update

.github/workflows/push_tests.yml

@@ -61,7 +61,7 @@ jobs:
       max-parallel: 1
       matrix:
         module: ${{ fromJson(needs.setup_torch_cuda_pipeline_matrix.outputs.pipeline_test_matrix) }}
-    runs-on: [single-gpu, nvidia-gpu, t4, ci]
+    runs-on: docker-gpu
     container:
       image: diffusers/diffusers-pytorch-cuda
       options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/ --gpus 0

docs/source/en/api/attnprocessor.md

@@ -41,6 +41,12 @@ An attention processor is a class for applying different types of attention mech
 ## FusedAttnProcessor2_0
 [[autodoc]] models.attention_processor.FusedAttnProcessor2_0
 
+## LoRAAttnProcessor
+[[autodoc]] models.attention_processor.LoRAAttnProcessor
+
+## LoRAAttnProcessor2_0
+[[autodoc]] models.attention_processor.LoRAAttnProcessor2_0
+
 ## LoRAAttnAddedKVProcessor
 [[autodoc]] models.attention_processor.LoRAAttnAddedKVProcessor
 

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

@@ -444,7 +444,7 @@ export_to_gif(frames, "animatelcm.gif")
         A space rocket, 4K.
         <br>
         <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatelcm-output.gif"
-            alt="A space rocket, 4K"
+            alt="masterpiece, bestquality, sunset"
             style="width: 300px;" />
         </center></td>
     </tr>
@@ -486,7 +486,7 @@ export_to_gif(frames, "animatelcm-motion-lora.gif")
         A space rocket, 4K.
         <br>
         <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatelcm-motion-lora.gif"
-            alt="A space rocket, 4K"
+            alt="masterpiece, bestquality, sunset"
             style="width: 300px;" />
         </center></td>
     </tr>

docs/source/en/optimization/fp16.md

@@ -66,9 +66,3 @@ image = pipe(prompt).images[0]
 Don't use [`torch.autocast`](https://pytorch.org/docs/stable/amp.html#torch.autocast) in any of the pipelines as it can lead to black images and is always slower than pure float16 precision.
 
 </Tip>
-
-## Distilled model
-
-You could also use a distilled Stable Diffusion model and autoencoder to speed up inference. During distillation, many of the UNet's residual and attention blocks are shed to reduce the model size. The distilled model is faster and uses less memory while generating images of comparable quality to the full Stable Diffusion model.
-
-Learn more about in the [Distilled Stable Diffusion inference](../using-diffusers/distilled_sd) guide!

docs/source/en/optimization/torch2.0.md

@@ -75,9 +75,6 @@ Compilation requires some time to complete, so it is best suited for situations
 
 For more information and different options about `torch.compile`, refer to the [`torch_compile`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) tutorial.
 
-> [!TIP]
-> Learn more about other ways PyTorch 2.0 can help optimize your model in the [Accelerate inference of text-to-image diffusion models](../tutorials/fast_diffusion) tutorial.
-
 ## Benchmark
 
 We conducted a comprehensive benchmark with PyTorch 2.0's efficient attention implementation and `torch.compile` across different GPUs and batch sizes for five of our most used pipelines. The code is benchmarked on 🤗 Diffusers v0.17.0.dev0 to optimize `torch.compile` usage (see [here](https://github.com/huggingface/diffusers/pull/3313) for more details).

docs/source/en/training/lora.md

@@ -113,50 +113,36 @@ The dataset preprocessing code and training loop are found in the [`main()`](htt
 
 As with the script parameters, a walkthrough of the training script is provided in the [Text-to-image](text2image#training-script) training guide. Instead, this guide takes a look at the LoRA relevant parts of the script.
 
-<hfoptions id="lora">
-<hfoption id="UNet">
-
-Diffusers uses [`~peft.LoraConfig`] from the [PEFT](https://hf.co/docs/peft) library to set up the parameters of the LoRA adapter such as the rank, alpha, and which modules to insert the LoRA weights into. The adapter is added to the UNet, and only the LoRA layers are filtered for optimization in `lora_layers`.
+The script begins by adding the [new LoRA weights](https://github.com/huggingface/diffusers/blob/dd9a5caf61f04d11c0fa9f3947b69ab0010c9a0f/examples/text_to_image/train_text_to_image_lora.py#L447) to the attention layers. This involves correctly configuring the weight size for each block in the UNet. You'll see the `rank` parameter is used to create the [`~models.attention_processor.LoRAAttnProcessor`]:
 
 ```py
-unet_lora_config = LoraConfig(
-    r=args.rank,
-    lora_alpha=args.rank,
-    init_lora_weights="gaussian",
-    target_modules=["to_k", "to_q", "to_v", "to_out.0"],
-)
+lora_attn_procs = {}
+for name in unet.attn_processors.keys():
+    cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
+    if name.startswith("mid_block"):
+        hidden_size = unet.config.block_out_channels[-1]
+    elif name.startswith("up_blocks"):
+        block_id = int(name[len("up_blocks.")])
+        hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
+    elif name.startswith("down_blocks"):
+        block_id = int(name[len("down_blocks.")])
+        hidden_size = unet.config.block_out_channels[block_id]
 
-unet.add_adapter(unet_lora_config)
-lora_layers = filter(lambda p: p.requires_grad, unet.parameters())
+    lora_attn_procs[name] = LoRAAttnProcessor(
+        hidden_size=hidden_size,
+        cross_attention_dim=cross_attention_dim,
+        rank=args.rank,
+    )
+
+unet.set_attn_processor(lora_attn_procs)
+lora_layers = AttnProcsLayers(unet.attn_processors)
 ```
 
-</hfoption>
-<hfoption id="text encoder">
-
-Diffusers also supports finetuning the text encoder with LoRA from the [PEFT](https://hf.co/docs/peft) library when necessary such as finetuning Stable Diffusion XL (SDXL). The [`~peft.LoraConfig`] is used to configure the parameters of the LoRA adapter which are then added to the text encoder, and only the LoRA layers are filtered for training.
-
-```py
-text_lora_config = LoraConfig(
-    r=args.rank,
-    lora_alpha=args.rank,
-    init_lora_weights="gaussian",
-    target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
-)
-
-text_encoder_one.add_adapter(text_lora_config)
-text_encoder_two.add_adapter(text_lora_config)
-text_lora_parameters_one = list(filter(lambda p: p.requires_grad, text_encoder_one.parameters()))
-text_lora_parameters_two = list(filter(lambda p: p.requires_grad, text_encoder_two.parameters()))
-```
-
-</hfoption>
-</hfoptions>
-
-The [optimizer](https://github.com/huggingface/diffusers/blob/e4b8f173b97731686e290b2eb98e7f5df2b1b322/examples/text_to_image/train_text_to_image_lora.py#L529) is initialized with the `lora_layers` because these are the only weights that'll be optimized:
+The [optimizer](https://github.com/huggingface/diffusers/blob/dd9a5caf61f04d11c0fa9f3947b69ab0010c9a0f/examples/text_to_image/train_text_to_image_lora.py#L519) is initialized with the `lora_layers` because these are the only weights that'll be optimized:
 
 ```py
 optimizer = optimizer_cls(
-    lora_layers,
+    lora_layers.parameters(),
     lr=args.learning_rate,
     betas=(args.adam_beta1, args.adam_beta2),
     weight_decay=args.adam_weight_decay,

docs/source/en/using-diffusers/sdxl.md

@@ -63,12 +63,11 @@ from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipelin
 import torch
 
 pipeline = StableDiffusionXLPipeline.from_single_file(
-    "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors", 
-    torch_dtype=torch.float16
+    "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 ).to("cuda")
 
 refiner = StableDiffusionXLImg2ImgPipeline.from_single_file(
-    "https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/blob/main/sd_xl_refiner_1.0.safetensors", torch_dtype=torch.float16
+    "https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/blob/main/sd_xl_refiner_1.0.safetensors", torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
 ).to("cuda")
 ```
 

docs/source/en/using-diffusers/stable_diffusion_jax_how_to.md

@@ -217,9 +217,3 @@ Check your image dimensions to see if they're correct:
 images.shape
 # (8, 1, 512, 512, 3)
 ```
-
-## Resources
-
-To learn more about how JAX works with Stable Diffusion, you may be interested in reading:
-
-* [Accelerating Stable Diffusion XL Inference with JAX on Cloud TPU v5e](https://hf.co/blog/sdxl_jax)

docs/source/en/using-diffusers/write_own_pipeline.md

@@ -273,7 +273,7 @@ Lastly, convert the image to a `PIL.Image` to see your generated image!
 ```py
 >>> image = (image / 2 + 0.5).clamp(0, 1).squeeze()
 >>> image = (image.permute(1, 2, 0) * 255).to(torch.uint8).cpu().numpy()
->>> image = (image * 255).round().astype("uint8")
+>>> images = (image * 255).round().astype("uint8")
 >>> image = Image.fromarray(image)
 >>> image
 ```

docs/source/ko/api/pipelines/stable_diffusion/stable_diffusion_xl.md

@@ -313,12 +313,12 @@ from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipelin
 import torch
 
 pipe = StableDiffusionXLPipeline.from_single_file(
-    "./sd_xl_base_1.0.safetensors", torch_dtype=torch.float16
+    "./sd_xl_base_1.0.safetensors", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 )
 pipe.to("cuda")
 
 refiner = StableDiffusionXLImg2ImgPipeline.from_single_file(
-    "./sd_xl_refiner_1.0.safetensors", torch_dtype=torch.float16
+    "./sd_xl_refiner_1.0.safetensors", torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
 )
 refiner.to("cuda")
 ```

examples/community/README.md

@@ -57,13 +57,12 @@ If a community doesn't work as expected, please open an issue and ping the autho
 |   DemoFusion Pipeline                                                                                                    | Implementation of [DemoFusion: Democratising High-Resolution Image Generation With No $$$](https://arxiv.org/abs/2311.16973)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [DemoFusion Pipeline](#DemoFusion)      | - |              [Ruoyi Du](https://github.com/RuoyiDu) |
 |   Instaflow Pipeline                                                                                                    | Implementation of [InstaFlow! One-Step Stable Diffusion with Rectified Flow](https://arxiv.org/abs/2309.06380)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [Instaflow Pipeline](#instaflow-pipeline)      | - |              [Ayush Mangal](https://github.com/ayushtues) |
 |   Null-Text Inversion Pipeline  | Implement [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://arxiv.org/abs/2211.09794) as a pipeline.                                                                                                                                                                                                                                                                                                                                                                                                                                      | [Null-Text Inversion](https://github.com/google/prompt-to-prompt/)      | - |              [Junsheng Luan](https://github.com/Junsheng121) |
-|   Rerender A Video Pipeline                                                                                                    | Implementation of [[SIGGRAPH Asia 2023] Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation](https://arxiv.org/abs/2306.07954)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [Rerender A Video Pipeline](#Rerender-A-Video)      | - |              [Yifan Zhou](https://github.com/SingleZombie) |
+|   Rerender A Video Pipeline                                                                                                    | Implementation of [[SIGGRAPH Asia 2023] Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation](https://arxiv.org/abs/2306.07954)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [Rerender A Video Pipeline](#Rerender_A_Video)      | - |              [Yifan Zhou](https://github.com/SingleZombie) |
 | StyleAligned Pipeline                                                                                                    | Implementation of [Style Aligned Image Generation via Shared Attention](https://arxiv.org/abs/2312.02133)                                                                                                                                                                                                                                                                                                                                                                                                                                   | [StyleAligned Pipeline](#stylealigned-pipeline) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/15X2E0jFPTajUIjS0FzX50OaHsCbP2lQ0/view?usp=sharing) | [Aryan V S](https://github.com/a-r-r-o-w) |
 | AnimateDiff Image-To-Video Pipeline | Experimental Image-To-Video support for AnimateDiff (open to improvements) | [AnimateDiff Image To Video Pipeline](#animatediff-image-to-video-pipeline) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1TvzCDPHhfFtdcJZe4RLloAwyoLKuttWK/view?usp=sharing) | [Aryan V S](https://github.com/a-r-r-o-w) |
 |   IP Adapter FaceID Stable Diffusion                                                                                               | Stable Diffusion Pipeline that supports IP Adapter Face ID                                                                                                                                                                                                                                                                                                                                                  |  [IP Adapter Face ID](#ip-adapter-face-id) | - | [Fabio Rigano](https://github.com/fabiorigano) |
 |   InstantID Pipeline                                                                                               | Stable Diffusion XL Pipeline that supports InstantID                                                                                                                                                                                                                                                                                                                                                 |  [InstantID Pipeline](#instantid-pipeline) | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/InstantX/InstantID) | [Haofan Wang](https://github.com/haofanwang) |
 |   UFOGen Scheduler                                                                                               | Scheduler for UFOGen Model (compatible with Stable Diffusion pipelines)                                                                                                                                                                                                                                                                                                                                                 |  [UFOGen Scheduler](#ufogen-scheduler) | - | [dg845](https://github.com/dg845) |
-| Stable Diffusion XL IPEX Pipeline | Accelerate Stable Diffusion XL inference pipeline with BF16/FP32 precision on Intel Xeon CPUs with [IPEX](https://github.com/intel/intel-extension-for-pytorch) | [Stable Diffusion XL on IPEX](#stable-diffusion-xl-on-ipex) | - | [Dan Li](https://github.com/ustcuna/) |
 
 To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.
 
@@ -1708,111 +1707,6 @@ print("Latency of StableDiffusionPipeline--fp32",latency)
 
 ```
 
-### Stable Diffusion XL on IPEX
-
-This diffusion pipeline aims to accelarate the inference of Stable-Diffusion XL on Intel Xeon CPUs with BF16/FP32 precision using [IPEX](https://github.com/intel/intel-extension-for-pytorch).
-
-To use this pipeline, you need to:
-1. Install [IPEX](https://github.com/intel/intel-extension-for-pytorch)
-
-**Note:** For each PyTorch release, there is a corresponding release of IPEX. Here is the mapping relationship. It is recommended to install Pytorch/IPEX2.0 to get the best performance.
-
-|PyTorch Version|IPEX Version|
-|--|--|
-|[v2.0.\*](https://github.com/pytorch/pytorch/tree/v2.0.1 "v2.0.1")|[v2.0.\*](https://github.com/intel/intel-extension-for-pytorch/tree/v2.0.100+cpu)|
-|[v1.13.\*](https://github.com/pytorch/pytorch/tree/v1.13.0 "v1.13.0")|[v1.13.\*](https://github.com/intel/intel-extension-for-pytorch/tree/v1.13.100+cpu)|
-
-You can simply use pip to install IPEX with the latest version.
-```python
-python -m pip install intel_extension_for_pytorch
-```
-**Note:** To install a specific version, run with the following command:
-```
-python -m pip install intel_extension_for_pytorch==<version_name> -f https://developer.intel.com/ipex-whl-stable-cpu
-```
-
-2. After pipeline initialization, `prepare_for_ipex()` should be called to enable IPEX accelaration. Supported inference datatypes are Float32 and BFloat16.
-
-**Note:** The values of `height` and `width` used during preparation with `prepare_for_ipex()` should be the same when running inference with the prepared pipeline.
-
-```python
-pipe = StableDiffusionXLPipelineIpex.from_pretrained("stabilityai/sdxl-turbo", low_cpu_mem_usage=True, use_safetensors=True)
-# value of image height/width should be consistent with the pipeline inference
-# For Float32
-pipe.prepare_for_ipex(torch.float32, prompt, height=512, width=512)
-# For BFloat16
-pipe.prepare_for_ipex(torch.bfloat16, prompt, height=512, width=512)
-```
-
-Then you can use the ipex pipeline in a similar way to the default stable diffusion xl pipeline.
-```python
-# value of image height/width should be consistent with 'prepare_for_ipex()'
-# For Float32
-image = pipe(prompt, num_inference_steps=num_inference_steps, height=512, width=512, guidance_scale=guidance_scale).images[0]
-# For BFloat16
-with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16):
-    image = pipe(prompt, num_inference_steps=num_inference_steps, height=512, width=512, guidance_scale=guidance_scale).images[0]
-```
-
-The following code compares the performance of the original stable diffusion xl pipeline with the ipex-optimized pipeline.
-By using this optimized pipeline, we can get about 1.4-2 times performance boost with BFloat16 on fourth generation of Intel Xeon CPUs, 
-code-named Sapphire Rapids.
-
-```python
-import torch
-from diffusers import StableDiffusionXLPipeline
-from pipeline_stable_diffusion_xl_ipex import StableDiffusionXLPipelineIpex
-import time
-
-prompt = "sailing ship in storm by Rembrandt"
-model_id = "stabilityai/sdxl-turbo"
-steps = 4
-
-# Helper function for time evaluation
-def elapsed_time(pipeline, nb_pass=3, num_inference_steps=1):
-    # warmup
-    for _ in range(2):
-        images = pipeline(prompt, num_inference_steps=num_inference_steps, height=512, width=512, guidance_scale=0.0).images
-    #time evaluation
-    start = time.time()
-    for _ in range(nb_pass):
-        pipeline(prompt, num_inference_steps=num_inference_steps, height=512, width=512, guidance_scale=0.0)
-    end = time.time()
-    return (end - start) / nb_pass
-
-##############     bf16 inference performance    ###############
-
-# 1. IPEX Pipeline initialization
-pipe = StableDiffusionXLPipelineIpex.from_pretrained(model_id, low_cpu_mem_usage=True, use_safetensors=True)
-pipe.prepare_for_ipex(torch.bfloat16, prompt, height=512, width=512)
-
-# 2. Original Pipeline initialization
-pipe2 = StableDiffusionXLPipeline.from_pretrained(model_id, low_cpu_mem_usage=True, use_safetensors=True)
-
-# 3. Compare performance between Original Pipeline and IPEX Pipeline
-with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16):
-    latency = elapsed_time(pipe, num_inference_steps=steps)
-    print("Latency of StableDiffusionXLPipelineIpex--bf16", latency, "s for total", steps, "steps")
-    latency = elapsed_time(pipe2, num_inference_steps=steps)
-    print("Latency of StableDiffusionXLPipeline--bf16", latency, "s for total", steps, "steps")
-
-##############     fp32 inference performance    ###############
-
-# 1. IPEX Pipeline initialization
-pipe3 = StableDiffusionXLPipelineIpex.from_pretrained(model_id, low_cpu_mem_usage=True, use_safetensors=True)
-pipe3.prepare_for_ipex(torch.float32, prompt, height=512, width=512)
-
-# 2. Original Pipeline initialization
-pipe4 = StableDiffusionXLPipeline.from_pretrained(model_id, low_cpu_mem_usage=True, use_safetensors=True)
-
-# 3. Compare performance between Original Pipeline and IPEX Pipeline
-latency = elapsed_time(pipe3, num_inference_steps=steps)
-print("Latency of StableDiffusionXLPipelineIpex--fp32", latency, "s for total", steps, "steps")
-latency = elapsed_time(pipe4, num_inference_steps=steps)
-print("Latency of StableDiffusionXLPipeline--fp32",latency, "s for total", steps, "steps")
-
-```
-
 ### CLIP Guided Images Mixing With Stable Diffusion
 
 ![clip_guided_images_mixing_examples](https://huggingface.co/datasets/TheDenk/images_mixing/resolve/main/main.png)
@@ -3412,9 +3306,10 @@ inverted_latent, uncond = pipeline.invert(input_image, invert_prompt, num_inner_
 pipeline(prompt, uncond, inverted_latent, guidance_scale=7.5, num_inference_steps=steps).images[0].save(input_image+".output.jpg")
 ```
 
-### Rerender A Video
+### Rerender_A_Video
 
-This is the Diffusers implementation of zero-shot video-to-video translation pipeline [Rerender A Video](https://github.com/williamyang1991/Rerender_A_Video) (without Ebsynth postprocessing). To run the code, please install gmflow. Then modify the path in `examples/community/rerender_a_video.py`:
+```
+This is the Diffusers implementation of zero-shot video-to-video translation pipeline [Rerender_A_Video](https://github.com/williamyang1991/Rerender_A_Video) (without Ebsynth postprocessing). To run the code, please install gmflow. Then modify the path in `examples/community/rerender_a_video.py`:
 
 ```py
 gmflow_dir = "/path/to/gmflow"
@@ -3561,17 +3456,14 @@ pipe.disable_style_aligned()
 
 This pipeline adds experimental support for the image-to-video task using AnimateDiff. Refer to [this](https://github.com/huggingface/diffusers/pull/6328) PR for more examples and results.
 
-This pipeline relies on a "hack" discovered by the community that allows the generation of videos given an input image with AnimateDiff. It works by creating a copy of the image `num_frames` times and progressively adding more noise to the image based on the strength and latent interpolation method.
-
 ```py
 import torch
 from diffusers import MotionAdapter, DiffusionPipeline, DDIMScheduler
 from diffusers.utils import export_to_gif, load_image
 
-model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
 adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2")
-pipe = DiffusionPipeline.from_pretrained(model_id, motion_adapter=adapter, custom_pipeline="pipeline_animatediff_img2video").to("cuda")
-pipe.scheduler = DDIMScheduler.from_pretrained(model_id, subfolder="scheduler", clip_sample=False, timestep_spacing="linspace", beta_schedule="linear", steps_offset=1)
+pipe = DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.1_noVAE", motion_adapter=adapter, custom_pipeline="pipeline_animatediff_img2video").to("cuda")
+pipe.scheduler = DDIMScheduler(beta_schedule="linear", steps_offset=1, clip_sample=False, timespace_spacing="linspace")
 
 image = load_image("snail.png")
 output = pipe(

examples/community/checkpoint_merger.py

@@ -81,8 +81,6 @@ class CheckpointMergerPipeline(DiffusionPipeline):
 
                 force - Whether to ignore mismatch in model_config.json for the current models. Defaults to False.
 
-                variant - which variant of a pretrained model to load, e.g. "fp16" (None)
-
         """
         # Default kwargs from DiffusionPipeline
         cache_dir = kwargs.pop("cache_dir", None)
@@ -91,7 +89,6 @@ class CheckpointMergerPipeline(DiffusionPipeline):
         proxies = kwargs.pop("proxies", None)
         local_files_only = kwargs.pop("local_files_only", False)
         token = kwargs.pop("token", None)
-        variant = kwargs.pop("variant", None)
         revision = kwargs.pop("revision", None)
         torch_dtype = kwargs.pop("torch_dtype", None)
         device_map = kwargs.pop("device_map", None)
@@ -176,10 +173,7 @@ class CheckpointMergerPipeline(DiffusionPipeline):
         # Step 3:-
         # Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
         final_pipe = DiffusionPipeline.from_pretrained(
-            cached_folders[0],
-            torch_dtype=torch_dtype,
-            device_map=device_map,
-            variant=variant,
+            cached_folders[0], torch_dtype=torch_dtype, device_map=device_map
         )
         final_pipe.to(self.device)
 

examples/community/imagic_stable_diffusion.py

@@ -346,9 +346,8 @@ class ImagicStableDiffusionPipeline(DiffusionPipeline):
         r"""
         Function invoked when calling the pipeline for generation.
         Args:
-            alpha (`float`, *optional*, defaults to 1.2):
-                The interpolation factor between the original and optimized text embeddings. A value closer to 0
-                will resemble the original input image.
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
             height (`int`, *optional*, defaults to 512):
                 The height in pixels of the generated image.
             width (`int`, *optional*, defaults to 512):
@@ -362,18 +361,22 @@ class ImagicStableDiffusionPipeline(DiffusionPipeline):
                 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.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
             generator (`torch.Generator`, *optional*):
                 A [torch generator](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`.
             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 `nd.array`.
             return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                 plain tuple.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
         Returns:
             [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
             [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.

examples/community/lpw_stable_diffusion_xl.py

@@ -1766,7 +1766,7 @@ class SDXLLongPromptWeightingPipeline(
 
         # 4. Prepare timesteps
         def denoising_value_valid(dnv):
-            return isinstance(dnv, float) and 0 < dnv < 1
+            return isinstance(self.denoising_end, float) and 0 < dnv < 1
 
         timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
         if image is not None:
@@ -1774,7 +1774,7 @@ class SDXLLongPromptWeightingPipeline(
                 num_inference_steps,
                 strength,
                 device,
-                denoising_start=self.denoising_start if denoising_value_valid(self.denoising_start) else None,
+                denoising_start=self.denoising_start if denoising_value_valid else None,
             )
 
             # check that number of inference steps is not < 1 - as this doesn't make sense

examples/community/pipeline_animatediff_controlnet.py

@@ -24,7 +24,7 @@ from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPV
 
 from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
 from diffusers.loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
-from diffusers.models import AutoencoderKL, ControlNetModel, ImageProjection, UNet2DConditionModel, UNetMotionModel
+from diffusers.models import AutoencoderKL, ControlNetModel, UNet2DConditionModel, UNetMotionModel
 from diffusers.models.lora import adjust_lora_scale_text_encoder
 from diffusers.models.unets.unet_motion_model import MotionAdapter
 from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
@@ -382,41 +382,6 @@ class AnimateDiffControlNetPipeline(DiffusionPipeline, TextualInversionLoaderMix
         uncond_image_embeds = torch.zeros_like(image_embeds)
         return image_embeds, uncond_image_embeds
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
-    def prepare_ip_adapter_image_embeds(
-        self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt
-    ):
-        if ip_adapter_image_embeds is None:
-            if not isinstance(ip_adapter_image, list):
-                ip_adapter_image = [ip_adapter_image]
-
-            if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
-                raise ValueError(
-                    f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
-                )
-
-            image_embeds = []
-            for single_ip_adapter_image, image_proj_layer in zip(
-                ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
-            ):
-                output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
-                single_image_embeds, single_negative_image_embeds = self.encode_image(
-                    single_ip_adapter_image, device, 1, output_hidden_state
-                )
-                single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)
-                single_negative_image_embeds = torch.stack(
-                    [single_negative_image_embeds] * num_images_per_prompt, dim=0
-                )
-
-                if self.do_classifier_free_guidance:
-                    single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
-                    single_image_embeds = single_image_embeds.to(device)
-
-                image_embeds.append(single_image_embeds)
-        else:
-            image_embeds = ip_adapter_image_embeds
-        return image_embeds
-
     # Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents
     def decode_latents(self, latents):
         latents = 1 / self.vae.config.scaling_factor * latents
@@ -802,7 +767,6 @@ class AnimateDiffControlNetPipeline(DiffusionPipeline, TextualInversionLoaderMix
         prompt_embeds: Optional[torch.FloatTensor] = None,
         negative_prompt_embeds: Optional[torch.FloatTensor] = None,
         ip_adapter_image: Optional[PipelineImageInput] = None,
-        ip_adapter_image_embeds: Optional[PipelineImageInput] = None,
         conditioning_frames: Optional[List[PipelineImageInput]] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
@@ -857,9 +821,6 @@ class AnimateDiffControlNetPipeline(DiffusionPipeline, TextualInversionLoaderMix
                 not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
             ip_adapter_image (`PipelineImageInput`, *optional*):
                 Optional image input to work with IP Adapters.
-            ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*):
-                Pre-generated image embeddings for IP-Adapter. If not
-                provided, embeddings are computed from the `ip_adapter_image` input argument.
             conditioning_frames (`List[PipelineImageInput]`, *optional*):
                 The ControlNet input condition to provide guidance to the `unet` for generation. If multiple ControlNets
                 are specified, images must be passed as a list such that each element of the list can be correctly
@@ -1004,9 +965,9 @@ class AnimateDiffControlNetPipeline(DiffusionPipeline, TextualInversionLoaderMix
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
 
         if ip_adapter_image is not None:
-            image_embeds = self.prepare_ip_adapter_image_embeds(
-                ip_adapter_image, ip_adapter_image_embeds, device, batch_size * num_videos_per_prompt
-            )
+            image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_videos_per_prompt)
+            if self.do_classifier_free_guidance:
+                image_embeds = torch.cat([negative_image_embeds, image_embeds])
 
         if isinstance(controlnet, ControlNetModel):
             conditioning_frames = self.prepare_image(
@@ -1062,11 +1023,7 @@ class AnimateDiffControlNetPipeline(DiffusionPipeline, TextualInversionLoaderMix
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 7. Add image embeds for IP-Adapter
-        added_cond_kwargs = (
-            {"image_embeds": image_embeds}
-            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
-            else None
-        )
+        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
 
         # 7.1 Create tensor stating which controlnets to keep
         controlnet_keep = []

examples/community/pipeline_animatediff_img2video.py

@@ -11,14 +11,9 @@
 # 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.
-#
-# Note:
-# This pipeline relies on a "hack" discovered by the community that allows
-# the generation of videos given an input image with AnimateDiff. It works
-# by creating a copy of the image `num_frames` times and progressively adding
-# more noise to the image based on the strength and latent interpolation method.
 
 import inspect
+from dataclasses import dataclass
 from types import FunctionType
 from typing import Any, Callable, Dict, List, Optional, Union
 
@@ -30,8 +25,7 @@ from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
 from diffusers.loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
 from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel, UNetMotionModel
 from diffusers.models.lora import adjust_lora_scale_text_encoder
-from diffusers.models.unets.unet_motion_model import MotionAdapter
-from diffusers.pipelines.animatediff.pipeline_output import AnimateDiffPipelineOutput
+from diffusers.models.unet_motion_model import MotionAdapter
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from diffusers.schedulers import (
     DDIMScheduler,
@@ -41,7 +35,7 @@ from diffusers.schedulers import (
     LMSDiscreteScheduler,
     PNDMScheduler,
 )
-from diffusers.utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
+from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, logging, scale_lora_layers, unscale_lora_layers
 from diffusers.utils.torch_utils import randn_tensor
 
 
@@ -54,10 +48,9 @@ EXAMPLE_DOC_STRING = """
         >>> from diffusers import MotionAdapter, DiffusionPipeline, DDIMScheduler
         >>> from diffusers.utils import export_to_gif, load_image
 
-        >>> model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
         >>> adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2")
         >>> pipe = DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.1_noVAE", motion_adapter=adapter, custom_pipeline="pipeline_animatediff_img2video").to("cuda")
-        >>> pipe.scheduler = pipe.scheduler = DDIMScheduler.from_pretrained(model_id, subfolder="scheduler", clip_sample=False, timestep_spacing="linspace", beta_schedule="linear", steps_offset=1)
+        >>> pipe.scheduler = DDIMScheduler(beta_schedule="linear", steps_offset=1, clip_sample=False, timespace_spacing="linspace")
 
         >>> image = load_image("snail.png")
         >>> output = pipe(image=image, prompt="A snail moving on the ground", strength=0.8, latent_interpolation_method="slerp")
@@ -232,9 +225,14 @@ def retrieve_timesteps(
     return timesteps, num_inference_steps
 
 
+@dataclass
+class AnimateDiffImgToVideoPipelineOutput(BaseOutput):
+    frames: Union[torch.Tensor, np.ndarray]
+
+
 class AnimateDiffImgToVideoPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin):
     r"""
-    Pipeline for image-to-video generation.
+    Pipeline for text-to-video generation.
 
     This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
     implemented for all pipelines (downloading, saving, running on a particular device, etc.).
@@ -505,41 +503,6 @@ class AnimateDiffImgToVideoPipeline(DiffusionPipeline, TextualInversionLoaderMix
 
             return image_embeds, uncond_image_embeds
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
-    def prepare_ip_adapter_image_embeds(
-        self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt
-    ):
-        if ip_adapter_image_embeds is None:
-            if not isinstance(ip_adapter_image, list):
-                ip_adapter_image = [ip_adapter_image]
-
-            if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
-                raise ValueError(
-                    f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
-                )
-
-            image_embeds = []
-            for single_ip_adapter_image, image_proj_layer in zip(
-                ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
-            ):
-                output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
-                single_image_embeds, single_negative_image_embeds = self.encode_image(
-                    single_ip_adapter_image, device, 1, output_hidden_state
-                )
-                single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)
-                single_negative_image_embeds = torch.stack(
-                    [single_negative_image_embeds] * num_images_per_prompt, dim=0
-                )
-
-                if self.do_classifier_free_guidance:
-                    single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
-                    single_image_embeds = single_image_embeds.to(device)
-
-                image_embeds.append(single_image_embeds)
-        else:
-            image_embeds = ip_adapter_image_embeds
-        return image_embeds
-
     # Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents
     def decode_latents(self, latents):
         latents = 1 / self.vae.config.scaling_factor * latents
@@ -802,7 +765,6 @@ class AnimateDiffImgToVideoPipeline(DiffusionPipeline, TextualInversionLoaderMix
         prompt_embeds: Optional[torch.FloatTensor] = None,
         negative_prompt_embeds: Optional[torch.FloatTensor] = None,
         ip_adapter_image: Optional[PipelineImageInput] = None,
-        ip_adapter_image_embeds: Optional[PipelineImageInput] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
         callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
@@ -856,9 +818,6 @@ class AnimateDiffImgToVideoPipeline(DiffusionPipeline, TextualInversionLoaderMix
                 not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
             ip_adapter_image: (`PipelineImageInput`, *optional*):
                 Optional image input to work with IP Adapters.
-            ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*):
-                Pre-generated image embeddings for IP-Adapter. If not
-                provided, embeddings are computed from the `ip_adapter_image` input argument.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generated video. Choose between `torch.FloatTensor`, `PIL.Image` or
                 `np.array`.
@@ -883,8 +842,8 @@ class AnimateDiffImgToVideoPipeline(DiffusionPipeline, TextualInversionLoaderMix
         Examples:
 
         Returns:
-            [`AnimateDiffPipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`AnimateDiffPipelineOutput`] is
+            [`AnimateDiffImgToVideoPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`AnimateDiffImgToVideoPipelineOutput`] is
                 returned, otherwise a `tuple` is returned where the first element is a list with the generated frames.
         """
         # 0. Default height and width to unet
@@ -943,9 +902,12 @@ class AnimateDiffImgToVideoPipeline(DiffusionPipeline, TextualInversionLoaderMix
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
 
         if ip_adapter_image is not None:
-            image_embeds = self.prepare_ip_adapter_image_embeds(
-                ip_adapter_image, ip_adapter_image_embeds, device, batch_size * num_videos_per_prompt
+            output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True
+            image_embeds, negative_image_embeds = self.encode_image(
+                ip_adapter_image, device, num_videos_per_prompt, output_hidden_state
             )
+            if do_classifier_free_guidance:
+                image_embeds = torch.cat([negative_image_embeds, image_embeds])
 
         # 4. Preprocess image
         image = self.image_processor.preprocess(image, height=height, width=width)
@@ -974,11 +936,7 @@ class AnimateDiffImgToVideoPipeline(DiffusionPipeline, TextualInversionLoaderMix
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 8. Add image embeds for IP-Adapter
-        added_cond_kwargs = (
-            {"image_embeds": image_embeds}
-            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
-            else None
-        )
+        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
 
         # 9. Denoising loop
         num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
@@ -1012,7 +970,7 @@ class AnimateDiffImgToVideoPipeline(DiffusionPipeline, TextualInversionLoaderMix
                         callback(i, t, latents)
 
         if output_type == "latent":
-            return AnimateDiffPipelineOutput(frames=latents)
+            return AnimateDiffImgToVideoPipelineOutput(frames=latents)
 
         # 10. Post-processing
         video_tensor = self.decode_latents(latents)
@@ -1028,4 +986,4 @@ class AnimateDiffImgToVideoPipeline(DiffusionPipeline, TextualInversionLoaderMix
         if not return_dict:
             return (video,)
 
-        return AnimateDiffPipelineOutput(frames=video)
+        return AnimateDiffImgToVideoPipelineOutput(frames=video)

examples/community/pipeline_sdxl_style_aligned.py

@@ -1769,7 +1769,7 @@ class StyleAlignedSDXLPipeline(
 
         # 4. Prepare timesteps
         def denoising_value_valid(dnv):
-            return isinstance(dnv, float) and 0 < dnv < 1
+            return isinstance(self.denoising_end, float) and 0 < dnv < 1
 
         timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
 
@@ -1778,7 +1778,7 @@ class StyleAlignedSDXLPipeline(
                 num_inference_steps,
                 strength,
                 device,
-                denoising_start=self.denoising_start if denoising_value_valid(self.denoising_start) else None,
+                denoising_start=self.denoising_start if denoising_value_valid else None,
             )
 
             # check that number of inference steps is not < 1 - as this doesn't make sense

examples/community/pipeline_stable_diffusion_xl_controlnet_adapter_inpaint.py

@@ -1563,14 +1563,14 @@ class StableDiffusionXLControlNetAdapterInpaintPipeline(DiffusionPipeline, FromS
 
         # 4. set timesteps
         def denoising_value_valid(dnv):
-            return isinstance(dnv, float) and 0 < dnv < 1
+            return isinstance(denoising_end, float) and 0 < dnv < 1
 
         self.scheduler.set_timesteps(num_inference_steps, device=device)
         timesteps, num_inference_steps = self.get_timesteps(
             num_inference_steps,
             strength,
             device,
-            denoising_start=denoising_start if denoising_value_valid(denoising_start) else None,
+            denoising_start=denoising_start if denoising_value_valid else None,
         )
         # check that number of inference steps is not < 1 - as this doesn't make sense
         if num_inference_steps < 1:

examples/community/stable_diffusion_reference.py

@@ -1,31 +1,16 @@
 # Inspired by: https://github.com/Mikubill/sd-webui-controlnet/discussions/1236 and https://github.com/Mikubill/sd-webui-controlnet/discussions/1280
-import inspect
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 import PIL.Image
 import torch
-from packaging import version
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
-from diffusers import AutoencoderKL, DiffusionPipeline, UNet2DConditionModel
-from diffusers.configuration_utils import FrozenDict, deprecate
-from diffusers.image_processor import VaeImageProcessor
-from diffusers.loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
+from diffusers import StableDiffusionPipeline
 from diffusers.models.attention import BasicTransformerBlock
-from diffusers.models.lora import adjust_lora_scale_text_encoder
 from diffusers.models.unets.unet_2d_blocks import CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UpBlock2D
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import rescale_noise_cfg
-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from diffusers.schedulers import KarrasDiffusionSchedulers
-from diffusers.utils import (
-    PIL_INTERPOLATION,
-    USE_PEFT_BACKEND,
-    logging,
-    scale_lora_layers,
-    unscale_lora_layers,
-)
+from diffusers.utils import PIL_INTERPOLATION, logging
 from diffusers.utils.torch_utils import randn_tensor
 
 
@@ -46,7 +31,7 @@ EXAMPLE_DOC_STRING = """
                 torch_dtype=torch.float16
                 ).to('cuda:0')
 
-        >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+        >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe_controlnet.scheduler.config)
 
         >>> result_img = pipe(ref_image=input_image,
                         prompt="1girl",
@@ -60,182 +45,14 @@ EXAMPLE_DOC_STRING = """
 
 
 def torch_dfs(model: torch.nn.Module):
-    r"""
-    Performs a depth-first search on the given PyTorch model and returns a list of all its child modules.
-
-    Args:
-        model (torch.nn.Module): The PyTorch model to perform the depth-first search on.
-
-    Returns:
-        list: A list of all child modules of the given model.
-    """
     result = [model]
     for child in model.children():
         result += torch_dfs(child)
     return result
 
 
-class StableDiffusionReferencePipeline(
-    DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin
-):
-    r""" "
-    Pipeline for Stable Diffusion Reference.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    The pipeline also inherits the following loading methods:
-    - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
-    - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
-    - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
-    - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
-    - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPImageProcessor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-
-    _optional_components = ["safety_checker", "feature_extractor"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: KarrasDiffusionSchedulers,
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
-                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
-                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
-                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
-                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
-                " file"
-            )
-            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["steps_offset"] = 1
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if hasattr(scheduler.config, "skip_prk_steps") and scheduler.config.skip_prk_steps is False:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} has not set the configuration"
-                " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
-                " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
-                " incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
-                " Hub, it would be very nice if you could open a Pull request for the"
-                " `scheduler/scheduler_config.json` file"
-            )
-            deprecate(
-                "skip_prk_steps not set",
-                "1.0.0",
-                deprecation_message,
-                standard_warn=False,
-            )
-            new_config = dict(scheduler.config)
-            new_config["skip_prk_steps"] = True
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
-        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
-            deprecation_message = (
-                "The configuration file of the unet has set the default `sample_size` to smaller than"
-                " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
-                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
-                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
-                " in the config might lead to incorrect results in future versions. If you have downloaded this"
-                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
-                " the `unet/config.json` file"
-            )
-            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(unet.config)
-            new_config["sample_size"] = 64
-            unet._internal_dict = FrozenDict(new_config)
-        # Check shapes, assume num_channels_latents == 4, num_channels_mask == 1, num_channels_masked == 4
-        if unet.config.in_channels != 4:
-            logger.warning(
-                f"You have loaded a UNet with {unet.config.in_channels} input channels, whereas by default,"
-                f" {self.__class__} assumes that `pipeline.unet` has 4 input channels: 4 for `num_channels_latents`,"
-                ". If you did not intend to modify"
-                " this behavior, please check whether you have loaded the right checkpoint."
-            )
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    def _default_height_width(
-        self,
-        height: Optional[int],
-        width: Optional[int],
-        image: Union[PIL.Image.Image, torch.Tensor, List[PIL.Image.Image]],
-    ) -> Tuple[int, int]:
-        r"""
-        Calculate the default height and width for the given image.
-
-        Args:
-            height (int or None): The desired height of the image. If None, the height will be determined based on the input image.
-            width (int or None): The desired width of the image. If None, the width will be determined based on the input image.
-            image (PIL.Image.Image or torch.Tensor or list[PIL.Image.Image]): The input image or a list of images.
-
-        Returns:
-            Tuple[int, int]: A tuple containing the calculated height and width.
-
-        """
+class StableDiffusionReferencePipeline(StableDiffusionPipeline):
+    def _default_height_width(self, height, width, image):
         # NOTE: It is possible that a list of images have different
         # dimensions for each image, so just checking the first image
         # is not _exactly_ correct, but it is simple.
@@ -260,430 +77,18 @@ class StableDiffusionReferencePipeline(
 
         return height, width
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
-    def check_inputs(
-        self,
-        prompt: Optional[Union[str, List[str]]],
-        height: int,
-        width: int,
-        callback_steps: Optional[int],
-        negative_prompt: Optional[str] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        ip_adapter_image: Optional[torch.Tensor] = None,
-        ip_adapter_image_embeds: Optional[torch.FloatTensor] = None,
-        callback_on_step_end_tensor_inputs: Optional[List[str]] = None,
-    ) -> None:
-        """
-        Check the validity of the input arguments for the diffusion model.
-
-        Args:
-            prompt (Optional[Union[str, List[str]]]): The prompt text or list of prompt texts.
-            height (int): The height of the input image.
-            width (int): The width of the input image.
-            callback_steps (Optional[int]): The number of steps to perform the callback on.
-            negative_prompt (Optional[str]): The negative prompt text.
-            prompt_embeds (Optional[torch.FloatTensor]): The prompt embeddings.
-            negative_prompt_embeds (Optional[torch.FloatTensor]): The negative prompt embeddings.
-            ip_adapter_image (Optional[torch.Tensor]): The input adapter image.
-            ip_adapter_image_embeds (Optional[torch.FloatTensor]): The input adapter image embeddings.
-            callback_on_step_end_tensor_inputs (Optional[List[str]]): The list of tensor inputs to perform the callback on.
-
-        Raises:
-            ValueError: If `height` or `width` is not divisible by 8.
-            ValueError: If `callback_steps` is not a positive integer.
-            ValueError: If `callback_on_step_end_tensor_inputs` contains invalid tensor inputs.
-            ValueError: If both `prompt` and `prompt_embeds` are provided.
-            ValueError: If neither `prompt` nor `prompt_embeds` are provided.
-            ValueError: If `prompt` is not of type `str` or `list`.
-            ValueError: If both `negative_prompt` and `negative_prompt_embeds` are provided.
-            ValueError: If both `prompt_embeds` and `negative_prompt_embeds` are provided and have different shapes.
-            ValueError: If both `ip_adapter_image` and `ip_adapter_image_embeds` are provided.
-
-        Returns:
-            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_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-        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 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}."
-                )
-
-        if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
-            raise ValueError(
-                "Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
-            )
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
-    def _encode_prompt(
-        self,
-        prompt: Union[str, List[str]],
-        device: torch.device,
-        num_images_per_prompt: int,
-        do_classifier_free_guidance: bool,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        lora_scale: Optional[float] = None,
-        **kwargs,
-    ) -> torch.FloatTensor:
-        r"""
-        Encodes the prompt into embeddings.
-
-        Args:
-            prompt (Union[str, List[str]]): The prompt text or a list of prompt texts.
-            device (torch.device): The device to use for encoding.
-            num_images_per_prompt (int): The number of images per prompt.
-            do_classifier_free_guidance (bool): Whether to use classifier-free guidance.
-            negative_prompt (Optional[Union[str, List[str]]], optional): The negative prompt text or a list of negative prompt texts. Defaults to None.
-            prompt_embeds (Optional[torch.FloatTensor], optional): The prompt embeddings. Defaults to None.
-            negative_prompt_embeds (Optional[torch.FloatTensor], optional): The negative prompt embeddings. Defaults to None.
-            lora_scale (Optional[float], optional): The LoRA scale. Defaults to None.
-            **kwargs: Additional keyword arguments.
-
-        Returns:
-            torch.FloatTensor: The encoded prompt embeddings.
-        """
-        deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
-        deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False)
-
-        prompt_embeds_tuple = self.encode_prompt(
-            prompt=prompt,
-            device=device,
-            num_images_per_prompt=num_images_per_prompt,
-            do_classifier_free_guidance=do_classifier_free_guidance,
-            negative_prompt=negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            lora_scale=lora_scale,
-            **kwargs,
-        )
-
-        # concatenate for backwards comp
-        prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])
-
-        return prompt_embeds
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
-    def encode_prompt(
-        self,
-        prompt: Optional[str],
-        device: torch.device,
-        num_images_per_prompt: int,
-        do_classifier_free_guidance: bool,
-        negative_prompt: Optional[str] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        lora_scale: Optional[float] = None,
-        clip_skip: Optional[int] = None,
-    ) -> torch.FloatTensor:
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            device: (`torch.device`):
-                torch device
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            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`).
-            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.
-            lora_scale (`float`, *optional*):
-                A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
-            clip_skip (`int`, *optional*):
-                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
-                the output of the pre-final layer will be used for computing the prompt embeddings.
-        """
-        # set lora scale so that monkey patched LoRA
-        # function of text encoder can correctly access it
-        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
-            self._lora_scale = lora_scale
-
-            # dynamically adjust the LoRA scale
-            if not USE_PEFT_BACKEND:
-                adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
-            else:
-                scale_lora_layers(self.text_encoder, lora_scale)
-
-        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]
-
-        if prompt_embeds is None:
-            # textual inversion: process multi-vector tokens if necessary
-            if isinstance(self, TextualInversionLoaderMixin):
-                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
-
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=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[:, self.tokenizer.model_max_length - 1 : -1]
-                )
-                logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-                )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = text_inputs.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            if clip_skip is None:
-                prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
-                prompt_embeds = prompt_embeds[0]
-            else:
-                prompt_embeds = self.text_encoder(
-                    text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
-                )
-                # Access the `hidden_states` first, that contains a tuple of
-                # all the hidden states from the encoder layers. Then index into
-                # the tuple to access the hidden states from the desired layer.
-                prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
-                # We also need to apply the final LayerNorm here to not mess with the
-                # representations. The `last_hidden_states` that we typically use for
-                # obtaining the final prompt representations passes through the LayerNorm
-                # layer.
-                prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
-
-        if self.text_encoder is not None:
-            prompt_embeds_dtype = self.text_encoder.dtype
-        elif self.unet is not None:
-            prompt_embeds_dtype = self.unet.dtype
-        else:
-            prompt_embeds_dtype = prompt_embeds.dtype
-
-        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
-
-        bs_embed, seq_len, _ = prompt_embeds.shape
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif 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 isinstance(negative_prompt, str):
-                uncond_tokens = [negative_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`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            # textual inversion: process multi-vector tokens if necessary
-            if isinstance(self, TextualInversionLoaderMixin):
-                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
-
-            max_length = prompt_embeds.shape[1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = uncond_input.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            negative_prompt_embeds = self.text_encoder(
-                uncond_input.input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            negative_prompt_embeds = negative_prompt_embeds[0]
-
-        if do_classifier_free_guidance:
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = negative_prompt_embeds.shape[1]
-
-            negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
-
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-        if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
-            # Retrieve the original scale by scaling back the LoRA layers
-            unscale_lora_layers(self.text_encoder, lora_scale)
-
-        return prompt_embeds, negative_prompt_embeds
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
-    def prepare_latents(
-        self,
-        batch_size: int,
-        num_channels_latents: int,
-        height: int,
-        width: int,
-        dtype: torch.dtype,
-        device: torch.device,
-        generator: Union[torch.Generator, List[torch.Generator]],
-        latents: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        r"""
-        Prepare the latent vectors for diffusion.
-
-        Args:
-            batch_size (int): The number of samples in the batch.
-            num_channels_latents (int): The number of channels in the latent vectors.
-            height (int): The height of the latent vectors.
-            width (int): The width of the latent vectors.
-            dtype (torch.dtype): The data type of the latent vectors.
-            device (torch.device): The device to place the latent vectors on.
-            generator (Union[torch.Generator, List[torch.Generator]]): The generator(s) to use for random number generation.
-            latents (Optional[torch.Tensor]): The pre-existing latent vectors. If None, new latent vectors will be generated.
-
-        Returns:
-            torch.Tensor: The prepared latent vectors.
-        """
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
-        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
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(
-        self, generator: Union[torch.Generator, List[torch.Generator]], eta: float
-    ) -> Dict[str, Any]:
-        r"""
-        Prepare extra keyword arguments for the scheduler step.
-
-        Args:
-            generator (Union[torch.Generator, List[torch.Generator]]): The generator used for sampling.
-            eta (float): The value of eta (η) used with the DDIMScheduler. Should be between 0 and 1.
-
-        Returns:
-            Dict[str, Any]: A dictionary containing the extra keyword arguments for the scheduler step.
-        """
-        # 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
-
     def prepare_image(
         self,
-        image: Union[torch.Tensor, PIL.Image.Image, List[Union[torch.Tensor, PIL.Image.Image]]],
-        width: int,
-        height: int,
-        batch_size: int,
-        num_images_per_prompt: int,
-        device: torch.device,
-        dtype: torch.dtype,
-        do_classifier_free_guidance: bool = False,
-        guess_mode: bool = False,
-    ) -> torch.Tensor:
-        r"""
-        Prepares the input image for processing.
-
-        Args:
-            image (torch.Tensor or PIL.Image.Image or list): The input image(s).
-            width (int): The desired width of the image.
-            height (int): The desired height of the image.
-            batch_size (int): The batch size for processing.
-            num_images_per_prompt (int): The number of images per prompt.
-            device (torch.device): The device to use for processing.
-            dtype (torch.dtype): The data type of the image.
-            do_classifier_free_guidance (bool, optional): Whether to perform classifier-free guidance. Defaults to False.
-            guess_mode (bool, optional): Whether to use guess mode. Defaults to False.
-
-        Returns:
-            torch.Tensor: The prepared image for processing.
-        """
+        image,
+        width,
+        height,
+        batch_size,
+        num_images_per_prompt,
+        device,
+        dtype,
+        do_classifier_free_guidance=False,
+        guess_mode=False,
+    ):
         if not isinstance(image, torch.Tensor):
             if isinstance(image, PIL.Image.Image):
                 image = [image]
@@ -725,29 +130,7 @@ class StableDiffusionReferencePipeline(
 
         return image
 
-    def prepare_ref_latents(
-        self,
-        refimage: torch.Tensor,
-        batch_size: int,
-        dtype: torch.dtype,
-        device: torch.device,
-        generator: Union[int, List[int]],
-        do_classifier_free_guidance: bool,
-    ) -> torch.Tensor:
-        r"""
-        Prepares reference latents for generating images.
-
-        Args:
-            refimage (torch.Tensor): The reference image.
-            batch_size (int): The desired batch size.
-            dtype (torch.dtype): The data type of the tensors.
-            device (torch.device): The device to perform computations on.
-            generator (int or list): The generator index or a list of generator indices.
-            do_classifier_free_guidance (bool): Whether to use classifier-free guidance.
-
-        Returns:
-            torch.Tensor: The prepared reference latents.
-        """
+    def prepare_ref_latents(self, refimage, batch_size, dtype, device, generator, do_classifier_free_guidance):
         refimage = refimage.to(device=device, dtype=dtype)
 
         # encode the mask image into latents space so we can concatenate it to the latents
@@ -775,35 +158,6 @@ class StableDiffusionReferencePipeline(
         ref_image_latents = ref_image_latents.to(device=device, dtype=dtype)
         return ref_image_latents
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
-    def run_safety_checker(
-        self, image: Union[torch.Tensor, PIL.Image.Image], device: torch.device, dtype: torch.dtype
-    ) -> Tuple[Union[torch.Tensor, PIL.Image.Image], Optional[bool]]:
-        r"""
-        Runs the safety checker on the given image.
-
-        Args:
-            image (Union[torch.Tensor, PIL.Image.Image]): The input image to be checked.
-            device (torch.device): The device to run the safety checker on.
-            dtype (torch.dtype): The data type of the input image.
-
-        Returns:
-            (image, has_nsfw_concept) Tuple[Union[torch.Tensor, PIL.Image.Image], Optional[bool]]: A tuple containing the processed image and
-            a boolean indicating whether the image has a NSFW (Not Safe for Work) concept.
-        """
-        if self.safety_checker is None:
-            has_nsfw_concept = None
-        else:
-            if torch.is_tensor(image):
-                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
-            else:
-                feature_extractor_input = self.image_processor.numpy_to_pil(image)
-            safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-            )
-        return image, has_nsfw_concept
-
     @torch.no_grad()
     def __call__(
         self,
@@ -1184,12 +538,7 @@ class StableDiffusionReferencePipeline(
 
             return hidden_states, output_states
 
-        def hacked_DownBlock2D_forward(
-            self,
-            hidden_states: torch.FloatTensor,
-            temb: Optional[torch.FloatTensor] = None,
-            **kwargs: Any,
-        ) -> Tuple[torch.FloatTensor, ...]:
+        def hacked_DownBlock2D_forward(self, hidden_states, temb=None, **kwargs):
             eps = 1e-6
 
             output_states = ()
@@ -1239,7 +588,7 @@ class StableDiffusionReferencePipeline(
             upsample_size: Optional[int] = None,
             attention_mask: Optional[torch.FloatTensor] = None,
             encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        ) -> torch.FloatTensor:
+        ):
             eps = 1e-6
             # TODO(Patrick, William) - attention mask is not used
             for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
@@ -1286,13 +635,8 @@ class StableDiffusionReferencePipeline(
             return hidden_states
 
         def hacked_UpBlock2D_forward(
-            self,
-            hidden_states: torch.FloatTensor,
-            res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
-            temb: Optional[torch.FloatTensor] = None,
-            upsample_size: Optional[int] = None,
-            **kwargs: Any,
-        ) -> torch.FloatTensor:
+            self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None, **kwargs
+        ):
             eps = 1e-6
             for i, resnet in enumerate(self.resnets):
                 # pop res hidden states

examples/community/stable_diffusion_tensorrt_inpaint.py

@@ -1011,7 +1011,7 @@ class TensorRTStableDiffusionInpaintPipeline(StableDiffusionInpaintPipeline):
         """
         self.generator = generator
         self.denoising_steps = num_inference_steps
-        self._guidance_scale = guidance_scale
+        self.guidance_scale = guidance_scale
 
         # Pre-compute latent input scales and linear multistep coefficients
         self.scheduler.set_timesteps(self.denoising_steps, device=self.torch_device)

examples/community/stable_diffusion_tensorrt_txt2img.py

@@ -882,7 +882,7 @@ class TensorRTStableDiffusionPipeline(StableDiffusionPipeline):
         """
         self.generator = generator
         self.denoising_steps = num_inference_steps
-        self._guidance_scale = guidance_scale
+        self.guidance_scale = guidance_scale
 
         # Pre-compute latent input scales and linear multistep coefficients
         self.scheduler.set_timesteps(self.denoising_steps, device=self.torch_device)

examples/dreambooth/train_dreambooth_lora.py

@@ -66,9 +66,6 @@ from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.torch_utils import is_compiled_module
 
 
-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("0.27.0.dev0")
 
@@ -116,71 +113,6 @@ LoRA for the text encoder was enabled: {train_text_encoder}.
     model_card.save(os.path.join(repo_folder, "README.md"))
 
 
-def log_validation(
-    pipeline,
-    args,
-    accelerator,
-    pipeline_args,
-    epoch,
-    is_final_validation=False,
-):
-    logger.info(
-        f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
-        f" {args.validation_prompt}."
-    )
-    # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
-    scheduler_args = {}
-
-    if "variance_type" in pipeline.scheduler.config:
-        variance_type = pipeline.scheduler.config.variance_type
-
-        if variance_type in ["learned", "learned_range"]:
-            variance_type = "fixed_small"
-
-        scheduler_args["variance_type"] = variance_type
-
-    pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args)
-
-    pipeline = pipeline.to(accelerator.device)
-    pipeline.set_progress_bar_config(disable=True)
-
-    # run inference
-    generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
-
-    if args.validation_images is None:
-        images = []
-        for _ in range(args.num_validation_images):
-            with torch.cuda.amp.autocast():
-                image = pipeline(**pipeline_args, generator=generator).images[0]
-                images.append(image)
-    else:
-        images = []
-        for image in args.validation_images:
-            image = Image.open(image)
-            with torch.cuda.amp.autocast():
-                image = pipeline(**pipeline_args, image=image, generator=generator).images[0]
-            images.append(image)
-
-    for tracker in accelerator.trackers:
-        phase_name = "test" if is_final_validation else "validation"
-        if tracker.name == "tensorboard":
-            np_images = np.stack([np.asarray(img) for img in images])
-            tracker.writer.add_images(phase_name, np_images, epoch, dataformats="NHWC")
-        if tracker.name == "wandb":
-            tracker.log(
-                {
-                    phase_name: [
-                        wandb.Image(image, caption=f"{i}: {args.validation_prompt}") for i, image in enumerate(images)
-                    ]
-                }
-            )
-
-    del pipeline
-    torch.cuda.empty_cache()
-
-    return images
-
-
 def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
     text_encoder_config = PretrainedConfig.from_pretrained(
         pretrained_model_name_or_path,
@@ -752,6 +684,7 @@ def main(args):
     if args.report_to == "wandb":
         if not is_wandb_available():
             raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
+        import wandb
 
     # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
     # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
@@ -1332,6 +1265,10 @@ def main(args):
 
         if accelerator.is_main_process:
             if args.validation_prompt is not None and epoch % args.validation_epochs == 0:
+                logger.info(
+                    f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
+                    f" {args.validation_prompt}."
+                )
                 # create pipeline
                 pipeline = DiffusionPipeline.from_pretrained(
                     args.pretrained_model_name_or_path,
@@ -1342,6 +1279,26 @@ def main(args):
                     torch_dtype=weight_dtype,
                 )
 
+                # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
+                scheduler_args = {}
+
+                if "variance_type" in pipeline.scheduler.config:
+                    variance_type = pipeline.scheduler.config.variance_type
+
+                    if variance_type in ["learned", "learned_range"]:
+                        variance_type = "fixed_small"
+
+                    scheduler_args["variance_type"] = variance_type
+
+                pipeline.scheduler = DPMSolverMultistepScheduler.from_config(
+                    pipeline.scheduler.config, **scheduler_args
+                )
+
+                pipeline = pipeline.to(accelerator.device)
+                pipeline.set_progress_bar_config(disable=True)
+
+                # run inference
+                generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
                 if args.pre_compute_text_embeddings:
                     pipeline_args = {
                         "prompt_embeds": validation_prompt_encoder_hidden_states,
@@ -1350,13 +1307,36 @@ def main(args):
                 else:
                     pipeline_args = {"prompt": args.validation_prompt}
 
-                images = log_validation(
-                    pipeline,
-                    args,
-                    accelerator,
-                    pipeline_args,
-                    epoch,
-                )
+                if args.validation_images is None:
+                    images = []
+                    for _ in range(args.num_validation_images):
+                        with torch.cuda.amp.autocast():
+                            image = pipeline(**pipeline_args, generator=generator).images[0]
+                            images.append(image)
+                else:
+                    images = []
+                    for image in args.validation_images:
+                        image = Image.open(image)
+                        with torch.cuda.amp.autocast():
+                            image = pipeline(**pipeline_args, image=image, generator=generator).images[0]
+                        images.append(image)
+
+                for tracker in accelerator.trackers:
+                    if tracker.name == "tensorboard":
+                        np_images = np.stack([np.asarray(img) for img in images])
+                        tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
+                    if tracker.name == "wandb":
+                        tracker.log(
+                            {
+                                "validation": [
+                                    wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
+                                    for i, image in enumerate(images)
+                                ]
+                            }
+                        )
+
+                del pipeline
+                torch.cuda.empty_cache()
 
     # Save the lora layers
     accelerator.wait_for_everyone()
@@ -1384,21 +1364,46 @@ def main(args):
             args.pretrained_model_name_or_path, revision=args.revision, variant=args.variant, torch_dtype=weight_dtype
         )
 
+        # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
+        scheduler_args = {}
+
+        if "variance_type" in pipeline.scheduler.config:
+            variance_type = pipeline.scheduler.config.variance_type
+
+            if variance_type in ["learned", "learned_range"]:
+                variance_type = "fixed_small"
+
+            scheduler_args["variance_type"] = variance_type
+
+        pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args)
+
+        pipeline = pipeline.to(accelerator.device)
+
         # load attention processors
         pipeline.load_lora_weights(args.output_dir, weight_name="pytorch_lora_weights.safetensors")
 
         # run inference
         images = []
         if args.validation_prompt and args.num_validation_images > 0:
-            pipeline_args = {"prompt": args.validation_prompt, "num_inference_steps": 25}
-            images = log_validation(
-                pipeline,
-                args,
-                accelerator,
-                pipeline_args,
-                epoch,
-                is_final_validation=True,
-            )
+            generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
+            images = [
+                pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
+                for _ in range(args.num_validation_images)
+            ]
+
+            for tracker in accelerator.trackers:
+                if tracker.name == "tensorboard":
+                    np_images = np.stack([np.asarray(img) for img in images])
+                    tracker.writer.add_images("test", np_images, epoch, dataformats="NHWC")
+                if tracker.name == "wandb":
+                    tracker.log(
+                        {
+                            "test": [
+                                wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
+                                for i, image in enumerate(images)
+                            ]
+                        }
+                    )
 
         if args.push_to_hub:
             save_model_card(

examples/dreambooth/train_dreambooth_lora_sdxl.py

@@ -67,9 +67,6 @@ from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.torch_utils import is_compiled_module
 
 
-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("0.27.0.dev0")
 
@@ -143,61 +140,6 @@ Weights for this model are available in Safetensors format.
     model_card.save(os.path.join(repo_folder, "README.md"))
 
 
-def log_validation(
-    pipeline,
-    args,
-    accelerator,
-    pipeline_args,
-    epoch,
-    is_final_validation=False,
-):
-    logger.info(
-        f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
-        f" {args.validation_prompt}."
-    )
-
-    # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
-    scheduler_args = {}
-
-    if "variance_type" in pipeline.scheduler.config:
-        variance_type = pipeline.scheduler.config.variance_type
-
-        if variance_type in ["learned", "learned_range"]:
-            variance_type = "fixed_small"
-
-        scheduler_args["variance_type"] = variance_type
-
-    pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args)
-
-    pipeline = pipeline.to(accelerator.device)
-    pipeline.set_progress_bar_config(disable=True)
-
-    # run inference
-    generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
-
-    with torch.cuda.amp.autocast():
-        images = [pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images)]
-
-    for tracker in accelerator.trackers:
-        phase_name = "test" if is_final_validation else "validation"
-        if tracker.name == "tensorboard":
-            np_images = np.stack([np.asarray(img) for img in images])
-            tracker.writer.add_images(phase_name, np_images, epoch, dataformats="NHWC")
-        if tracker.name == "wandb":
-            tracker.log(
-                {
-                    phase_name: [
-                        wandb.Image(image, caption=f"{i}: {args.validation_prompt}") for i, image in enumerate(images)
-                    ]
-                }
-            )
-
-    del pipeline
-    torch.cuda.empty_cache()
-
-    return images
-
-
 def import_model_class_from_model_name_or_path(
     pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
 ):
@@ -920,6 +862,7 @@ def main(args):
     if args.report_to == "wandb":
         if not is_wandb_available():
             raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
+        import wandb
 
     # Make one log on every process with the configuration for debugging.
     logging.basicConfig(
@@ -1672,6 +1615,10 @@ def main(args):
 
         if accelerator.is_main_process:
             if args.validation_prompt is not None and epoch % args.validation_epochs == 0:
+                logger.info(
+                    f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
+                    f" {args.validation_prompt}."
+                )
                 # create pipeline
                 if not args.train_text_encoder:
                     text_encoder_one = text_encoder_cls_one.from_pretrained(
@@ -1697,15 +1644,50 @@ def main(args):
                     torch_dtype=weight_dtype,
                 )
 
+                # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
+                scheduler_args = {}
+
+                if "variance_type" in pipeline.scheduler.config:
+                    variance_type = pipeline.scheduler.config.variance_type
+
+                    if variance_type in ["learned", "learned_range"]:
+                        variance_type = "fixed_small"
+
+                    scheduler_args["variance_type"] = variance_type
+
+                pipeline.scheduler = DPMSolverMultistepScheduler.from_config(
+                    pipeline.scheduler.config, **scheduler_args
+                )
+
+                pipeline = pipeline.to(accelerator.device)
+                pipeline.set_progress_bar_config(disable=True)
+
+                # run inference
+                generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
                 pipeline_args = {"prompt": args.validation_prompt}
 
-                images = log_validation(
-                    pipeline,
-                    args,
-                    accelerator,
-                    pipeline_args,
-                    epoch,
-                )
+                with torch.cuda.amp.autocast():
+                    images = [
+                        pipeline(**pipeline_args, generator=generator).images[0]
+                        for _ in range(args.num_validation_images)
+                    ]
+
+                for tracker in accelerator.trackers:
+                    if tracker.name == "tensorboard":
+                        np_images = np.stack([np.asarray(img) for img in images])
+                        tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
+                    if tracker.name == "wandb":
+                        tracker.log(
+                            {
+                                "validation": [
+                                    wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
+                                    for i, image in enumerate(images)
+                                ]
+                            }
+                        )
+
+                del pipeline
+                torch.cuda.empty_cache()
 
     # Save the lora layers
     accelerator.wait_for_everyone()
@@ -1751,21 +1733,45 @@ def main(args):
             torch_dtype=weight_dtype,
         )
 
+        # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
+        scheduler_args = {}
+
+        if "variance_type" in pipeline.scheduler.config:
+            variance_type = pipeline.scheduler.config.variance_type
+
+            if variance_type in ["learned", "learned_range"]:
+                variance_type = "fixed_small"
+
+            scheduler_args["variance_type"] = variance_type
+
+        pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args)
+
         # load attention processors
         pipeline.load_lora_weights(args.output_dir)
 
         # run inference
         images = []
         if args.validation_prompt and args.num_validation_images > 0:
-            pipeline_args = {"prompt": args.validation_prompt, "num_inference_steps": 25}
-            images = log_validation(
-                pipeline,
-                args,
-                accelerator,
-                pipeline_args,
-                epoch,
-                final_validation=True,
-            )
+            pipeline = pipeline.to(accelerator.device)
+            generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
+            images = [
+                pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
+                for _ in range(args.num_validation_images)
+            ]
+
+            for tracker in accelerator.trackers:
+                if tracker.name == "tensorboard":
+                    np_images = np.stack([np.asarray(img) for img in images])
+                    tracker.writer.add_images("test", np_images, epoch, dataformats="NHWC")
+                if tracker.name == "wandb":
+                    tracker.log(
+                        {
+                            "test": [
+                                wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
+                                for i, image in enumerate(images)
+                            ]
+                        }
+                    )
 
         if args.push_to_hub:
             save_model_card(

examples/text_to_image/README.md

@@ -4,7 +4,7 @@ The `train_text_to_image.py` script shows how to fine-tune stable diffusion mode
 
 ___Note___:
 
-___This script is experimental. The script fine-tunes the whole model and often times the model overfits and runs into issues like catastrophic forgetting. It's recommended to try different hyperparameters to get the best result on your dataset.___
+___This script is experimental. The script fine-tunes the whole model and often times the model overfits and runs into issues like catastrophic forgetting. It's recommended to try different hyperparamters to get the best result on your dataset.___
 
 
 ## Running locally with PyTorch

examples/text_to_image/README_sdxl.md

@@ -2,7 +2,7 @@
 
 The `train_text_to_image_sdxl.py` script shows how to fine-tune Stable Diffusion XL (SDXL) on your own dataset.
 
-🚨 This script is experimental. The script fine-tunes the whole model and often times the model overfits and runs into issues like catastrophic forgetting. It's recommended to try different hyperparameters to get the best result on your dataset. 🚨
+🚨 This script is experimental. The script fine-tunes the whole model and often times the model overfits and runs into issues like catastrophic forgetting. It's recommended to try different hyperparamters to get the best result on your dataset. 🚨
 
 ## Running locally with PyTorch
 
@@ -238,8 +238,8 @@ accelerate launch  --config_file $ACCELERATE_CONFIG_FILE train_text_to_image_lor
   --validation_epochs=20 \
   --seed=1234 \
   --output_dir="sd-pokemon-model-lora-sdxl" \
-  --validation_prompt="cute dragon creature"
-
+  --validation_prompt="cute dragon creature" 
+  
 ```
 
 

examples/text_to_image/test_text_to_image.py

@@ -1,6 +1,5 @@
-#!/usr/bin/env python
 # coding=utf-8
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+# 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.

examples/text_to_image/test_text_to_image_lora.py

@@ -1,6 +1,5 @@
-#!/usr/bin/env python
 # coding=utf-8
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+# 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.

examples/text_to_image/train_text_to_image.py

@@ -12,7 +12,6 @@
 # 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 argparse
 import logging
@@ -396,7 +395,7 @@ def parse_args():
         "--prediction_type",
         type=str,
         default=None,
-        help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediction_type` is chosen.",
+        help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediciton_type` is chosen.",
     )
     parser.add_argument(
         "--hub_model_id",
@@ -636,7 +635,7 @@ def main():
                 ema_unet.to(accelerator.device)
                 del load_model
 
-            for _ in range(len(models)):
+            for i in range(len(models)):
                 # pop models so that they are not loaded again
                 model = models.pop()
 
@@ -811,7 +810,7 @@ def main():
     if args.use_ema:
         ema_unet.to(accelerator.device)
 
-    # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision
+    # For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) to half-precision
     # as these weights are only used for inference, keeping weights in full precision is not required.
     weight_dtype = torch.float32
     if accelerator.mixed_precision == "fp16":

examples/text_to_image/train_text_to_image_flax.py

@@ -1,19 +1,3 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2024 The HuggingFace Inc. 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 argparse
 import logging
 import math

examples/text_to_image/train_text_to_image_lora.py

@@ -1,4 +1,3 @@
-#!/usr/bin/env python
 # coding=utf-8
 # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
 #
@@ -58,17 +57,12 @@ logger = get_logger(__name__, log_level="INFO")
 
 
 def save_model_card(
-    repo_id: str,
-    images: list = None,
-    base_model: str = None,
-    dataset_name: str = None,
-    repo_folder: str = None,
+    repo_id: str, images: list = None, base_model: str = None, dataset_name: str = None, repo_folder: str = None
 ):
     img_str = ""
-    if images is not None:
-        for i, image in enumerate(images):
-            image.save(os.path.join(repo_folder, f"image_{i}.png"))
-            img_str += f"![img_{i}](./image_{i}.png)\n"
+    for i, image in enumerate(images):
+        image.save(os.path.join(repo_folder, f"image_{i}.png"))
+        img_str += f"![img_{i}](./image_{i}.png)\n"
 
     model_description = f"""
 # LoRA text2image fine-tuning - {repo_id}
@@ -299,7 +293,7 @@ def parse_args():
         "--prediction_type",
         type=str,
         default=None,
-        help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediction_type` is chosen.",
+        help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediciton_type` is chosen.",
     )
     parser.add_argument(
         "--hub_model_id",
@@ -460,7 +454,7 @@ def main():
     vae.requires_grad_(False)
     text_encoder.requires_grad_(False)
 
-    # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision
+    # For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) to half-precision
     # as these weights are only used for inference, keeping weights in full precision is not required.
     weight_dtype = torch.float32
     if accelerator.mixed_precision == "fp16":

examples/text_to_image/train_text_to_image_lora_sdxl.py

@@ -370,7 +370,7 @@ def parse_args(input_args=None):
         "--prediction_type",
         type=str,
         default=None,
-        help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediction_type` is chosen.",
+        help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediciton_type` is chosen.",
     )
     parser.add_argument(
         "--hub_model_id",
@@ -585,7 +585,7 @@ def main(args):
     text_encoder_two.requires_grad_(False)
     unet.requires_grad_(False)
 
-    # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision
+    # For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) to half-precision
     # as these weights are only used for inference, keeping weights in full precision is not required.
     weight_dtype = torch.float32
     if accelerator.mixed_precision == "fp16":
@@ -648,7 +648,7 @@ def main(args):
     def save_model_hook(models, weights, output_dir):
         if accelerator.is_main_process:
             # there are only two options here. Either are just the unet attn processor layers
-            # or there are the unet and text encoder attn layers
+            # or there are the unet and text encoder atten layers
             unet_lora_layers_to_save = None
             text_encoder_one_lora_layers_to_save = None
             text_encoder_two_lora_layers_to_save = None

examples/text_to_image/train_text_to_image_sdxl.py

@@ -74,10 +74,9 @@ def save_model_card(
     vae_path: str = None,
 ):
     img_str = ""
-    if images is not None:
-        for i, image in enumerate(images):
-            image.save(os.path.join(repo_folder, f"image_{i}.png"))
-            img_str += f"![img_{i}](./image_{i}.png)\n"
+    for i, image in enumerate(images):
+        image.save(os.path.join(repo_folder, f"image_{i}.png"))
+        img_str += f"![img_{i}](./image_{i}.png)\n"
 
     model_description = f"""
 # Text-to-image finetuning - {repo_id}
@@ -420,7 +419,7 @@ def parse_args(input_args=None):
         "--prediction_type",
         type=str,
         default=None,
-        help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediction_type` is chosen.",
+        help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediciton_type` is chosen.",
     )
     parser.add_argument(
         "--hub_model_id",
@@ -684,7 +683,7 @@ def main(args):
     # Set unet as trainable.
     unet.train()
 
-    # For mixed precision training we cast all non-trainable weights to half-precision
+    # For mixed precision training we cast all non-trainable weigths to half-precision
     # as these weights are only used for inference, keeping weights in full precision is not required.
     weight_dtype = torch.float32
     if accelerator.mixed_precision == "fp16":
@@ -739,7 +738,7 @@ def main(args):
                 ema_unet.to(accelerator.device)
                 del load_model
 
-            for _ in range(len(models)):
+            for i in range(len(models)):
                 # pop models so that they are not loaded again
                 model = models.pop()
 
@@ -963,7 +962,7 @@ def main(args):
     if accelerator.is_main_process:
         accelerator.init_trackers("text2image-fine-tune-sdxl", config=vars(args))
 
-    # Function for unwrapping if torch.compile() was used in accelerate.
+    # Function for unwraping if torch.compile() was used in accelerate.
     def unwrap_model(model):
         model = accelerator.unwrap_model(model)
         model = model._orig_mod if is_compiled_module(model) else model

src/diffusers/loaders/autoencoder.py

@@ -75,6 +75,10 @@ class FromOriginalVAEMixin:
                 diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z
                 = 1 / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution
                 Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
+            use_safetensors (`bool`, *optional*, defaults to `None`):
+                If set to `None`, the safetensors weights are downloaded if they're available **and** if the
+                safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
+                weights. If set to `False`, safetensors weights are not loaded.
             kwargs (remaining dictionary of keyword arguments, *optional*):
                 Can be used to overwrite load and saveable variables (for example the pipeline components of the
                 specific pipeline class). The overwritten components are directly passed to the pipelines `__init__`
@@ -107,6 +111,7 @@ class FromOriginalVAEMixin:
         local_files_only = kwargs.pop("local_files_only", None)
         revision = kwargs.pop("revision", None)
         torch_dtype = kwargs.pop("torch_dtype", None)
+        use_safetensors = kwargs.pop("use_safetensors", True)
 
         class_name = cls.__name__
 
@@ -126,6 +131,7 @@ class FromOriginalVAEMixin:
             token=token,
             revision=revision,
             local_files_only=local_files_only,
+            use_safetensors=use_safetensors,
             cache_dir=cache_dir,
         )
 

src/diffusers/loaders/controlnet.py

@@ -65,6 +65,10 @@ class FromOriginalControlNetMixin:
             revision (`str`, *optional*, defaults to `"main"`):
                 The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
                 allowed by Git.
+            use_safetensors (`bool`, *optional*, defaults to `None`):
+                If set to `None`, the safetensors weights are downloaded if they're available **and** if the
+                safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
+                weights. If set to `False`, safetensors weights are not loaded.
             image_size (`int`, *optional*, defaults to 512):
                 The image size the model was trained on. Use 512 for all Stable Diffusion v1 models and the Stable
                 Diffusion v2 base model. Use 768 for Stable Diffusion v2.
@@ -97,6 +101,7 @@ class FromOriginalControlNetMixin:
         local_files_only = kwargs.pop("local_files_only", None)
         revision = kwargs.pop("revision", None)
         torch_dtype = kwargs.pop("torch_dtype", None)
+        use_safetensors = kwargs.pop("use_safetensors", True)
 
         class_name = cls.__name__
         if (config_file is not None) and (original_config_file is not None):
@@ -115,6 +120,7 @@ class FromOriginalControlNetMixin:
             token=token,
             revision=revision,
             local_files_only=local_files_only,
+            use_safetensors=use_safetensors,
             cache_dir=cache_dir,
         )
 

src/diffusers/loaders/lora.py

@@ -1192,7 +1192,7 @@ class LoraLoaderMixin:
 class StableDiffusionXLLoraLoaderMixin(LoraLoaderMixin):
     """This class overrides `LoraLoaderMixin` with LoRA loading/saving code that's specific to SDXL"""
 
-    # Override to properly handle the loading and unloading of the additional text encoder.
+    # Overrride to properly handle the loading and unloading of the additional text encoder.
     def load_lora_weights(
         self,
         pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],

src/diffusers/loaders/single_file.py

@@ -181,6 +181,10 @@ class FromSingleFileMixin:
             revision (`str`, *optional*, defaults to `"main"`):
                 The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
                 allowed by Git.
+            use_safetensors (`bool`, *optional*, defaults to `None`):
+                If set to `None`, the safetensors weights are downloaded if they're available **and** if the
+                safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
+                weights. If set to `False`, safetensors weights are not loaded.
         Examples:
 
         ```py
@@ -212,6 +216,7 @@ class FromSingleFileMixin:
         local_files_only = kwargs.pop("local_files_only", False)
         revision = kwargs.pop("revision", None)
         torch_dtype = kwargs.pop("torch_dtype", None)
+        use_safetensors = kwargs.pop("use_safetensors", True)
 
         class_name = cls.__name__
 
@@ -225,6 +230,7 @@ class FromSingleFileMixin:
             token=token,
             revision=revision,
             local_files_only=local_files_only,
+            use_safetensors=use_safetensors,
             cache_dir=cache_dir,
         )
 

src/diffusers/loaders/single_file_utils.py

@@ -227,7 +227,14 @@ def fetch_ldm_config_and_checkpoint(
     cache_dir=None,
     local_files_only=None,
     revision=None,
+    use_safetensors=True,
 ):
+    file_extension = pretrained_model_link_or_path.rsplit(".", 1)[-1]
+    from_safetensors = file_extension == "safetensors"
+
+    if from_safetensors and use_safetensors is False:
+        raise ValueError("Make sure to install `safetensors` with `pip install safetensors`.")
+
     if os.path.isfile(pretrained_model_link_or_path):
         checkpoint = load_state_dict(pretrained_model_link_or_path)
 
@@ -869,7 +876,7 @@ def create_diffusers_controlnet_model_from_ldm(
         from ..models.modeling_utils import load_model_dict_into_meta
 
         unexpected_keys = load_model_dict_into_meta(
-            controlnet, diffusers_format_controlnet_checkpoint, dtype=torch_dtype
+            controlnet, diffusers_format_controlnet_checkpoint, torch_dtype=torch_dtype
         )
         if controlnet._keys_to_ignore_on_load_unexpected is not None:
             for pat in controlnet._keys_to_ignore_on_load_unexpected:

src/diffusers/loaders/textual_inversion.py

@@ -215,7 +215,7 @@ class TextualInversionLoaderMixin:
                 embedding = state_dict["string_to_param"]["*"]
             else:
                 raise ValueError(
-                    f"Loaded state dictionary is incorrect: {state_dict}. \n\n"
+                    f"Loaded state dictonary is incorrect: {state_dict}. \n\n"
                     "Please verify that the loaded state dictionary of the textual embedding either only has a single key or includes the `string_to_param`"
                     " input key."
                 )
@@ -457,8 +457,6 @@ class TextualInversionLoaderMixin:
     def unload_textual_inversion(
         self,
         tokens: Optional[Union[str, List[str]]] = None,
-        tokenizer: Optional["PreTrainedTokenizer"] = None,
-        text_encoder: Optional["PreTrainedModel"] = None,
     ):
         r"""
         Unload Textual Inversion embeddings from the text encoder of [`StableDiffusionPipeline`]
@@ -483,28 +481,11 @@ class TextualInversionLoaderMixin:
 
         # Remove just one token
         pipeline.unload_textual_inversion("<moe-bius>")
-
-        # Example 3: unload from SDXL
-        pipeline = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
-        embedding_path = hf_hub_download(repo_id="linoyts/web_y2k", filename="web_y2k_emb.safetensors", repo_type="model")
-
-        # load embeddings to the text encoders
-        state_dict = load_file(embedding_path)
-
-        # load embeddings of text_encoder 1 (CLIP ViT-L/14)
-        pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
-        # load embeddings of text_encoder 2 (CLIP ViT-G/14)
-        pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
-
-        # Unload explicitly from both text encoders abd tokenizers
-        pipeline.unload_textual_inversion(tokens=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
-        pipeline.unload_textual_inversion(tokens=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
-
         ```
         """
 
-        tokenizer = tokenizer or getattr(self, "tokenizer", None)
-        text_encoder = text_encoder or getattr(self, "text_encoder", None)
+        tokenizer = getattr(self, "tokenizer", None)
+        text_encoder = getattr(self, "text_encoder", None)
 
         # Get textual inversion tokens and ids
         token_ids = []

src/diffusers/models/attention_processor.py

@@ -559,16 +559,12 @@ class Attention(nn.Module):
             `torch.Tensor`: The reshaped tensor.
         """
         head_size = self.heads
-        if tensor.ndim == 3:
-            batch_size, seq_len, dim = tensor.shape
-            extra_dim = 1
-        else:
-            batch_size, extra_dim, seq_len, dim = tensor.shape
-        tensor = tensor.reshape(batch_size, seq_len * extra_dim, head_size, dim // head_size)
+        batch_size, seq_len, dim = tensor.shape
+        tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
         tensor = tensor.permute(0, 2, 1, 3)
 
         if out_dim == 3:
-            tensor = tensor.reshape(batch_size * head_size, seq_len * extra_dim, dim // head_size)
+            tensor = tensor.reshape(batch_size * head_size, seq_len, dim // head_size)
 
         return tensor
 

src/diffusers/models/unets/unet_2d_blocks.py

@@ -249,81 +249,6 @@ def get_down_block(
     raise ValueError(f"{down_block_type} does not exist.")
 
 
-def get_mid_block(
-    mid_block_type: str,
-    temb_channels: int,
-    in_channels: int,
-    resnet_eps: float,
-    resnet_act_fn: str,
-    resnet_groups: int,
-    output_scale_factor: float = 1.0,
-    transformer_layers_per_block: int = 1,
-    num_attention_heads: Optional[int] = None,
-    cross_attention_dim: Optional[int] = None,
-    dual_cross_attention: bool = False,
-    use_linear_projection: bool = False,
-    mid_block_only_cross_attention: bool = False,
-    upcast_attention: bool = False,
-    resnet_time_scale_shift: str = "default",
-    attention_type: str = "default",
-    resnet_skip_time_act: bool = False,
-    cross_attention_norm: Optional[str] = None,
-    attention_head_dim: Optional[int] = 1,
-    dropout: float = 0.0,
-):
-    if mid_block_type == "UNetMidBlock2DCrossAttn":
-        return UNetMidBlock2DCrossAttn(
-            transformer_layers_per_block=transformer_layers_per_block,
-            in_channels=in_channels,
-            temb_channels=temb_channels,
-            dropout=dropout,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            output_scale_factor=output_scale_factor,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            cross_attention_dim=cross_attention_dim,
-            num_attention_heads=num_attention_heads,
-            resnet_groups=resnet_groups,
-            dual_cross_attention=dual_cross_attention,
-            use_linear_projection=use_linear_projection,
-            upcast_attention=upcast_attention,
-            attention_type=attention_type,
-        )
-    elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn":
-        return UNetMidBlock2DSimpleCrossAttn(
-            in_channels=in_channels,
-            temb_channels=temb_channels,
-            dropout=dropout,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            output_scale_factor=output_scale_factor,
-            cross_attention_dim=cross_attention_dim,
-            attention_head_dim=attention_head_dim,
-            resnet_groups=resnet_groups,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            skip_time_act=resnet_skip_time_act,
-            only_cross_attention=mid_block_only_cross_attention,
-            cross_attention_norm=cross_attention_norm,
-        )
-    elif mid_block_type == "UNetMidBlock2D":
-        return UNetMidBlock2D(
-            in_channels=in_channels,
-            temb_channels=temb_channels,
-            dropout=dropout,
-            num_layers=0,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            output_scale_factor=output_scale_factor,
-            resnet_groups=resnet_groups,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            add_attention=False,
-        )
-    elif mid_block_type is None:
-        return None
-    else:
-        raise ValueError(f"unknown mid_block_type : {mid_block_type}")
-
-
 def get_up_block(
     up_block_type: str,
     num_layers: int,

src/diffusers/models/unets/unet_2d_condition.py

@@ -44,8 +44,10 @@ from ..embeddings import (
 )
 from ..modeling_utils import ModelMixin
 from .unet_2d_blocks import (
+    UNetMidBlock2D,
+    UNetMidBlock2DCrossAttn,
+    UNetMidBlock2DSimpleCrossAttn,
     get_down_block,
-    get_mid_block,
     get_up_block,
 )
 
@@ -237,18 +239,44 @@ class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin,
         num_attention_heads = num_attention_heads or attention_head_dim
 
         # Check inputs
-        self._check_config(
-            down_block_types=down_block_types,
-            up_block_types=up_block_types,
-            only_cross_attention=only_cross_attention,
-            block_out_channels=block_out_channels,
-            layers_per_block=layers_per_block,
-            cross_attention_dim=cross_attention_dim,
-            transformer_layers_per_block=transformer_layers_per_block,
-            reverse_transformer_layers_per_block=reverse_transformer_layers_per_block,
-            attention_head_dim=attention_head_dim,
-            num_attention_heads=num_attention_heads,
-        )
+        if len(down_block_types) != len(up_block_types):
+            raise ValueError(
+                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
+            )
+
+        if len(block_out_channels) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
+            )
+
+        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
+            )
+        if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None:
+            for layer_number_per_block in transformer_layers_per_block:
+                if isinstance(layer_number_per_block, list):
+                    raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.")
 
         # input
         conv_in_padding = (conv_in_kernel - 1) // 2
@@ -257,13 +285,23 @@ class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin,
         )
 
         # time
-        time_embed_dim, timestep_input_dim = self._set_time_proj(
-            time_embedding_type,
-            block_out_channels=block_out_channels,
-            flip_sin_to_cos=flip_sin_to_cos,
-            freq_shift=freq_shift,
-            time_embedding_dim=time_embedding_dim,
-        )
+        if time_embedding_type == "fourier":
+            time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
+            if time_embed_dim % 2 != 0:
+                raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
+            self.time_proj = GaussianFourierProjection(
+                time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
+            )
+            timestep_input_dim = time_embed_dim
+        elif time_embedding_type == "positional":
+            time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
+
+            self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
+            timestep_input_dim = block_out_channels[0]
+        else:
+            raise ValueError(
+                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
+            )
 
         self.time_embedding = TimestepEmbedding(
             timestep_input_dim,
@@ -273,33 +311,96 @@ class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin,
             cond_proj_dim=time_cond_proj_dim,
         )
 
-        self._set_encoder_hid_proj(
-            encoder_hid_dim_type,
-            cross_attention_dim=cross_attention_dim,
-            encoder_hid_dim=encoder_hid_dim,
-        )
+        if encoder_hid_dim_type is None and encoder_hid_dim is not None:
+            encoder_hid_dim_type = "text_proj"
+            self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
+            logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")
+
+        if encoder_hid_dim is None and encoder_hid_dim_type is not None:
+            raise ValueError(
+                f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
+            )
+
+        if encoder_hid_dim_type == "text_proj":
+            self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
+        elif encoder_hid_dim_type == "text_image_proj":
+            # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
+            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
+            # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
+            self.encoder_hid_proj = TextImageProjection(
+                text_embed_dim=encoder_hid_dim,
+                image_embed_dim=cross_attention_dim,
+                cross_attention_dim=cross_attention_dim,
+            )
+        elif encoder_hid_dim_type == "image_proj":
+            # Kandinsky 2.2
+            self.encoder_hid_proj = ImageProjection(
+                image_embed_dim=encoder_hid_dim,
+                cross_attention_dim=cross_attention_dim,
+            )
+        elif encoder_hid_dim_type is not None:
+            raise ValueError(
+                f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
+            )
+        else:
+            self.encoder_hid_proj = None
 
         # class embedding
-        self._set_class_embedding(
-            class_embed_type,
-            act_fn=act_fn,
-            num_class_embeds=num_class_embeds,
-            projection_class_embeddings_input_dim=projection_class_embeddings_input_dim,
-            time_embed_dim=time_embed_dim,
-            timestep_input_dim=timestep_input_dim,
-        )
+        if class_embed_type is None and num_class_embeds is not None:
+            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
+        elif class_embed_type == "timestep":
+            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
+        elif class_embed_type == "identity":
+            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
+        elif class_embed_type == "projection":
+            if projection_class_embeddings_input_dim is None:
+                raise ValueError(
+                    "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
+                )
+            # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
+            # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
+            # 2. it projects from an arbitrary input dimension.
+            #
+            # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
+            # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
+            # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
+            self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+        elif class_embed_type == "simple_projection":
+            if projection_class_embeddings_input_dim is None:
+                raise ValueError(
+                    "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
+                )
+            self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
+        else:
+            self.class_embedding = None
 
-        self._set_add_embedding(
-            addition_embed_type,
-            addition_embed_type_num_heads=addition_embed_type_num_heads,
-            addition_time_embed_dim=addition_time_embed_dim,
-            cross_attention_dim=cross_attention_dim,
-            encoder_hid_dim=encoder_hid_dim,
-            flip_sin_to_cos=flip_sin_to_cos,
-            freq_shift=freq_shift,
-            projection_class_embeddings_input_dim=projection_class_embeddings_input_dim,
-            time_embed_dim=time_embed_dim,
-        )
+        if addition_embed_type == "text":
+            if encoder_hid_dim is not None:
+                text_time_embedding_from_dim = encoder_hid_dim
+            else:
+                text_time_embedding_from_dim = cross_attention_dim
+
+            self.add_embedding = TextTimeEmbedding(
+                text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
+            )
+        elif addition_embed_type == "text_image":
+            # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
+            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
+            # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
+            self.add_embedding = TextImageTimeEmbedding(
+                text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
+            )
+        elif addition_embed_type == "text_time":
+            self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
+            self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+        elif addition_embed_type == "image":
+            # Kandinsky 2.2
+            self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
+        elif addition_embed_type == "image_hint":
+            # Kandinsky 2.2 ControlNet
+            self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
+        elif addition_embed_type is not None:
+            raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
 
         if time_embedding_act_fn is None:
             self.time_embed_act = None
@@ -377,28 +478,57 @@ class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin,
             self.down_blocks.append(down_block)
 
         # mid
-        self.mid_block = get_mid_block(
-            mid_block_type,
-            temb_channels=blocks_time_embed_dim,
-            in_channels=block_out_channels[-1],
-            resnet_eps=norm_eps,
-            resnet_act_fn=act_fn,
-            resnet_groups=norm_num_groups,
-            output_scale_factor=mid_block_scale_factor,
-            transformer_layers_per_block=transformer_layers_per_block[-1],
-            num_attention_heads=num_attention_heads[-1],
-            cross_attention_dim=cross_attention_dim[-1],
-            dual_cross_attention=dual_cross_attention,
-            use_linear_projection=use_linear_projection,
-            mid_block_only_cross_attention=mid_block_only_cross_attention,
-            upcast_attention=upcast_attention,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            attention_type=attention_type,
-            resnet_skip_time_act=resnet_skip_time_act,
-            cross_attention_norm=cross_attention_norm,
-            attention_head_dim=attention_head_dim[-1],
-            dropout=dropout,
-        )
+        if mid_block_type == "UNetMidBlock2DCrossAttn":
+            self.mid_block = UNetMidBlock2DCrossAttn(
+                transformer_layers_per_block=transformer_layers_per_block[-1],
+                in_channels=block_out_channels[-1],
+                temb_channels=blocks_time_embed_dim,
+                dropout=dropout,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                cross_attention_dim=cross_attention_dim[-1],
+                num_attention_heads=num_attention_heads[-1],
+                resnet_groups=norm_num_groups,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                upcast_attention=upcast_attention,
+                attention_type=attention_type,
+            )
+        elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn":
+            self.mid_block = UNetMidBlock2DSimpleCrossAttn(
+                in_channels=block_out_channels[-1],
+                temb_channels=blocks_time_embed_dim,
+                dropout=dropout,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                cross_attention_dim=cross_attention_dim[-1],
+                attention_head_dim=attention_head_dim[-1],
+                resnet_groups=norm_num_groups,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                skip_time_act=resnet_skip_time_act,
+                only_cross_attention=mid_block_only_cross_attention,
+                cross_attention_norm=cross_attention_norm,
+            )
+        elif mid_block_type == "UNetMidBlock2D":
+            self.mid_block = UNetMidBlock2D(
+                in_channels=block_out_channels[-1],
+                temb_channels=blocks_time_embed_dim,
+                dropout=dropout,
+                num_layers=0,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_groups=norm_num_groups,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                add_attention=False,
+            )
+        elif mid_block_type is None:
+            self.mid_block = None
+        else:
+            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
 
         # count how many layers upsample the images
         self.num_upsamplers = 0
@@ -477,206 +607,6 @@ class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin,
             block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
         )
 
-        self._set_pos_net_if_use_gligen(attention_type=attention_type, cross_attention_dim=cross_attention_dim)
-
-    def _check_config(
-        self,
-        down_block_types: Tuple[str],
-        up_block_types: Tuple[str],
-        only_cross_attention: Union[bool, Tuple[bool]],
-        block_out_channels: Tuple[int],
-        layers_per_block: [int, Tuple[int]],
-        cross_attention_dim: Union[int, Tuple[int]],
-        transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]],
-        reverse_transformer_layers_per_block: bool,
-        attention_head_dim: int,
-        num_attention_heads: Optional[Union[int, Tuple[int]]],
-    ):
-        if len(down_block_types) != len(up_block_types):
-            raise ValueError(
-                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
-            )
-
-        if len(block_out_channels) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
-            )
-
-        if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
-            )
-
-        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
-            )
-
-        if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
-            )
-
-        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
-            )
-
-        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
-            )
-        if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None:
-            for layer_number_per_block in transformer_layers_per_block:
-                if isinstance(layer_number_per_block, list):
-                    raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.")
-
-    def _set_time_proj(
-        self,
-        time_embedding_type: str,
-        block_out_channels: int,
-        flip_sin_to_cos: bool,
-        freq_shift: float,
-        time_embedding_dim: int,
-    ) -> Tuple[int, int]:
-        if time_embedding_type == "fourier":
-            time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
-            if time_embed_dim % 2 != 0:
-                raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
-            self.time_proj = GaussianFourierProjection(
-                time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
-            )
-            timestep_input_dim = time_embed_dim
-        elif time_embedding_type == "positional":
-            time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
-
-            self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
-            timestep_input_dim = block_out_channels[0]
-        else:
-            raise ValueError(
-                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
-            )
-
-        return time_embed_dim, timestep_input_dim
-
-    def _set_encoder_hid_proj(
-        self,
-        encoder_hid_dim_type: Optional[str],
-        cross_attention_dim: Union[int, Tuple[int]],
-        encoder_hid_dim: Optional[int],
-    ):
-        if encoder_hid_dim_type is None and encoder_hid_dim is not None:
-            encoder_hid_dim_type = "text_proj"
-            self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
-            logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")
-
-        if encoder_hid_dim is None and encoder_hid_dim_type is not None:
-            raise ValueError(
-                f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
-            )
-
-        if encoder_hid_dim_type == "text_proj":
-            self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
-        elif encoder_hid_dim_type == "text_image_proj":
-            # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
-            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
-            # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
-            self.encoder_hid_proj = TextImageProjection(
-                text_embed_dim=encoder_hid_dim,
-                image_embed_dim=cross_attention_dim,
-                cross_attention_dim=cross_attention_dim,
-            )
-        elif encoder_hid_dim_type == "image_proj":
-            # Kandinsky 2.2
-            self.encoder_hid_proj = ImageProjection(
-                image_embed_dim=encoder_hid_dim,
-                cross_attention_dim=cross_attention_dim,
-            )
-        elif encoder_hid_dim_type is not None:
-            raise ValueError(
-                f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
-            )
-        else:
-            self.encoder_hid_proj = None
-
-    def _set_class_embedding(
-        self,
-        class_embed_type: Optional[str],
-        act_fn: str,
-        num_class_embeds: Optional[int],
-        projection_class_embeddings_input_dim: Optional[int],
-        time_embed_dim: int,
-        timestep_input_dim: int,
-    ):
-        if class_embed_type is None and num_class_embeds is not None:
-            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
-        elif class_embed_type == "timestep":
-            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
-        elif class_embed_type == "identity":
-            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
-        elif class_embed_type == "projection":
-            if projection_class_embeddings_input_dim is None:
-                raise ValueError(
-                    "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
-                )
-            # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
-            # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
-            # 2. it projects from an arbitrary input dimension.
-            #
-            # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
-            # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
-            # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
-            self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
-        elif class_embed_type == "simple_projection":
-            if projection_class_embeddings_input_dim is None:
-                raise ValueError(
-                    "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
-                )
-            self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
-        else:
-            self.class_embedding = None
-
-    def _set_add_embedding(
-        self,
-        addition_embed_type: str,
-        addition_embed_type_num_heads: int,
-        addition_time_embed_dim: Optional[int],
-        flip_sin_to_cos: bool,
-        freq_shift: float,
-        cross_attention_dim: Optional[int],
-        encoder_hid_dim: Optional[int],
-        projection_class_embeddings_input_dim: Optional[int],
-        time_embed_dim: int,
-    ):
-        if addition_embed_type == "text":
-            if encoder_hid_dim is not None:
-                text_time_embedding_from_dim = encoder_hid_dim
-            else:
-                text_time_embedding_from_dim = cross_attention_dim
-
-            self.add_embedding = TextTimeEmbedding(
-                text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
-            )
-        elif addition_embed_type == "text_image":
-            # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
-            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
-            # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
-            self.add_embedding = TextImageTimeEmbedding(
-                text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
-            )
-        elif addition_embed_type == "text_time":
-            self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
-            self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
-        elif addition_embed_type == "image":
-            # Kandinsky 2.2
-            self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
-        elif addition_embed_type == "image_hint":
-            # Kandinsky 2.2 ControlNet
-            self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
-        elif addition_embed_type is not None:
-            raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
-
-    def _set_pos_net_if_use_gligen(self, attention_type: str, cross_attention_dim: int):
         if attention_type in ["gated", "gated-text-image"]:
             positive_len = 768
             if isinstance(cross_attention_dim, int):
@@ -910,130 +840,6 @@ class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin,
             if hasattr(module, "set_lora_layer"):
                 module.set_lora_layer(None)
 
-    def get_time_embed(
-        self, sample: torch.Tensor, timestep: Union[torch.Tensor, float, int]
-    ) -> Optional[torch.Tensor]:
-        timesteps = timestep
-        if not torch.is_tensor(timesteps):
-            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
-            # This would be a good case for the `match` statement (Python 3.10+)
-            is_mps = sample.device.type == "mps"
-            if isinstance(timestep, float):
-                dtype = torch.float32 if is_mps else torch.float64
-            else:
-                dtype = torch.int32 if is_mps else torch.int64
-            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
-        elif len(timesteps.shape) == 0:
-            timesteps = timesteps[None].to(sample.device)
-
-        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-        timesteps = timesteps.expand(sample.shape[0])
-
-        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=sample.dtype)
-        return t_emb
-
-    def get_class_embed(self, sample: torch.Tensor, class_labels: Optional[torch.Tensor]) -> Optional[torch.Tensor]:
-        class_emb = None
-        if self.class_embedding is not None:
-            if class_labels is None:
-                raise ValueError("class_labels should be provided when num_class_embeds > 0")
-
-            if self.config.class_embed_type == "timestep":
-                class_labels = self.time_proj(class_labels)
-
-                # `Timesteps` does not contain any weights and will always return f32 tensors
-                # there might be better ways to encapsulate this.
-                class_labels = class_labels.to(dtype=sample.dtype)
-
-            class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
-        return class_emb
-
-    def get_aug_embed(
-        self, emb: torch.Tensor, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict
-    ) -> Optional[torch.Tensor]:
-        aug_emb = None
-        if self.config.addition_embed_type == "text":
-            aug_emb = self.add_embedding(encoder_hidden_states)
-        elif self.config.addition_embed_type == "text_image":
-            # Kandinsky 2.1 - style
-            if "image_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
-                )
-
-            image_embs = added_cond_kwargs.get("image_embeds")
-            text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
-            aug_emb = self.add_embedding(text_embs, image_embs)
-        elif self.config.addition_embed_type == "text_time":
-            # SDXL - style
-            if "text_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
-                )
-            text_embeds = added_cond_kwargs.get("text_embeds")
-            if "time_ids" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
-                )
-            time_ids = added_cond_kwargs.get("time_ids")
-            time_embeds = self.add_time_proj(time_ids.flatten())
-            time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
-            add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
-            add_embeds = add_embeds.to(emb.dtype)
-            aug_emb = self.add_embedding(add_embeds)
-        elif self.config.addition_embed_type == "image":
-            # Kandinsky 2.2 - style
-            if "image_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
-                )
-            image_embs = added_cond_kwargs.get("image_embeds")
-            aug_emb = self.add_embedding(image_embs)
-        elif self.config.addition_embed_type == "image_hint":
-            # Kandinsky 2.2 - style
-            if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`"
-                )
-            image_embs = added_cond_kwargs.get("image_embeds")
-            hint = added_cond_kwargs.get("hint")
-            aug_emb = self.add_embedding(image_embs, hint)
-        return aug_emb
-
-    def process_encoder_hidden_states(self, encoder_hidden_states: torch.Tensor, added_cond_kwargs) -> torch.Tensor:
-        if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
-            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
-        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
-            # Kadinsky 2.1 - style
-            if "image_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
-                )
-
-            image_embeds = added_cond_kwargs.get("image_embeds")
-            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
-        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj":
-            # Kandinsky 2.2 - style
-            if "image_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
-                )
-            image_embeds = added_cond_kwargs.get("image_embeds")
-            encoder_hidden_states = self.encoder_hid_proj(image_embeds)
-        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj":
-            if "image_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
-                )
-            image_embeds = added_cond_kwargs.get("image_embeds")
-            image_embeds = self.encoder_hid_proj(image_embeds)
-            encoder_hidden_states = (encoder_hidden_states, image_embeds)
-        return encoder_hidden_states
-
     def forward(
         self,
         sample: torch.FloatTensor,
@@ -1146,22 +952,96 @@ class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin,
             sample = 2 * sample - 1.0
 
         # 1. time
-        t_emb = self.get_time_embed(sample=sample, timestep=timestep)
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        timesteps = timesteps.expand(sample.shape[0])
+
+        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=sample.dtype)
+
         emb = self.time_embedding(t_emb, timestep_cond)
         aug_emb = None
 
-        class_emb = self.get_class_embed(sample=sample, class_labels=class_labels)
-        if class_emb is not None:
+        if self.class_embedding is not None:
+            if class_labels is None:
+                raise ValueError("class_labels should be provided when num_class_embeds > 0")
+
+            if self.config.class_embed_type == "timestep":
+                class_labels = self.time_proj(class_labels)
+
+                # `Timesteps` does not contain any weights and will always return f32 tensors
+                # there might be better ways to encapsulate this.
+                class_labels = class_labels.to(dtype=sample.dtype)
+
+            class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
+
             if self.config.class_embeddings_concat:
                 emb = torch.cat([emb, class_emb], dim=-1)
             else:
                 emb = emb + class_emb
 
-        aug_emb = self.get_aug_embed(
-            emb=emb, encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
-        )
-        if self.config.addition_embed_type == "image_hint":
-            aug_emb, hint = aug_emb
+        if self.config.addition_embed_type == "text":
+            aug_emb = self.add_embedding(encoder_hidden_states)
+        elif self.config.addition_embed_type == "text_image":
+            # Kandinsky 2.1 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
+                )
+
+            image_embs = added_cond_kwargs.get("image_embeds")
+            text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
+            aug_emb = self.add_embedding(text_embs, image_embs)
+        elif self.config.addition_embed_type == "text_time":
+            # SDXL - style
+            if "text_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
+                )
+            text_embeds = added_cond_kwargs.get("text_embeds")
+            if "time_ids" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
+                )
+            time_ids = added_cond_kwargs.get("time_ids")
+            time_embeds = self.add_time_proj(time_ids.flatten())
+            time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
+            add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
+            add_embeds = add_embeds.to(emb.dtype)
+            aug_emb = self.add_embedding(add_embeds)
+        elif self.config.addition_embed_type == "image":
+            # Kandinsky 2.2 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
+                )
+            image_embs = added_cond_kwargs.get("image_embeds")
+            aug_emb = self.add_embedding(image_embs)
+        elif self.config.addition_embed_type == "image_hint":
+            # Kandinsky 2.2 - style
+            if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`"
+                )
+            image_embs = added_cond_kwargs.get("image_embeds")
+            hint = added_cond_kwargs.get("hint")
+            aug_emb, hint = self.add_embedding(image_embs, hint)
             sample = torch.cat([sample, hint], dim=1)
 
         emb = emb + aug_emb if aug_emb is not None else emb
@@ -1169,9 +1049,33 @@ class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin,
         if self.time_embed_act is not None:
             emb = self.time_embed_act(emb)
 
-        encoder_hidden_states = self.process_encoder_hidden_states(
-            encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
-        )
+        if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
+            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
+        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
+            # Kadinsky 2.1 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
+                )
+
+            image_embeds = added_cond_kwargs.get("image_embeds")
+            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
+        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj":
+            # Kandinsky 2.2 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
+                )
+            image_embeds = added_cond_kwargs.get("image_embeds")
+            encoder_hidden_states = self.encoder_hid_proj(image_embeds)
+        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj":
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
+                )
+            image_embeds = added_cond_kwargs.get("image_embeds")
+            image_embeds = self.encoder_hid_proj(image_embeds)
+            encoder_hidden_states = (encoder_hidden_states, image_embeds)
 
         # 2. pre-process
         sample = self.conv_in(sample)

src/diffusers/models/unets/unet_motion_model.py

@@ -217,7 +217,6 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
         use_motion_mid_block: int = True,
         encoder_hid_dim: Optional[int] = None,
         encoder_hid_dim_type: Optional[str] = None,
-        time_cond_proj_dim: Optional[int] = None,
     ):
         super().__init__()
 
@@ -253,7 +252,9 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
         timestep_input_dim = block_out_channels[0]
 
         self.time_embedding = TimestepEmbedding(
-            timestep_input_dim, time_embed_dim, act_fn=act_fn, cond_proj_dim=time_cond_proj_dim
+            timestep_input_dim,
+            time_embed_dim,
+            act_fn=act_fn,
         )
 
         if encoder_hid_dim_type is None:
@@ -305,7 +306,6 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
                 num_attention_heads=num_attention_heads[-1],
                 resnet_groups=norm_num_groups,
                 dual_cross_attention=False,
-                use_linear_projection=use_linear_projection,
                 temporal_num_attention_heads=motion_num_attention_heads,
                 temporal_max_seq_length=motion_max_seq_length,
             )
@@ -321,7 +321,6 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
                 num_attention_heads=num_attention_heads[-1],
                 resnet_groups=norm_num_groups,
                 dual_cross_attention=False,
-                use_linear_projection=use_linear_projection,
             )
 
         # count how many layers upsample the images

src/diffusers/pipelines/animatediff/pipeline_animatediff.py

@@ -797,11 +797,7 @@ class AnimateDiffPipeline(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 7. Add image embeds for IP-Adapter
-        added_cond_kwargs = (
-            {"image_embeds": image_embeds}
-            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
-            else None
-        )
+        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
 
         num_free_init_iters = self._free_init_num_iters if self.free_init_enabled else 1
         for free_init_iter in range(num_free_init_iters):

src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py

@@ -441,41 +441,6 @@ class AnimateDiffVideoToVideoPipeline(
 
             return image_embeds, uncond_image_embeds
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
-    def prepare_ip_adapter_image_embeds(
-        self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt
-    ):
-        if ip_adapter_image_embeds is None:
-            if not isinstance(ip_adapter_image, list):
-                ip_adapter_image = [ip_adapter_image]
-
-            if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
-                raise ValueError(
-                    f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
-                )
-
-            image_embeds = []
-            for single_ip_adapter_image, image_proj_layer in zip(
-                ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
-            ):
-                output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
-                single_image_embeds, single_negative_image_embeds = self.encode_image(
-                    single_ip_adapter_image, device, 1, output_hidden_state
-                )
-                single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)
-                single_negative_image_embeds = torch.stack(
-                    [single_negative_image_embeds] * num_images_per_prompt, dim=0
-                )
-
-                if self.do_classifier_free_guidance:
-                    single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
-                    single_image_embeds = single_image_embeds.to(device)
-
-                image_embeds.append(single_image_embeds)
-        else:
-            image_embeds = ip_adapter_image_embeds
-        return image_embeds
-
     # Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents
     def decode_latents(self, latents):
         latents = 1 / self.vae.config.scaling_factor * latents
@@ -770,7 +735,6 @@ class AnimateDiffVideoToVideoPipeline(
         prompt_embeds: Optional[torch.FloatTensor] = None,
         negative_prompt_embeds: Optional[torch.FloatTensor] = None,
         ip_adapter_image: Optional[PipelineImageInput] = None,
-        ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
@@ -820,9 +784,6 @@ class AnimateDiffVideoToVideoPipeline(
                 not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
             ip_adapter_image: (`PipelineImageInput`, *optional*):
                 Optional image input to work with IP Adapters.
-            ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*):
-                Pre-generated image embeddings for IP-Adapter. If not
-                provided, embeddings are computed from the `ip_adapter_image` input argument.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generated video. Choose between `torch.FloatTensor`, `PIL.Image` or
                 `np.array`.
@@ -909,10 +870,13 @@ class AnimateDiffVideoToVideoPipeline(
         if self.do_classifier_free_guidance:
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
 
-        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
-            image_embeds = self.prepare_ip_adapter_image_embeds(
-                ip_adapter_image, ip_adapter_image_embeds, device, batch_size * num_videos_per_prompt
+        if ip_adapter_image is not None:
+            output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True
+            image_embeds, negative_image_embeds = self.encode_image(
+                ip_adapter_image, device, num_videos_per_prompt, output_hidden_state
             )
+            if self.do_classifier_free_guidance:
+                image_embeds = torch.cat([negative_image_embeds, image_embeds])
 
         # 4. Prepare timesteps
         timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
@@ -938,11 +902,7 @@ class AnimateDiffVideoToVideoPipeline(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 7. Add image embeds for IP-Adapter
-        added_cond_kwargs = (
-            {"image_embeds": image_embeds}
-            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
-            else None
-        )
+        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
 
         num_free_init_iters = self._free_init_num_iters if self.free_init_enabled else 1
         for free_init_iter in range(num_free_init_iters):

src/diffusers/pipelines/controlnet/pipeline_controlnet.py

@@ -1206,11 +1206,7 @@ class StableDiffusionControlNetPipeline(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 7.1 Add image embeds for IP-Adapter
-        added_cond_kwargs = (
-            {"image_embeds": image_embeds}
-            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
-            else None
-        )
+        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
 
         # 7.2 Create tensor stating which controlnets to keep
         controlnet_keep = []

src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py

@@ -1206,11 +1206,7 @@ class StableDiffusionControlNetImg2ImgPipeline(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 7.1 Add image embeds for IP-Adapter
-        added_cond_kwargs = (
-            {"image_embeds": image_embeds}
-            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
-            else None
-        )
+        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
 
         # 7.2 Create tensor stating which controlnets to keep
         controlnet_keep = []

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py

@@ -1495,11 +1495,7 @@ class StableDiffusionControlNetInpaintPipeline(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 7.1 Add image embeds for IP-Adapter
-        added_cond_kwargs = (
-            {"image_embeds": image_embeds}
-            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
-            else None
-        )
+        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
 
         # 7.2 Create tensor stating which controlnets to keep
         controlnet_keep = []

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py

@@ -19,22 +19,11 @@ import numpy as np
 import PIL.Image
 import torch
 import torch.nn.functional as F
-from transformers import (
-    CLIPImageProcessor,
-    CLIPTextModel,
-    CLIPTextModelWithProjection,
-    CLIPTokenizer,
-    CLIPVisionModelWithProjection,
-)
+from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
 
 from ...image_processor import PipelineImageInput, VaeImageProcessor
-from ...loaders import (
-    FromSingleFileMixin,
-    IPAdapterMixin,
-    StableDiffusionXLLoraLoaderMixin,
-    TextualInversionLoaderMixin,
-)
-from ...models import AutoencoderKL, ControlNetModel, ImageProjection, UNet2DConditionModel
+from ...loaders import FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin
+from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
 from ...models.attention_processor import (
     AttnProcessor2_0,
     LoRAAttnProcessor2_0,
@@ -151,7 +140,7 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
 
 
 class StableDiffusionXLControlNetInpaintPipeline(
-    DiffusionPipeline, StableDiffusionXLLoraLoaderMixin, FromSingleFileMixin, IPAdapterMixin
+    DiffusionPipeline, StableDiffusionXLLoraLoaderMixin, FromSingleFileMixin
 ):
     r"""
     Pipeline for text-to-image generation using Stable Diffusion XL.
@@ -163,7 +152,6 @@ class StableDiffusionXLControlNetInpaintPipeline(
         - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
         - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
         - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
-        - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
 
     Args:
         vae ([`AutoencoderKL`]):
@@ -207,8 +195,6 @@ class StableDiffusionXLControlNetInpaintPipeline(
         requires_aesthetics_score: bool = False,
         force_zeros_for_empty_prompt: bool = True,
         add_watermarker: Optional[bool] = None,
-        feature_extractor: Optional[CLIPImageProcessor] = None,
-        image_encoder: Optional[CLIPVisionModelWithProjection] = None,
     ):
         super().__init__()
 
@@ -224,8 +210,6 @@ class StableDiffusionXLControlNetInpaintPipeline(
             unet=unet,
             controlnet=controlnet,
             scheduler=scheduler,
-            feature_extractor=feature_extractor,
-            image_encoder=image_encoder,
         )
         self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
         self.register_to_config(requires_aesthetics_score=requires_aesthetics_score)
@@ -513,66 +497,6 @@ class StableDiffusionXLControlNetInpaintPipeline(
 
         return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
-    def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
-        dtype = next(self.image_encoder.parameters()).dtype
-
-        if not isinstance(image, torch.Tensor):
-            image = self.feature_extractor(image, return_tensors="pt").pixel_values
-
-        image = image.to(device=device, dtype=dtype)
-        if output_hidden_states:
-            image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
-            image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
-            uncond_image_enc_hidden_states = self.image_encoder(
-                torch.zeros_like(image), output_hidden_states=True
-            ).hidden_states[-2]
-            uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
-                num_images_per_prompt, dim=0
-            )
-            return image_enc_hidden_states, uncond_image_enc_hidden_states
-        else:
-            image_embeds = self.image_encoder(image).image_embeds
-            image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-            uncond_image_embeds = torch.zeros_like(image_embeds)
-
-            return image_embeds, uncond_image_embeds
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
-    def prepare_ip_adapter_image_embeds(
-        self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt
-    ):
-        if ip_adapter_image_embeds is None:
-            if not isinstance(ip_adapter_image, list):
-                ip_adapter_image = [ip_adapter_image]
-
-            if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
-                raise ValueError(
-                    f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
-                )
-
-            image_embeds = []
-            for single_ip_adapter_image, image_proj_layer in zip(
-                ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
-            ):
-                output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
-                single_image_embeds, single_negative_image_embeds = self.encode_image(
-                    single_ip_adapter_image, device, 1, output_hidden_state
-                )
-                single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)
-                single_negative_image_embeds = torch.stack(
-                    [single_negative_image_embeds] * num_images_per_prompt, dim=0
-                )
-
-                if self.do_classifier_free_guidance:
-                    single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
-                    single_image_embeds = single_image_embeds.to(device)
-
-                image_embeds.append(single_image_embeds)
-        else:
-            image_embeds = ip_adapter_image_embeds
-        return image_embeds
-
     # 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
@@ -642,8 +566,6 @@ class StableDiffusionXLControlNetInpaintPipeline(
         negative_prompt_2=None,
         prompt_embeds=None,
         negative_prompt_embeds=None,
-        ip_adapter_image=None,
-        ip_adapter_image_embeds=None,
         pooled_prompt_embeds=None,
         negative_pooled_prompt_embeds=None,
         controlnet_conditioning_scale=1.0,
@@ -830,11 +752,6 @@ class StableDiffusionXLControlNetInpaintPipeline(
             if end > 1.0:
                 raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")
 
-        if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
-            raise ValueError(
-                "Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
-            )
-
     def prepare_control_image(
         self,
         image,
@@ -1183,8 +1100,6 @@ class StableDiffusionXLControlNetInpaintPipeline(
         latents: Optional[torch.FloatTensor] = None,
         prompt_embeds: Optional[torch.FloatTensor] = None,
         negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        ip_adapter_image: Optional[PipelineImageInput] = None,
-        ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None,
         pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
         negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
         output_type: Optional[str] = "pil",
@@ -1279,10 +1194,6 @@ class StableDiffusionXLControlNetInpaintPipeline(
                 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.
-            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
-            ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*):
-                Pre-generated image embeddings for IP-Adapter. If not
-                provided, embeddings are computed from the `ip_adapter_image` input argument.
             pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                 Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
                 If not provided, pooled text embeddings will be generated from `prompt` input argument.
@@ -1415,8 +1326,6 @@ class StableDiffusionXLControlNetInpaintPipeline(
             negative_prompt_2,
             prompt_embeds,
             negative_prompt_embeds,
-            ip_adapter_image,
-            ip_adapter_image_embeds,
             pooled_prompt_embeds,
             negative_pooled_prompt_embeds,
             controlnet_conditioning_scale,
@@ -1469,22 +1378,13 @@ class StableDiffusionXLControlNetInpaintPipeline(
             clip_skip=self.clip_skip,
         )
 
-        # 3.1 Encode ip_adapter_image
-        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
-            image_embeds = self.prepare_ip_adapter_image_embeds(
-                ip_adapter_image, ip_adapter_image_embeds, device, batch_size * num_images_per_prompt
-            )
-
         # 4. set timesteps
         def denoising_value_valid(dnv):
-            return isinstance(dnv, float) and 0 < dnv < 1
+            return isinstance(denoising_end, float) and 0 < dnv < 1
 
         self.scheduler.set_timesteps(num_inference_steps, device=device)
         timesteps, num_inference_steps = self.get_timesteps(
-            num_inference_steps,
-            strength,
-            device,
-            denoising_start=denoising_start if denoising_value_valid(denoising_start) else None,
+            num_inference_steps, strength, device, denoising_start=denoising_start if denoising_value_valid else None
         )
         # check that number of inference steps is not < 1 - as this doesn't make sense
         if num_inference_steps < 1:
@@ -1749,9 +1649,6 @@ class StableDiffusionXLControlNetInpaintPipeline(
                     down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
                     mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
 
-                if ip_adapter_image is not None:
-                    added_cond_kwargs["image_embeds"] = image_embeds
-
                 if num_channels_unet == 9:
                     latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
 

src/diffusers/pipelines/deprecated/versatile_diffusion/modeling_text_unet.py

@@ -268,6 +268,7 @@ class GLIGENTextBoundingboxProjection(nn.Module):
         return objs
 
 
+# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel with UNet2DConditionModel->UNetFlatConditionModel, nn.Conv2d->LinearMultiDim, Block2D->BlockFlat
 class UNetFlatConditionModel(ModelMixin, ConfigMixin):
     r"""
     A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample

src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py

@@ -477,9 +477,8 @@ class LatentConsistencyModelImg2ImgPipeline(
 
             return image_embeds, uncond_image_embeds
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
     def prepare_ip_adapter_image_embeds(
-        self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt
+        self, ip_adapter_image, ip_adapter_image_embeds, do_classifier_free_guidance, device, num_images_per_prompt
     ):
         if ip_adapter_image_embeds is None:
             if not isinstance(ip_adapter_image, list):
@@ -503,7 +502,7 @@ class LatentConsistencyModelImg2ImgPipeline(
                     [single_negative_image_embeds] * num_images_per_prompt, dim=0
                 )
 
-                if self.do_classifier_free_guidance:
+                if do_classifier_free_guidance:
                     single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
                     single_image_embeds = single_image_embeds.to(device)
 
@@ -700,10 +699,6 @@ class LatentConsistencyModelImg2ImgPipeline(
     def clip_skip(self):
         return self._clip_skip
 
-    @property
-    def do_classifier_free_guidance(self):
-        return False
-
     @property
     def num_timesteps(self):
         return self._num_timesteps
@@ -850,7 +845,7 @@ class LatentConsistencyModelImg2ImgPipeline(
 
         if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
             image_embeds = self.prepare_ip_adapter_image_embeds(
-                ip_adapter_image, ip_adapter_image_embeds, device, batch_size * num_images_per_prompt
+                ip_adapter_image, ip_adapter_image_embeds, False, device, batch_size * num_images_per_prompt
             )
 
         # 3. Encode input prompt
@@ -865,7 +860,7 @@ class LatentConsistencyModelImg2ImgPipeline(
             prompt,
             device,
             num_images_per_prompt,
-            self.do_classifier_free_guidance,
+            False,
             negative_prompt=None,
             prompt_embeds=prompt_embeds,
             negative_prompt_embeds=None,
@@ -911,11 +906,7 @@ class LatentConsistencyModelImg2ImgPipeline(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, None)
 
         # 7.1 Add image embeds for IP-Adapter
-        added_cond_kwargs = (
-            {"image_embeds": image_embeds}
-            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
-            else None
-        )
+        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
 
         # 8. LCM Multistep Sampling Loop
         num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order

src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py

@@ -461,41 +461,6 @@ class LatentConsistencyModelPipeline(
 
             return image_embeds, uncond_image_embeds
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
-    def prepare_ip_adapter_image_embeds(
-        self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt
-    ):
-        if ip_adapter_image_embeds is None:
-            if not isinstance(ip_adapter_image, list):
-                ip_adapter_image = [ip_adapter_image]
-
-            if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
-                raise ValueError(
-                    f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
-                )
-
-            image_embeds = []
-            for single_ip_adapter_image, image_proj_layer in zip(
-                ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
-            ):
-                output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
-                single_image_embeds, single_negative_image_embeds = self.encode_image(
-                    single_ip_adapter_image, device, 1, output_hidden_state
-                )
-                single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)
-                single_negative_image_embeds = torch.stack(
-                    [single_negative_image_embeds] * num_images_per_prompt, dim=0
-                )
-
-                if self.do_classifier_free_guidance:
-                    single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
-                    single_image_embeds = single_image_embeds.to(device)
-
-                image_embeds.append(single_image_embeds)
-        else:
-            image_embeds = ip_adapter_image_embeds
-        return image_embeds
-
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
     def run_safety_checker(self, image, device, dtype):
         if self.safety_checker is None:
@@ -625,10 +590,6 @@ class LatentConsistencyModelPipeline(
     def clip_skip(self):
         return self._clip_skip
 
-    @property
-    def do_classifier_free_guidance(self):
-        return False
-
     @property
     def num_timesteps(self):
         return self._num_timesteps
@@ -649,7 +610,6 @@ class LatentConsistencyModelPipeline(
         latents: Optional[torch.FloatTensor] = None,
         prompt_embeds: Optional[torch.FloatTensor] = None,
         ip_adapter_image: Optional[PipelineImageInput] = None,
-        ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
@@ -700,9 +660,6 @@ class LatentConsistencyModelPipeline(
                 provided, text embeddings are generated from the `prompt` input argument.
             ip_adapter_image: (`PipelineImageInput`, *optional*):
                 Optional image input to work with IP Adapters.
-            ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*):
-                Pre-generated image embeddings for IP-Adapter. If not
-                provided, embeddings are computed from the `ip_adapter_image` input argument.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generated image. Choose between `PIL.Image` or `np.array`.
             return_dict (`bool`, *optional*, defaults to `True`):
@@ -769,10 +726,12 @@ class LatentConsistencyModelPipeline(
             batch_size = prompt_embeds.shape[0]
 
         device = self._execution_device
+        # do_classifier_free_guidance = guidance_scale > 1.0
 
-        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
-            image_embeds = self.prepare_ip_adapter_image_embeds(
-                ip_adapter_image, ip_adapter_image_embeds, device, batch_size * num_images_per_prompt
+        if ip_adapter_image is not None:
+            output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True
+            image_embeds, negative_image_embeds = self.encode_image(
+                ip_adapter_image, device, num_images_per_prompt, output_hidden_state
             )
 
         # 3. Encode input prompt
@@ -787,7 +746,7 @@ class LatentConsistencyModelPipeline(
             prompt,
             device,
             num_images_per_prompt,
-            self.do_classifier_free_guidance,
+            False,
             negative_prompt=None,
             prompt_embeds=prompt_embeds,
             negative_prompt_embeds=None,
@@ -827,11 +786,7 @@ class LatentConsistencyModelPipeline(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, None)
 
         # 7.1 Add image embeds for IP-Adapter
-        added_cond_kwargs = (
-            {"image_embeds": image_embeds}
-            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
-            else None
-        )
+        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
 
         # 8. LCM MultiStep Sampling Loop:
         num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order

src/diffusers/pipelines/pia/pipeline_pia.py

@@ -987,11 +987,7 @@ class PIAPipeline(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 7. Add image embeds for IP-Adapter
-        added_cond_kwargs = (
-            {"image_embeds": image_embeds}
-            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
-            else None
-        )
+        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
 
         # 8. Denoising loop
         num_free_init_iters = self._free_init_num_iters if self.free_init_enabled else 1

src/diffusers/pipelines/pipeline_utils.py

@@ -170,7 +170,7 @@ def is_safetensors_compatible(filenames, variant=None, passed_components=None) -
             sf_filenames.add(os.path.normpath(filename))
 
     for filename in pt_filenames:
-        #  filename = 'foo/bar/baz.bam' -> path = 'foo/bar', filename = 'baz', extension = '.bam'
+        #  filename = 'foo/bar/baz.bam' -> path = 'foo/bar', filename = 'baz', extention = '.bam'
         path, filename = os.path.split(filename)
         filename, extension = os.path.splitext(filename)
 
@@ -375,7 +375,7 @@ def _get_pipeline_class(
 
         if repo_id is not None and hub_revision is not None:
             # if we load the pipeline code from the Hub
-            # make sure to overwrite the `revision`
+            # make sure to overwrite the `revison`
             revision = hub_revision
 
         return get_class_from_dynamic_module(
@@ -451,7 +451,7 @@ def load_sub_model(
     )
 
     load_method_name = None
-    # retrieve load method name
+    # retrive load method name
     for class_name, class_candidate in class_candidates.items():
         if class_candidate is not None and issubclass(class_obj, class_candidate):
             load_method_name = importable_classes[class_name][1]
@@ -1897,7 +1897,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
             else:
                 # 2. we forced `local_files_only=True` when `model_info` failed
                 raise EnvironmentError(
-                    f"Cannot load model {pretrained_model_name}: model is not cached locally and an error occurred"
+                    f"Cannot load model {pretrained_model_name}: model is not cached locally and an error occured"
                     " while trying to fetch metadata from the Hub. Please check out the root cause in the stacktrace"
                     " above."
                 ) from model_info_call_error

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -1111,11 +1111,7 @@ class StableDiffusionImg2ImgPipeline(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 7.1 Add image embeds for IP-Adapter
-        added_cond_kwargs = (
-            {"image_embeds": image_embeds}
-            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
-            else None
-        )
+        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
 
         # 7.2 Optionally get Guidance Scale Embedding
         timestep_cond = None

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -1397,11 +1397,7 @@ class StableDiffusionInpaintPipeline(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 9.1 Add image embeds for IP-Adapter
-        added_cond_kwargs = (
-            {"image_embeds": image_embeds}
-            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
-            else None
-        )
+        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
 
         # 9.2 Optionally get Guidance Scale Embedding
         timestep_cond = None

src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py

@@ -777,11 +777,7 @@ class StableDiffusionPanoramaPipeline(DiffusionPipeline, TextualInversionLoaderM
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 7.1 Add image embeds for IP-Adapter
-        added_cond_kwargs = (
-            {"image_embeds": image_embeds}
-            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
-            else None
-        )
+        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
 
         # 8. Denoising loop
         # Each denoising step also includes refinement of the latents with respect to the

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -1315,14 +1315,14 @@ class StableDiffusionXLImg2ImgPipeline(
 
         # 5. Prepare timesteps
         def denoising_value_valid(dnv):
-            return isinstance(dnv, float) and 0 < dnv < 1
+            return isinstance(self.denoising_end, float) and 0 < dnv < 1
 
         timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
         timesteps, num_inference_steps = self.get_timesteps(
             num_inference_steps,
             strength,
             device,
-            denoising_start=self.denoising_start if denoising_value_valid(self.denoising_start) else None,
+            denoising_start=self.denoising_start if denoising_value_valid else None,
         )
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
 

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py

@@ -1581,14 +1581,14 @@ class StableDiffusionXLInpaintPipeline(
 
         # 4. set timesteps
         def denoising_value_valid(dnv):
-            return isinstance(dnv, float) and 0 < dnv < 1
+            return isinstance(self.denoising_end, float) and 0 < dnv < 1
 
         timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
         timesteps, num_inference_steps = self.get_timesteps(
             num_inference_steps,
             strength,
             device,
-            denoising_start=self.denoising_start if denoising_value_valid(self.denoising_start) else None,
+            denoising_start=self.denoising_start if denoising_value_valid else None,
         )
         # check that number of inference steps is not < 1 - as this doesn't make sense
         if num_inference_steps < 1:

tests/models/unets/test_models_unet_2d_condition.py

@@ -62,10 +62,7 @@ def create_ip_adapter_state_dict(model):
     key_id = 1
 
     for name in model.attn_processors.keys():
-        cross_attention_dim = (
-            None if name.endswith("attn1.processor") or "motion_module" in name else model.config.cross_attention_dim
-        )
-
+        cross_attention_dim = None if name.endswith("attn1.processor") else model.config.cross_attention_dim
         if name.startswith("mid_block"):
             hidden_size = model.config.block_out_channels[-1]
         elif name.startswith("up_blocks"):
@@ -74,7 +71,6 @@ def create_ip_adapter_state_dict(model):
         elif name.startswith("down_blocks"):
             block_id = int(name[len("down_blocks.")])
             hidden_size = model.config.block_out_channels[block_id]
-
         if cross_attention_dim is not None:
             sd = IPAdapterAttnProcessor(
                 hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0

tests/pipelines/animatediff/test_animatediff.py

@@ -18,7 +18,7 @@ from diffusers.utils import is_xformers_available, logging
 from diffusers.utils.testing_utils import numpy_cosine_similarity_distance, require_torch_gpu, slow, torch_device
 
 from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
-from ..test_pipelines_common import IPAdapterTesterMixin, PipelineTesterMixin
+from ..test_pipelines_common import PipelineTesterMixin
 
 
 def to_np(tensor):
@@ -28,7 +28,7 @@ def to_np(tensor):
     return tensor
 
 
-class AnimateDiffPipelineFastTests(IPAdapterTesterMixin, PipelineTesterMixin, unittest.TestCase):
+class AnimateDiffPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
     pipeline_class = AnimateDiffPipeline
     params = TEXT_TO_IMAGE_PARAMS
     batch_params = TEXT_TO_IMAGE_BATCH_PARAMS

tests/pipelines/animatediff/test_animatediff_video2video.py

@@ -18,7 +18,7 @@ from diffusers.utils import is_xformers_available, logging
 from diffusers.utils.testing_utils import torch_device
 
 from ..pipeline_params import TEXT_TO_IMAGE_PARAMS, VIDEO_TO_VIDEO_BATCH_PARAMS
-from ..test_pipelines_common import IPAdapterTesterMixin, PipelineTesterMixin
+from ..test_pipelines_common import PipelineTesterMixin
 
 
 def to_np(tensor):
@@ -28,7 +28,7 @@ def to_np(tensor):
     return tensor
 
 
-class AnimateDiffVideoToVideoPipelineFastTests(IPAdapterTesterMixin, PipelineTesterMixin, unittest.TestCase):
+class AnimateDiffVideoToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
     pipeline_class = AnimateDiffVideoToVideoPipeline
     params = TEXT_TO_IMAGE_PARAMS
     batch_params = VIDEO_TO_VIDEO_BATCH_PARAMS

tests/pipelines/controlnet/test_controlnet.py

@@ -54,7 +54,6 @@ from ..pipeline_params import (
     TEXT_TO_IMAGE_PARAMS,
 )
 from ..test_pipelines_common import (
-    IPAdapterTesterMixin,
     PipelineKarrasSchedulerTesterMixin,
     PipelineLatentTesterMixin,
     PipelineTesterMixin,
@@ -111,11 +110,7 @@ def _test_stable_diffusion_compile(in_queue, out_queue, timeout):
 
 
 class ControlNetPipelineFastTests(
-    IPAdapterTesterMixin,
-    PipelineLatentTesterMixin,
-    PipelineKarrasSchedulerTesterMixin,
-    PipelineTesterMixin,
-    unittest.TestCase,
+    PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
 ):
     pipeline_class = StableDiffusionControlNetPipeline
     params = TEXT_TO_IMAGE_PARAMS
@@ -278,7 +273,7 @@ class ControlNetPipelineFastTests(
 
 
 class StableDiffusionMultiControlNetPipelineFastTests(
-    IPAdapterTesterMixin, PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
+    PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
 ):
     pipeline_class = StableDiffusionControlNetPipeline
     params = TEXT_TO_IMAGE_PARAMS
@@ -495,7 +490,7 @@ class StableDiffusionMultiControlNetPipelineFastTests(
 
 
 class StableDiffusionMultiControlNetOneModelPipelineFastTests(
-    IPAdapterTesterMixin, PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
+    PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
 ):
     pipeline_class = StableDiffusionControlNetPipeline
     params = TEXT_TO_IMAGE_PARAMS

tests/pipelines/controlnet/test_controlnet_img2img.py

@@ -52,7 +52,6 @@ from ..pipeline_params import (
     TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
 )
 from ..test_pipelines_common import (
-    IPAdapterTesterMixin,
     PipelineKarrasSchedulerTesterMixin,
     PipelineLatentTesterMixin,
     PipelineTesterMixin,
@@ -63,11 +62,7 @@ enable_full_determinism()
 
 
 class ControlNetImg2ImgPipelineFastTests(
-    IPAdapterTesterMixin,
-    PipelineLatentTesterMixin,
-    PipelineKarrasSchedulerTesterMixin,
-    PipelineTesterMixin,
-    unittest.TestCase,
+    PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
 ):
     pipeline_class = StableDiffusionControlNetImg2ImgPipeline
     params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
@@ -186,7 +181,7 @@ class ControlNetImg2ImgPipelineFastTests(
 
 
 class StableDiffusionMultiControlNetPipelineFastTests(
-    IPAdapterTesterMixin, PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
+    PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
 ):
     pipeline_class = StableDiffusionControlNetImg2ImgPipeline
     params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}

tests/pipelines/controlnet/test_controlnet_inpaint.py

@@ -51,7 +51,11 @@ from ..pipeline_params import (
     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
     TEXT_TO_IMAGE_IMAGE_PARAMS,
 )
-from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
+from ..test_pipelines_common import (
+    PipelineKarrasSchedulerTesterMixin,
+    PipelineLatentTesterMixin,
+    PipelineTesterMixin,
+)
 
 
 enable_full_determinism()
@@ -555,16 +559,17 @@ class ControlNetInpaintPipelineSlowTests(unittest.TestCase):
     def test_load_local(self):
         controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
         pipe_1 = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-            "runwayml/stable-diffusion-inpainting", safety_checker=None, controlnet=controlnet
+            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
         )
 
         controlnet = ControlNetModel.from_single_file(
             "https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth"
         )
         pipe_2 = StableDiffusionControlNetInpaintPipeline.from_single_file(
-            "https://huggingface.co/runwayml/stable-diffusion-inpainting/blob/main/sd-v1-5-inpainting.ckpt",
+            "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors",
             safety_checker=None,
             controlnet=controlnet,
+            scheduler_type="pndm",
         )
         control_image = load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"

tests/pipelines/controlnet/test_controlnet_sdxl.py

@@ -48,7 +48,6 @@ from ..pipeline_params import (
     TEXT_TO_IMAGE_PARAMS,
 )
 from ..test_pipelines_common import (
-    IPAdapterTesterMixin,
     PipelineKarrasSchedulerTesterMixin,
     PipelineLatentTesterMixin,
     PipelineTesterMixin,
@@ -60,7 +59,6 @@ enable_full_determinism()
 
 
 class StableDiffusionXLControlNetPipelineFastTests(
-    IPAdapterTesterMixin,
     PipelineLatentTesterMixin,
     PipelineKarrasSchedulerTesterMixin,
     PipelineTesterMixin,

tests/pipelines/controlnet/test_controlnet_sdxl_img2img.py

@@ -36,7 +36,6 @@ from ..pipeline_params import (
     TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
 )
 from ..test_pipelines_common import (
-    IPAdapterTesterMixin,
     PipelineKarrasSchedulerTesterMixin,
     PipelineLatentTesterMixin,
     PipelineTesterMixin,
@@ -47,11 +46,7 @@ enable_full_determinism()
 
 
 class ControlNetPipelineSDXLImg2ImgFastTests(
-    IPAdapterTesterMixin,
-    PipelineLatentTesterMixin,
-    PipelineKarrasSchedulerTesterMixin,
-    PipelineTesterMixin,
-    unittest.TestCase,
+    PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
 ):
     pipeline_class = StableDiffusionXLControlNetImg2ImgPipeline
     params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS

tests/pipelines/latent_consistency_models/test_latent_consistency_models.py

@@ -20,15 +20,13 @@ from diffusers.utils.testing_utils import (
 )
 
 from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
-from ..test_pipelines_common import IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
+from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
 
 
 enable_full_determinism()
 
 
-class LatentConsistencyModelPipelineFastTests(
-    IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase
-):
+class LatentConsistencyModelPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
     pipeline_class = LatentConsistencyModelPipeline
     params = TEXT_TO_IMAGE_PARAMS - {"negative_prompt", "negative_prompt_embeds"}
     batch_params = TEXT_TO_IMAGE_BATCH_PARAMS - {"negative_prompt"}

tests/pipelines/latent_consistency_models/test_latent_consistency_models_img2img.py

@@ -27,14 +27,14 @@ from ..pipeline_params import (
     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
     TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
 )
-from ..test_pipelines_common import IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
+from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
 
 
 enable_full_determinism()
 
 
 class LatentConsistencyModelImg2ImgPipelineFastTests(
-    IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase
+    PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase
 ):
     pipeline_class = LatentConsistencyModelImg2ImgPipeline
     params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "negative_prompt", "negative_prompt_embeds"}

tests/pipelines/pia/test_pia.py

@@ -17,7 +17,7 @@ from diffusers import (
 from diffusers.utils import is_xformers_available, logging
 from diffusers.utils.testing_utils import floats_tensor, torch_device
 
-from ..test_pipelines_common import IPAdapterTesterMixin, PipelineTesterMixin
+from ..test_pipelines_common import PipelineTesterMixin
 
 
 def to_np(tensor):
@@ -27,7 +27,7 @@ def to_np(tensor):
     return tensor
 
 
-class PIAPipelineFastTests(IPAdapterTesterMixin, PipelineTesterMixin, unittest.TestCase):
+class PIAPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
     pipeline_class = PIAPipeline
     params = frozenset(
         [

tests/pipelines/stable_diffusion/test_stable_diffusion.py

@@ -23,11 +23,7 @@ import unittest
 import numpy as np
 import torch
 from huggingface_hub import hf_hub_download
-from transformers import (
-    CLIPTextConfig,
-    CLIPTextModel,
-    CLIPTokenizer,
-)
+from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
 
 from diffusers import (
     AutoencoderKL,
@@ -64,12 +60,7 @@ from ..pipeline_params import (
     TEXT_TO_IMAGE_IMAGE_PARAMS,
     TEXT_TO_IMAGE_PARAMS,
 )
-from ..test_pipelines_common import (
-    IPAdapterTesterMixin,
-    PipelineKarrasSchedulerTesterMixin,
-    PipelineLatentTesterMixin,
-    PipelineTesterMixin,
-)
+from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
 
 
 enable_full_determinism()
@@ -109,11 +100,7 @@ def _test_stable_diffusion_compile(in_queue, out_queue, timeout):
 
 
 class StableDiffusionPipelineFastTests(
-    IPAdapterTesterMixin,
-    PipelineLatentTesterMixin,
-    PipelineKarrasSchedulerTesterMixin,
-    PipelineTesterMixin,
-    unittest.TestCase,
+    PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
 ):
     pipeline_class = StableDiffusionPipeline
     params = TEXT_TO_IMAGE_PARAMS
@@ -190,7 +177,7 @@ class StableDiffusionPipelineFastTests(
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "np",
+            "output_type": "numpy",
         }
         return inputs
 

tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py

@@ -55,12 +55,7 @@ from ..pipeline_params import (
     TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
     TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
 )
-from ..test_pipelines_common import (
-    IPAdapterTesterMixin,
-    PipelineKarrasSchedulerTesterMixin,
-    PipelineLatentTesterMixin,
-    PipelineTesterMixin,
-)
+from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
 
 
 enable_full_determinism()
@@ -99,11 +94,7 @@ def _test_img2img_compile(in_queue, out_queue, timeout):
 
 
 class StableDiffusionImg2ImgPipelineFastTests(
-    IPAdapterTesterMixin,
-    PipelineLatentTesterMixin,
-    PipelineKarrasSchedulerTesterMixin,
-    PipelineTesterMixin,
-    unittest.TestCase,
+    PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
 ):
     pipeline_class = StableDiffusionImg2ImgPipeline
     params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py

@@ -57,12 +57,7 @@ from ..pipeline_params import (
     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
     TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
 )
-from ..test_pipelines_common import (
-    IPAdapterTesterMixin,
-    PipelineKarrasSchedulerTesterMixin,
-    PipelineLatentTesterMixin,
-    PipelineTesterMixin,
-)
+from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
 
 
 enable_full_determinism()
@@ -103,11 +98,7 @@ def _test_inpaint_compile(in_queue, out_queue, timeout):
 
 
 class StableDiffusionInpaintPipelineFastTests(
-    IPAdapterTesterMixin,
-    PipelineLatentTesterMixin,
-    PipelineKarrasSchedulerTesterMixin,
-    PipelineTesterMixin,
-    unittest.TestCase,
+    PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
 ):
     pipeline_class = StableDiffusionInpaintPipeline
     params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS

tests/pipelines/stable_diffusion/test_stable_diffusion_instruction_pix2pix.py

@@ -47,11 +47,7 @@ from ..pipeline_params import (
     TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
     TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
 )
-from ..test_pipelines_common import (
-    PipelineKarrasSchedulerTesterMixin,
-    PipelineLatentTesterMixin,
-    PipelineTesterMixin,
-)
+from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
 
 
 enable_full_determinism()

tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl.py

@@ -49,23 +49,14 @@ from ..pipeline_params import (
     TEXT_TO_IMAGE_IMAGE_PARAMS,
     TEXT_TO_IMAGE_PARAMS,
 )
-from ..test_pipelines_common import (
-    IPAdapterTesterMixin,
-    PipelineLatentTesterMixin,
-    PipelineTesterMixin,
-    SDXLOptionalComponentsTesterMixin,
-)
+from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin, SDXLOptionalComponentsTesterMixin
 
 
 enable_full_determinism()
 
 
 class StableDiffusionXLPipelineFastTests(
-    IPAdapterTesterMixin,
-    PipelineLatentTesterMixin,
-    PipelineTesterMixin,
-    SDXLOptionalComponentsTesterMixin,
-    unittest.TestCase,
+    PipelineLatentTesterMixin, PipelineTesterMixin, SDXLOptionalComponentsTesterMixin, unittest.TestCase
 ):
     pipeline_class = StableDiffusionXLPipeline
     params = TEXT_TO_IMAGE_PARAMS

tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_adapter.py

@@ -44,7 +44,6 @@ from diffusers.utils.testing_utils import (
 
 from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
 from ..test_pipelines_common import (
-    IPAdapterTesterMixin,
     PipelineTesterMixin,
     SDXLOptionalComponentsTesterMixin,
     assert_mean_pixel_difference,
@@ -55,7 +54,7 @@ enable_full_determinism()
 
 
 class StableDiffusionXLAdapterPipelineFastTests(
-    IPAdapterTesterMixin, PipelineTesterMixin, SDXLOptionalComponentsTesterMixin, unittest.TestCase
+    PipelineTesterMixin, SDXLOptionalComponentsTesterMixin, unittest.TestCase
 ):
     pipeline_class = StableDiffusionXLAdapterPipeline
     params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS

tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_img2img.py

@@ -54,20 +54,13 @@ from ..pipeline_params import (
     TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
     TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
 )
-from ..test_pipelines_common import (
-    IPAdapterTesterMixin,
-    PipelineLatentTesterMixin,
-    PipelineTesterMixin,
-    SDXLOptionalComponentsTesterMixin,
-)
+from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin, SDXLOptionalComponentsTesterMixin
 
 
 enable_full_determinism()
 
 
-class StableDiffusionXLImg2ImgPipelineFastTests(
-    IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase
-):
+class StableDiffusionXLImg2ImgPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
     pipeline_class = StableDiffusionXLImg2ImgPipeline
     params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
     required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}

tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_inpaint.py

@@ -48,15 +48,13 @@ from ..pipeline_params import (
     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
     TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
 )
-from ..test_pipelines_common import IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
+from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
 
 
 enable_full_determinism()
 
 
-class StableDiffusionXLInpaintPipelineFastTests(
-    IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase
-):
+class StableDiffusionXLInpaintPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
     pipeline_class = StableDiffusionXLInpaintPipeline
     params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
     batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS

tests/pipelines/stable_video_diffusion/test_stable_video_diffusion.py

@@ -22,6 +22,7 @@ from diffusers.utils import is_accelerate_available, is_accelerate_version, load
 from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.testing_utils import (
     CaptureLogger,
+    disable_full_determinism,
     enable_full_determinism,
     floats_tensor,
     numpy_cosine_similarity_distance,
@@ -33,9 +34,6 @@ from diffusers.utils.testing_utils import (
 from ..test_pipelines_common import PipelineTesterMixin
 
 
-enable_full_determinism()
-
-
 def to_np(tensor):
     if isinstance(tensor, torch.Tensor):
         tensor = tensor.detach().cpu().numpy()
@@ -467,6 +465,8 @@ class StableVideoDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCa
         reason="XFormers attention is only available with CUDA and `xformers` installed",
     )
     def test_xformers_attention_forwardGenerator_pass(self):
+        disable_full_determinism()
+
         expected_max_diff = 9e-4
 
         if not self.test_xformers_attention:
@@ -496,6 +496,8 @@ class StableVideoDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCa
         max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()
         self.assertLess(max_diff, expected_max_diff, "XFormers attention should not affect the inference results")
 
+        enable_full_determinism()
+
 
 @slow
 @require_torch_gpu

tests/pipelines/test_pipelines_common.py

@@ -8,7 +8,7 @@ import re
 import tempfile
 import unittest
 import uuid
-from typing import Any, Callable, Dict, Union
+from typing import Callable, Union
 
 import numpy as np
 import PIL.Image
@@ -29,7 +29,6 @@ from diffusers import (
     UNet2DConditionModel,
 )
 from diffusers.image_processor import VaeImageProcessor
-from diffusers.loaders import IPAdapterMixin
 from diffusers.schedulers import KarrasDiffusionSchedulers
 from diffusers.utils import logging
 from diffusers.utils.import_utils import is_accelerate_available, is_accelerate_version, is_xformers_available
@@ -45,7 +44,6 @@ from ..models.autoencoders.test_models_vae import (
     get_autoencoder_tiny_config,
     get_consistency_vae_config,
 )
-from ..models.unets.test_models_unet_2d_condition import create_ip_adapter_state_dict
 from ..others.test_utils import TOKEN, USER, is_staging_test
 
 
@@ -61,118 +59,6 @@ def check_same_shape(tensor_list):
     return all(shape == shapes[0] for shape in shapes[1:])
 
 
-class IPAdapterTesterMixin:
-    """
-    This mixin is designed to be used with PipelineTesterMixin and unittest.TestCase classes.
-    It provides a set of common tests for pipelines that support IP Adapters.
-    """
-
-    def test_pipeline_signature(self):
-        parameters = inspect.signature(self.pipeline_class.__call__).parameters
-
-        assert issubclass(self.pipeline_class, IPAdapterMixin)
-        self.assertIn(
-            "ip_adapter_image",
-            parameters,
-            "`ip_adapter_image` argument must be supported by the `__call__` method",
-        )
-        self.assertIn(
-            "ip_adapter_image_embeds",
-            parameters,
-            "`ip_adapter_image_embeds` argument must be supported by the `__call__` method",
-        )
-
-    def _get_dummy_image_embeds(self, cross_attention_dim: int = 32):
-        return torch.randn((2, 1, cross_attention_dim), device=torch_device)
-
-    def _modify_inputs_for_ip_adapter_test(self, inputs: Dict[str, Any]):
-        parameters = inspect.signature(self.pipeline_class.__call__).parameters
-        if "image" in parameters.keys() and "strength" in parameters.keys():
-            inputs["num_inference_steps"] = 4
-
-        inputs["output_type"] = "np"
-        inputs["return_dict"] = False
-        return inputs
-
-    def test_ip_adapter_single(self, expected_max_diff: float = 1e-4):
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components).to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        cross_attention_dim = pipe.unet.config.get("cross_attention_dim", 32)
-
-        # forward pass without ip adapter
-        inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))
-        output_without_adapter = pipe(**inputs)[0]
-
-        adapter_state_dict = create_ip_adapter_state_dict(pipe.unet)
-        pipe.unet._load_ip_adapter_weights(adapter_state_dict)
-
-        # forward pass with single ip adapter, but scale=0 which should have no effect
-        inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))
-        inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)]
-        pipe.set_ip_adapter_scale(0.0)
-        output_without_adapter_scale = pipe(**inputs)[0]
-
-        # forward pass with single ip adapter, but with scale of adapter weights
-        inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))
-        inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)]
-        pipe.set_ip_adapter_scale(42.0)
-        output_with_adapter_scale = pipe(**inputs)[0]
-
-        max_diff_without_adapter_scale = np.abs(output_without_adapter_scale - output_without_adapter).max()
-        max_diff_with_adapter_scale = np.abs(output_with_adapter_scale - output_without_adapter).max()
-
-        self.assertLess(
-            max_diff_without_adapter_scale,
-            expected_max_diff,
-            "Output without ip-adapter must be same as normal inference",
-        )
-        self.assertGreater(
-            max_diff_with_adapter_scale, 1e-2, "Output with ip-adapter must be different from normal inference"
-        )
-
-    def test_ip_adapter_multi(self, expected_max_diff: float = 1e-4):
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components).to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        cross_attention_dim = pipe.unet.config.get("cross_attention_dim", 32)
-
-        # forward pass without ip adapter
-        inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))
-        output_without_adapter = pipe(**inputs)[0]
-
-        adapter_state_dict_1 = create_ip_adapter_state_dict(pipe.unet)
-        adapter_state_dict_2 = create_ip_adapter_state_dict(pipe.unet)
-        pipe.unet._load_ip_adapter_weights([adapter_state_dict_1, adapter_state_dict_2])
-
-        # forward pass with multi ip adapter, but scale=0 which should have no effect
-        inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))
-        inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)] * 2
-        pipe.set_ip_adapter_scale([0.0, 0.0])
-        output_without_multi_adapter_scale = pipe(**inputs)[0]
-
-        # forward pass with multi ip adapter, but with scale of adapter weights
-        inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))
-        inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)] * 2
-        pipe.set_ip_adapter_scale([42.0, 42.0])
-        output_with_multi_adapter_scale = pipe(**inputs)[0]
-
-        max_diff_without_multi_adapter_scale = np.abs(
-            output_without_multi_adapter_scale - output_without_adapter
-        ).max()
-        max_diff_with_multi_adapter_scale = np.abs(output_with_multi_adapter_scale - output_without_adapter).max()
-        self.assertLess(
-            max_diff_without_multi_adapter_scale,
-            expected_max_diff,
-            "Output without multi-ip-adapter must be same as normal inference",
-        )
-        self.assertGreater(
-            max_diff_with_multi_adapter_scale,
-            1e-2,
-            "Output with multi-ip-adapter scale must be different from normal inference",
-        )
-
-
 class PipelineLatentTesterMixin:
     """
     This mixin is designed to be used with PipelineTesterMixin and unittest.TestCase classes.