@ class IPAdapterMixin:
                             pretrained_model_name_or_path_or_dict,
                             subfolder=image_encoder_subfolder,
                             low_cpu_mem_usage=low_cpu_mem_usage,
-                            cache_dir=cache_dir,
-                            local_files_only=local_files_only,
                         ).to(self.device, dtype=self.dtype)
                         self.register_modules(image_encoder=image_encoder)
                     else:

src/diffusers/loaders/lora_pipeline.py

@@ -1489,10 +1489,10 @@ class FluxLoraLoaderMixin(LoraBaseMixin):
 
     @classmethod
     @validate_hf_hub_args
+    # Copied from diffusers.loaders.lora_pipeline.SD3LoraLoaderMixin.lora_state_dict
     def lora_state_dict(
         cls,
         pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
-        return_alphas: bool = False,
         **kwargs,
     ):
         r"""
@@ -1577,26 +1577,7 @@ class FluxLoraLoaderMixin(LoraBaseMixin):
             allow_pickle=allow_pickle,
         )
 
-        # For state dicts like
-        # https://huggingface.co/TheLastBen/Jon_Snow_Flux_LoRA
-        keys = list(state_dict.keys())
-        network_alphas = {}
-        for k in keys:
-            if "alpha" in k:
-                alpha_value = state_dict.get(k)
-                if (torch.is_tensor(alpha_value) and torch.is_floating_point(alpha_value)) or isinstance(
-                    alpha_value, float
-                ):
-                    network_alphas[k] = state_dict.pop(k)
-                else:
-                    raise ValueError(
-                        f"The alpha key ({k}) seems to be incorrect. If you think this error is unexpected, please open as issue."
-                    )
-
-        if return_alphas:
-            return state_dict, network_alphas
-        else:
-            return state_dict
+        return state_dict
 
     def load_lora_weights(
         self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], adapter_name=None, **kwargs
@@ -1630,9 +1611,7 @@ class FluxLoraLoaderMixin(LoraBaseMixin):
             pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy()
 
         # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
-        state_dict, network_alphas = self.lora_state_dict(
-            pretrained_model_name_or_path_or_dict, return_alphas=True, **kwargs
-        )
+        state_dict = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)
 
         is_correct_format = all("lora" in key or "dora_scale" in key for key in state_dict.keys())
         if not is_correct_format:
@@ -1640,7 +1619,6 @@ class FluxLoraLoaderMixin(LoraBaseMixin):
 
         self.load_lora_into_transformer(
             state_dict,
-            network_alphas=network_alphas,
             transformer=getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer,
             adapter_name=adapter_name,
             _pipeline=self,
@@ -1650,7 +1628,7 @@ class FluxLoraLoaderMixin(LoraBaseMixin):
         if len(text_encoder_state_dict) > 0:
             self.load_lora_into_text_encoder(
                 text_encoder_state_dict,
-                network_alphas=network_alphas,
+                network_alphas=None,
                 text_encoder=self.text_encoder,
                 prefix="text_encoder",
                 lora_scale=self.lora_scale,
@@ -1659,7 +1637,8 @@ class FluxLoraLoaderMixin(LoraBaseMixin):
             )
 
     @classmethod
-    def load_lora_into_transformer(cls, state_dict, network_alphas, transformer, adapter_name=None, _pipeline=None):
+    # Copied from diffusers.loaders.lora_pipeline.SD3LoraLoaderMixin.load_lora_into_transformer
+    def load_lora_into_transformer(cls, state_dict, transformer, adapter_name=None, _pipeline=None):
         """
         This will load the LoRA layers specified in `state_dict` into `transformer`.
 
@@ -1668,10 +1647,6 @@ class FluxLoraLoaderMixin(LoraBaseMixin):
                 A standard state dict containing the lora layer parameters. The keys can either be indexed directly
                 into the unet or prefixed with an additional `unet` which can be used to distinguish between text
                 encoder lora layers.
-            network_alphas (`Dict[str, float]`):
-                The value of the network alpha used for stable learning and preventing underflow. This value has the
-                same meaning as the `--network_alpha` option in the kohya-ss trainer script. Refer to [this
-                link](https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning).
             transformer (`SD3Transformer2DModel`):
                 The Transformer model to load the LoRA layers into.
             adapter_name (`str`, *optional*):
@@ -1703,12 +1678,7 @@ class FluxLoraLoaderMixin(LoraBaseMixin):
                 if "lora_B" in key:
                     rank[key] = val.shape[1]
 
-            if network_alphas is not None and len(network_alphas) >= 1:
-                prefix = cls.transformer_name
-                alpha_keys = [k for k in network_alphas.keys() if k.startswith(prefix) and k.split(".")[0] == prefix]
-                network_alphas = {k.replace(f"{prefix}.", ""): v for k, v in network_alphas.items() if k in alpha_keys}
-
-            lora_config_kwargs = get_peft_kwargs(rank, network_alpha_dict=network_alphas, peft_state_dict=state_dict)
+            lora_config_kwargs = get_peft_kwargs(rank, network_alpha_dict=None, peft_state_dict=state_dict)
             if "use_dora" in lora_config_kwargs:
                 if lora_config_kwargs["use_dora"] and is_peft_version("<", "0.9.0"):
                     raise ValueError(

src/diffusers/loaders/single_file.py

@@ -23,7 +23,6 @@ from packaging import version
 from ..utils import deprecate, is_transformers_available, logging
 from .single_file_utils import (
     SingleFileComponentError,
-    _is_legacy_scheduler_kwargs,
     _is_model_weights_in_cached_folder,
     _legacy_load_clip_tokenizer,
     _legacy_load_safety_checker,
@@ -43,6 +42,7 @@ logger = logging.get_logger(__name__)
 # Legacy behaviour. `from_single_file` does not load the safety checker unless explicitly provided
 SINGLE_FILE_OPTIONAL_COMPONENTS = ["safety_checker"]
 
+
 if is_transformers_available():
     import transformers
     from transformers import PreTrainedModel, PreTrainedTokenizer
@@ -135,7 +135,7 @@ def load_single_file_sub_model(
             class_obj, checkpoint=checkpoint, config=cached_model_config_path, local_files_only=local_files_only
         )
 
-    elif is_diffusers_scheduler and (is_legacy_loading or _is_legacy_scheduler_kwargs(kwargs)):
+    elif is_diffusers_scheduler and is_legacy_loading:
         loaded_sub_model = _legacy_load_scheduler(
             class_obj, checkpoint=checkpoint, component_name=name, original_config=original_config, **kwargs
         )

src/diffusers/loaders/single_file_model.py

@@ -75,9 +75,6 @@ SINGLE_FILE_LOADABLE_CLASSES = {
     "MotionAdapter": {
         "checkpoint_mapping_fn": convert_animatediff_checkpoint_to_diffusers,
     },
-    "SparseControlNetModel": {
-        "checkpoint_mapping_fn": convert_animatediff_checkpoint_to_diffusers,
-    },
     "FluxTransformer2DModel": {
         "checkpoint_mapping_fn": convert_flux_transformer_checkpoint_to_diffusers,
         "default_subfolder": "transformer",

src/diffusers/loaders/single_file_utils.py

@@ -74,15 +74,10 @@ CHECKPOINT_KEY_NAMES = {
     "stable_cascade_stage_b": "down_blocks.1.0.channelwise.0.weight",
     "stable_cascade_stage_c": "clip_txt_mapper.weight",
     "sd3": "model.diffusion_model.joint_blocks.0.context_block.adaLN_modulation.1.bias",
-    "animatediff": "down_blocks.0.motion_modules.0.temporal_transformer.transformer_blocks.0.attention_blocks.0.pos_encoder.pe",
+    "animatediff": "down_blocks.0.motion_modules.0.temporal_transformer.transformer_blocks.0.attention_blocks.1.pos_encoder.pe",
     "animatediff_v2": "mid_block.motion_modules.0.temporal_transformer.norm.bias",
     "animatediff_sdxl_beta": "up_blocks.2.motion_modules.0.temporal_transformer.norm.weight",
-    "animatediff_scribble": "controlnet_cond_embedding.conv_in.weight",
-    "animatediff_rgb": "controlnet_cond_embedding.weight",
-    "flux": [
-        "double_blocks.0.img_attn.norm.key_norm.scale",
-        "model.diffusion_model.double_blocks.0.img_attn.norm.key_norm.scale",
-    ],
+    "flux": "double_blocks.0.img_attn.norm.key_norm.scale",
 }
 
 DIFFUSERS_DEFAULT_PIPELINE_PATHS = {
@@ -91,11 +86,11 @@ DIFFUSERS_DEFAULT_PIPELINE_PATHS = {
     "xl_inpaint": {"pretrained_model_name_or_path": "diffusers/stable-diffusion-xl-1.0-inpainting-0.1"},
     "playground-v2-5": {"pretrained_model_name_or_path": "playgroundai/playground-v2.5-1024px-aesthetic"},
     "upscale": {"pretrained_model_name_or_path": "stabilityai/stable-diffusion-x4-upscaler"},
-    "inpainting": {"pretrained_model_name_or_path": "Lykon/dreamshaper-8-inpainting"},
+    "inpainting": {"pretrained_model_name_or_path": "runwayml/stable-diffusion-inpainting"},
     "inpainting_v2": {"pretrained_model_name_or_path": "stabilityai/stable-diffusion-2-inpainting"},
     "controlnet": {"pretrained_model_name_or_path": "lllyasviel/control_v11p_sd15_canny"},
     "v2": {"pretrained_model_name_or_path": "stabilityai/stable-diffusion-2-1"},
-    "v1": {"pretrained_model_name_or_path": "Lykon/dreamshaper-8"},
+    "v1": {"pretrained_model_name_or_path": "runwayml/stable-diffusion-v1-5"},
     "stable_cascade_stage_b": {"pretrained_model_name_or_path": "stabilityai/stable-cascade", "subfolder": "decoder"},
     "stable_cascade_stage_b_lite": {
         "pretrained_model_name_or_path": "stabilityai/stable-cascade",
@@ -116,8 +111,6 @@ DIFFUSERS_DEFAULT_PIPELINE_PATHS = {
     "animatediff_v2": {"pretrained_model_name_or_path": "guoyww/animatediff-motion-adapter-v1-5-2"},
     "animatediff_v3": {"pretrained_model_name_or_path": "guoyww/animatediff-motion-adapter-v1-5-3"},
     "animatediff_sdxl_beta": {"pretrained_model_name_or_path": "guoyww/animatediff-motion-adapter-sdxl-beta"},
-    "animatediff_scribble": {"pretrained_model_name_or_path": "guoyww/animatediff-sparsectrl-scribble"},
-    "animatediff_rgb": {"pretrained_model_name_or_path": "guoyww/animatediff-sparsectrl-rgb"},
     "flux-dev": {"pretrained_model_name_or_path": "black-forest-labs/FLUX.1-dev"},
     "flux-schnell": {"pretrained_model_name_or_path": "black-forest-labs/FLUX.1-schnell"},
 }
@@ -261,7 +254,7 @@ SCHEDULER_DEFAULT_CONFIG = {
     "timestep_spacing": "leading",
 }
 
-LDM_VAE_KEYS = ["first_stage_model.", "vae."]
+LDM_VAE_KEY = "first_stage_model."
 LDM_VAE_DEFAULT_SCALING_FACTOR = 0.18215
 PLAYGROUND_VAE_SCALING_FACTOR = 0.5
 LDM_UNET_KEY = "model.diffusion_model."
@@ -270,8 +263,8 @@ LDM_CLIP_PREFIX_TO_REMOVE = [
     "cond_stage_model.transformer.",
     "conditioner.embedders.0.transformer.",
 ]
+OPEN_CLIP_PREFIX = "conditioner.embedders.0.model."
 LDM_OPEN_CLIP_TEXT_PROJECTION_DIM = 1024
-SCHEDULER_LEGACY_KWARGS = ["prediction_type", "scheduler_type"]
 
 VALID_URL_PREFIXES = ["https://huggingface.co/", "huggingface.co/", "hf.co/", "https://hf.co/"]
 
@@ -321,10 +314,6 @@ def _is_model_weights_in_cached_folder(cached_folder, name):
     return weights_exist
 
 
-def _is_legacy_scheduler_kwargs(kwargs):
-    return any(k in SCHEDULER_LEGACY_KWARGS for k in kwargs.keys())
-
-
 def load_single_file_checkpoint(
     pretrained_model_link_or_path,
     force_download=False,
@@ -505,13 +494,7 @@ def infer_diffusers_model_type(checkpoint):
         model_type = "sd3"
 
     elif CHECKPOINT_KEY_NAMES["animatediff"] in checkpoint:
-        if CHECKPOINT_KEY_NAMES["animatediff_scribble"] in checkpoint:
-            model_type = "animatediff_scribble"
-
-        elif CHECKPOINT_KEY_NAMES["animatediff_rgb"] in checkpoint:
-            model_type = "animatediff_rgb"
-
-        elif CHECKPOINT_KEY_NAMES["animatediff_v2"] in checkpoint:
+        if CHECKPOINT_KEY_NAMES["animatediff_v2"] in checkpoint:
             model_type = "animatediff_v2"
 
         elif checkpoint[CHECKPOINT_KEY_NAMES["animatediff_sdxl_beta"]].shape[-1] == 320:
@@ -523,10 +506,8 @@ def infer_diffusers_model_type(checkpoint):
         else:
             model_type = "animatediff_v3"
 
-    elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["flux"]):
-        if any(
-            g in checkpoint for g in ["guidance_in.in_layer.bias", "model.diffusion_model.guidance_in.in_layer.bias"]
-        ):
+    elif CHECKPOINT_KEY_NAMES["flux"] in checkpoint:
+        if "guidance_in.in_layer.bias" in checkpoint:
             model_type = "flux-dev"
         else:
             model_type = "flux-schnell"
@@ -1185,11 +1166,7 @@ def convert_ldm_vae_checkpoint(checkpoint, config):
     # remove the LDM_VAE_KEY prefix from the ldm checkpoint keys so that it is easier to map them to diffusers keys
     vae_state_dict = {}
     keys = list(checkpoint.keys())
-    vae_key = ""
-    for ldm_vae_key in LDM_VAE_KEYS:
-        if any(k.startswith(ldm_vae_key) for k in keys):
-            vae_key = ldm_vae_key
-
+    vae_key = LDM_VAE_KEY if any(k.startswith(LDM_VAE_KEY) for k in keys) else ""
     for key in keys:
         if key.startswith(vae_key):
             vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
@@ -1490,22 +1467,14 @@ def _legacy_load_scheduler(
 
     if scheduler_type is not None:
         deprecation_message = (
-            "Please pass an instance of a Scheduler object directly to the `scheduler` argument in `from_single_file`\n\n"
-            "Example:\n\n"
-            "from diffusers import StableDiffusionPipeline, DDIMScheduler\n\n"
-            "scheduler = DDIMScheduler()\n"
-            "pipe = StableDiffusionPipeline.from_single_file(<checkpoint path>, scheduler=scheduler)\n"
+            "Please pass an instance of a Scheduler object directly to the `scheduler` argument in `from_single_file`."
         )
         deprecate("scheduler_type", "1.0.0", deprecation_message)
 
     if prediction_type is not None:
         deprecation_message = (
-            "Please configure an instance of a Scheduler with the appropriate `prediction_type` and "
-            "pass the object directly to the `scheduler` argument in `from_single_file`.\n\n"
-            "Example:\n\n"
-            "from diffusers import StableDiffusionPipeline, DDIMScheduler\n\n"
-            'scheduler = DDIMScheduler(prediction_type="v_prediction")\n'
-            "pipe = StableDiffusionPipeline.from_single_file(<checkpoint path>, scheduler=scheduler)\n"
+            "Please configure an instance of a Scheduler with the appropriate `prediction_type` "
+            "and pass the object directly to the `scheduler` argument in `from_single_file`."
         )
         deprecate("prediction_type", "1.0.0", deprecation_message)
 
@@ -1902,10 +1871,6 @@ def convert_animatediff_checkpoint_to_diffusers(checkpoint, **kwargs):
 
 def convert_flux_transformer_checkpoint_to_diffusers(checkpoint, **kwargs):
     converted_state_dict = {}
-    keys = list(checkpoint.keys())
-    for k in keys:
-        if "model.diffusion_model." in k:
-            checkpoint[k.replace("model.diffusion_model.", "")] = checkpoint.pop(k)
 
     num_layers = list(set(int(k.split(".", 2)[1]) for k in checkpoint if "double_blocks." in k))[-1] + 1  # noqa: C401
     num_single_layers = list(set(int(k.split(".", 2)[1]) for k in checkpoint if "single_blocks." in k))[-1] + 1  # noqa: C401

src/diffusers/models/__init__.py

@@ -28,7 +28,6 @@ if is_torch_available():
     _import_structure["adapter"] = ["MultiAdapter", "T2IAdapter"]
     _import_structure["autoencoders.autoencoder_asym_kl"] = ["AsymmetricAutoencoderKL"]
     _import_structure["autoencoders.autoencoder_kl"] = ["AutoencoderKL"]
-    _import_structure["autoencoders.autoencoder_kl_cogvideox"] = ["AutoencoderKLCogVideoX"]
     _import_structure["autoencoders.autoencoder_kl_temporal_decoder"] = ["AutoencoderKLTemporalDecoder"]
     _import_structure["autoencoders.autoencoder_oobleck"] = ["AutoencoderOobleck"]
     _import_structure["autoencoders.autoencoder_tiny"] = ["AutoencoderTiny"]
@@ -42,7 +41,6 @@ if is_torch_available():
     _import_structure["embeddings"] = ["ImageProjection"]
     _import_structure["modeling_utils"] = ["ModelMixin"]
     _import_structure["transformers.auraflow_transformer_2d"] = ["AuraFlowTransformer2DModel"]
-    _import_structure["transformers.cogvideox_transformer_3d"] = ["CogVideoXTransformer3DModel"]
     _import_structure["transformers.dit_transformer_2d"] = ["DiTTransformer2DModel"]
     _import_structure["transformers.dual_transformer_2d"] = ["DualTransformer2DModel"]
     _import_structure["transformers.hunyuan_transformer_2d"] = ["HunyuanDiT2DModel"]
@@ -79,7 +77,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .autoencoders import (
             AsymmetricAutoencoderKL,
             AutoencoderKL,
-            AutoencoderKLCogVideoX,
             AutoencoderKLTemporalDecoder,
             AutoencoderOobleck,
             AutoencoderTiny,
@@ -95,7 +92,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .modeling_utils import ModelMixin
         from .transformers import (
             AuraFlowTransformer2DModel,
-            CogVideoXTransformer3DModel,
             DiTTransformer2DModel,
             DualTransformer2DModel,
             FluxTransformer2DModel,

src/diffusers/models/attention_processor.py

@@ -1868,148 +1868,6 @@ class FluxAttnProcessor2_0:
         return hidden_states, encoder_hidden_states
 
 
-class CogVideoXAttnProcessor2_0:
-    r"""
-    Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on
-    query and key vectors, but does not include spatial normalization.
-    """
-
-    def __init__(self):
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("CogVideoXAttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-
-    def __call__(
-        self,
-        attn: Attention,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        image_rotary_emb: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        text_seq_length = encoder_hidden_states.size(1)
-
-        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-
-        if attention_mask is not None:
-            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
-        query = attn.to_q(hidden_states)
-        key = attn.to_k(hidden_states)
-        value = attn.to_v(hidden_states)
-
-        inner_dim = key.shape[-1]
-        head_dim = inner_dim // attn.heads
-
-        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        if attn.norm_q is not None:
-            query = attn.norm_q(query)
-        if attn.norm_k is not None:
-            key = attn.norm_k(key)
-
-        # Apply RoPE if needed
-        if image_rotary_emb is not None:
-            from .embeddings import apply_rotary_emb
-
-            query[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb)
-            if not attn.is_cross_attention:
-                key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], image_rotary_emb)
-
-        hidden_states = F.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-        )
-
-        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        encoder_hidden_states, hidden_states = hidden_states.split(
-            [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
-        )
-        return hidden_states, encoder_hidden_states
-
-
-class FusedCogVideoXAttnProcessor2_0:
-    r"""
-    Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on
-    query and key vectors, but does not include spatial normalization.
-    """
-
-    def __init__(self):
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("CogVideoXAttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-
-    def __call__(
-        self,
-        attn: Attention,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        image_rotary_emb: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        text_seq_length = encoder_hidden_states.size(1)
-
-        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-
-        if attention_mask is not None:
-            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
-        qkv = attn.to_qkv(hidden_states)
-        split_size = qkv.shape[-1] // 3
-        query, key, value = torch.split(qkv, split_size, dim=-1)
-
-        inner_dim = key.shape[-1]
-        head_dim = inner_dim // attn.heads
-
-        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        if attn.norm_q is not None:
-            query = attn.norm_q(query)
-        if attn.norm_k is not None:
-            key = attn.norm_k(key)
-
-        # Apply RoPE if needed
-        if image_rotary_emb is not None:
-            from .embeddings import apply_rotary_emb
-
-            query[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb)
-            if not attn.is_cross_attention:
-                key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], image_rotary_emb)
-
-        hidden_states = F.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-        )
-
-        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        encoder_hidden_states, hidden_states = hidden_states.split(
-            [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
-        )
-        return hidden_states, encoder_hidden_states
-
-
 class XFormersAttnAddedKVProcessor:
     r"""
     Processor for implementing memory efficient attention using xFormers.

src/diffusers/models/autoencoders/__init__.py

@@ -1,6 +1,5 @@
 from .autoencoder_asym_kl import AsymmetricAutoencoderKL
 from .autoencoder_kl import AutoencoderKL
-from .autoencoder_kl_cogvideox import AutoencoderKLCogVideoX
 from .autoencoder_kl_temporal_decoder import AutoencoderKLTemporalDecoder
 from .autoencoder_oobleck import AutoencoderOobleck
 from .autoencoder_tiny import AutoencoderTiny

src/diffusers/models/controlnet_sparsectrl.py

@@ -20,7 +20,6 @@ from torch import nn
 from torch.nn import functional as F
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..loaders import FromOriginalModelMixin
 from ..utils import BaseOutput, logging
 from .attention_processor import (
     ADDED_KV_ATTENTION_PROCESSORS,
@@ -93,7 +92,7 @@ class SparseControlNetConditioningEmbedding(nn.Module):
         return embedding
 
 
-class SparseControlNetModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
+class SparseControlNetModel(ModelMixin, ConfigMixin):
     """
     A SparseControlNet model as described in [SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion
     Models](https://arxiv.org/abs/2311.16933).
@@ -315,7 +314,6 @@ class SparseControlNetModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
                     temporal_num_attention_heads=motion_num_attention_heads[i],
                     temporal_max_seq_length=motion_max_seq_length,
                     temporal_transformer_layers_per_block=temporal_transformer_layers_per_block[i],
-                    temporal_double_self_attention=False,
                 )
             elif down_block_type == "DownBlockMotion":
                 down_block = DownBlockMotion(
@@ -333,7 +331,6 @@ class SparseControlNetModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
                     temporal_num_attention_heads=motion_num_attention_heads[i],
                     temporal_max_seq_length=motion_max_seq_length,
                     temporal_transformer_layers_per_block=temporal_transformer_layers_per_block[i],
-                    temporal_double_self_attention=False,
                 )
             else:
                 raise ValueError(

src/diffusers/models/downsampling.py

@@ -285,74 +285,6 @@ class KDownsample2D(nn.Module):
         return F.conv2d(inputs, weight, stride=2)
 
 
-class CogVideoXDownsample3D(nn.Module):
-    # Todo: Wait for paper relase.
-    r"""
-    A 3D Downsampling layer using in [CogVideoX]() by Tsinghua University & ZhipuAI
-
-    Args:
-        in_channels (`int`):
-            Number of channels in the input image.
-        out_channels (`int`):
-            Number of channels produced by the convolution.
-        kernel_size (`int`, defaults to `3`):
-            Size of the convolving kernel.
-        stride (`int`, defaults to `2`):
-            Stride of the convolution.
-        padding (`int`, defaults to `0`):
-            Padding added to all four sides of the input.
-        compress_time (`bool`, defaults to `False`):
-            Whether or not to compress the time dimension.
-    """
-
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        kernel_size: int = 3,
-        stride: int = 2,
-        padding: int = 0,
-        compress_time: bool = False,
-    ):
-        super().__init__()
-
-        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
-        self.compress_time = compress_time
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        if self.compress_time:
-            batch_size, channels, frames, height, width = x.shape
-
-            # (batch_size, channels, frames, height, width) -> (batch_size, height, width, channels, frames) -> (batch_size * height * width, channels, frames)
-            x = x.permute(0, 3, 4, 1, 2).reshape(batch_size * height * width, channels, frames)
-
-            if x.shape[-1] % 2 == 1:
-                x_first, x_rest = x[..., 0], x[..., 1:]
-                if x_rest.shape[-1] > 0:
-                    # (batch_size * height * width, channels, frames - 1) -> (batch_size * height * width, channels, (frames - 1) // 2)
-                    x_rest = F.avg_pool1d(x_rest, kernel_size=2, stride=2)
-
-                x = torch.cat([x_first[..., None], x_rest], dim=-1)
-                # (batch_size * height * width, channels, (frames // 2) + 1) -> (batch_size, height, width, channels, (frames // 2) + 1) -> (batch_size, channels, (frames // 2) + 1, height, width)
-                x = x.reshape(batch_size, height, width, channels, x.shape[-1]).permute(0, 3, 4, 1, 2)
-            else:
-                # (batch_size * height * width, channels, frames) -> (batch_size * height * width, channels, frames // 2)
-                x = F.avg_pool1d(x, kernel_size=2, stride=2)
-                # (batch_size * height * width, channels, frames // 2) -> (batch_size, height, width, channels, frames // 2) -> (batch_size, channels, frames // 2, height, width)
-                x = x.reshape(batch_size, height, width, channels, x.shape[-1]).permute(0, 3, 4, 1, 2)
-
-        # Pad the tensor
-        pad = (0, 1, 0, 1)
-        x = F.pad(x, pad, mode="constant", value=0)
-        batch_size, channels, frames, height, width = x.shape
-        # (batch_size, channels, frames, height, width) -> (batch_size, frames, channels, height, width) -> (batch_size * frames, channels, height, width)
-        x = x.permute(0, 2, 1, 3, 4).reshape(batch_size * frames, channels, height, width)
-        x = self.conv(x)
-        # (batch_size * frames, channels, height, width) -> (batch_size, frames, channels, height, width) -> (batch_size, channels, frames, height, width)
-        x = x.reshape(batch_size, frames, x.shape[1], x.shape[2], x.shape[3]).permute(0, 2, 1, 3, 4)
-        return x
-
-
 def downsample_2d(
     hidden_states: torch.Tensor,
     kernel: Optional[torch.Tensor] = None,

src/diffusers/models/embeddings.py

@@ -78,53 +78,6 @@ def get_timestep_embedding(
     return emb
 
 
-def get_3d_sincos_pos_embed(
-    embed_dim: int,
-    spatial_size: Union[int, Tuple[int, int]],
-    temporal_size: int,
-    spatial_interpolation_scale: float = 1.0,
-    temporal_interpolation_scale: float = 1.0,
-) -> np.ndarray:
-    r"""
-    Args:
-        embed_dim (`int`):
-        spatial_size (`int` or `Tuple[int, int]`):
-        temporal_size (`int`):
-        spatial_interpolation_scale (`float`, defaults to 1.0):
-        temporal_interpolation_scale (`float`, defaults to 1.0):
-    """
-    if embed_dim % 4 != 0:
-        raise ValueError("`embed_dim` must be divisible by 4")
-    if isinstance(spatial_size, int):
-        spatial_size = (spatial_size, spatial_size)
-
-    embed_dim_spatial = 3 * embed_dim // 4
-    embed_dim_temporal = embed_dim // 4
-
-    # 1. Spatial
-    grid_h = np.arange(spatial_size[1], dtype=np.float32) / spatial_interpolation_scale
-    grid_w = np.arange(spatial_size[0], dtype=np.float32) / spatial_interpolation_scale
-    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
-    grid = np.stack(grid, axis=0)
-
-    grid = grid.reshape([2, 1, spatial_size[1], spatial_size[0]])
-    pos_embed_spatial = get_2d_sincos_pos_embed_from_grid(embed_dim_spatial, grid)
-
-    # 2. Temporal
-    grid_t = np.arange(temporal_size, dtype=np.float32) / temporal_interpolation_scale
-    pos_embed_temporal = get_1d_sincos_pos_embed_from_grid(embed_dim_temporal, grid_t)
-
-    # 3. Concat
-    pos_embed_spatial = pos_embed_spatial[np.newaxis, :, :]
-    pos_embed_spatial = np.repeat(pos_embed_spatial, temporal_size, axis=0)  # [T, H*W, D // 4 * 3]
-
-    pos_embed_temporal = pos_embed_temporal[:, np.newaxis, :]
-    pos_embed_temporal = np.repeat(pos_embed_temporal, spatial_size[0] * spatial_size[1], axis=1)  # [T, H*W, D // 4]
-
-    pos_embed = np.concatenate([pos_embed_temporal, pos_embed_spatial], axis=-1)  # [T, H*W, D]
-    return pos_embed
-
-
 def get_2d_sincos_pos_embed(
     embed_dim, grid_size, cls_token=False, extra_tokens=0, interpolation_scale=1.0, base_size=16
 ):
@@ -334,130 +287,6 @@ class LuminaPatchEmbed(nn.Module):
         )
 
 
-class CogVideoXPatchEmbed(nn.Module):
-    def __init__(
-        self,
-        patch_size: int = 2,
-        in_channels: int = 16,
-        embed_dim: int = 1920,
-        text_embed_dim: int = 4096,
-        bias: bool = True,
-    ) -> None:
-        super().__init__()
-        self.patch_size = patch_size
-
-        self.proj = nn.Conv2d(
-            in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
-        )
-        self.text_proj = nn.Linear(text_embed_dim, embed_dim)
-
-    def forward(self, text_embeds: torch.Tensor, image_embeds: torch.Tensor):
-        r"""
-        Args:
-            text_embeds (`torch.Tensor`):
-                Input text embeddings. Expected shape: (batch_size, seq_length, embedding_dim).
-            image_embeds (`torch.Tensor`):
-                Input image embeddings. Expected shape: (batch_size, num_frames, channels, height, width).
-        """
-        text_embeds = self.text_proj(text_embeds)
-
-        batch, num_frames, channels, height, width = image_embeds.shape
-        image_embeds = image_embeds.reshape(-1, channels, height, width)
-        image_embeds = self.proj(image_embeds)
-        image_embeds = image_embeds.view(batch, num_frames, *image_embeds.shape[1:])
-        image_embeds = image_embeds.flatten(3).transpose(2, 3)  # [batch, num_frames, height x width, channels]
-        image_embeds = image_embeds.flatten(1, 2)  # [batch, num_frames x height x width, channels]
-
-        embeds = torch.cat(
-            [text_embeds, image_embeds], dim=1
-        ).contiguous()  # [batch, seq_length + num_frames x height x width, channels]
-        return embeds
-
-
-def get_3d_rotary_pos_embed(
-    embed_dim, crops_coords, grid_size, temporal_size, theta: int = 10000, use_real: bool = True
-) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
-    """
-    RoPE for video tokens with 3D structure.
-
-    Args:
-    embed_dim: (`int`):
-        The embedding dimension size, corresponding to hidden_size_head.
-    crops_coords (`Tuple[int]`):
-        The top-left and bottom-right coordinates of the crop.
-    grid_size (`Tuple[int]`):
-        The grid size of the spatial positional embedding (height, width).
-    temporal_size (`int`):
-        The size of the temporal dimension.
-    theta (`float`):
-        Scaling factor for frequency computation.
-    use_real (`bool`):
-        If True, return real part and imaginary part separately. Otherwise, return complex numbers.
-
-    Returns:
-        `torch.Tensor`: positional embedding with shape `(temporal_size * grid_size[0] * grid_size[1], embed_dim/2)`.
-    """
-    start, stop = crops_coords
-    grid_h = np.linspace(start[0], stop[0], grid_size[0], endpoint=False, dtype=np.float32)
-    grid_w = np.linspace(start[1], stop[1], grid_size[1], endpoint=False, dtype=np.float32)
-    grid_t = np.linspace(0, temporal_size, temporal_size, endpoint=False, dtype=np.float32)
-
-    # Compute dimensions for each axis
-    dim_t = embed_dim // 4
-    dim_h = embed_dim // 8 * 3
-    dim_w = embed_dim // 8 * 3
-
-    # Temporal frequencies
-    freqs_t = 1.0 / (theta ** (torch.arange(0, dim_t, 2).float() / dim_t))
-    grid_t = torch.from_numpy(grid_t).float()
-    freqs_t = torch.einsum("n , f -> n f", grid_t, freqs_t)
-    freqs_t = freqs_t.repeat_interleave(2, dim=-1)
-
-    # Spatial frequencies for height and width
-    freqs_h = 1.0 / (theta ** (torch.arange(0, dim_h, 2).float() / dim_h))
-    freqs_w = 1.0 / (theta ** (torch.arange(0, dim_w, 2).float() / dim_w))
-    grid_h = torch.from_numpy(grid_h).float()
-    grid_w = torch.from_numpy(grid_w).float()
-    freqs_h = torch.einsum("n , f -> n f", grid_h, freqs_h)
-    freqs_w = torch.einsum("n , f -> n f", grid_w, freqs_w)
-    freqs_h = freqs_h.repeat_interleave(2, dim=-1)
-    freqs_w = freqs_w.repeat_interleave(2, dim=-1)
-
-    # Broadcast and concatenate tensors along specified dimension
-    def broadcast(tensors, dim=-1):
-        num_tensors = len(tensors)
-        shape_lens = {len(t.shape) for t in tensors}
-        assert len(shape_lens) == 1, "tensors must all have the same number of dimensions"
-        shape_len = list(shape_lens)[0]
-        dim = (dim + shape_len) if dim < 0 else dim
-        dims = list(zip(*(list(t.shape) for t in tensors)))
-        expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim]
-        assert all(
-            [*(len(set(t[1])) <= 2 for t in expandable_dims)]
-        ), "invalid dimensions for broadcastable concatenation"
-        max_dims = [(t[0], max(t[1])) for t in expandable_dims]
-        expanded_dims = [(t[0], (t[1],) * num_tensors) for t in max_dims]
-        expanded_dims.insert(dim, (dim, dims[dim]))
-        expandable_shapes = list(zip(*(t[1] for t in expanded_dims)))
-        tensors = [t[0].expand(*t[1]) for t in zip(tensors, expandable_shapes)]
-        return torch.cat(tensors, dim=dim)
-
-    freqs = broadcast((freqs_t[:, None, None, :], freqs_h[None, :, None, :], freqs_w[None, None, :, :]), dim=-1)
-
-    t, h, w, d = freqs.shape
-    freqs = freqs.view(t * h * w, d)
-
-    # Generate sine and cosine components
-    sin = freqs.sin()
-    cos = freqs.cos()
-
-    if use_real:
-        return cos, sin
-    else:
-        freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
-        return freqs_cis
-
-
 def get_2d_rotary_pos_embed(embed_dim, crops_coords, grid_size, use_real=True):
     """
     RoPE for image tokens with 2d structure.

src/diffusers/models/normalization.py

@@ -34,53 +34,19 @@ class AdaLayerNorm(nn.Module):
 
     Parameters:
         embedding_dim (`int`): The size of each embedding vector.
-        num_embeddings (`int`, *optional*): The size of the embeddings dictionary.
-        output_dim (`int`, *optional*):
-        norm_elementwise_affine (`bool`, defaults to `False):
-        norm_eps (`bool`, defaults to `False`):
-        chunk_dim (`int`, defaults to `0`):
+        num_embeddings (`int`): The size of the embeddings dictionary.
     """
 
-    def __init__(
-        self,
-        embedding_dim: int,
-        num_embeddings: Optional[int] = None,
-        output_dim: Optional[int] = None,
-        norm_elementwise_affine: bool = False,
-        norm_eps: float = 1e-5,
-        chunk_dim: int = 0,
-    ):
+    def __init__(self, embedding_dim: int, num_embeddings: int):
         super().__init__()
-
-        self.chunk_dim = chunk_dim
-        output_dim = output_dim or embedding_dim * 2
-
-        if num_embeddings is not None:
-            self.emb = nn.Embedding(num_embeddings, embedding_dim)
-        else:
-            self.emb = None
-
+        self.emb = nn.Embedding(num_embeddings, embedding_dim)
         self.silu = nn.SiLU()
-        self.linear = nn.Linear(embedding_dim, output_dim)
-        self.norm = nn.LayerNorm(output_dim // 2, norm_eps, norm_elementwise_affine)
-
-    def forward(
-        self, x: torch.Tensor, timestep: Optional[torch.Tensor] = None, temb: Optional[torch.Tensor] = None
-    ) -> torch.Tensor:
-        if self.emb is not None:
-            temb = self.emb(timestep)
-
-        temb = self.linear(self.silu(temb))
-
-        if self.chunk_dim == 1:
-            # This is a bit weird why we have the order of "shift, scale" here and "scale, shift" in the
-            # other if-branch. This branch is specific to CogVideoX for now.
-            shift, scale = temb.chunk(2, dim=1)
-            shift = shift[:, None, :]
-            scale = scale[:, None, :]
-        else:
-            scale, shift = temb.chunk(2, dim=0)
+        self.linear = nn.Linear(embedding_dim, embedding_dim * 2)
+        self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False)
 
+    def forward(self, x: torch.Tensor, timestep: torch.Tensor) -> torch.Tensor:
+        emb = self.linear(self.silu(self.emb(timestep)))
+        scale, shift = torch.chunk(emb, 2)
         x = self.norm(x) * (1 + scale) + shift
         return x
 
@@ -355,30 +321,6 @@ class LuminaLayerNormContinuous(nn.Module):
         return x
 
 
-class CogVideoXLayerNormZero(nn.Module):
-    def __init__(
-        self,
-        conditioning_dim: int,
-        embedding_dim: int,
-        elementwise_affine: bool = True,
-        eps: float = 1e-5,
-        bias: bool = True,
-    ) -> None:
-        super().__init__()
-
-        self.silu = nn.SiLU()
-        self.linear = nn.Linear(conditioning_dim, 6 * embedding_dim, bias=bias)
-        self.norm = nn.LayerNorm(embedding_dim, eps=eps, elementwise_affine=elementwise_affine)
-
-    def forward(
-        self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, temb: torch.Tensor
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        shift, scale, gate, enc_shift, enc_scale, enc_gate = self.linear(self.silu(temb)).chunk(6, dim=1)
-        hidden_states = self.norm(hidden_states) * (1 + scale)[:, None, :] + shift[:, None, :]
-        encoder_hidden_states = self.norm(encoder_hidden_states) * (1 + enc_scale)[:, None, :] + enc_shift[:, None, :]
-        return hidden_states, encoder_hidden_states, gate[:, None, :], enc_gate[:, None, :]
-
-
 if is_torch_version(">=", "2.1.0"):
     LayerNorm = nn.LayerNorm
 else:

src/diffusers/models/transformers/__init__.py

@@ -3,7 +3,6 @@ from ...utils import is_torch_available
 
 if is_torch_available():
     from .auraflow_transformer_2d import AuraFlowTransformer2DModel
-    from .cogvideox_transformer_3d import CogVideoXTransformer3DModel
     from .dit_transformer_2d import DiTTransformer2DModel
     from .dual_transformer_2d import DualTransformer2DModel
     from .hunyuan_transformer_2d import HunyuanDiT2DModel

src/diffusers/models/transformers/auraflow_transformer_2d.py

@@ -68,21 +68,6 @@ class AuraFlowPatchEmbed(nn.Module):
         self.height, self.width = height // patch_size, width // patch_size
         self.base_size = height // patch_size
 
-    def pe_selection_index_based_on_dim(self, h, w):
-        # select subset of positional embedding based on H, W, where H, W is size of latent
-        # PE will be viewed as 2d-grid, and H/p x W/p of the PE will be selected
-        # because original input are in flattened format, we have to flatten this 2d grid as well.
-        h_p, w_p = h // self.patch_size, w // self.patch_size
-        original_pe_indexes = torch.arange(self.pos_embed.shape[1])
-        h_max, w_max = int(self.pos_embed_max_size**0.5), int(self.pos_embed_max_size**0.5)
-        original_pe_indexes = original_pe_indexes.view(h_max, w_max)
-        starth = h_max // 2 - h_p // 2
-        endh = starth + h_p
-        startw = w_max // 2 - w_p // 2
-        endw = startw + w_p
-        original_pe_indexes = original_pe_indexes[starth:endh, startw:endw]
-        return original_pe_indexes.flatten()
-
     def forward(self, latent):
         batch_size, num_channels, height, width = latent.size()
         latent = latent.view(
@@ -95,8 +80,7 @@ class AuraFlowPatchEmbed(nn.Module):
         )
         latent = latent.permute(0, 2, 4, 1, 3, 5).flatten(-3).flatten(1, 2)
         latent = self.proj(latent)
-        pe_index = self.pe_selection_index_based_on_dim(height, width)
-        return latent + self.pos_embed[:, pe_index]
+        return latent + self.pos_embed
 
 
 # Taken from the original Aura flow inference code.

src/diffusers/models/transformers/cogvideox_transformer_3d.py

@@ -1,485 +0,0 @@
-# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI and 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.
-
-from typing import Any, Dict, Optional, Tuple, Union
-
-import torch
-from torch import nn
-
-from ...configuration_utils import ConfigMixin, register_to_config
-from ...utils import is_torch_version, logging
-from ...utils.torch_utils import maybe_allow_in_graph
-from ..attention import Attention, FeedForward
-from ..attention_processor import AttentionProcessor, CogVideoXAttnProcessor2_0, FusedCogVideoXAttnProcessor2_0
-from ..embeddings import CogVideoXPatchEmbed, TimestepEmbedding, Timesteps, get_3d_sincos_pos_embed
-from ..modeling_outputs import Transformer2DModelOutput
-from ..modeling_utils import ModelMixin
-from ..normalization import AdaLayerNorm, CogVideoXLayerNormZero
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-@maybe_allow_in_graph
-class CogVideoXBlock(nn.Module):
-    r"""
-    Transformer block used in [CogVideoX](https://github.com/THUDM/CogVideo) model.
-
-    Parameters:
-        dim (`int`):
-            The number of channels in the input and output.
-        num_attention_heads (`int`):
-            The number of heads to use for multi-head attention.
-        attention_head_dim (`int`):
-            The number of channels in each head.
-        time_embed_dim (`int`):
-            The number of channels in timestep embedding.
-        dropout (`float`, defaults to `0.0`):
-            The dropout probability to use.
-        activation_fn (`str`, defaults to `"gelu-approximate"`):
-            Activation function to be used in feed-forward.
-        attention_bias (`bool`, defaults to `False`):
-            Whether or not to use bias in attention projection layers.
-        qk_norm (`bool`, defaults to `True`):
-            Whether or not to use normalization after query and key projections in Attention.
-        norm_elementwise_affine (`bool`, defaults to `True`):
-            Whether to use learnable elementwise affine parameters for normalization.
-        norm_eps (`float`, defaults to `1e-5`):
-            Epsilon value for normalization layers.
-        final_dropout (`bool` defaults to `False`):
-            Whether to apply a final dropout after the last feed-forward layer.
-        ff_inner_dim (`int`, *optional*, defaults to `None`):
-            Custom hidden dimension of Feed-forward layer. If not provided, `4 * dim` is used.
-        ff_bias (`bool`, defaults to `True`):
-            Whether or not to use bias in Feed-forward layer.
-        attention_out_bias (`bool`, defaults to `True`):
-            Whether or not to use bias in Attention output projection layer.
-    """
-
-    def __init__(
-        self,
-        dim: int,
-        num_attention_heads: int,
-        attention_head_dim: int,
-        time_embed_dim: int,
-        dropout: float = 0.0,
-        activation_fn: str = "gelu-approximate",
-        attention_bias: bool = False,
-        qk_norm: bool = True,
-        norm_elementwise_affine: bool = True,
-        norm_eps: float = 1e-5,
-        final_dropout: bool = True,
-        ff_inner_dim: Optional[int] = None,
-        ff_bias: bool = True,
-        attention_out_bias: bool = True,
-    ):
-        super().__init__()
-
-        # 1. Self Attention
-        self.norm1 = CogVideoXLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True)
-
-        self.attn1 = Attention(
-            query_dim=dim,
-            dim_head=attention_head_dim,
-            heads=num_attention_heads,
-            qk_norm="layer_norm" if qk_norm else None,
-            eps=1e-6,
-            bias=attention_bias,
-            out_bias=attention_out_bias,
-            processor=CogVideoXAttnProcessor2_0(),
-        )
-
-        # 2. Feed Forward
-        self.norm2 = CogVideoXLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True)
-
-        self.ff = FeedForward(
-            dim,
-            dropout=dropout,
-            activation_fn=activation_fn,
-            final_dropout=final_dropout,
-            inner_dim=ff_inner_dim,
-            bias=ff_bias,
-        )
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        temb: torch.Tensor,
-        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    ) -> torch.Tensor:
-        text_seq_length = encoder_hidden_states.size(1)
-
-        # norm & modulate
-        norm_hidden_states, norm_encoder_hidden_states, gate_msa, enc_gate_msa = self.norm1(
-            hidden_states, encoder_hidden_states, temb
-        )
-
-        # attention
-        attn_hidden_states, attn_encoder_hidden_states = self.attn1(
-            hidden_states=norm_hidden_states,
-            encoder_hidden_states=norm_encoder_hidden_states,
-            image_rotary_emb=image_rotary_emb,
-        )
-
-        hidden_states = hidden_states + gate_msa * attn_hidden_states
-        encoder_hidden_states = encoder_hidden_states + enc_gate_msa * attn_encoder_hidden_states
-
-        # norm & modulate
-        norm_hidden_states, norm_encoder_hidden_states, gate_ff, enc_gate_ff = self.norm2(
-            hidden_states, encoder_hidden_states, temb
-        )
-
-        # feed-forward
-        norm_hidden_states = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1)
-        ff_output = self.ff(norm_hidden_states)
-
-        hidden_states = hidden_states + gate_ff * ff_output[:, text_seq_length:]
-        encoder_hidden_states = encoder_hidden_states + enc_gate_ff * ff_output[:, :text_seq_length]
-
-        return hidden_states, encoder_hidden_states
-
-
-class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin):
-    """
-    A Transformer model for video-like data in [CogVideoX](https://github.com/THUDM/CogVideo).
-
-    Parameters:
-        num_attention_heads (`int`, defaults to `30`):
-            The number of heads to use for multi-head attention.
-        attention_head_dim (`int`, defaults to `64`):
-            The number of channels in each head.
-        in_channels (`int`, defaults to `16`):
-            The number of channels in the input.
-        out_channels (`int`, *optional*, defaults to `16`):
-            The number of channels in the output.
-        flip_sin_to_cos (`bool`, defaults to `True`):
-            Whether to flip the sin to cos in the time embedding.
-        time_embed_dim (`int`, defaults to `512`):
-            Output dimension of timestep embeddings.
-        text_embed_dim (`int`, defaults to `4096`):
-            Input dimension of text embeddings from the text encoder.
-        num_layers (`int`, defaults to `30`):
-            The number of layers of Transformer blocks to use.
-        dropout (`float`, defaults to `0.0`):
-            The dropout probability to use.
-        attention_bias (`bool`, defaults to `True`):
-            Whether or not to use bias in the attention projection layers.
-        sample_width (`int`, defaults to `90`):
-            The width of the input latents.
-        sample_height (`int`, defaults to `60`):
-            The height of the input latents.
-        sample_frames (`int`, defaults to `49`):
-            The number of frames in the input latents. Note that this parameter was incorrectly initialized to 49
-            instead of 13 because CogVideoX processed 13 latent frames at once in its default and recommended settings,
-            but cannot be changed to the correct value to ensure backwards compatibility. To create a transformer with
-            K latent frames, the correct value to pass here would be: ((K - 1) * temporal_compression_ratio + 1).
-        patch_size (`int`, defaults to `2`):
-            The size of the patches to use in the patch embedding layer.
-        temporal_compression_ratio (`int`, defaults to `4`):
-            The compression ratio across the temporal dimension. See documentation for `sample_frames`.
-        max_text_seq_length (`int`, defaults to `226`):
-            The maximum sequence length of the input text embeddings.
-        activation_fn (`str`, defaults to `"gelu-approximate"`):
-            Activation function to use in feed-forward.
-        timestep_activation_fn (`str`, defaults to `"silu"`):
-            Activation function to use when generating the timestep embeddings.
-        norm_elementwise_affine (`bool`, defaults to `True`):
-            Whether or not to use elementwise affine in normalization layers.
-        norm_eps (`float`, defaults to `1e-5`):
-            The epsilon value to use in normalization layers.
-        spatial_interpolation_scale (`float`, defaults to `1.875`):
-            Scaling factor to apply in 3D positional embeddings across spatial dimensions.
-        temporal_interpolation_scale (`float`, defaults to `1.0`):
-            Scaling factor to apply in 3D positional embeddings across temporal dimensions.
-    """
-
-    _supports_gradient_checkpointing = True
-
-    @register_to_config
-    def __init__(
-        self,
-        num_attention_heads: int = 30,
-        attention_head_dim: int = 64,
-        in_channels: int = 16,
-        out_channels: Optional[int] = 16,
-        flip_sin_to_cos: bool = True,
-        freq_shift: int = 0,
-        time_embed_dim: int = 512,
-        text_embed_dim: int = 4096,
-        num_layers: int = 30,
-        dropout: float = 0.0,
-        attention_bias: bool = True,
-        sample_width: int = 90,
-        sample_height: int = 60,
-        sample_frames: int = 49,
-        patch_size: int = 2,
-        temporal_compression_ratio: int = 4,
-        max_text_seq_length: int = 226,
-        activation_fn: str = "gelu-approximate",
-        timestep_activation_fn: str = "silu",
-        norm_elementwise_affine: bool = True,
-        norm_eps: float = 1e-5,
-        spatial_interpolation_scale: float = 1.875,
-        temporal_interpolation_scale: float = 1.0,
-        use_rotary_positional_embeddings: bool = False,
-    ):
-        super().__init__()
-        inner_dim = num_attention_heads * attention_head_dim
-
-        post_patch_height = sample_height // patch_size
-        post_patch_width = sample_width // patch_size
-        post_time_compression_frames = (sample_frames - 1) // temporal_compression_ratio + 1
-        self.num_patches = post_patch_height * post_patch_width * post_time_compression_frames
-
-        # 1. Patch embedding
-        self.patch_embed = CogVideoXPatchEmbed(patch_size, in_channels, inner_dim, text_embed_dim, bias=True)
-        self.embedding_dropout = nn.Dropout(dropout)
-
-        # 2. 3D positional embeddings
-        spatial_pos_embedding = get_3d_sincos_pos_embed(
-            inner_dim,
-            (post_patch_width, post_patch_height),
-            post_time_compression_frames,
-            spatial_interpolation_scale,
-            temporal_interpolation_scale,
-        )
-        spatial_pos_embedding = torch.from_numpy(spatial_pos_embedding).flatten(0, 1)
-        pos_embedding = torch.zeros(1, max_text_seq_length + self.num_patches, inner_dim, requires_grad=False)
-        pos_embedding.data[:, max_text_seq_length:].copy_(spatial_pos_embedding)
-        self.register_buffer("pos_embedding", pos_embedding, persistent=False)
-
-        # 3. Time embeddings
-        self.time_proj = Timesteps(inner_dim, flip_sin_to_cos, freq_shift)
-        self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, timestep_activation_fn)
-
-        # 4. Define spatio-temporal transformers blocks
-        self.transformer_blocks = nn.ModuleList(
-            [
-                CogVideoXBlock(
-                    dim=inner_dim,
-                    num_attention_heads=num_attention_heads,
-                    attention_head_dim=attention_head_dim,
-                    time_embed_dim=time_embed_dim,
-                    dropout=dropout,
-                    activation_fn=activation_fn,
-                    attention_bias=attention_bias,
-                    norm_elementwise_affine=norm_elementwise_affine,
-                    norm_eps=norm_eps,
-                )
-                for _ in range(num_layers)
-            ]
-        )
-        self.norm_final = nn.LayerNorm(inner_dim, norm_eps, norm_elementwise_affine)
-
-        # 5. Output blocks
-        self.norm_out = AdaLayerNorm(
-            embedding_dim=time_embed_dim,
-            output_dim=2 * inner_dim,
-            norm_elementwise_affine=norm_elementwise_affine,
-            norm_eps=norm_eps,
-            chunk_dim=1,
-        )
-        self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * out_channels)
-
-        self.gradient_checkpointing = False
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        self.gradient_checkpointing = value
-
-    @property
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
-    def attn_processors(self) -> Dict[str, AttentionProcessor]:
-        r"""
-        Returns:
-            `dict` of attention processors: A dictionary containing all attention processors used in the model with
-            indexed by its weight name.
-        """
-        # set recursively
-        processors = {}
-
-        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
-            if hasattr(module, "get_processor"):
-                processors[f"{name}.processor"] = module.get_processor()
-
-            for sub_name, child in module.named_children():
-                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
-
-            return processors
-
-        for name, module in self.named_children():
-            fn_recursive_add_processors(name, module, processors)
-
-        return processors
-
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
-    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
-        r"""
-        Sets the attention processor to use to compute attention.
-
-        Parameters:
-            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
-                The instantiated processor class or a dictionary of processor classes that will be set as the processor
-                for **all** `Attention` layers.
-
-                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
-                processor. This is strongly recommended when setting trainable attention processors.
-
-        """
-        count = len(self.attn_processors.keys())
-
-        if isinstance(processor, dict) and len(processor) != count:
-            raise ValueError(
-                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
-                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
-            )
-
-        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
-            if hasattr(module, "set_processor"):
-                if not isinstance(processor, dict):
-                    module.set_processor(processor)
-                else:
-                    module.set_processor(processor.pop(f"{name}.processor"))
-
-            for sub_name, child in module.named_children():
-                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
-
-        for name, module in self.named_children():
-            fn_recursive_attn_processor(name, module, processor)
-
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedCogVideoXAttnProcessor2_0
-    def fuse_qkv_projections(self):
-        """
-        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
-        are fused. For cross-attention modules, key and value projection matrices are fused.
-
-        <Tip warning={true}>
-
-        This API is 🧪 experimental.
-
-        </Tip>
-        """
-        self.original_attn_processors = None
-
-        for _, attn_processor in self.attn_processors.items():
-            if "Added" in str(attn_processor.__class__.__name__):
-                raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
-
-        self.original_attn_processors = self.attn_processors
-
-        for module in self.modules():
-            if isinstance(module, Attention):
-                module.fuse_projections(fuse=True)
-
-        self.set_attn_processor(FusedCogVideoXAttnProcessor2_0())
-
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
-    def unfuse_qkv_projections(self):
-        """Disables the fused QKV projection if enabled.
-
-        <Tip warning={true}>
-
-        This API is 🧪 experimental.
-
-        </Tip>
-
-        """
-        if self.original_attn_processors is not None:
-            self.set_attn_processor(self.original_attn_processors)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        timestep: Union[int, float, torch.LongTensor],
-        timestep_cond: Optional[torch.Tensor] = None,
-        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        return_dict: bool = True,
-    ):
-        batch_size, num_frames, channels, height, width = hidden_states.shape
-
-        # 1. Time embedding
-        timesteps = timestep
-        t_emb = self.time_proj(timesteps)
-
-        # timesteps does not contain any weights and will always return f32 tensors
-        # but time_embedding might actually be running in fp16. so we need to cast here.
-        # there might be better ways to encapsulate this.
-        t_emb = t_emb.to(dtype=hidden_states.dtype)
-        emb = self.time_embedding(t_emb, timestep_cond)
-
-        # 2. Patch embedding
-        hidden_states = self.patch_embed(encoder_hidden_states, hidden_states)
-
-        # 3. Position embedding
-        text_seq_length = encoder_hidden_states.shape[1]
-        if not self.config.use_rotary_positional_embeddings:
-            seq_length = height * width * num_frames // (self.config.patch_size**2)
-
-            pos_embeds = self.pos_embedding[:, : text_seq_length + seq_length]
-            hidden_states = hidden_states + pos_embeds
-            hidden_states = self.embedding_dropout(hidden_states)
-
-        encoder_hidden_states = hidden_states[:, :text_seq_length]
-        hidden_states = hidden_states[:, text_seq_length:]
-
-        # 4. Transformer blocks
-        for i, block in enumerate(self.transformer_blocks):
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs)
-
-                    return custom_forward
-
-                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
-                hidden_states, encoder_hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(block),
-                    hidden_states,
-                    encoder_hidden_states,
-                    emb,
-                    image_rotary_emb,
-                    **ckpt_kwargs,
-                )
-            else:
-                hidden_states, encoder_hidden_states = block(
-                    hidden_states=hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    temb=emb,
-                    image_rotary_emb=image_rotary_emb,
-                )
-
-        if not self.config.use_rotary_positional_embeddings:
-            # CogVideoX-2B
-            hidden_states = self.norm_final(hidden_states)
-        else:
-            # CogVideoX-5B
-            hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
-            hidden_states = self.norm_final(hidden_states)
-            hidden_states = hidden_states[:, text_seq_length:]
-
-        # 5. Final block
-        hidden_states = self.norm_out(hidden_states, temb=emb)
-        hidden_states = self.proj_out(hidden_states)
-
-        # 6. Unpatchify
-        p = self.config.patch_size
-        output = hidden_states.reshape(batch_size, num_frames, height // p, width // p, channels, p, p)
-        output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
-
-        if not return_dict:
-            return (output,)
-        return Transformer2DModelOutput(sample=output)

src/diffusers/models/transformers/transformer_flux.py

@@ -125,8 +125,6 @@ class FluxSingleTransformerBlock(nn.Module):
         gate = gate.unsqueeze(1)
         hidden_states = gate * self.proj_out(hidden_states)
         hidden_states = residual + hidden_states
-        if hidden_states.dtype == torch.float16:
-            hidden_states = hidden_states.clip(-65504, 65504)
 
         return hidden_states
 
@@ -225,8 +223,6 @@ class FluxTransformerBlock(nn.Module):
 
         context_ff_output = self.ff_context(norm_encoder_hidden_states)
         encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output
-        if encoder_hidden_states.dtype == torch.float16:
-            encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504)
 
         return encoder_hidden_states, hidden_states
 

src/diffusers/models/unets/unet_motion_model.py

@@ -233,7 +233,6 @@ class DownBlockMotion(nn.Module):
         temporal_cross_attention_dim: Optional[int] = None,
         temporal_max_seq_length: int = 32,
         temporal_transformer_layers_per_block: Union[int, Tuple[int]] = 1,
-        temporal_double_self_attention: bool = True,
     ):
         super().__init__()
         resnets = []
@@ -283,7 +282,6 @@ class DownBlockMotion(nn.Module):
                     positional_embeddings="sinusoidal",
                     num_positional_embeddings=temporal_max_seq_length,
                     attention_head_dim=out_channels // temporal_num_attention_heads[i],
-                    double_self_attention=temporal_double_self_attention,
                 )
             )
 
@@ -387,7 +385,6 @@ class CrossAttnDownBlockMotion(nn.Module):
         temporal_num_attention_heads: int = 8,
         temporal_max_seq_length: int = 32,
         temporal_transformer_layers_per_block: Union[int, Tuple[int]] = 1,
-        temporal_double_self_attention: bool = True,
     ):
         super().__init__()
         resnets = []
@@ -469,7 +466,6 @@ class CrossAttnDownBlockMotion(nn.Module):
                     positional_embeddings="sinusoidal",
                     num_positional_embeddings=temporal_max_seq_length,
                     attention_head_dim=out_channels // temporal_num_attention_heads,
-                    double_self_attention=temporal_double_self_attention,
                 )
             )
 

src/diffusers/models/upsampling.py

@@ -348,70 +348,6 @@ class KUpsample2D(nn.Module):
         return F.conv_transpose2d(inputs, weight, stride=2, padding=self.pad * 2 + 1)
 
 
-class CogVideoXUpsample3D(nn.Module):
-    r"""
-    A 3D Upsample layer using in CogVideoX by Tsinghua University & ZhipuAI # Todo: Wait for paper relase.
-
-    Args:
-        in_channels (`int`):
-            Number of channels in the input image.
-        out_channels (`int`):
-            Number of channels produced by the convolution.
-        kernel_size (`int`, defaults to `3`):
-            Size of the convolving kernel.
-        stride (`int`, defaults to `1`):
-            Stride of the convolution.
-        padding (`int`, defaults to `1`):
-            Padding added to all four sides of the input.
-        compress_time (`bool`, defaults to `False`):
-            Whether or not to compress the time dimension.
-    """
-
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        kernel_size: int = 3,
-        stride: int = 1,
-        padding: int = 1,
-        compress_time: bool = False,
-    ) -> None:
-        super().__init__()
-
-        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
-        self.compress_time = compress_time
-
-    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
-        if self.compress_time:
-            if inputs.shape[2] > 1 and inputs.shape[2] % 2 == 1:
-                # split first frame
-                x_first, x_rest = inputs[:, :, 0], inputs[:, :, 1:]
-
-                x_first = F.interpolate(x_first, scale_factor=2.0)
-                x_rest = F.interpolate(x_rest, scale_factor=2.0)
-                x_first = x_first[:, :, None, :, :]
-                inputs = torch.cat([x_first, x_rest], dim=2)
-            elif inputs.shape[2] > 1:
-                inputs = F.interpolate(inputs, scale_factor=2.0)
-            else:
-                inputs = inputs.squeeze(2)
-                inputs = F.interpolate(inputs, scale_factor=2.0)
-                inputs = inputs[:, :, None, :, :]
-        else:
-            # only interpolate 2D
-            b, c, t, h, w = inputs.shape
-            inputs = inputs.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
-            inputs = F.interpolate(inputs, scale_factor=2.0)
-            inputs = inputs.reshape(b, t, c, *inputs.shape[2:]).permute(0, 2, 1, 3, 4)
-
-        b, c, t, h, w = inputs.shape
-        inputs = inputs.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
-        inputs = self.conv(inputs)
-        inputs = inputs.reshape(b, t, *inputs.shape[1:]).permute(0, 2, 1, 3, 4)
-
-        return inputs
-
-
 def upfirdn2d_native(
     tensor: torch.Tensor,
     kernel: torch.Tensor,

src/diffusers/pipelines/__init__.py

@@ -132,7 +132,6 @@ else:
         "AudioLDM2UNet2DConditionModel",
     ]
     _import_structure["blip_diffusion"] = ["BlipDiffusionPipeline"]
-    _import_structure["cogvideo"] = ["CogVideoXPipeline"]
     _import_structure["controlnet"].extend(
         [
             "BlipDiffusionControlNetPipeline",
@@ -452,7 +451,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         )
         from .aura_flow import AuraFlowPipeline
         from .blip_diffusion import BlipDiffusionPipeline
-        from .cogvideo import CogVideoXPipeline
         from .controlnet import (
             BlipDiffusionControlNetPipeline,
             StableDiffusionControlNetImg2ImgPipeline,

src/diffusers/pipelines/cogvideo/__init__.py

@@ -1,48 +0,0 @@
-from typing import TYPE_CHECKING
-
-from ...utils import (
-    DIFFUSERS_SLOW_IMPORT,
-    OptionalDependencyNotAvailable,
-    _LazyModule,
-    get_objects_from_module,
-    is_torch_available,
-    is_transformers_available,
-)
-
-
-_dummy_objects = {}
-_import_structure = {}
-
-
-try:
-    if not (is_transformers_available() and is_torch_available()):
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from ...utils import dummy_torch_and_transformers_objects  # noqa F403
-
-    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
-else:
-    _import_structure["pipeline_cogvideox"] = ["CogVideoXPipeline"]
-
-if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
-    try:
-        if not (is_transformers_available() and is_torch_available()):
-            raise OptionalDependencyNotAvailable()
-
-    except OptionalDependencyNotAvailable:
-        from ...utils.dummy_torch_and_transformers_objects import *
-    else:
-        from .pipeline_cogvideox import CogVideoXPipeline
-
-else:
-    import sys
-
-    sys.modules[__name__] = _LazyModule(
-        __name__,
-        globals()["__file__"],
-        _import_structure,
-        module_spec=__spec__,
-    )
-
-    for name, value in _dummy_objects.items():
-        setattr(sys.modules[__name__], name, value)

src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py

@@ -1,746 +0,0 @@
-# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI and 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.
-
-import inspect
-import math
-from dataclasses import dataclass
-from typing import Callable, Dict, List, Optional, Tuple, Union
-
-import torch
-from transformers import T5EncoderModel, T5Tokenizer
-
-from ...callbacks import MultiPipelineCallbacks, PipelineCallback
-from ...models import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel
-from ...models.embeddings import get_3d_rotary_pos_embed
-from ...pipelines.pipeline_utils import DiffusionPipeline
-from ...schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler
-from ...utils import BaseOutput, logging, replace_example_docstring
-from ...utils.torch_utils import randn_tensor
-from ...video_processor import VideoProcessor
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```python
-        >>> import torch
-        >>> from diffusers import CogVideoXPipeline
-        >>> from diffusers.utils import export_to_video
-
-        >>> # Models: "THUDM/CogVideoX-2b" or "THUDM/CogVideoX-5b"
-        >>> pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-2b", torch_dtype=torch.float16).to("cuda")
-        >>> prompt = (
-        ...     "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. "
-        ...     "The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other "
-        ...     "pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, "
-        ...     "casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. "
-        ...     "The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical "
-        ...     "atmosphere of this unique musical performance."
-        ... )
-        >>> video = pipe(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]
-        >>> export_to_video(video, "output.mp4", fps=8)
-        ```
-"""
-
-
-# Similar to diffusers.pipelines.hunyuandit.pipeline_hunyuandit.get_resize_crop_region_for_grid
-def get_resize_crop_region_for_grid(src, tgt_width, tgt_height):
-    tw = tgt_width
-    th = tgt_height
-    h, w = src
-    r = h / w
-    if r > (th / tw):
-        resize_height = th
-        resize_width = int(round(th / h * w))
-    else:
-        resize_width = tw
-        resize_height = int(round(tw / w * h))
-
-    crop_top = int(round((th - resize_height) / 2.0))
-    crop_left = int(round((tw - resize_width) / 2.0))
-
-    return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
-def retrieve_timesteps(
-    scheduler,
-    num_inference_steps: Optional[int] = None,
-    device: Optional[Union[str, torch.device]] = None,
-    timesteps: Optional[List[int]] = None,
-    sigmas: Optional[List[float]] = None,
-    **kwargs,
-):
-    """
-    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
-    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
-
-    Args:
-        scheduler (`SchedulerMixin`):
-            The scheduler to get timesteps from.
-        num_inference_steps (`int`):
-            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
-            must be `None`.
-        device (`str` or `torch.device`, *optional*):
-            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
-        timesteps (`List[int]`, *optional*):
-            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
-            `num_inference_steps` and `sigmas` must be `None`.
-        sigmas (`List[float]`, *optional*):
-            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
-            `num_inference_steps` and `timesteps` must be `None`.
-
-    Returns:
-        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
-        second element is the number of inference steps.
-    """
-    if timesteps is not None and sigmas is not None:
-        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
-    if timesteps is not None:
-        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
-        if not accepts_timesteps:
-            raise ValueError(
-                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
-                f" timestep schedules. Please check whether you are using the correct scheduler."
-            )
-        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-        num_inference_steps = len(timesteps)
-    elif sigmas is not None:
-        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
-        if not accept_sigmas:
-            raise ValueError(
-                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
-                f" sigmas schedules. Please check whether you are using the correct scheduler."
-            )
-        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-        num_inference_steps = len(timesteps)
-    else:
-        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-    return timesteps, num_inference_steps
-
-
-@dataclass
-class CogVideoXPipelineOutput(BaseOutput):
-    r"""
-    Output class for CogVideo pipelines.
-
-    Args:
-        frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
-            List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
-            denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape
-            `(batch_size, num_frames, channels, height, width)`.
-    """
-
-    frames: torch.Tensor
-
-
-class CogVideoXPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for text-to-video generation using CogVideoX.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
-        text_encoder ([`T5EncoderModel`]):
-            Frozen text-encoder. CogVideoX uses
-            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel); specifically the
-            [t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
-        tokenizer (`T5Tokenizer`):
-            Tokenizer of class
-            [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
-        transformer ([`CogVideoXTransformer3DModel`]):
-            A text conditioned `CogVideoXTransformer3DModel` to denoise the encoded video latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `transformer` to denoise the encoded video latents.
-    """
-
-    _optional_components = []
-    model_cpu_offload_seq = "text_encoder->transformer->vae"
-
-    _callback_tensor_inputs = [
-        "latents",
-        "prompt_embeds",
-        "negative_prompt_embeds",
-    ]
-
-    def __init__(
-        self,
-        tokenizer: T5Tokenizer,
-        text_encoder: T5EncoderModel,
-        vae: AutoencoderKLCogVideoX,
-        transformer: CogVideoXTransformer3DModel,
-        scheduler: Union[CogVideoXDDIMScheduler, CogVideoXDPMScheduler],
-    ):
-        super().__init__()
-
-        self.register_modules(
-            tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler
-        )
-        self.vae_scale_factor_spatial = (
-            2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
-        )
-        self.vae_scale_factor_temporal = (
-            self.vae.config.temporal_compression_ratio if hasattr(self, "vae") and self.vae is not None else 4
-        )
-
-        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
-
-    def _get_t5_prompt_embeds(
-        self,
-        prompt: Union[str, List[str]] = None,
-        num_videos_per_prompt: int = 1,
-        max_sequence_length: int = 226,
-        device: Optional[torch.device] = None,
-        dtype: Optional[torch.dtype] = None,
-    ):
-        device = device or self._execution_device
-        dtype = dtype or self.text_encoder.dtype
-
-        prompt = [prompt] if isinstance(prompt, str) else prompt
-        batch_size = len(prompt)
-
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=max_sequence_length,
-            truncation=True,
-            add_special_tokens=True,
-            return_tensors="pt",
-        )
-        text_input_ids = text_inputs.input_ids
-        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
-            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
-            logger.warning(
-                "The following part of your input was truncated because `max_sequence_length` is set to "
-                f" {max_sequence_length} tokens: {removed_text}"
-            )
-
-        prompt_embeds = self.text_encoder(text_input_ids.to(device))[0]
-        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
-
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        _, seq_len, _ = prompt_embeds.shape
-        prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
-
-        return prompt_embeds
-
-    def encode_prompt(
-        self,
-        prompt: Union[str, List[str]],
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        do_classifier_free_guidance: bool = True,
-        num_videos_per_prompt: int = 1,
-        prompt_embeds: Optional[torch.Tensor] = None,
-        negative_prompt_embeds: Optional[torch.Tensor] = None,
-        max_sequence_length: int = 226,
-        device: Optional[torch.device] = None,
-        dtype: Optional[torch.dtype] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
-                Whether to use classifier free guidance or not.
-            num_videos_per_prompt (`int`, *optional*, defaults to 1):
-                Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
-            prompt_embeds (`torch.Tensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.Tensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            device: (`torch.device`, *optional*):
-                torch device
-            dtype: (`torch.dtype`, *optional*):
-                torch dtype
-        """
-        device = device or self._execution_device
-
-        prompt = [prompt] if isinstance(prompt, str) else prompt
-        if prompt is not None:
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-            prompt_embeds = self._get_t5_prompt_embeds(
-                prompt=prompt,
-                num_videos_per_prompt=num_videos_per_prompt,
-                max_sequence_length=max_sequence_length,
-                device=device,
-                dtype=dtype,
-            )
-
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            negative_prompt = negative_prompt or ""
-            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
-
-            if prompt is not None and type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-
-            negative_prompt_embeds = self._get_t5_prompt_embeds(
-                prompt=negative_prompt,
-                num_videos_per_prompt=num_videos_per_prompt,
-                max_sequence_length=max_sequence_length,
-                device=device,
-                dtype=dtype,
-            )
-
-        return prompt_embeds, negative_prompt_embeds
-
-    def prepare_latents(
-        self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
-    ):
-        shape = (
-            batch_size,
-            (num_frames - 1) // self.vae_scale_factor_temporal + 1,
-            num_channels_latents,
-            height // self.vae_scale_factor_spatial,
-            width // self.vae_scale_factor_spatial,
-        )
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            latents = latents.to(device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-
-    def decode_latents(self, latents: torch.Tensor) -> torch.Tensor:
-        latents = latents.permute(0, 2, 1, 3, 4)  # [batch_size, num_channels, num_frames, height, width]
-        latents = 1 / self.vae.config.scaling_factor * latents
-
-        frames = self.vae.decode(latents).sample
-        return frames
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    # Copied from diffusers.pipelines.latte.pipeline_latte.LattePipeline.check_inputs
-    def check_inputs(
-        self,
-        prompt,
-        height,
-        width,
-        negative_prompt,
-        callback_on_step_end_tensor_inputs,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-    ):
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        if callback_on_step_end_tensor_inputs is not None and not all(
-            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
-        ):
-            raise ValueError(
-                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
-            )
-        if prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-
-        if negative_prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-
-        if prompt_embeds is not None and negative_prompt_embeds is not None:
-            if prompt_embeds.shape != negative_prompt_embeds.shape:
-                raise ValueError(
-                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
-                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
-
-    def fuse_qkv_projections(self) -> None:
-        r"""Enables fused QKV projections."""
-        self.fusing_transformer = True
-        self.transformer.fuse_qkv_projections()
-
-    def unfuse_qkv_projections(self) -> None:
-        r"""Disable QKV projection fusion if enabled."""
-        if not self.fusing_transformer:
-            logger.warning("The Transformer was not initially fused for QKV projections. Doing nothing.")
-        else:
-            self.transformer.unfuse_qkv_projections()
-            self.fusing_transformer = False
-
-    def _prepare_rotary_positional_embeddings(
-        self,
-        height: int,
-        width: int,
-        num_frames: int,
-        device: torch.device,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
-        grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
-        base_size_width = 720 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
-        base_size_height = 480 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
-
-        grid_crops_coords = get_resize_crop_region_for_grid(
-            (grid_height, grid_width), base_size_width, base_size_height
-        )
-        freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
-            embed_dim=self.transformer.config.attention_head_dim,
-            crops_coords=grid_crops_coords,
-            grid_size=(grid_height, grid_width),
-            temporal_size=num_frames,
-            use_real=True,
-        )
-
-        freqs_cos = freqs_cos.to(device=device)
-        freqs_sin = freqs_sin.to(device=device)
-        return freqs_cos, freqs_sin
-
-    @property
-    def guidance_scale(self):
-        return self._guidance_scale
-
-    @property
-    def num_timesteps(self):
-        return self._num_timesteps
-
-    @property
-    def interrupt(self):
-        return self._interrupt
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: Optional[Union[str, List[str]]] = None,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        height: int = 480,
-        width: int = 720,
-        num_frames: int = 49,
-        num_inference_steps: int = 50,
-        timesteps: Optional[List[int]] = None,
-        guidance_scale: float = 6,
-        use_dynamic_cfg: bool = False,
-        num_videos_per_prompt: int = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: str = "pil",
-        return_dict: bool = True,
-        callback_on_step_end: Optional[
-            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
-        ] = None,
-        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
-        max_sequence_length: int = 226,
-    ) -> Union[CogVideoXPipelineOutput, Tuple]:
-        """
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
-                instead.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The height in pixels of the generated image. This is set to 1024 by default for the best results.
-            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The width in pixels of the generated image. This is set to 1024 by default for the best results.
-            num_frames (`int`, defaults to `48`):
-                Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will
-                contain 1 extra frame because CogVideoX is conditioned with (num_seconds * fps + 1) frames where
-                num_seconds is 6 and fps is 4. However, since videos can be saved at any fps, the only condition that
-                needs to be satisfied is that of divisibility mentioned above.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            timesteps (`List[int]`, *optional*):
-                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
-                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
-                passed will be used. Must be in descending order.
-            guidance_scale (`float`, *optional*, defaults to 7.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            num_videos_per_prompt (`int`, *optional*, defaults to 1):
-                The number of videos to generate per prompt.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
-                of a plain tuple.
-            callback_on_step_end (`Callable`, *optional*):
-                A function that calls at the end of each denoising steps during the inference. The function is called
-                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
-                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
-                `callback_on_step_end_tensor_inputs`.
-            callback_on_step_end_tensor_inputs (`List`, *optional*):
-                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
-                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
-                `._callback_tensor_inputs` attribute of your pipeline class.
-            max_sequence_length (`int`, defaults to `226`):
-                Maximum sequence length in encoded prompt. Must be consistent with
-                `self.transformer.config.max_text_seq_length` otherwise may lead to poor results.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput`] or `tuple`:
-            [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput`] if `return_dict` is True, otherwise a
-            `tuple`. When returning a tuple, the first element is a list with the generated images.
-        """
-
-        if num_frames > 49:
-            raise ValueError(
-                "The number of frames must be less than 49 for now due to static positional embeddings. This will be updated in the future to remove this limitation."
-            )
-
-        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
-            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
-
-        height = height or self.transformer.config.sample_size * self.vae_scale_factor_spatial
-        width = width or self.transformer.config.sample_size * self.vae_scale_factor_spatial
-        num_videos_per_prompt = 1
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(
-            prompt,
-            height,
-            width,
-            negative_prompt,
-            callback_on_step_end_tensor_inputs,
-            prompt_embeds,
-            negative_prompt_embeds,
-        )
-        self._guidance_scale = guidance_scale
-        self._interrupt = False
-
-        # 2. Default call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        device = self._execution_device
-
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input prompt
-        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
-            prompt,
-            negative_prompt,
-            do_classifier_free_guidance,
-            num_videos_per_prompt=num_videos_per_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            max_sequence_length=max_sequence_length,
-            device=device,
-        )
-        if do_classifier_free_guidance:
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
-
-        # 4. Prepare timesteps
-        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
-        self._num_timesteps = len(timesteps)
-
-        # 5. Prepare latents.
-        latent_channels = self.transformer.config.in_channels
-        latents = self.prepare_latents(
-            batch_size * num_videos_per_prompt,
-            latent_channels,
-            num_frames,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-        )
-
-        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 7. Create rotary embeds if required
-        image_rotary_emb = (
-            self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device)
-            if self.transformer.config.use_rotary_positional_embeddings
-            else None
-        )
-
-        # 8. Denoising loop
-        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
-
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            # for DPM-solver++
-            old_pred_original_sample = None
-            for i, t in enumerate(timesteps):
-                if self.interrupt:
-                    continue
-
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-                timestep = t.expand(latent_model_input.shape[0])
-
-                # predict noise model_output
-                noise_pred = self.transformer(
-                    hidden_states=latent_model_input,
-                    encoder_hidden_states=prompt_embeds,
-                    timestep=timestep,
-                    image_rotary_emb=image_rotary_emb,
-                    return_dict=False,
-                )[0]
-                noise_pred = noise_pred.float()
-
-                # perform guidance
-                if use_dynamic_cfg:
-                    self._guidance_scale = 1 + guidance_scale * (
-                        (1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2
-                    )
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                if not isinstance(self.scheduler, CogVideoXDPMScheduler):
-                    latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
-                else:
-                    latents, old_pred_original_sample = self.scheduler.step(
-                        noise_pred,
-                        old_pred_original_sample,
-                        t,
-                        timesteps[i - 1] if i > 0 else None,
-                        latents,
-                        **extra_step_kwargs,
-                        return_dict=False,
-                    )
-                latents = latents.to(prompt_embeds.dtype)
-
-                # call the callback, if provided
-                if callback_on_step_end is not None:
-                    callback_kwargs = {}
-                    for k in callback_on_step_end_tensor_inputs:
-                        callback_kwargs[k] = locals()[k]
-                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
-
-                    latents = callback_outputs.pop("latents", latents)
-                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
-                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
-
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-
-        if not output_type == "latent":
-            video = self.decode_latents(latents)
-            video = self.video_processor.postprocess_video(video=video, output_type=output_type)
-        else:
-            video = latents
-
-        # Offload all models
-        self.maybe_free_model_hooks()
-
-        if not return_dict:
-            return (video,)
-
-        return CogVideoXPipelineOutput(frames=video)

src/diffusers/pipelines/flux/pipeline_flux.py

@@ -280,7 +280,7 @@ class FluxPipeline(DiffusionPipeline, FluxLoraLoaderMixin):
         prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
 
         # duplicate text embeddings for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt)
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
         prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, -1)
 
         return prompt_embeds

src/diffusers/schedulers/__init__.py

@@ -43,14 +43,12 @@ else:
     _import_structure["scheduling_consistency_decoder"] = ["ConsistencyDecoderScheduler"]
     _import_structure["scheduling_consistency_models"] = ["CMStochasticIterativeScheduler"]
     _import_structure["scheduling_ddim"] = ["DDIMScheduler"]
-    _import_structure["scheduling_ddim_cogvideox"] = ["CogVideoXDDIMScheduler"]
     _import_structure["scheduling_ddim_inverse"] = ["DDIMInverseScheduler"]
     _import_structure["scheduling_ddim_parallel"] = ["DDIMParallelScheduler"]
     _import_structure["scheduling_ddpm"] = ["DDPMScheduler"]
     _import_structure["scheduling_ddpm_parallel"] = ["DDPMParallelScheduler"]
     _import_structure["scheduling_ddpm_wuerstchen"] = ["DDPMWuerstchenScheduler"]
     _import_structure["scheduling_deis_multistep"] = ["DEISMultistepScheduler"]
-    _import_structure["scheduling_dpm_cogvideox"] = ["CogVideoXDPMScheduler"]
     _import_structure["scheduling_dpmsolver_multistep"] = ["DPMSolverMultistepScheduler"]
     _import_structure["scheduling_dpmsolver_multistep_inverse"] = ["DPMSolverMultistepInverseScheduler"]
     _import_structure["scheduling_dpmsolver_singlestep"] = ["DPMSolverSinglestepScheduler"]
@@ -143,14 +141,12 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .scheduling_consistency_decoder import ConsistencyDecoderScheduler
         from .scheduling_consistency_models import CMStochasticIterativeScheduler
         from .scheduling_ddim import DDIMScheduler
-        from .scheduling_ddim_cogvideox import CogVideoXDDIMScheduler
         from .scheduling_ddim_inverse import DDIMInverseScheduler
         from .scheduling_ddim_parallel import DDIMParallelScheduler
         from .scheduling_ddpm import DDPMScheduler
         from .scheduling_ddpm_parallel import DDPMParallelScheduler
         from .scheduling_ddpm_wuerstchen import DDPMWuerstchenScheduler
         from .scheduling_deis_multistep import DEISMultistepScheduler
-        from .scheduling_dpm_cogvideox import CogVideoXDPMScheduler
         from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
         from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
         from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler

src/diffusers/schedulers/scheduling_ddim_cogvideox.py

@@ -1,449 +0,0 @@
-# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI and 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.
-
-# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
-# and https://github.com/hojonathanho/diffusion
-
-import math
-from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput
-from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
-
-
-@dataclass
-# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
-class DDIMSchedulerOutput(BaseOutput):
-    """
-    Output class for the scheduler's `step` function output.
-
-    Args:
-        prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-        pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
-            The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
-            `pred_original_sample` can be used to preview progress or for guidance.
-    """
-
-    prev_sample: torch.Tensor
-    pred_original_sample: Optional[torch.Tensor] = None
-
-
-# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
-def betas_for_alpha_bar(
-    num_diffusion_timesteps,
-    max_beta=0.999,
-    alpha_transform_type="cosine",
-):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
-    (1-beta) over time from t = [0,1].
-
-    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
-    to that part of the diffusion process.
-
-
-    Args:
-        num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-        alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
-                     Choose from `cosine` or `exp`
-
-    Returns:
-        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
-    """
-    if alpha_transform_type == "cosine":
-
-        def alpha_bar_fn(t):
-            return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
-
-    elif alpha_transform_type == "exp":
-
-        def alpha_bar_fn(t):
-            return math.exp(t * -12.0)
-
-    else:
-        raise ValueError(f"Unsupported alpha_transform_type: {alpha_transform_type}")
-
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
-    return torch.tensor(betas, dtype=torch.float32)
-
-
-def rescale_zero_terminal_snr(alphas_cumprod):
-    """
-    Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)
-
-
-    Args:
-        betas (`torch.Tensor`):
-            the betas that the scheduler is being initialized with.
-
-    Returns:
-        `torch.Tensor`: rescaled betas with zero terminal SNR
-    """
-
-    alphas_bar_sqrt = alphas_cumprod.sqrt()
-
-    # Store old values.
-    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
-    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
-
-    # Shift so the last timestep is zero.
-    alphas_bar_sqrt -= alphas_bar_sqrt_T
-
-    # Scale so the first timestep is back to the old value.
-    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
-
-    # Convert alphas_bar_sqrt to betas
-    alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
-
-    return alphas_bar
-
-
-class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
-    """
-    `DDIMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with
-    non-Markovian guidance.
-
-    This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
-    methods the library implements for all schedulers such as loading and saving.
-
-    Args:
-        num_train_timesteps (`int`, defaults to 1000):
-            The number of diffusion steps to train the model.
-        beta_start (`float`, defaults to 0.0001):
-            The starting `beta` value of inference.
-        beta_end (`float`, defaults to 0.02):
-            The final `beta` value.
-        beta_schedule (`str`, defaults to `"linear"`):
-            The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
-            `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
-        trained_betas (`np.ndarray`, *optional*):
-            Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
-        clip_sample (`bool`, defaults to `True`):
-            Clip the predicted sample for numerical stability.
-        clip_sample_range (`float`, defaults to 1.0):
-            The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
-        set_alpha_to_one (`bool`, defaults to `True`):
-            Each diffusion step uses the alphas product value at that step and at the previous one. For the final step
-            there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
-            otherwise it uses the alpha value at step 0.
-        steps_offset (`int`, defaults to 0):
-            An offset added to the inference steps, as required by some model families.
-        prediction_type (`str`, defaults to `epsilon`, *optional*):
-            Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
-            `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
-        thresholding (`bool`, defaults to `False`):
-            Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
-            as Stable Diffusion.
-        dynamic_thresholding_ratio (`float`, defaults to 0.995):
-            The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.
-        sample_max_value (`float`, defaults to 1.0):
-            The threshold value for dynamic thresholding. Valid only when `thresholding=True`.
-        timestep_spacing (`str`, defaults to `"leading"`):
-            The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
-            Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
-        rescale_betas_zero_snr (`bool`, defaults to `False`):
-            Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
-            dark samples instead of limiting it to samples with medium brightness. Loosely related to
-            [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).
-    """
-
-    _compatibles = [e.name for e in KarrasDiffusionSchedulers]
-    order = 1
-
-    @register_to_config
-    def __init__(
-        self,
-        num_train_timesteps: int = 1000,
-        beta_start: float = 0.00085,
-        beta_end: float = 0.0120,
-        beta_schedule: str = "scaled_linear",
-        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
-        clip_sample: bool = True,
-        set_alpha_to_one: bool = True,
-        steps_offset: int = 0,
-        prediction_type: str = "epsilon",
-        clip_sample_range: float = 1.0,
-        sample_max_value: float = 1.0,
-        timestep_spacing: str = "leading",
-        rescale_betas_zero_snr: bool = False,
-        snr_shift_scale: float = 3.0,
-    ):
-        if trained_betas is not None:
-            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
-        elif beta_schedule == "linear":
-            self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
-        elif beta_schedule == "scaled_linear":
-            # this schedule is very specific to the latent diffusion model.
-            self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float64) ** 2
-        elif beta_schedule == "squaredcos_cap_v2":
-            # Glide cosine schedule
-            self.betas = betas_for_alpha_bar(num_train_timesteps)
-        else:
-            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
-
-        self.alphas = 1.0 - self.betas
-        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
-
-        # Modify: SNR shift following SD3
-        self.alphas_cumprod = self.alphas_cumprod / (snr_shift_scale + (1 - snr_shift_scale) * self.alphas_cumprod)
-
-        # Rescale for zero SNR
-        if rescale_betas_zero_snr:
-            self.alphas_cumprod = rescale_zero_terminal_snr(self.alphas_cumprod)
-
-        # At every step in ddim, we are looking into the previous alphas_cumprod
-        # For the final step, there is no previous alphas_cumprod because we are already at 0
-        # `set_alpha_to_one` decides whether we set this parameter simply to one or
-        # whether we use the final alpha of the "non-previous" one.
-        self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
-
-        # standard deviation of the initial noise distribution
-        self.init_noise_sigma = 1.0
-
-        # setable values
-        self.num_inference_steps = None
-        self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64))
-
-    def _get_variance(self, timestep, prev_timestep):
-        alpha_prod_t = self.alphas_cumprod[timestep]
-        alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
-        beta_prod_t = 1 - alpha_prod_t
-        beta_prod_t_prev = 1 - alpha_prod_t_prev
-
-        variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
-
-        return variance
-
-    def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:
-        """
-        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
-        current timestep.
-
-        Args:
-            sample (`torch.Tensor`):
-                The input sample.
-            timestep (`int`, *optional*):
-                The current timestep in the diffusion chain.
-
-        Returns:
-            `torch.Tensor`:
-                A scaled input sample.
-        """
-        return sample
-
-    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
-        """
-        Sets the discrete timesteps used for the diffusion chain (to be run before inference).
-
-        Args:
-            num_inference_steps (`int`):
-                The number of diffusion steps used when generating samples with a pre-trained model.
-        """
-
-        if num_inference_steps > self.config.num_train_timesteps:
-            raise ValueError(
-                f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
-                f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
-                f" maximal {self.config.num_train_timesteps} timesteps."
-            )
-
-        self.num_inference_steps = num_inference_steps
-
-        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
-        if self.config.timestep_spacing == "linspace":
-            timesteps = (
-                np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps)
-                .round()[::-1]
-                .copy()
-                .astype(np.int64)
-            )
-        elif self.config.timestep_spacing == "leading":
-            step_ratio = self.config.num_train_timesteps // self.num_inference_steps
-            # creates integer timesteps by multiplying by ratio
-            # casting to int to avoid issues when num_inference_step is power of 3
-            timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64)
-            timesteps += self.config.steps_offset
-        elif self.config.timestep_spacing == "trailing":
-            step_ratio = self.config.num_train_timesteps / self.num_inference_steps
-            # creates integer timesteps by multiplying by ratio
-            # casting to int to avoid issues when num_inference_step is power of 3
-            timesteps = np.round(np.arange(self.config.num_train_timesteps, 0, -step_ratio)).astype(np.int64)
-            timesteps -= 1
-        else:
-            raise ValueError(
-                f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'leading' or 'trailing'."
-            )
-
-        self.timesteps = torch.from_numpy(timesteps).to(device)
-
-    def step(
-        self,
-        model_output: torch.Tensor,
-        timestep: int,
-        sample: torch.Tensor,
-        eta: float = 0.0,
-        use_clipped_model_output: bool = False,
-        generator=None,
-        variance_noise: Optional[torch.Tensor] = None,
-        return_dict: bool = True,
-    ) -> Union[DDIMSchedulerOutput, Tuple]:
-        """
-        Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
-        process from the learned model outputs (most often the predicted noise).
-
-        Args:
-            model_output (`torch.Tensor`):
-                The direct output from learned diffusion model.
-            timestep (`float`):
-                The current discrete timestep in the diffusion chain.
-            sample (`torch.Tensor`):
-                A current instance of a sample created by the diffusion process.
-            eta (`float`):
-                The weight of noise for added noise in diffusion step.
-            use_clipped_model_output (`bool`, defaults to `False`):
-                If `True`, computes "corrected" `model_output` from the clipped predicted original sample. Necessary
-                because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no
-                clipping has happened, "corrected" `model_output` would coincide with the one provided as input and
-                `use_clipped_model_output` has no effect.
-            generator (`torch.Generator`, *optional*):
-                A random number generator.
-            variance_noise (`torch.Tensor`):
-                Alternative to generating noise with `generator` by directly providing the noise for the variance
-                itself. Useful for methods such as [`CycleDiffusion`].
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~schedulers.scheduling_ddim.DDIMSchedulerOutput`] or `tuple`.
-
-        Returns:
-            [`~schedulers.scheduling_ddim.DDIMSchedulerOutput`] or `tuple`:
-                If return_dict is `True`, [`~schedulers.scheduling_ddim.DDIMSchedulerOutput`] is returned, otherwise a
-                tuple is returned where the first element is the sample tensor.
-
-        """
-        if self.num_inference_steps is None:
-            raise ValueError(
-                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
-            )
-
-        # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
-        # Ideally, read DDIM paper in-detail understanding
-
-        # Notation (<variable name> -> <name in paper>
-        # - pred_noise_t -> e_theta(x_t, t)
-        # - pred_original_sample -> f_theta(x_t, t) or x_0
-        # - std_dev_t -> sigma_t
-        # - eta -> η
-        # - pred_sample_direction -> "direction pointing to x_t"
-        # - pred_prev_sample -> "x_t-1"
-
-        # 1. get previous step value (=t-1)
-        prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
-
-        # 2. compute alphas, betas
-        alpha_prod_t = self.alphas_cumprod[timestep]
-        alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
-
-        beta_prod_t = 1 - alpha_prod_t
-
-        # 3. compute predicted original sample from predicted noise also called
-        # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-        # To make style tests pass, commented out `pred_epsilon` as it is an unused variable
-        if self.config.prediction_type == "epsilon":
-            pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
-            # pred_epsilon = model_output
-        elif self.config.prediction_type == "sample":
-            pred_original_sample = model_output
-            # pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
-        elif self.config.prediction_type == "v_prediction":
-            pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
-            # pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
-        else:
-            raise ValueError(
-                f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
-                " `v_prediction`"
-            )
-
-        a_t = ((1 - alpha_prod_t_prev) / (1 - alpha_prod_t)) ** 0.5
-        b_t = alpha_prod_t_prev**0.5 - alpha_prod_t**0.5 * a_t
-
-        prev_sample = a_t * sample + b_t * pred_original_sample
-
-        if not return_dict:
-            return (prev_sample,)
-
-        return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
-    def add_noise(
-        self,
-        original_samples: torch.Tensor,
-        noise: torch.Tensor,
-        timesteps: torch.IntTensor,
-    ) -> torch.Tensor:
-        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
-        # Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement
-        # for the subsequent add_noise calls
-        self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device)
-        alphas_cumprod = self.alphas_cumprod.to(dtype=original_samples.dtype)
-        timesteps = timesteps.to(original_samples.device)
-
-        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
-        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
-        while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
-
-        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
-        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
-        while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
-
-        noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
-        return noisy_samples
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity
-    def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor) -> torch.Tensor:
-        # Make sure alphas_cumprod and timestep have same device and dtype as sample
-        self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device)
-        alphas_cumprod = self.alphas_cumprod.to(dtype=sample.dtype)
-        timesteps = timesteps.to(sample.device)
-
-        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
-        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
-        while len(sqrt_alpha_prod.shape) < len(sample.shape):
-            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
-
-        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
-        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
-        while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):
-            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
-
-        velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
-        return velocity
-
-    def __len__(self):
-        return self.config.num_train_timesteps

src/diffusers/schedulers/scheduling_dpm_cogvideox.py

@@ -1,489 +0,0 @@
-# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI and 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.
-
-# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
-# and https://github.com/hojonathanho/diffusion
-
-import math
-from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput
-from ..utils.torch_utils import randn_tensor
-from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
-
-
-@dataclass
-# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
-class DDIMSchedulerOutput(BaseOutput):
-    """
-    Output class for the scheduler's `step` function output.
-
-    Args:
-        prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-        pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
-            The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
-            `pred_original_sample` can be used to preview progress or for guidance.
-    """
-
-    prev_sample: torch.Tensor
-    pred_original_sample: Optional[torch.Tensor] = None
-
-
-# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
-def betas_for_alpha_bar(
-    num_diffusion_timesteps,
-    max_beta=0.999,
-    alpha_transform_type="cosine",
-):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
-    (1-beta) over time from t = [0,1].
-
-    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
-    to that part of the diffusion process.
-
-
-    Args:
-        num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-        alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
-                     Choose from `cosine` or `exp`
-
-    Returns:
-        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
-    """
-    if alpha_transform_type == "cosine":
-
-        def alpha_bar_fn(t):
-            return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
-
-    elif alpha_transform_type == "exp":
-
-        def alpha_bar_fn(t):
-            return math.exp(t * -12.0)
-
-    else:
-        raise ValueError(f"Unsupported alpha_transform_type: {alpha_transform_type}")
-
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
-    return torch.tensor(betas, dtype=torch.float32)
-
-
-def rescale_zero_terminal_snr(alphas_cumprod):
-    """
-    Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)
-
-
-    Args:
-        betas (`torch.Tensor`):
-            the betas that the scheduler is being initialized with.
-
-    Returns:
-        `torch.Tensor`: rescaled betas with zero terminal SNR
-    """
-
-    alphas_bar_sqrt = alphas_cumprod.sqrt()
-
-    # Store old values.
-    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
-    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
-
-    # Shift so the last timestep is zero.
-    alphas_bar_sqrt -= alphas_bar_sqrt_T
-
-    # Scale so the first timestep is back to the old value.
-    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
-
-    # Convert alphas_bar_sqrt to betas
-    alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
-
-    return alphas_bar
-
-
-class CogVideoXDPMScheduler(SchedulerMixin, ConfigMixin):
-    """
-    `DDIMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with
-    non-Markovian guidance.
-
-    This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
-    methods the library implements for all schedulers such as loading and saving.
-
-    Args:
-        num_train_timesteps (`int`, defaults to 1000):
-            The number of diffusion steps to train the model.
-        beta_start (`float`, defaults to 0.0001):
-            The starting `beta` value of inference.
-        beta_end (`float`, defaults to 0.02):
-            The final `beta` value.
-        beta_schedule (`str`, defaults to `"linear"`):
-            The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
-            `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
-        trained_betas (`np.ndarray`, *optional*):
-            Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
-        clip_sample (`bool`, defaults to `True`):
-            Clip the predicted sample for numerical stability.
-        clip_sample_range (`float`, defaults to 1.0):
-            The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
-        set_alpha_to_one (`bool`, defaults to `True`):
-            Each diffusion step uses the alphas product value at that step and at the previous one. For the final step
-            there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
-            otherwise it uses the alpha value at step 0.
-        steps_offset (`int`, defaults to 0):
-            An offset added to the inference steps, as required by some model families.
-        prediction_type (`str`, defaults to `epsilon`, *optional*):
-            Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
-            `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
-        thresholding (`bool`, defaults to `False`):
-            Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
-            as Stable Diffusion.
-        dynamic_thresholding_ratio (`float`, defaults to 0.995):
-            The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.
-        sample_max_value (`float`, defaults to 1.0):
-            The threshold value for dynamic thresholding. Valid only when `thresholding=True`.
-        timestep_spacing (`str`, defaults to `"leading"`):
-            The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
-            Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
-        rescale_betas_zero_snr (`bool`, defaults to `False`):
-            Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
-            dark samples instead of limiting it to samples with medium brightness. Loosely related to
-            [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).
-    """
-
-    _compatibles = [e.name for e in KarrasDiffusionSchedulers]
-    order = 1
-
-    @register_to_config
-    def __init__(
-        self,
-        num_train_timesteps: int = 1000,
-        beta_start: float = 0.00085,
-        beta_end: float = 0.0120,
-        beta_schedule: str = "scaled_linear",
-        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
-        clip_sample: bool = True,
-        set_alpha_to_one: bool = True,
-        steps_offset: int = 0,
-        prediction_type: str = "epsilon",
-        clip_sample_range: float = 1.0,
-        sample_max_value: float = 1.0,
-        timestep_spacing: str = "leading",
-        rescale_betas_zero_snr: bool = False,
-        snr_shift_scale: float = 3.0,
-    ):
-        if trained_betas is not None:
-            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
-        elif beta_schedule == "linear":
-            self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
-        elif beta_schedule == "scaled_linear":
-            # this schedule is very specific to the latent diffusion model.
-            self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float64) ** 2
-        elif beta_schedule == "squaredcos_cap_v2":
-            # Glide cosine schedule
-            self.betas = betas_for_alpha_bar(num_train_timesteps)
-        else:
-            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
-
-        self.alphas = 1.0 - self.betas
-        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
-
-        # Modify: SNR shift following SD3
-        self.alphas_cumprod = self.alphas_cumprod / (snr_shift_scale + (1 - snr_shift_scale) * self.alphas_cumprod)
-
-        # Rescale for zero SNR
-        if rescale_betas_zero_snr:
-            self.alphas_cumprod = rescale_zero_terminal_snr(self.alphas_cumprod)
-
-        # At every step in ddim, we are looking into the previous alphas_cumprod
-        # For the final step, there is no previous alphas_cumprod because we are already at 0
-        # `set_alpha_to_one` decides whether we set this parameter simply to one or
-        # whether we use the final alpha of the "non-previous" one.
-        self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
-
-        # standard deviation of the initial noise distribution
-        self.init_noise_sigma = 1.0
-
-        # setable values
-        self.num_inference_steps = None
-        self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64))
-
-    def _get_variance(self, timestep, prev_timestep):
-        alpha_prod_t = self.alphas_cumprod[timestep]
-        alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
-        beta_prod_t = 1 - alpha_prod_t
-        beta_prod_t_prev = 1 - alpha_prod_t_prev
-
-        variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
-
-        return variance
-
-    def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:
-        """
-        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
-        current timestep.
-
-        Args:
-            sample (`torch.Tensor`):
-                The input sample.
-            timestep (`int`, *optional*):
-                The current timestep in the diffusion chain.
-
-        Returns:
-            `torch.Tensor`:
-                A scaled input sample.
-        """
-        return sample
-
-    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
-        """
-        Sets the discrete timesteps used for the diffusion chain (to be run before inference).
-
-        Args:
-            num_inference_steps (`int`):
-                The number of diffusion steps used when generating samples with a pre-trained model.
-        """
-
-        if num_inference_steps > self.config.num_train_timesteps:
-            raise ValueError(
-                f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
-                f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
-                f" maximal {self.config.num_train_timesteps} timesteps."
-            )
-
-        self.num_inference_steps = num_inference_steps
-
-        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
-        if self.config.timestep_spacing == "linspace":
-            timesteps = (
-                np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps)
-                .round()[::-1]
-                .copy()
-                .astype(np.int64)
-            )
-        elif self.config.timestep_spacing == "leading":
-            step_ratio = self.config.num_train_timesteps // self.num_inference_steps
-            # creates integer timesteps by multiplying by ratio
-            # casting to int to avoid issues when num_inference_step is power of 3
-            timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64)
-            timesteps += self.config.steps_offset
-        elif self.config.timestep_spacing == "trailing":
-            step_ratio = self.config.num_train_timesteps / self.num_inference_steps
-            # creates integer timesteps by multiplying by ratio
-            # casting to int to avoid issues when num_inference_step is power of 3
-            timesteps = np.round(np.arange(self.config.num_train_timesteps, 0, -step_ratio)).astype(np.int64)
-            timesteps -= 1
-        else:
-            raise ValueError(
-                f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'leading' or 'trailing'."
-            )
-
-        self.timesteps = torch.from_numpy(timesteps).to(device)
-
-    def get_variables(self, alpha_prod_t, alpha_prod_t_prev, alpha_prod_t_back=None):
-        lamb = ((alpha_prod_t / (1 - alpha_prod_t)) ** 0.5).log()
-        lamb_next = ((alpha_prod_t_prev / (1 - alpha_prod_t_prev)) ** 0.5).log()
-        h = lamb_next - lamb
-
-        if alpha_prod_t_back is not None:
-            lamb_previous = ((alpha_prod_t_back / (1 - alpha_prod_t_back)) ** 0.5).log()
-            h_last = lamb - lamb_previous
-            r = h_last / h
-            return h, r, lamb, lamb_next
-        else:
-            return h, None, lamb, lamb_next
-
-    def get_mult(self, h, r, alpha_prod_t, alpha_prod_t_prev, alpha_prod_t_back):
-        mult1 = ((1 - alpha_prod_t_prev) / (1 - alpha_prod_t)) ** 0.5 * (-h).exp()
-        mult2 = (-2 * h).expm1() * alpha_prod_t_prev**0.5
-
-        if alpha_prod_t_back is not None:
-            mult3 = 1 + 1 / (2 * r)
-            mult4 = 1 / (2 * r)
-            return mult1, mult2, mult3, mult4
-        else:
-            return mult1, mult2
-
-    def step(
-        self,
-        model_output: torch.Tensor,
-        old_pred_original_sample: torch.Tensor,
-        timestep: int,
-        timestep_back: int,
-        sample: torch.Tensor,
-        eta: float = 0.0,
-        use_clipped_model_output: bool = False,
-        generator=None,
-        variance_noise: Optional[torch.Tensor] = None,
-        return_dict: bool = False,
-    ) -> Union[DDIMSchedulerOutput, Tuple]:
-        """
-        Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
-        process from the learned model outputs (most often the predicted noise).
-
-        Args:
-            model_output (`torch.Tensor`):
-                The direct output from learned diffusion model.
-            timestep (`float`):
-                The current discrete timestep in the diffusion chain.
-            sample (`torch.Tensor`):
-                A current instance of a sample created by the diffusion process.
-            eta (`float`):
-                The weight of noise for added noise in diffusion step.
-            use_clipped_model_output (`bool`, defaults to `False`):
-                If `True`, computes "corrected" `model_output` from the clipped predicted original sample. Necessary
-                because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no
-                clipping has happened, "corrected" `model_output` would coincide with the one provided as input and
-                `use_clipped_model_output` has no effect.
-            generator (`torch.Generator`, *optional*):
-                A random number generator.
-            variance_noise (`torch.Tensor`):
-                Alternative to generating noise with `generator` by directly providing the noise for the variance
-                itself. Useful for methods such as [`CycleDiffusion`].
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~schedulers.scheduling_ddim.DDIMSchedulerOutput`] or `tuple`.
-
-        Returns:
-            [`~schedulers.scheduling_ddim.DDIMSchedulerOutput`] or `tuple`:
-                If return_dict is `True`, [`~schedulers.scheduling_ddim.DDIMSchedulerOutput`] is returned, otherwise a
-                tuple is returned where the first element is the sample tensor.
-
-        """
-        if self.num_inference_steps is None:
-            raise ValueError(
-                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
-            )
-
-        # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
-        # Ideally, read DDIM paper in-detail understanding
-
-        # Notation (<variable name> -> <name in paper>
-        # - pred_noise_t -> e_theta(x_t, t)
-        # - pred_original_sample -> f_theta(x_t, t) or x_0
-        # - std_dev_t -> sigma_t
-        # - eta -> η
-        # - pred_sample_direction -> "direction pointing to x_t"
-        # - pred_prev_sample -> "x_t-1"
-
-        # 1. get previous step value (=t-1)
-        prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
-
-        # 2. compute alphas, betas
-        alpha_prod_t = self.alphas_cumprod[timestep]
-        alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
-        alpha_prod_t_back = self.alphas_cumprod[timestep_back] if timestep_back is not None else None
-
-        beta_prod_t = 1 - alpha_prod_t
-
-        # 3. compute predicted original sample from predicted noise also called
-        # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-        # To make style tests pass, commented out `pred_epsilon` as it is an unused variable
-        if self.config.prediction_type == "epsilon":
-            pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
-            # pred_epsilon = model_output
-        elif self.config.prediction_type == "sample":
-            pred_original_sample = model_output
-            # pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
-        elif self.config.prediction_type == "v_prediction":
-            pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
-            # pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
-        else:
-            raise ValueError(
-                f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
-                " `v_prediction`"
-            )
-
-        h, r, lamb, lamb_next = self.get_variables(alpha_prod_t, alpha_prod_t_prev, alpha_prod_t_back)
-        mult = list(self.get_mult(h, r, alpha_prod_t, alpha_prod_t_prev, alpha_prod_t_back))
-        mult_noise = (1 - alpha_prod_t_prev) ** 0.5 * (1 - (-2 * h).exp()) ** 0.5
-
-        noise = randn_tensor(sample.shape, generator=generator, device=sample.device, dtype=sample.dtype)
-        prev_sample = mult[0] * sample - mult[1] * pred_original_sample + mult_noise * noise
-
-        if old_pred_original_sample is None or prev_timestep < 0:
-            # Save a network evaluation if all noise levels are 0 or on the first step
-            return prev_sample, pred_original_sample
-        else:
-            denoised_d = mult[2] * pred_original_sample - mult[3] * old_pred_original_sample
-            noise = randn_tensor(sample.shape, generator=generator, device=sample.device, dtype=sample.dtype)
-            x_advanced = mult[0] * sample - mult[1] * denoised_d + mult_noise * noise
-
-            prev_sample = x_advanced
-
-        if not return_dict:
-            return (prev_sample, pred_original_sample)
-
-        return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
-    def add_noise(
-        self,
-        original_samples: torch.Tensor,
-        noise: torch.Tensor,
-        timesteps: torch.IntTensor,
-    ) -> torch.Tensor:
-        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
-        # Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement
-        # for the subsequent add_noise calls
-        self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device)
-        alphas_cumprod = self.alphas_cumprod.to(dtype=original_samples.dtype)
-        timesteps = timesteps.to(original_samples.device)
-
-        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
-        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
-        while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
-
-        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
-        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
-        while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
-
-        noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
-        return noisy_samples
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity
-    def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor) -> torch.Tensor:
-        # Make sure alphas_cumprod and timestep have same device and dtype as sample
-        self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device)
-        alphas_cumprod = self.alphas_cumprod.to(dtype=sample.dtype)
-        timesteps = timesteps.to(sample.device)
-
-        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
-        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
-        while len(sqrt_alpha_prod.shape) < len(sample.shape):
-            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
-
-        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
-        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
-        while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):
-            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
-
-        velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
-        return velocity
-
-    def __len__(self):
-        return self.config.num_train_timesteps

src/diffusers/utils/dummy_pt_objects.py

@@ -47,21 +47,6 @@ class AutoencoderKL(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
-class AutoencoderKLCogVideoX(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-
 class AutoencoderKLTemporalDecoder(metaclass=DummyObject):
     _backends = ["torch"]
 
@@ -107,21 +92,6 @@ class AutoencoderTiny(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
-class CogVideoXTransformer3DModel(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-
 class ConsistencyDecoderVAE(metaclass=DummyObject):
     _backends = ["torch"]
 
@@ -1005,36 +975,6 @@ class CMStochasticIterativeScheduler(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
-class CogVideoXDDIMScheduler(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-
-class CogVideoXDPMScheduler(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-
 class DDIMInverseScheduler(metaclass=DummyObject):
     _backends = ["torch"]
 

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -257,21 +257,6 @@ class CLIPImageProjection(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
-class CogVideoXPipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-
 class CycleDiffusionPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

src/diffusers/utils/export_utils.py

@@ -9,7 +9,7 @@ import numpy as np
 import PIL.Image
 import PIL.ImageOps
 
-from .import_utils import BACKENDS_MAPPING, is_imageio_available, is_opencv_available
+from .import_utils import BACKENDS_MAPPING, is_opencv_available
 from .logging import get_logger
 
 
@@ -112,9 +112,9 @@ def export_to_obj(mesh, output_obj_path: str = None):
         f.writelines("\n".join(combined_data))
 
 
-def _legacy_export_to_video(
+def export_to_video(
     video_frames: Union[List[np.ndarray], List[PIL.Image.Image]], output_video_path: str = None, fps: int = 10
-):
+) -> str:
     if is_opencv_available():
         import cv2
     else:
@@ -134,51 +134,4 @@ def _legacy_export_to_video(
     for i in range(len(video_frames)):
         img = cv2.cvtColor(video_frames[i], cv2.COLOR_RGB2BGR)
         video_writer.write(img)
-
-    return output_video_path
-
-
-def export_to_video(
-    video_frames: Union[List[np.ndarray], List[PIL.Image.Image]], output_video_path: str = None, fps: int = 10
-) -> str:
-    # TODO: Dhruv. Remove by Diffusers release 0.33.0
-    # Added to prevent breaking existing code
-    if not is_imageio_available():
-        logger.warning(
-            (
-                "It is recommended to use `export_to_video` with `imageio` and `imageio-ffmpeg` as a backend. \n"
-                "These libraries are not present in your environment. Attempting to use legacy OpenCV backend to export video. \n"
-                "Support for the OpenCV backend will be deprecated in a future Diffusers version"
-            )
-        )
-        return _legacy_export_to_video(video_frames, output_video_path, fps)
-
-    if is_imageio_available():
-        import imageio
-    else:
-        raise ImportError(BACKENDS_MAPPING["imageio"][1].format("export_to_video"))
-
-    try:
-        imageio.plugins.ffmpeg.get_exe()
-    except AttributeError:
-        raise AttributeError(
-            (
-                "Found an existing imageio backend in your environment. Attempting to export video with imageio. \n"
-                "Unable to find a compatible ffmpeg installation in your environment to use with imageio. Please install via `pip install imageio-ffmpeg"
-            )
-        )
-
-    if output_video_path is None:
-        output_video_path = tempfile.NamedTemporaryFile(suffix=".mp4").name
-
-    if isinstance(video_frames[0], np.ndarray):
-        video_frames = [(frame * 255).astype(np.uint8) for frame in video_frames]
-
-    elif isinstance(video_frames[0], PIL.Image.Image):
-        video_frames = [np.array(frame) for frame in video_frames]
-
-    with imageio.get_writer(output_video_path, fps=fps) as writer:
-        for frame in video_frames:
-            writer.append_data(frame)
-
     return output_video_path

src/diffusers/utils/import_utils.py

@@ -330,15 +330,6 @@ except importlib_metadata.PackageNotFoundError:
 
 _is_google_colab = "google.colab" in sys.modules or any(k.startswith("COLAB_") for k in os.environ)
 
-_imageio_available = importlib.util.find_spec("imageio") is not None
-if _imageio_available:
-    try:
-        _imageio_version = importlib_metadata.version("imageio")
-        logger.debug(f"Successfully imported imageio version {_imageio_version}")
-
-    except importlib_metadata.PackageNotFoundError:
-        _imageio_available = False
-
 
 def is_torch_available():
     return _torch_available
@@ -456,10 +447,6 @@ def is_sentencepiece_available():
     return _sentencepiece_available
 
 
-def is_imageio_available():
-    return _imageio_available
-
-
 # docstyle-ignore
 FLAX_IMPORT_ERROR = """
 {0} requires the FLAX library but it was not found in your environment. Checkout the instructions on the
@@ -588,11 +575,6 @@ BITSANDBYTES_IMPORT_ERROR = """
 {0} requires the bitsandbytes library but it was not found in your environment. You can install it with pip: `pip install bitsandbytes`
 """
 
-# docstyle-ignore
-IMAGEIO_IMPORT_ERROR = """
-{0} requires the imageio library and ffmpeg but it was not found in your environment. You can install it with pip: `pip install imageio imageio-ffmpeg`
-"""
-
 BACKENDS_MAPPING = OrderedDict(
     [
         ("bs4", (is_bs4_available, BS4_IMPORT_ERROR)),
@@ -617,7 +599,6 @@ BACKENDS_MAPPING = OrderedDict(
         ("safetensors", (is_safetensors_available, SAFETENSORS_IMPORT_ERROR)),
         ("bitsandbytes", (is_bitsandbytes_available, BITSANDBYTES_IMPORT_ERROR)),
         ("sentencepiece", (is_sentencepiece_available, SENTENCEPIECE_IMPORT_ERROR)),
-        ("imageio", (is_imageio_available, IMAGEIO_IMPORT_ERROR)),
     ]
 )
 

src/diffusers/utils/loading_utils.py

@@ -6,7 +6,7 @@ import PIL.Image
 import PIL.ImageOps
 import requests
 
-from .import_utils import BACKENDS_MAPPING, is_imageio_available
+from .import_utils import BACKENDS_MAPPING, is_opencv_available
 
 
 def load_image(
@@ -81,8 +81,7 @@ def load_video(
 
     if is_url:
         video_data = requests.get(video, stream=True).raw
-        suffix = os.path.splitext(video)[1] or ".mp4"
-        video_path = tempfile.NamedTemporaryFile(suffix=suffix, delete=False).name
+        video_path = tempfile.NamedTemporaryFile(suffix=os.path.splitext(video)[1], delete=False).name
         was_tempfile_created = True
         with open(video_path, "wb") as f:
             f.write(video_data.read())
@@ -100,22 +99,19 @@ def load_video(
             pass
 
     else:
-        if is_imageio_available():
-            import imageio
+        if is_opencv_available():
+            import cv2
         else:
-            raise ImportError(BACKENDS_MAPPING["imageio"][1].format("load_video"))
+            raise ImportError(BACKENDS_MAPPING["opencv"][1].format("load_video"))
 
-        try:
-            imageio.plugins.ffmpeg.get_exe()
-        except AttributeError:
-            raise AttributeError(
-                "`Unable to find an ffmpeg installation on your machine. Please install via `pip install imageio-ffmpeg"
-            )
+        video_capture = cv2.VideoCapture(video)
+        success, frame = video_capture.read()
+        while success:
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            pil_images.append(PIL.Image.fromarray(frame))
+            success, frame = video_capture.read()
 
-        with imageio.get_reader(video) as reader:
-            # Read all frames
-            for frame in reader:
-                pil_images.append(PIL.Image.fromarray(frame))
+        video_capture.release()
 
     if was_tempfile_created:
         os.remove(video_path)

tests/lora/test_lora_layers_flux.py

@@ -12,26 +12,19 @@
 # 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.
-import os
 import sys
-import tempfile
 import unittest
 
-import numpy as np
-import safetensors.torch
 import torch
 from transformers import AutoTokenizer, CLIPTextModel, CLIPTokenizer, T5EncoderModel
 
 from diffusers import FlowMatchEulerDiscreteScheduler, FluxPipeline, FluxTransformer2DModel
-from diffusers.utils.testing_utils import floats_tensor, is_peft_available, require_peft_backend, torch_device
+from diffusers.utils.testing_utils import floats_tensor, require_peft_backend
 
 
-if is_peft_available():
-    from peft.utils import get_peft_model_state_dict
-
 sys.path.append(".")
 
-from utils import PeftLoraLoaderMixinTests, check_if_lora_correctly_set  # noqa: E402
+from utils import PeftLoraLoaderMixinTests  # noqa: E402
 
 
 @require_peft_backend
@@ -97,51 +90,3 @@ class FluxLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
             pipeline_inputs.update({"generator": generator})
 
         return noise, input_ids, pipeline_inputs
-
-    def test_with_alpha_in_state_dict(self):
-        components, _, denoiser_lora_config = self.get_dummy_components(FlowMatchEulerDiscreteScheduler)
-        pipe = self.pipeline_class(**components)
-        pipe = pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        _, _, inputs = self.get_dummy_inputs(with_generator=False)
-
-        output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
-        self.assertTrue(output_no_lora.shape == self.output_shape)
-
-        pipe.transformer.add_adapter(denoiser_lora_config)
-        self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
-
-        images_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
-
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            denoiser_state_dict = get_peft_model_state_dict(pipe.transformer)
-            self.pipeline_class.save_lora_weights(tmpdirname, transformer_lora_layers=denoiser_state_dict)
-
-            self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.safetensors")))
-            pipe.unload_lora_weights()
-            pipe.load_lora_weights(os.path.join(tmpdirname, "pytorch_lora_weights.safetensors"))
-
-            # modify the state dict to have alpha values following
-            # https://huggingface.co/TheLastBen/Jon_Snow_Flux_LoRA/blob/main/jon_snow.safetensors
-            state_dict_with_alpha = safetensors.torch.load_file(
-                os.path.join(tmpdirname, "pytorch_lora_weights.safetensors")
-            )
-            alpha_dict = {}
-            for k, v in state_dict_with_alpha.items():
-                # only do for `transformer` and for the k projections -- should be enough to test.
-                if "transformer" in k and "to_k" in k and "lora_A" in k:
-                    alpha_dict[f"{k}.alpha"] = float(torch.randint(10, 100, size=()))
-            state_dict_with_alpha.update(alpha_dict)
-
-        images_lora_from_pretrained = pipe(**inputs, generator=torch.manual_seed(0)).images
-        self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in denoiser")
-
-        pipe.unload_lora_weights()
-        pipe.load_lora_weights(state_dict_with_alpha)
-        images_lora_with_alpha = pipe(**inputs, generator=torch.manual_seed(0)).images
-
-        self.assertTrue(
-            np.allclose(images_lora, images_lora_from_pretrained, atol=1e-3, rtol=1e-3),
-            "Loading from saved checkpoints should give same results.",
-        )
-        self.assertFalse(np.allclose(images_lora_with_alpha, images_lora, atol=1e-3, rtol=1e-3))

tests/models/transformers/test_models_transformer_cogvideox.py

@@ -1,82 +0,0 @@
-# coding=utf-8
-# Copyright 2024 HuggingFace Inc.
-#
-# 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.
-
-import unittest
-
-import torch
-
-from diffusers import CogVideoXTransformer3DModel
-from diffusers.utils.testing_utils import (
-    enable_full_determinism,
-    torch_device,
-)
-
-from ..test_modeling_common import ModelTesterMixin
-
-
-enable_full_determinism()
-
-
-class CogVideoXTransformerTests(ModelTesterMixin, unittest.TestCase):
-    model_class = CogVideoXTransformer3DModel
-    main_input_name = "hidden_states"
-
-    @property
-    def dummy_input(self):
-        batch_size = 2
-        num_channels = 4
-        num_frames = 1
-        height = 8
-        width = 8
-        embedding_dim = 8
-        sequence_length = 8
-
-        hidden_states = torch.randn((batch_size, num_frames, num_channels, height, width)).to(torch_device)
-        encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
-        timestep = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
-
-        return {
-            "hidden_states": hidden_states,
-            "encoder_hidden_states": encoder_hidden_states,
-            "timestep": timestep,
-        }
-
-    @property
-    def input_shape(self):
-        return (1, 4, 8, 8)
-
-    @property
-    def output_shape(self):
-        return (1, 4, 8, 8)
-
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
-            # Product of num_attention_heads * attention_head_dim must be divisible by 16 for 3D positional embeddings.
-            "num_attention_heads": 2,
-            "attention_head_dim": 8,
-            "in_channels": 4,
-            "out_channels": 4,
-            "time_embed_dim": 2,
-            "text_embed_dim": 8,
-            "num_layers": 1,
-            "sample_width": 8,
-            "sample_height": 8,
-            "sample_frames": 8,
-            "patch_size": 2,
-            "temporal_compression_ratio": 4,
-            "max_text_seq_length": 8,
-        }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict

tests/pipelines/animatediff/test_animatediff_controlnet.py

@@ -20,7 +20,6 @@ from diffusers import (
 )
 from diffusers.models.attention import FreeNoiseTransformerBlock
 from diffusers.utils import logging
-from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.testing_utils import torch_device
 
 from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
@@ -330,13 +329,6 @@ class AnimateDiffControlNetPipelineFastTests(
         inputs["prompt_embeds"] = torch.randn((1, 4, pipe.text_encoder.config.hidden_size), device=torch_device)
         pipe(**inputs)
 
-    @unittest.skipIf(
-        torch_device != "cuda" or not is_xformers_available(),
-        reason="XFormers attention is only available with CUDA and `xformers` installed",
-    )
-    def test_xformers_attention_forwardGenerator_pass(self):
-        super()._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False)
-
     def test_free_init(self):
         components = self.get_dummy_components()
         pipe: AnimateDiffControlNetPipeline = self.pipeline_class(**components)

tests/pipelines/animatediff/test_animatediff_sparsectrl.py

@@ -19,7 +19,6 @@ from diffusers import (
     UNetMotionModel,
 )
 from diffusers.utils import logging
-from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.testing_utils import torch_device
 
 from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
@@ -394,13 +393,6 @@ class AnimateDiffSparseControlNetPipelineFastTests(
         inputs["prompt_embeds"] = torch.randn((1, 4, pipe.text_encoder.config.hidden_size), device=torch_device)
         pipe(**inputs)
 
-    @unittest.skipIf(
-        torch_device != "cuda" or not is_xformers_available(),
-        reason="XFormers attention is only available with CUDA and `xformers` installed",
-    )
-    def test_xformers_attention_forwardGenerator_pass(self):
-        super()._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False)
-
     def test_free_init(self):
         components = self.get_dummy_components()
         pipe: AnimateDiffSparseControlNetPipeline = self.pipeline_class(**components)

tests/pipelines/cogvideox/test_cogvideox.py

@@ -1,362 +0,0 @@
-# Copyright 2024 The HuggingFace Team.
-#
-# 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.
-
-import gc
-import inspect
-import unittest
-
-import numpy as np
-import torch
-from transformers import AutoTokenizer, T5EncoderModel
-
-from diffusers import AutoencoderKLCogVideoX, CogVideoXPipeline, CogVideoXTransformer3DModel, DDIMScheduler
-from diffusers.utils.testing_utils import (
-    enable_full_determinism,
-    numpy_cosine_similarity_distance,
-    require_torch_gpu,
-    slow,
-    torch_device,
-)
-
-from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
-from ..test_pipelines_common import (
-    PipelineTesterMixin,
-    check_qkv_fusion_matches_attn_procs_length,
-    check_qkv_fusion_processors_exist,
-    to_np,
-)
-
-
-enable_full_determinism()
-
-
-class CogVideoXPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
-    pipeline_class = CogVideoXPipeline
-    params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
-    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
-    image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
-    image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
-    required_optional_params = frozenset(
-        [
-            "num_inference_steps",
-            "generator",
-            "latents",
-            "return_dict",
-            "callback_on_step_end",
-            "callback_on_step_end_tensor_inputs",
-        ]
-    )
-
-    def get_dummy_components(self):
-        torch.manual_seed(0)
-        transformer = CogVideoXTransformer3DModel(
-            # Product of num_attention_heads * attention_head_dim must be divisible by 16 for 3D positional embeddings
-            # But, since we are using tiny-random-t5 here, we need the internal dim of CogVideoXTransformer3DModel
-            # to be 32. The internal dim is product of num_attention_heads and attention_head_dim
-            num_attention_heads=4,
-            attention_head_dim=8,
-            in_channels=4,
-            out_channels=4,
-            time_embed_dim=2,
-            text_embed_dim=32,  # Must match with tiny-random-t5
-            num_layers=1,
-            sample_width=16,  # latent width: 2 -> final width: 16
-            sample_height=16,  # latent height: 2 -> final height: 16
-            sample_frames=9,  # latent frames: (9 - 1) / 4 + 1 = 3 -> final frames: 9
-            patch_size=2,
-            temporal_compression_ratio=4,
-            max_text_seq_length=16,
-        )
-
-        torch.manual_seed(0)
-        vae = AutoencoderKLCogVideoX(
-            in_channels=3,
-            out_channels=3,
-            down_block_types=(
-                "CogVideoXDownBlock3D",
-                "CogVideoXDownBlock3D",
-                "CogVideoXDownBlock3D",
-                "CogVideoXDownBlock3D",
-            ),
-            up_block_types=(
-                "CogVideoXUpBlock3D",
-                "CogVideoXUpBlock3D",
-                "CogVideoXUpBlock3D",
-                "CogVideoXUpBlock3D",
-            ),
-            block_out_channels=(8, 8, 8, 8),
-            latent_channels=4,
-            layers_per_block=1,
-            norm_num_groups=2,
-            temporal_compression_ratio=4,
-        )
-
-        torch.manual_seed(0)
-        scheduler = DDIMScheduler()
-        text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
-        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
-
-        components = {
-            "transformer": transformer,
-            "vae": vae,
-            "scheduler": scheduler,
-            "text_encoder": text_encoder,
-            "tokenizer": tokenizer,
-        }
-        return components
-
-    def get_dummy_inputs(self, device, seed=0):
-        if str(device).startswith("mps"):
-            generator = torch.manual_seed(seed)
-        else:
-            generator = torch.Generator(device=device).manual_seed(seed)
-        inputs = {
-            "prompt": "dance monkey",
-            "negative_prompt": "",
-            "generator": generator,
-            "num_inference_steps": 2,
-            "guidance_scale": 6.0,
-            # Cannot reduce because convolution kernel becomes bigger than sample
-            "height": 16,
-            "width": 16,
-            "num_frames": 8,
-            "max_sequence_length": 16,
-            "output_type": "pt",
-        }
-        return inputs
-
-    def test_inference(self):
-        device = "cpu"
-
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        pipe.to(device)
-        pipe.set_progress_bar_config(disable=None)
-
-        inputs = self.get_dummy_inputs(device)
-        video = pipe(**inputs).frames
-        generated_video = video[0]
-
-        self.assertEqual(generated_video.shape, (8, 3, 16, 16))
-        expected_video = torch.randn(8, 3, 16, 16)
-        max_diff = np.abs(generated_video - expected_video).max()
-        self.assertLessEqual(max_diff, 1e10)
-
-    def test_callback_inputs(self):
-        sig = inspect.signature(self.pipeline_class.__call__)
-        has_callback_tensor_inputs = "callback_on_step_end_tensor_inputs" in sig.parameters
-        has_callback_step_end = "callback_on_step_end" in sig.parameters
-
-        if not (has_callback_tensor_inputs and has_callback_step_end):
-            return
-
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        pipe = pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        self.assertTrue(
-            hasattr(pipe, "_callback_tensor_inputs"),
-            f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs",
-        )
-
-        def callback_inputs_subset(pipe, i, t, callback_kwargs):
-            # iterate over callback args
-            for tensor_name, tensor_value in callback_kwargs.items():
-                # check that we're only passing in allowed tensor inputs
-                assert tensor_name in pipe._callback_tensor_inputs
-
-            return callback_kwargs
-
-        def callback_inputs_all(pipe, i, t, callback_kwargs):
-            for tensor_name in pipe._callback_tensor_inputs:
-                assert tensor_name in callback_kwargs
-
-            # iterate over callback args
-            for tensor_name, tensor_value in callback_kwargs.items():
-                # check that we're only passing in allowed tensor inputs
-                assert tensor_name in pipe._callback_tensor_inputs
-
-            return callback_kwargs
-
-        inputs = self.get_dummy_inputs(torch_device)
-
-        # Test passing in a subset
-        inputs["callback_on_step_end"] = callback_inputs_subset
-        inputs["callback_on_step_end_tensor_inputs"] = ["latents"]
-        output = pipe(**inputs)[0]
-
-        # Test passing in a everything
-        inputs["callback_on_step_end"] = callback_inputs_all
-        inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
-        output = pipe(**inputs)[0]
-
-        def callback_inputs_change_tensor(pipe, i, t, callback_kwargs):
-            is_last = i == (pipe.num_timesteps - 1)
-            if is_last:
-                callback_kwargs["latents"] = torch.zeros_like(callback_kwargs["latents"])
-            return callback_kwargs
-
-        inputs["callback_on_step_end"] = callback_inputs_change_tensor
-        inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
-        output = pipe(**inputs)[0]
-        assert output.abs().sum() < 1e10
-
-    def test_inference_batch_single_identical(self):
-        self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-3)
-
-    def test_attention_slicing_forward_pass(
-        self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3
-    ):
-        if not self.test_attention_slicing:
-            return
-
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        for component in pipe.components.values():
-            if hasattr(component, "set_default_attn_processor"):
-                component.set_default_attn_processor()
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-
-        generator_device = "cpu"
-        inputs = self.get_dummy_inputs(generator_device)
-        output_without_slicing = pipe(**inputs)[0]
-
-        pipe.enable_attention_slicing(slice_size=1)
-        inputs = self.get_dummy_inputs(generator_device)
-        output_with_slicing1 = pipe(**inputs)[0]
-
-        pipe.enable_attention_slicing(slice_size=2)
-        inputs = self.get_dummy_inputs(generator_device)
-        output_with_slicing2 = pipe(**inputs)[0]
-
-        if test_max_difference:
-            max_diff1 = np.abs(to_np(output_with_slicing1) - to_np(output_without_slicing)).max()
-            max_diff2 = np.abs(to_np(output_with_slicing2) - to_np(output_without_slicing)).max()
-            self.assertLess(
-                max(max_diff1, max_diff2),
-                expected_max_diff,
-                "Attention slicing should not affect the inference results",
-            )
-
-    def test_vae_tiling(self, expected_diff_max: float = 0.2):
-        generator_device = "cpu"
-        components = self.get_dummy_components()
-
-        pipe = self.pipeline_class(**components)
-        pipe.to("cpu")
-        pipe.set_progress_bar_config(disable=None)
-
-        # Without tiling
-        inputs = self.get_dummy_inputs(generator_device)
-        inputs["height"] = inputs["width"] = 128
-        output_without_tiling = pipe(**inputs)[0]
-
-        # With tiling
-        pipe.vae.enable_tiling(
-            tile_sample_min_height=96,
-            tile_sample_min_width=96,
-            tile_overlap_factor_height=1 / 12,
-            tile_overlap_factor_width=1 / 12,
-        )
-        inputs = self.get_dummy_inputs(generator_device)
-        inputs["height"] = inputs["width"] = 128
-        output_with_tiling = pipe(**inputs)[0]
-
-        self.assertLess(
-            (to_np(output_without_tiling) - to_np(output_with_tiling)).max(),
-            expected_diff_max,
-            "VAE tiling should not affect the inference results",
-        )
-
-    @unittest.skip("xformers attention processor does not exist for CogVideoX")
-    def test_xformers_attention_forwardGenerator_pass(self):
-        pass
-
-    def test_fused_qkv_projections(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        pipe = pipe.to(device)
-        pipe.set_progress_bar_config(disable=None)
-
-        inputs = self.get_dummy_inputs(device)
-        frames = pipe(**inputs).frames  # [B, F, C, H, W]
-        original_image_slice = frames[0, -2:, -1, -3:, -3:]
-
-        pipe.fuse_qkv_projections()
-        assert check_qkv_fusion_processors_exist(
-            pipe.transformer
-        ), "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
-        assert check_qkv_fusion_matches_attn_procs_length(
-            pipe.transformer, pipe.transformer.original_attn_processors
-        ), "Something wrong with the attention processors concerning the fused QKV projections."
-
-        inputs = self.get_dummy_inputs(device)
-        frames = pipe(**inputs).frames
-        image_slice_fused = frames[0, -2:, -1, -3:, -3:]
-
-        pipe.transformer.unfuse_qkv_projections()
-        inputs = self.get_dummy_inputs(device)
-        frames = pipe(**inputs).frames
-        image_slice_disabled = frames[0, -2:, -1, -3:, -3:]
-
-        assert np.allclose(
-            original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3
-        ), "Fusion of QKV projections shouldn't affect the outputs."
-        assert np.allclose(
-            image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3
-        ), "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
-        assert np.allclose(
-            original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2
-        ), "Original outputs should match when fused QKV projections are disabled."
-
-
-@slow
-@require_torch_gpu
-class CogVideoXPipelineIntegrationTests(unittest.TestCase):
-    prompt = "A painting of a squirrel eating a burger."
-
-    def setUp(self):
-        super().setUp()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def test_cogvideox(self):
-        generator = torch.Generator("cpu").manual_seed(0)
-
-        pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-2b", torch_dtype=torch.float16)
-        pipe.enable_model_cpu_offload()
-        prompt = self.prompt
-
-        videos = pipe(
-            prompt=prompt,
-            height=480,
-            width=720,
-            num_frames=16,
-            generator=generator,
-            num_inference_steps=2,
-            output_type="pt",
-        ).frames
-
-        video = videos[0]
-        expected_video = torch.randn(1, 16, 480, 720, 3).numpy()
-
-        max_diff = numpy_cosine_similarity_distance(video, expected_video)
-        assert max_diff < 1e-3, f"Max diff is too high. got {video}"

tests/pipelines/latte/test_latte.py

@@ -28,7 +28,6 @@ from diffusers import (
     LattePipeline,
     LatteTransformer3DModel,
 )
-from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.testing_utils import (
     enable_full_determinism,
     numpy_cosine_similarity_distance,
@@ -257,13 +256,6 @@ class LattePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
         self.assertLess(max_diff, 1.0)
 
-    @unittest.skipIf(
-        torch_device != "cuda" or not is_xformers_available(),
-        reason="XFormers attention is only available with CUDA and `xformers` installed",
-    )
-    def test_xformers_attention_forwardGenerator_pass(self):
-        super()._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False)
-
 
 @slow
 @require_torch_gpu