up

* update

* update

---------

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

.github/workflows/nightly_tests.yml

@@ -65,6 +65,7 @@ jobs:
           python -m uv pip install -e [quality,test]
           python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers
           python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate
+          python -m uv pip install pytest-reportlog
 
       - name: Environment
         run: |
@@ -150,6 +151,7 @@ jobs:
           ${CONDA_RUN} python -m uv pip install -e [quality,test]
           ${CONDA_RUN} python -m uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
           ${CONDA_RUN} python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate
+          ${CONDA_RUN} python -m uv pip install pytest-reportlog
 
       - name: Environment
         shell: arch -arch arm64 bash {0}

.github/workflows/pypi_publish.yaml

@@ -53,6 +53,8 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           pip install -U setuptools wheel twine
+          pip install -U torch --index-url https://download.pytorch.org/whl/cpu
+          pip install -U transformers
       
       - name: Build the dist files
         run: python setup.py bdist_wheel && python setup.py sdist

README.md

@@ -77,7 +77,7 @@ Please refer to the [How to use Stable Diffusion in Apple Silicon](https://huggi
 
 ## Quickstart
 
-Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 19000+ checkpoints):
+Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 22000+ checkpoints):
 
 ```python
 from diffusers import DiffusionPipeline
@@ -219,7 +219,7 @@ Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz9
 - https://github.com/deep-floyd/IF
 - https://github.com/bentoml/BentoML
 - https://github.com/bmaltais/kohya_ss
-- +8000 other amazing GitHub repositories 💪
+- +9000 other amazing GitHub repositories 💪
 
 Thank you for using us ❤️.
 

docs/source/en/_toctree.yml

@@ -400,6 +400,10 @@
       title: DPMSolverSDEScheduler
     - local: api/schedulers/singlestep_dpm_solver
       title: DPMSolverSinglestepScheduler
+    - local: api/schedulers/edm_multistep_dpm_solver
+      title: EDMDPMSolverMultistepScheduler
+    - local: api/schedulers/edm_euler
+      title: EDMEulerScheduler
     - local: api/schedulers/euler_ancestral
       title: EulerAncestralDiscreteScheduler
     - local: api/schedulers/euler

docs/source/en/api/pipelines/stable_diffusion/overview.md

@@ -172,3 +172,41 @@ inpaint = StableDiffusionInpaintPipeline(**text2img.components)
 
 # now you can use text2img(...), img2img(...), inpaint(...) just like the call methods of each respective pipeline
 ```
+
+### Create web demos using `gradio`
+
+The Stable Diffusion pipelines are automatically supported in [Gradio](https://github.com/gradio-app/gradio/), a library that makes creating beautiful and user-friendly machine learning apps on the web a breeze. First, make sure you have Gradio installed:
+
+```
+pip install -U gradio
+```
+
+Then, create a web demo around any Stable Diffusion-based pipeline. For example, you can create an image generation pipeline in a single line of code with Gradio's [`Interface.from_pipeline`](https://www.gradio.app/docs/interface#interface-from-pipeline) function:
+
+```py
+from diffusers import StableDiffusionPipeline
+import gradio as gr
+
+pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+
+gr.Interface.from_pipeline(pipe).launch()
+```
+
+which opens an intuitive drag-and-drop interface in your browser:
+
+![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/gradio-panda.png)
+
+Similarly, you could create a demo for an image-to-image pipeline with:
+
+```py
+from diffusers import StableDiffusionImg2ImgPipeline
+import gradio as gr
+
+
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+
+gr.Interface.from_pipeline(pipe).launch()
+```
+
+By default, the web demo runs on a local server. If you'd like to share it with others, you can generate a temporary public
+link by setting `share=True` in `launch()`. Or, you can host your demo on [Hugging Face Spaces](https://huggingface.co/spaces)https://huggingface.co/spaces for a permanent link. 

docs/source/en/api/schedulers/edm_euler.md

@@ -0,0 +1,22 @@
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# EDMEulerScheduler
+
+The Karras formulation of the Euler scheduler (Algorithm 2) from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper by Karras et al. This is a fast scheduler which can often generate good outputs in 20-30 steps. The scheduler is based on the original [k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51) implementation by [Katherine Crowson](https://github.com/crowsonkb/).
+
+
+## EDMEulerScheduler
+[[autodoc]] EDMEulerScheduler
+
+## EDMEulerSchedulerOutput
+[[autodoc]] schedulers.scheduling_edm_euler.EDMEulerSchedulerOutput

docs/source/en/api/schedulers/edm_multistep_dpm_solver.md

@@ -0,0 +1,24 @@
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# EDMDPMSolverMultistepScheduler
+
+`EDMDPMSolverMultistepScheduler` is a [Karras formulation](https://huggingface.co/papers/2206.00364) of `DPMSolverMultistep`, a multistep scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.
+
+DPMSolver (and the improved version DPMSolver++) is a fast dedicated high-order solver for diffusion ODEs with convergence order guarantee. Empirically, DPMSolver sampling with only 20 steps can generate high-quality
+samples, and it can generate quite good samples even in 10 steps.
+
+## EDMDPMSolverMultistepScheduler
+[[autodoc]] EDMDPMSolverMultistepScheduler
+
+## SchedulerOutput
+[[autodoc]] schedulers.scheduling_utils.SchedulerOutput

examples/advanced_diffusion_training/README.md

@@ -259,6 +259,50 @@ pip install git+https://github.com/huggingface/peft.git
 **Inference** 
 The inference is the same as if you train a regular LoRA 🤗
 
+## Conducting EDM-style training
+
+It's now possible to perform EDM-style training as proposed in [Elucidating the Design Space of Diffusion-Based Generative Models](https://arxiv.org/abs/2206.00364). 
+
+simply set:
+
+```diff
++  --do_edm_style_training \
+```
+
+Other SDXL-like models that use the EDM formulation, such as [playgroundai/playground-v2.5-1024px-aesthetic](https://huggingface.co/playgroundai/playground-v2.5-1024px-aesthetic), can also be DreamBooth'd with the script. Below is an example command:
+
+```bash
+accelerate launch train_dreambooth_lora_sdxl_advanced.py \
+  --pretrained_model_name_or_path="playgroundai/playground-v2.5-1024px-aesthetic"  \
+  --dataset_name="linoyts/3d_icon" \
+  --instance_prompt="3d icon in the style of TOK" \
+  --validation_prompt="a TOK icon of an astronaut riding a horse, in the style of TOK" \
+  --output_dir="3d-icon-SDXL-LoRA" \
+  --do_edm_style_training \
+  --caption_column="prompt" \
+  --mixed_precision="bf16" \
+  --resolution=1024 \
+  --train_batch_size=3 \
+  --repeats=1 \
+  --report_to="wandb"\
+  --gradient_accumulation_steps=1 \
+  --gradient_checkpointing \
+  --learning_rate=1.0 \
+  --text_encoder_lr=1.0 \
+  --optimizer="prodigy"\
+  --train_text_encoder_ti\
+  --train_text_encoder_ti_frac=0.5\
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=0 \
+  --rank=8 \
+  --max_train_steps=1000 \
+  --checkpointing_steps=2000 \
+  --seed="0" \
+  --push_to_hub
+```
+
+> [!CAUTION]
+> Min-SNR gamma is not supported with the EDM-style training yet. When training with the PlaygroundAI model, it's recommended to not pass any "variant".
 
 ### Tips and Tricks
 Check out [these recommended practices](https://huggingface.co/blog/sdxl_lora_advanced_script#additional-good-practices)

examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py

@@ -70,7 +70,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__)
 

examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py

@@ -14,9 +14,11 @@
 # See the License for the specific language governing permissions and
 
 import argparse
+import contextlib
 import gc
 import hashlib
 import itertools
+import json
 import logging
 import math
 import os
@@ -37,7 +39,7 @@ import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed
-from huggingface_hub import create_repo, upload_folder
+from huggingface_hub import create_repo, hf_hub_download, upload_folder
 from packaging import version
 from peft import LoraConfig, set_peft_model_state_dict
 from peft.utils import get_peft_model_state_dict
@@ -55,6 +57,8 @@ from diffusers import (
     AutoencoderKL,
     DDPMScheduler,
     DPMSolverMultistepScheduler,
+    EDMEulerScheduler,
+    EulerDiscreteScheduler,
     StableDiffusionXLPipeline,
     UNet2DConditionModel,
 )
@@ -74,11 +78,25 @@ from diffusers.utils.torch_utils import is_compiled_module
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__)
 
 
+def determine_scheduler_type(pretrained_model_name_or_path, revision):
+    model_index_filename = "model_index.json"
+    if os.path.isdir(pretrained_model_name_or_path):
+        model_index = os.path.join(pretrained_model_name_or_path, model_index_filename)
+    else:
+        model_index = hf_hub_download(
+            repo_id=pretrained_model_name_or_path, filename=model_index_filename, revision=revision
+        )
+
+    with open(model_index, "r") as f:
+        scheduler_type = json.load(f)["scheduler"][1]
+    return scheduler_type
+
+
 def save_model_card(
     repo_id: str,
     use_dora: bool,
@@ -370,6 +388,11 @@ def parse_args(input_args=None):
             " `args.validation_prompt` multiple times: `args.num_validation_images`."
         ),
     )
+    parser.add_argument(
+        "--do_edm_style_training",
+        action="store_true",
+        help="Flag to conduct training using the EDM formulation as introduced in https://arxiv.org/abs/2206.00364.",
+    )
     parser.add_argument(
         "--with_prior_preservation",
         default=False,
@@ -1117,6 +1140,8 @@ def main(args):
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
             " Please use `huggingface-cli login` to authenticate with the Hub."
         )
+    if args.do_edm_style_training and args.snr_gamma is not None:
+        raise ValueError("Min-SNR formulation is not supported when conducting EDM-style training.")
 
     logging_dir = Path(args.output_dir, args.logging_dir)
 
@@ -1234,7 +1259,19 @@ def main(args):
     )
 
     # Load scheduler and models
-    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
+    scheduler_type = determine_scheduler_type(args.pretrained_model_name_or_path, args.revision)
+    if "EDM" in scheduler_type:
+        args.do_edm_style_training = True
+        noise_scheduler = EDMEulerScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
+        logger.info("Performing EDM-style training!")
+    elif args.do_edm_style_training:
+        noise_scheduler = EulerDiscreteScheduler.from_pretrained(
+            args.pretrained_model_name_or_path, subfolder="scheduler"
+        )
+        logger.info("Performing EDM-style training!")
+    else:
+        noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
+
     text_encoder_one = text_encoder_cls_one.from_pretrained(
         args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
     )
@@ -1252,7 +1289,12 @@ def main(args):
         revision=args.revision,
         variant=args.variant,
     )
-    vae_scaling_factor = vae.config.scaling_factor
+    latents_mean = latents_std = None
+    if hasattr(vae.config, "latents_mean") and vae.config.latents_mean is not None:
+        latents_mean = torch.tensor(vae.config.latents_mean).view(1, 4, 1, 1)
+    if hasattr(vae.config, "latents_std") and vae.config.latents_std is not None:
+        latents_std = torch.tensor(vae.config.latents_std).view(1, 4, 1, 1)
+
     unet = UNet2DConditionModel.from_pretrained(
         args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
     )
@@ -1790,6 +1832,19 @@ def main(args):
         disable=not accelerator.is_local_main_process,
     )
 
+    def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
+        # TODO: revisit other sampling algorithms
+        sigmas = noise_scheduler.sigmas.to(device=accelerator.device, dtype=dtype)
+        schedule_timesteps = noise_scheduler.timesteps.to(accelerator.device)
+        timesteps = timesteps.to(accelerator.device)
+
+        step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
+
+        sigma = sigmas[step_indices].flatten()
+        while len(sigma.shape) < n_dim:
+            sigma = sigma.unsqueeze(-1)
+        return sigma
+
     if args.train_text_encoder:
         num_train_epochs_text_encoder = int(args.train_text_encoder_frac * args.num_train_epochs)
     elif args.train_text_encoder_ti:  # args.train_text_encoder_ti
@@ -1841,9 +1896,15 @@ def main(args):
                     pixel_values = batch["pixel_values"].to(dtype=vae.dtype)
                     model_input = vae.encode(pixel_values).latent_dist.sample()
 
-                model_input = model_input * vae_scaling_factor
-                if args.pretrained_vae_model_name_or_path is None:
-                    model_input = model_input.to(weight_dtype)
+                if latents_mean is None and latents_std is None:
+                    model_input = model_input * vae.config.scaling_factor
+                    if args.pretrained_vae_model_name_or_path is None:
+                        model_input = model_input.to(weight_dtype)
+                else:
+                    latents_mean = latents_mean.to(device=model_input.device, dtype=model_input.dtype)
+                    latents_std = latents_std.to(device=model_input.device, dtype=model_input.dtype)
+                    model_input = (model_input - latents_mean) * vae.config.scaling_factor / latents_std
+                    model_input = model_input.to(dtype=weight_dtype)
 
                 # Sample noise that we'll add to the latents
                 noise = torch.randn_like(model_input)
@@ -1854,15 +1915,32 @@ def main(args):
                     )
 
                 bsz = model_input.shape[0]
+
                 # Sample a random timestep for each image
-                timesteps = torch.randint(
-                    0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device
-                )
-                timesteps = timesteps.long()
+                if not args.do_edm_style_training:
+                    timesteps = torch.randint(
+                        0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device
+                    )
+                    timesteps = timesteps.long()
+                else:
+                    # in EDM formulation, the model is conditioned on the pre-conditioned noise levels
+                    # instead of discrete timesteps, so here we sample indices to get the noise levels
+                    # from `scheduler.timesteps`
+                    indices = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,))
+                    timesteps = noise_scheduler.timesteps[indices].to(device=model_input.device)
 
                 # Add noise to the model input according to the noise magnitude at each timestep
                 # (this is the forward diffusion process)
                 noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)
+                # For EDM-style training, we first obtain the sigmas based on the continuous timesteps.
+                # We then precondition the final model inputs based on these sigmas instead of the timesteps.
+                # Follow: Section 5 of https://arxiv.org/abs/2206.00364.
+                if args.do_edm_style_training:
+                    sigmas = get_sigmas(timesteps, len(noisy_model_input.shape), noisy_model_input.dtype)
+                    if "EDM" in scheduler_type:
+                        inp_noisy_latents = noise_scheduler.precondition_inputs(noisy_model_input, sigmas)
+                    else:
+                        inp_noisy_latents = noisy_model_input / ((sigmas**2 + 1) ** 0.5)
 
                 # time ids
                 add_time_ids = torch.cat(
@@ -1888,7 +1966,7 @@ def main(args):
                     }
                     prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat_text_embeds, 1, 1)
                     model_pred = unet(
-                        noisy_model_input,
+                        inp_noisy_latents if args.do_edm_style_training else noisy_model_input,
                         timesteps,
                         prompt_embeds_input,
                         added_cond_kwargs=unet_added_conditions,
@@ -1906,14 +1984,42 @@ def main(args):
                     )
                     prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat_text_embeds, 1, 1)
                     model_pred = unet(
-                        noisy_model_input, timesteps, prompt_embeds_input, added_cond_kwargs=unet_added_conditions
+                        inp_noisy_latents if args.do_edm_style_training else noisy_model_input,
+                        timesteps,
+                        prompt_embeds_input,
+                        added_cond_kwargs=unet_added_conditions,
                     ).sample
 
+                weighting = None
+                if args.do_edm_style_training:
+                    # Similar to the input preconditioning, the model predictions are also preconditioned
+                    # on noised model inputs (before preconditioning) and the sigmas.
+                    # Follow: Section 5 of https://arxiv.org/abs/2206.00364.
+                    if "EDM" in scheduler_type:
+                        model_pred = noise_scheduler.precondition_outputs(noisy_model_input, model_pred, sigmas)
+                    else:
+                        if noise_scheduler.config.prediction_type == "epsilon":
+                            model_pred = model_pred * (-sigmas) + noisy_model_input
+                        elif noise_scheduler.config.prediction_type == "v_prediction":
+                            model_pred = model_pred * (-sigmas / (sigmas**2 + 1) ** 0.5) + (
+                                noisy_model_input / (sigmas**2 + 1)
+                            )
+                    # We are not doing weighting here because it tends result in numerical problems.
+                    # See: https://github.com/huggingface/diffusers/pull/7126#issuecomment-1968523051
+                    # There might be other alternatives for weighting as well:
+                    # https://github.com/huggingface/diffusers/pull/7126#discussion_r1505404686
+                    if "EDM" not in scheduler_type:
+                        weighting = (sigmas**-2.0).float()
+
                 # Get the target for loss depending on the prediction type
                 if noise_scheduler.config.prediction_type == "epsilon":
-                    target = noise
+                    target = model_input if args.do_edm_style_training else noise
                 elif noise_scheduler.config.prediction_type == "v_prediction":
-                    target = noise_scheduler.get_velocity(model_input, noise, timesteps)
+                    target = (
+                        model_input
+                        if args.do_edm_style_training
+                        else noise_scheduler.get_velocity(model_input, noise, timesteps)
+                    )
                 else:
                     raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
 
@@ -1923,10 +2029,28 @@ def main(args):
                     target, target_prior = torch.chunk(target, 2, dim=0)
 
                     # Compute prior loss
-                    prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
+                    if weighting is not None:
+                        prior_loss = torch.mean(
+                            (weighting.float() * (model_pred_prior.float() - target_prior.float()) ** 2).reshape(
+                                target_prior.shape[0], -1
+                            ),
+                            1,
+                        )
+                        prior_loss = prior_loss.mean()
+                    else:
+                        prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
 
                 if args.snr_gamma is None:
-                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+                    if weighting is not None:
+                        loss = torch.mean(
+                            (weighting.float() * (model_pred.float() - target.float()) ** 2).reshape(
+                                target.shape[0], -1
+                            ),
+                            1,
+                        )
+                        loss = loss.mean()
+                    else:
+                        loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
                 else:
                     # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
                     # Since we predict the noise instead of x_0, the original formulation is slightly changed.
@@ -2049,17 +2173,18 @@ def main(args):
                 # 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 not args.do_edm_style_training:
+                    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"
+                        if variance_type in ["learned", "learned_range"]:
+                            variance_type = "fixed_small"
 
-                    scheduler_args["variance_type"] = variance_type
+                        scheduler_args["variance_type"] = variance_type
 
-                pipeline.scheduler = DPMSolverMultistepScheduler.from_config(
-                    pipeline.scheduler.config, **scheduler_args
-                )
+                    pipeline.scheduler = DPMSolverMultistepScheduler.from_config(
+                        pipeline.scheduler.config, **scheduler_args
+                    )
 
                 pipeline = pipeline.to(accelerator.device)
                 pipeline.set_progress_bar_config(disable=True)
@@ -2067,8 +2192,13 @@ def main(args):
                 # run inference
                 generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
                 pipeline_args = {"prompt": args.validation_prompt}
+                inference_ctx = (
+                    contextlib.nullcontext()
+                    if "playground" in args.pretrained_model_name_or_path
+                    else torch.cuda.amp.autocast()
+                )
 
-                with torch.cuda.amp.autocast():
+                with inference_ctx:
                     images = [
                         pipeline(**pipeline_args, generator=generator).images[0]
                         for _ in range(args.num_validation_images)
@@ -2144,15 +2274,18 @@ def main(args):
             # 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 not args.do_edm_style_training:
+                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"
+                    if variance_type in ["learned", "learned_range"]:
+                        variance_type = "fixed_small"
 
-                scheduler_args["variance_type"] = variance_type
+                    scheduler_args["variance_type"] = variance_type
 
-            pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args)
+                pipeline.scheduler = DPMSolverMultistepScheduler.from_config(
+                    pipeline.scheduler.config, **scheduler_args
+                )
 
             # load attention processors
             pipeline.load_lora_weights(args.output_dir)

examples/community/marigold_depth_estimation.py

@@ -40,7 +40,7 @@ from diffusers.utils import BaseOutput, check_min_version
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 
 class MarigoldDepthOutput(BaseOutput):

examples/consistency_distillation/train_lcm_distill_lora_sd_wds.py

@@ -72,7 +72,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__)
 

examples/consistency_distillation/train_lcm_distill_lora_sdxl.py

@@ -65,7 +65,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__)
 

examples/consistency_distillation/train_lcm_distill_lora_sdxl_wds.py

@@ -78,7 +78,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__)
 

examples/consistency_distillation/train_lcm_distill_sd_wds.py

@@ -71,7 +71,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__)
 

examples/consistency_distillation/train_lcm_distill_sdxl_wds.py

@@ -77,7 +77,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__)
 

examples/controlnet/train_controlnet.py

@@ -60,7 +60,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__)
 

examples/controlnet/train_controlnet_flax.py

@@ -60,7 +60,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = logging.getLogger(__name__)
 

examples/controlnet/train_controlnet_sdxl.py

@@ -61,7 +61,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__)
 

examples/custom_diffusion/train_custom_diffusion.py

@@ -63,7 +63,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__)
 

examples/dreambooth/train_dreambooth.py

@@ -63,7 +63,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__)
 

examples/dreambooth/train_dreambooth_flax.py

@@ -35,7 +35,7 @@ from diffusers.utils import check_min_version
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 # Cache compiled models across invocations of this script.
 cc.initialize_cache(os.path.expanduser("~/.cache/jax/compilation_cache"))

examples/dreambooth/train_dreambooth_lora.py

@@ -70,7 +70,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__)
 

examples/dreambooth/train_dreambooth_lora_sdxl.py

@@ -75,7 +75,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__)
 

examples/instruct_pix2pix/train_instruct_pix2pix.py

@@ -53,7 +53,7 @@ from diffusers.utils.torch_utils import is_compiled_module
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py

@@ -59,7 +59,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/kandinsky2_2/text_to_image/train_text_to_image_decoder.py

@@ -52,7 +52,7 @@ if is_wandb_available():
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/kandinsky2_2/text_to_image/train_text_to_image_lora_decoder.py

@@ -46,7 +46,7 @@ from diffusers.utils import check_min_version, is_wandb_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/kandinsky2_2/text_to_image/train_text_to_image_lora_prior.py

@@ -46,7 +46,7 @@ from diffusers.utils import check_min_version, is_wandb_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/kandinsky2_2/text_to_image/train_text_to_image_prior.py

@@ -51,7 +51,7 @@ if is_wandb_available():
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/research_projects/onnxruntime/unconditional_image_generation/train_unconditional.py

@@ -637,7 +637,7 @@ def main(args):
                     generator=generator,
                     batch_size=args.eval_batch_size,
                     num_inference_steps=args.ddpm_num_inference_steps,
-                    output_type="numpy",
+                    output_type="np",
                 ).images
 
                 if args.use_ema:

examples/t2i_adapter/train_t2i_adapter_sdxl.py

@@ -60,7 +60,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__)
 

examples/text_to_image/train_text_to_image.py

@@ -56,7 +56,7 @@ if is_wandb_available():
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/text_to_image/train_text_to_image_flax.py

@@ -49,7 +49,7 @@ from diffusers.utils import check_min_version
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = logging.getLogger(__name__)
 

examples/text_to_image/train_text_to_image_lora.py

@@ -52,7 +52,7 @@ from diffusers.utils.torch_utils import is_compiled_module
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/text_to_image/train_text_to_image_lora_sdxl.py

@@ -64,7 +64,7 @@ from diffusers.utils.torch_utils import is_compiled_module
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__)
 
@@ -425,6 +425,11 @@ def parse_args(input_args=None):
         default=4,
         help=("The dimension of the LoRA update matrices."),
     )
+    parser.add_argument(
+        "--debug_loss",
+        action="store_true",
+        help="debug loss for each image, if filenames are awailable in the dataset",
+    )
 
     if input_args is not None:
         args = parser.parse_args(input_args)
@@ -603,6 +608,7 @@ def main(args):
     # Move unet, vae and text_encoder to device and cast to weight_dtype
     # The VAE is in float32 to avoid NaN losses.
     unet.to(accelerator.device, dtype=weight_dtype)
+
     if args.pretrained_vae_model_name_or_path is None:
         vae.to(accelerator.device, dtype=torch.float32)
     else:
@@ -890,13 +896,17 @@ def main(args):
         tokens_one, tokens_two = tokenize_captions(examples)
         examples["input_ids_one"] = tokens_one
         examples["input_ids_two"] = tokens_two
+        if args.debug_loss:
+            fnames = [os.path.basename(image.filename) for image in examples[image_column] if image.filename]
+            if fnames:
+                examples["filenames"] = fnames
         return examples
 
     with accelerator.main_process_first():
         if args.max_train_samples is not None:
             dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
         # Set the training transforms
-        train_dataset = dataset["train"].with_transform(preprocess_train)
+        train_dataset = dataset["train"].with_transform(preprocess_train, output_all_columns=True)
 
     def collate_fn(examples):
         pixel_values = torch.stack([example["pixel_values"] for example in examples])
@@ -905,7 +915,7 @@ def main(args):
         crop_top_lefts = [example["crop_top_lefts"] for example in examples]
         input_ids_one = torch.stack([example["input_ids_one"] for example in examples])
         input_ids_two = torch.stack([example["input_ids_two"] for example in examples])
-        return {
+        result = {
             "pixel_values": pixel_values,
             "input_ids_one": input_ids_one,
             "input_ids_two": input_ids_two,
@@ -913,6 +923,11 @@ def main(args):
             "crop_top_lefts": crop_top_lefts,
         }
 
+        filenames = [example["filenames"] for example in examples if "filenames" in example]
+        if filenames:
+            result["filenames"] = filenames
+        return result
+
     # DataLoaders creation:
     train_dataloader = torch.utils.data.DataLoader(
         train_dataset,
@@ -1105,7 +1120,9 @@ def main(args):
                     loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
                     loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
                     loss = loss.mean()
-
+                if args.debug_loss and "filenames" in batch:
+                    for fname in batch["filenames"]:
+                        accelerator.log({"loss_for_" + fname: loss}, step=global_step)
                 # Gather the losses across all processes for logging (if we use distributed training).
                 avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
                 train_loss += avg_loss.item() / args.gradient_accumulation_steps

examples/text_to_image/train_text_to_image_sdxl.py

@@ -54,7 +54,7 @@ from diffusers.utils.torch_utils import is_compiled_module
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__)
 

examples/textual_inversion/textual_inversion.py

@@ -80,7 +80,7 @@ else:
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__)
 

examples/textual_inversion/textual_inversion_flax.py

@@ -56,7 +56,7 @@ else:
 # ------------------------------------------------------------------------------
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = logging.getLogger(__name__)
 

examples/textual_inversion/textual_inversion_sdxl.py

@@ -76,7 +76,7 @@ else:
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__)
 

examples/unconditional_image_generation/train_unconditional.py

@@ -29,7 +29,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 
@@ -648,7 +648,7 @@ def main(args):
                     generator=generator,
                     batch_size=args.eval_batch_size,
                     num_inference_steps=args.ddpm_num_inference_steps,
-                    output_type="numpy",
+                    output_type="np",
                 ).images
 
                 if args.use_ema:

examples/wuerstchen/text_to_image/train_text_to_image_lora_prior.py

@@ -50,7 +50,7 @@ if is_wandb_available():
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/wuerstchen/text_to_image/train_text_to_image_prior.py

@@ -51,7 +51,7 @@ if is_wandb_available():
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.27.0.dev0")
+check_min_version("0.28.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

setup.py

@@ -249,7 +249,7 @@ version_range_max = max(sys.version_info[1], 10) + 1
 
 setup(
     name="diffusers",
-    version="0.27.0.dev0",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
+    version="0.28.0.dev0",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
     description="State-of-the-art diffusion in PyTorch and JAX.",
     long_description=open("README.md", "r", encoding="utf-8").read(),
     long_description_content_type="text/markdown",

src/diffusers/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.27.0.dev0"
+__version__ = "0.28.0.dev0"
 
 from typing import TYPE_CHECKING
 

src/diffusers/models/attention.py

@@ -293,7 +293,7 @@ class BasicTransformerBlock(nn.Module):
     ) -> torch.FloatTensor:
         if cross_attention_kwargs is not None:
             if cross_attention_kwargs.get("scale", None) is not None:
-                logger.warning("Passing `scale` to `cross_attention_kwargs` is depcrecated. `scale` will be ignored.")
+                logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
 
         # Notice that normalization is always applied before the real computation in the following blocks.
         # 0. Self-Attention

src/diffusers/models/attention_processor.py

@@ -767,7 +767,18 @@ class AttnProcessor:
         query = attn.to_q(hidden_states)
 
         if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
+            # encoder_hidden_states = hidden_states
+            batch, seq, dim = hidden_states.shape
+            height = width = seq**0.5
+            # reshape to (batch, height, width, dim)
+            encoder_hidden_states = hidden_states.view(batch, height, width, dim)
+            # reshape to (batch, dim, height, width)
+            encoder_hidden_states = encoder_hidden_states.permute(0, 3, 1, 2)
+            encoder_hidden_states = torch.nn.functional.avg_pool2d(hidden_states, kernel_size=4)
+            # reshape to (batch, dim, seq)
+            encoder_hidden_states = encoder_hidden_states.view(batch, dim, -1)
+            # reshape to (batch, seq, dim)
+            encoder_hidden_states = encoder_hidden_states.permute(0, 2, 1)
         elif attn.norm_cross:
             encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
 
@@ -1259,7 +1270,18 @@ class AttnProcessor2_0:
         query = attn.to_q(hidden_states)
 
         if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
+            # encoder_hidden_states = hidden_states
+            batch, seq, dim = hidden_states.shape
+            height = width = seq**0.5
+            # reshape to (batch, height, width, dim)
+            encoder_hidden_states = hidden_states.view(batch, height, width, dim)
+            # reshape to (batch, dim, height, width)
+            encoder_hidden_states = encoder_hidden_states.permute(0, 3, 1, 2)
+            encoder_hidden_states = torch.nn.functional.avg_pool2d(hidden_states, kernel_size=4)
+            # reshape to (batch, dim, seq)
+            encoder_hidden_states = encoder_hidden_states.view(batch, dim, -1)
+            # reshape to (batch, seq, dim)
+            encoder_hidden_states = encoder_hidden_states.permute(0, 2, 1)
         elif attn.norm_cross:
             encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
 

src/diffusers/models/transformers/transformer_2d.py

@@ -308,7 +308,7 @@ class Transformer2DModel(ModelMixin, ConfigMixin):
         """
         if cross_attention_kwargs is not None:
             if cross_attention_kwargs.get("scale", None) is not None:
-                logger.warning("Passing `scale` to `cross_attention_kwargs` is depcrecated. `scale` will be ignored.")
+                logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
         # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
         #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
         #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.

src/diffusers/models/unets/unet_2d_blocks.py

@@ -846,7 +846,7 @@ class UNetMidBlock2DCrossAttn(nn.Module):
     ) -> torch.FloatTensor:
         if cross_attention_kwargs is not None:
             if cross_attention_kwargs.get("scale", None) is not None:
-                logger.warning("Passing `scale` to `cross_attention_kwargs` is depcrecated. `scale` will be ignored.")
+                logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
 
         hidden_states = self.resnets[0](hidden_states, temb)
         for attn, resnet in zip(self.attentions, self.resnets[1:]):
@@ -986,7 +986,7 @@ class UNetMidBlock2DSimpleCrossAttn(nn.Module):
     ) -> torch.FloatTensor:
         cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
         if cross_attention_kwargs.get("scale", None) is not None:
-            logger.warning("Passing `scale` to `cross_attention_kwargs` is depcrecated. `scale` will be ignored.")
+            logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
 
         if attention_mask is None:
             # if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask.
@@ -1116,7 +1116,7 @@ class AttnDownBlock2D(nn.Module):
     ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
         cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
         if cross_attention_kwargs.get("scale", None) is not None:
-            logger.warning("Passing `scale` to `cross_attention_kwargs` is depcrecated. `scale` will be ignored.")
+            logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
 
         output_states = ()
 
@@ -1241,7 +1241,7 @@ class CrossAttnDownBlock2D(nn.Module):
     ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
         if cross_attention_kwargs is not None:
             if cross_attention_kwargs.get("scale", None) is not None:
-                logger.warning("Passing `scale` to `cross_attention_kwargs` is depcrecated. `scale` will be ignored.")
+                logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
 
         output_states = ()
 
@@ -1986,7 +1986,7 @@ class SimpleCrossAttnDownBlock2D(nn.Module):
     ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
         cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
         if cross_attention_kwargs.get("scale", None) is not None:
-            logger.warning("Passing `scale` to `cross_attention_kwargs` is depcrecated. `scale` will be ignored.")
+            logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
 
         output_states = ()
 
@@ -2201,7 +2201,7 @@ class KCrossAttnDownBlock2D(nn.Module):
     ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
         cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
         if cross_attention_kwargs.get("scale", None) is not None:
-            logger.warning("Passing `scale` to `cross_attention_kwargs` is depcrecated. `scale` will be ignored.")
+            logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
 
         output_states = ()
 
@@ -2483,7 +2483,7 @@ class CrossAttnUpBlock2D(nn.Module):
     ) -> torch.FloatTensor:
         if cross_attention_kwargs is not None:
             if cross_attention_kwargs.get("scale", None) is not None:
-                logger.warning("Passing `scale` to `cross_attention_kwargs` is depcrecated. `scale` will be ignored.")
+                logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
 
         is_freeu_enabled = (
             getattr(self, "s1", None)
@@ -3312,7 +3312,7 @@ class SimpleCrossAttnUpBlock2D(nn.Module):
     ) -> torch.FloatTensor:
         cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
         if cross_attention_kwargs.get("scale", None) is not None:
-            logger.warning("Passing `scale` to `cross_attention_kwargs` is depcrecated. `scale` will be ignored.")
+            logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
 
         if attention_mask is None:
             # if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask.
@@ -3694,7 +3694,7 @@ class KAttentionBlock(nn.Module):
     ) -> torch.FloatTensor:
         cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
         if cross_attention_kwargs.get("scale", None) is not None:
-            logger.warning("Passing `scale` to `cross_attention_kwargs` is depcrecated. `scale` will be ignored.")
+            logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
 
         # 1. Self-Attention
         if self.add_self_attention:

src/diffusers/models/unets/unet_3d_blocks.py

@@ -1183,7 +1183,7 @@ class CrossAttnDownBlockMotion(nn.Module):
     ):
         if cross_attention_kwargs is not None:
             if cross_attention_kwargs.get("scale", None) is not None:
-                logger.warning("Passing `scale` to `cross_attention_kwargs` is depcrecated. `scale` will be ignored.")
+                logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
 
         output_states = ()
 
@@ -1367,7 +1367,7 @@ class CrossAttnUpBlockMotion(nn.Module):
     ) -> torch.FloatTensor:
         if cross_attention_kwargs is not None:
             if cross_attention_kwargs.get("scale", None) is not None:
-                logger.warning("Passing `scale` to `cross_attention_kwargs` is depcrecated. `scale` will be ignored.")
+                logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
 
         is_freeu_enabled = (
             getattr(self, "s1", None)
@@ -1707,7 +1707,7 @@ class UNetMidBlockCrossAttnMotion(nn.Module):
     ) -> torch.FloatTensor:
         if cross_attention_kwargs is not None:
             if cross_attention_kwargs.get("scale", None) is not None:
-                logger.warning("Passing `scale` to `cross_attention_kwargs` is depcrecated. `scale` will be ignored.")
+                logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
 
         hidden_states = self.resnets[0](hidden_states, temb)
 

src/diffusers/pipelines/amused/pipeline_amused_img2img.py

@@ -127,7 +127,7 @@ class AmusedImg2ImgPipeline(DiffusionPipeline):
                 on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
                 process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
                 essentially ignores `image`.
-            num_inference_steps (`int`, *optional*, defaults to 16):
+            num_inference_steps (`int`, *optional*, defaults to 12):
                 The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                 expense of slower inference.
             guidance_scale (`float`, *optional*, defaults to 10.0):
@@ -191,7 +191,7 @@ class AmusedImg2ImgPipeline(DiffusionPipeline):
             negative_prompt_embeds is None and negative_encoder_hidden_states is not None
         ):
             raise ValueError(
-                "pass either both `negatve_prompt_embeds` and `negative_encoder_hidden_states` or neither"
+                "pass either both `negative_prompt_embeds` and `negative_encoder_hidden_states` or neither"
             )
 
         if (prompt is None and prompt_embeds is None) or (prompt is not None and prompt_embeds is not None):

src/diffusers/pipelines/controlnet/pipeline_controlnet.py

@@ -824,20 +824,22 @@ class StableDiffusionControlNetPipeline(
         return latents
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
-    def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+    def get_guidance_scale_embedding(
+        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
+    ) -> torch.FloatTensor:
         """
         See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
 
         Args:
-            timesteps (`torch.Tensor`):
-                generate embedding vectors at these timesteps
+            w (`torch.Tensor`):
+                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
             embedding_dim (`int`, *optional*, defaults to 512):
-                dimension of the embeddings to generate
-            dtype:
-                data type of the generated embeddings
+                Dimension of the embeddings to generate.
+            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
+                Data type of the generated embeddings.
 
         Returns:
-            `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+            `torch.FloatTensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
         """
         assert len(w.shape) == 1
         w = w * 1000.0

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py

@@ -869,20 +869,22 @@ class StableDiffusionXLControlNetPipeline(
             self.vae.decoder.mid_block.to(dtype)
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
-    def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+    def get_guidance_scale_embedding(
+        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
+    ) -> torch.FloatTensor:
         """
         See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
 
         Args:
-            timesteps (`torch.Tensor`):
-                generate embedding vectors at these timesteps
+            w (`torch.Tensor`):
+                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
             embedding_dim (`int`, *optional*, defaults to 512):
-                dimension of the embeddings to generate
-            dtype:
-                data type of the generated embeddings
+                Dimension of the embeddings to generate.
+            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
+                Data type of the generated embeddings.
 
         Returns:
-            `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+            `torch.FloatTensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
         """
         assert len(w.shape) == 1
         w = w * 1000.0

src/diffusers/pipelines/deprecated/spectrogram_diffusion/pipeline_spectrogram_diffusion.py

@@ -133,7 +133,7 @@ class SpectrogramDiffusionPipeline(DiffusionPipeline):
         generator: Optional[torch.Generator] = None,
         num_inference_steps: int = 100,
         return_dict: bool = True,
-        output_type: str = "numpy",
+        output_type: str = "np",
         callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
         callback_steps: int = 1,
     ) -> Union[AudioPipelineOutput, Tuple]:
@@ -157,7 +157,7 @@ class SpectrogramDiffusionPipeline(DiffusionPipeline):
                 expense of slower inference.
             return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~pipelines.AudioPipelineOutput`] instead of a plain tuple.
-            output_type (`str`, *optional*, defaults to `"numpy"`):
+            output_type (`str`, *optional*, defaults to `"np"`):
                 The output format of the generated audio.
             callback (`Callable`, *optional*):
                 A function that calls every `callback_steps` steps during inference. The function is called with the
@@ -249,16 +249,16 @@ class SpectrogramDiffusionPipeline(DiffusionPipeline):
 
             logger.info("Generated segment", i)
 
-        if output_type == "numpy" and not is_onnx_available():
+        if output_type == "np" and not is_onnx_available():
             raise ValueError(
                 "Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'."
             )
-        elif output_type == "numpy" and self.melgan is None:
+        elif output_type == "np" and self.melgan is None:
             raise ValueError(
                 "Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'."
             )
 
-        if output_type == "numpy":
+        if output_type == "np":
             output = self.melgan(input_features=full_pred_mel.astype(np.float32))
         else:
             output = full_pred_mel

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

@@ -2004,7 +2004,7 @@ class CrossAttnUpBlockFlat(nn.Module):
     ) -> torch.FloatTensor:
         if cross_attention_kwargs is not None:
             if cross_attention_kwargs.get("scale", None) is not None:
-                logger.warning("Passing `scale` to `cross_attention_kwargs` is depcrecated. `scale` will be ignored.")
+                logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
 
         is_freeu_enabled = (
             getattr(self, "s1", None)
@@ -2338,7 +2338,7 @@ class UNetMidBlockFlatCrossAttn(nn.Module):
     ) -> torch.FloatTensor:
         if cross_attention_kwargs is not None:
             if cross_attention_kwargs.get("scale", None) is not None:
-                logger.warning("Passing `scale` to `cross_attention_kwargs` is depcrecated. `scale` will be ignored.")
+                logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
 
         hidden_states = self.resnets[0](hidden_states, temb)
         for attn, resnet in zip(self.attentions, self.resnets[1:]):
@@ -2479,7 +2479,7 @@ class UNetMidBlockFlatSimpleCrossAttn(nn.Module):
     ) -> torch.FloatTensor:
         cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
         if cross_attention_kwargs.get("scale", None) is not None:
-            logger.warning("Passing `scale` to `cross_attention_kwargs` is depcrecated. `scale` will be ignored.")
+            logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
 
         if attention_mask is None:
             # if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask.

src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py

@@ -548,20 +548,22 @@ class LatentConsistencyModelImg2ImgPipeline(
         return latents
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
-    def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+    def get_guidance_scale_embedding(
+        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
+    ) -> torch.FloatTensor:
         """
         See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
 
         Args:
-            timesteps (`torch.Tensor`):
-                generate embedding vectors at these timesteps
+            w (`torch.Tensor`):
+                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
             embedding_dim (`int`, *optional*, defaults to 512):
-                dimension of the embeddings to generate
-            dtype:
-                data type of the generated embeddings
+                Dimension of the embeddings to generate.
+            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
+                Data type of the generated embeddings.
 
         Returns:
-            `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+            `torch.FloatTensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
         """
         assert len(w.shape) == 1
         w = w * 1000.0

src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py

@@ -490,20 +490,22 @@ class LatentConsistencyModelPipeline(
         latents = latents * self.scheduler.init_noise_sigma
         return latents
 
-    def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+    def get_guidance_scale_embedding(
+        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
+    ) -> torch.FloatTensor:
         """
         See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
 
         Args:
-            timesteps (`torch.Tensor`):
-                generate embedding vectors at these timesteps
+            w (`torch.Tensor`):
+                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
             embedding_dim (`int`, *optional*, defaults to 512):
-                dimension of the embeddings to generate
-            dtype:
-                data type of the generated embeddings
+                Dimension of the embeddings to generate.
+            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
+                Data type of the generated embeddings.
 
         Returns:
-            `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+            `torch.FloatTensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
         """
         assert len(w.shape) == 1
         w = w * 1000.0

src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py

@@ -713,20 +713,22 @@ class LEditsPPPipelineStableDiffusionXL(
             self.vae.decoder.mid_block.to(dtype)
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
-    def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+    def get_guidance_scale_embedding(
+        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
+    ) -> torch.FloatTensor:
         """
         See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
 
         Args:
-            timesteps (`torch.Tensor`):
-                generate embedding vectors at these timesteps
+            w (`torch.Tensor`):
+                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
             embedding_dim (`int`, *optional*, defaults to 512):
-                dimension of the embeddings to generate
-            dtype:
-                data type of the generated embeddings
+                Dimension of the embeddings to generate.
+            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
+                Data type of the generated embeddings.
 
         Returns:
-            `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+            `torch.FloatTensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
         """
         assert len(w.shape) == 1
         w = w * 1000.0

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -669,20 +669,22 @@ class StableDiffusionPipeline(
         return latents
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
-    def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+    def get_guidance_scale_embedding(
+        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
+    ) -> torch.FloatTensor:
         """
         See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
 
         Args:
-            timesteps (`torch.Tensor`):
-                generate embedding vectors at these timesteps
+            w (`torch.Tensor`):
+                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
             embedding_dim (`int`, *optional*, defaults to 512):
-                dimension of the embeddings to generate
-            dtype:
-                data type of the generated embeddings
+                Dimension of the embeddings to generate.
+            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
+                Data type of the generated embeddings.
 
         Returns:
-            `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+            `torch.FloatTensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
         """
         assert len(w.shape) == 1
         w = w * 1000.0

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -767,20 +767,22 @@ class StableDiffusionImg2ImgPipeline(
         return latents
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
-    def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+    def get_guidance_scale_embedding(
+        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
+    ) -> torch.FloatTensor:
         """
         See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
 
         Args:
-            timesteps (`torch.Tensor`):
-                generate embedding vectors at these timesteps
+            w (`torch.Tensor`):
+                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
             embedding_dim (`int`, *optional*, defaults to 512):
-                dimension of the embeddings to generate
-            dtype:
-                data type of the generated embeddings
+                Dimension of the embeddings to generate.
+            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
+                Data type of the generated embeddings.
 
         Returns:
-            `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+            `torch.FloatTensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
         """
         assert len(w.shape) == 1
         w = w * 1000.0

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -909,20 +909,22 @@ class StableDiffusionInpaintPipeline(
         return timesteps, num_inference_steps - t_start
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
-    def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+    def get_guidance_scale_embedding(
+        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
+    ) -> torch.FloatTensor:
         """
         See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
 
         Args:
-            timesteps (`torch.Tensor`):
-                generate embedding vectors at these timesteps
+            w (`torch.Tensor`):
+                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
             embedding_dim (`int`, *optional*, defaults to 512):
-                dimension of the embeddings to generate
-            dtype:
-                data type of the generated embeddings
+                Dimension of the embeddings to generate.
+            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
+                Data type of the generated embeddings.
 
         Returns:
-            `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+            `torch.FloatTensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
         """
         assert len(w.shape) == 1
         w = w * 1000.0

src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py

@@ -1304,7 +1304,7 @@ class StableDiffusionDiffEditPipeline(
         callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
         callback_steps: int = 1,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        clip_ckip: int = None,
+        clip_skip: int = None,
     ):
         r"""
         The call function to the pipeline for generation.
@@ -1426,7 +1426,7 @@ class StableDiffusionDiffEditPipeline(
             prompt_embeds=prompt_embeds,
             negative_prompt_embeds=negative_prompt_embeds,
             lora_scale=text_encoder_lora_scale,
-            clip_skip=clip_ckip,
+            clip_skip=clip_skip,
         )
         # For classifier free guidance, we need to do two forward passes.
         # Here we concatenate the unconditional and text embeddings into a single batch

src/diffusers/pipelines/stable_diffusion_ldm3d/pipeline_stable_diffusion_ldm3d.py

@@ -644,20 +644,22 @@ class StableDiffusionLDM3DPipeline(
         return latents
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
-    def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+    def get_guidance_scale_embedding(
+        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
+    ) -> torch.FloatTensor:
         """
         See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
 
         Args:
-            timesteps (`torch.Tensor`):
-                generate embedding vectors at these timesteps
+            w (`torch.Tensor`):
+                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
             embedding_dim (`int`, *optional*, defaults to 512):
-                dimension of the embeddings to generate
-            dtype:
-                data type of the generated embeddings
+                Dimension of the embeddings to generate.
+            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
+                Data type of the generated embeddings.
 
         Returns:
-            `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+            `torch.FloatTensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
         """
         assert len(w.shape) == 1
         w = w * 1000.0

src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py

@@ -632,7 +632,7 @@ class StableDiffusionSAGPipeline(DiffusionPipeline, StableDiffusionMixin, Textua
         # corresponds to doing no classifier free guidance.
         do_classifier_free_guidance = guidance_scale > 1.0
         # and `sag_scale` is` `s` of equation (16)
-        # of the self-attentnion guidance paper: https://arxiv.org/pdf/2210.00939.pdf
+        # of the self-attention guidance paper: https://arxiv.org/pdf/2210.00939.pdf
         # `sag_scale = 0` means no self-attention guidance
         do_self_attention_guidance = sag_scale > 0.0
 
@@ -667,7 +667,7 @@ class StableDiffusionSAGPipeline(DiffusionPipeline, StableDiffusionMixin, Textua
 
         if timesteps.dtype not in [torch.int16, torch.int32, torch.int64]:
             raise ValueError(
-                f"{self.__class__.__name__} does not support using a scheduler of type {self.scheduler.__class__.__name__}. Please make sure to use one of 'DDIMScheduler, PNDMScheduler, DDPMScheduler, DEISMultistepScheduler, UniPCMultistepScheduler, DPMSolverMultistepScheduler, DPMSolverSinlgestepScheduler'."
+                f"{self.__class__.__name__} does not support using a scheduler of type {self.scheduler.__class__.__name__}. Please make sure to use one of 'DDIMScheduler, PNDMScheduler, DDPMScheduler, DEISMultistepScheduler, UniPCMultistepScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler'."
             )
 
         # 5. Prepare latent variables
@@ -723,7 +723,7 @@ class StableDiffusionSAGPipeline(DiffusionPipeline, StableDiffusionMixin, Textua
                         noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                         noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
 
-                    # perform self-attention guidance with the stored self-attentnion map
+                    # perform self-attention guidance with the stored self-attention map
                     if do_self_attention_guidance:
                         # classifier-free guidance produces two chunks of attention map
                         # and we only use unconditional one according to equation (25)

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py

@@ -740,20 +740,22 @@ class StableDiffusionXLPipeline(
             self.vae.decoder.mid_block.to(dtype)
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
-    def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+    def get_guidance_scale_embedding(
+        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
+    ) -> torch.FloatTensor:
         """
         See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
 
         Args:
-            timesteps (`torch.Tensor`):
-                generate embedding vectors at these timesteps
+            w (`torch.Tensor`):
+                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
             embedding_dim (`int`, *optional*, defaults to 512):
-                dimension of the embeddings to generate
-            dtype:
-                data type of the generated embeddings
+                Dimension of the embeddings to generate.
+            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
+                Data type of the generated embeddings.
 
         Returns:
-            `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+            `torch.FloatTensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
         """
         assert len(w.shape) == 1
         w = w * 1000.0

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -874,20 +874,22 @@ class StableDiffusionXLImg2ImgPipeline(
             self.vae.decoder.mid_block.to(dtype)
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
-    def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+    def get_guidance_scale_embedding(
+        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
+    ) -> torch.FloatTensor:
         """
         See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
 
         Args:
-            timesteps (`torch.Tensor`):
-                generate embedding vectors at these timesteps
+            w (`torch.Tensor`):
+                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
             embedding_dim (`int`, *optional*, defaults to 512):
-                dimension of the embeddings to generate
-            dtype:
-                data type of the generated embeddings
+                Dimension of the embeddings to generate.
+            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
+                Data type of the generated embeddings.
 
         Returns:
-            `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+            `torch.FloatTensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
         """
         assert len(w.shape) == 1
         w = w * 1000.0

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py

@@ -1110,20 +1110,22 @@ class StableDiffusionXLInpaintPipeline(
             self.vae.decoder.mid_block.to(dtype)
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
-    def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+    def get_guidance_scale_embedding(
+        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
+    ) -> torch.FloatTensor:
         """
         See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
 
         Args:
-            timesteps (`torch.Tensor`):
-                generate embedding vectors at these timesteps
+            w (`torch.Tensor`):
+                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
             embedding_dim (`int`, *optional*, defaults to 512):
-                dimension of the embeddings to generate
-            dtype:
-                data type of the generated embeddings
+                Dimension of the embeddings to generate.
+            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
+                Data type of the generated embeddings.
 
         Returns:
-            `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+            `torch.FloatTensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
         """
         assert len(w.shape) == 1
         w = w * 1000.0

src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py

@@ -613,20 +613,22 @@ class StableDiffusionAdapterPipeline(DiffusionPipeline, StableDiffusionMixin):
         return height, width
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
-    def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+    def get_guidance_scale_embedding(
+        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
+    ) -> torch.FloatTensor:
         """
         See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
 
         Args:
-            timesteps (`torch.Tensor`):
-                generate embedding vectors at these timesteps
+            w (`torch.Tensor`):
+                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
             embedding_dim (`int`, *optional*, defaults to 512):
-                dimension of the embeddings to generate
-            dtype:
-                data type of the generated embeddings
+                Dimension of the embeddings to generate.
+            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
+                Data type of the generated embeddings.
 
         Returns:
-            `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+            `torch.FloatTensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
         """
         assert len(w.shape) == 1
         w = w * 1000.0

src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py

@@ -784,20 +784,22 @@ class StableDiffusionXLAdapterPipeline(
         return height, width
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
-    def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+    def get_guidance_scale_embedding(
+        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
+    ) -> torch.FloatTensor:
         """
         See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
 
         Args:
-            timesteps (`torch.Tensor`):
-                generate embedding vectors at these timesteps
+            w (`torch.Tensor`):
+                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
             embedding_dim (`int`, *optional*, defaults to 512):
-                dimension of the embeddings to generate
-            dtype:
-                data type of the generated embeddings
+                Dimension of the embeddings to generate.
+            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
+                Data type of the generated embeddings.
 
         Returns:
-            `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+            `torch.FloatTensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
         """
         assert len(w.shape) == 1
         w = w * 1000.0

src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py

@@ -575,8 +575,8 @@ class TextToVideoZeroPipeline(DiffusionPipeline, StableDiffusionMixin, TextualIn
                 Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video
                 generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                 tensor is generated by sampling using the supplied random `generator`.
-            output_type (`str`, *optional*, defaults to `"numpy"`):
-                The output format of the generated video. Choose between `"latent"` and `"numpy"`.
+            output_type (`str`, *optional*, defaults to `"np"`):
+                The output format of the generated video. Choose between `"latent"` and `"np"`.
             return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a
                 [`~pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoPipelineOutput`] instead of

src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py

@@ -223,6 +223,8 @@ class DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin):
         """
         steps = num_inference_steps
         order = self.config.solver_order
+        if order > 3:
+            raise ValueError("Order > 3 is not supported by this scheduler")
         if self.config.lower_order_final:
             if order == 3:
                 if steps % 3 == 0:

tests/models/autoencoders/test_models_vae.py

@@ -829,7 +829,7 @@ class AutoencoderKLIntegrationTests(unittest.TestCase):
             "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/blob/main/vae-ft-mse-840000-ema-pruned.safetensors",
         )
 
-        assert vae_default.config.scaling_factor == 0.18125
+        assert vae_default.config.scaling_factor == 0.18215
         assert vae_default.config.sample_size == 512
         assert vae_default.dtype == torch.float32
 

tests/models/unets/test_models_unet_stable_cascade.py

@@ -50,9 +50,7 @@ class StableCascadeUNetModelSlowTests(unittest.TestCase):
         gc.collect()
         torch.cuda.empty_cache()
 
-        unet = StableCascadeUNet.from_pretrained(
-            "stabilityai/stable-cascade-prior", subfolder="prior", revision="refs/pr/2", variant="bf16"
-        )
+        unet = StableCascadeUNet.from_pretrained("stabilityai/stable-cascade-prior", subfolder="prior", variant="bf16")
         unet_config = unet.config
         del unet
         gc.collect()
@@ -74,9 +72,7 @@ class StableCascadeUNetModelSlowTests(unittest.TestCase):
         gc.collect()
         torch.cuda.empty_cache()
 
-        unet = StableCascadeUNet.from_pretrained(
-            "stabilityai/stable-cascade", subfolder="decoder", revision="refs/pr/44", variant="bf16"
-        )
+        unet = StableCascadeUNet.from_pretrained("stabilityai/stable-cascade", subfolder="decoder", variant="bf16")
         unet_config = unet.config
         del unet
         gc.collect()

tests/pipelines/controlnet/test_controlnet.py

@@ -211,7 +211,7 @@ class ControlNetPipelineFastTests(
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
             "image": image,
         }
 
@@ -402,7 +402,7 @@ class StableDiffusionMultiControlNetPipelineFastTests(
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
             "image": images,
         }
 
@@ -602,7 +602,7 @@ class StableDiffusionMultiControlNetOneModelPipelineFastTests(
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
             "image": images,
         }
 
@@ -1092,6 +1092,13 @@ class ControlNetPipelineSlowTests(unittest.TestCase):
         for param_name, param_value in single_file_pipe.controlnet.config.items():
             if param_name in PARAMS_TO_IGNORE:
                 continue
+
+            # This parameter doesn't appear to be loaded from the config.
+            # So when it is registered to config, it remains a tuple as this is the default in the class definition
+            # from_pretrained, does load from config and converts to a list when registering to config
+            if param_name == "conditioning_embedding_out_channels" and isinstance(param_value, tuple):
+                param_value = list(param_value)
+
             assert (
                 pipe.controlnet.config[param_name] == param_value
             ), f"{param_name} differs between single file loading and pretrained loading"

tests/pipelines/controlnet/test_controlnet_img2img.py

@@ -164,7 +164,7 @@ class ControlNetImg2ImgPipelineFastTests(
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
             "image": image,
             "control_image": control_image,
         }
@@ -313,7 +313,7 @@ class StableDiffusionMultiControlNetPipelineFastTests(
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
             "image": image,
             "control_image": control_image,
         }

tests/pipelines/controlnet/test_controlnet_inpaint.py

@@ -155,7 +155,7 @@ class ControlNetInpaintPipelineFastTests(
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
             "image": image,
             "mask_image": mask_image,
             "control_image": control_image,
@@ -375,7 +375,7 @@ class MultiControlNetInpaintPipelineFastTests(
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
             "image": image,
             "mask_image": mask_image,
             "control_image": control_image,

tests/pipelines/controlnet/test_controlnet_inpaint_sdxl.py

@@ -172,7 +172,7 @@ class ControlNetPipelineSDXLFastTests(
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
             "image": init_image,
             "mask_image": mask_image,
             "control_image": control_image,

tests/pipelines/controlnet/test_controlnet_sdxl.py

@@ -1002,6 +1002,11 @@ class ControlNetSDXLPipelineSlowTests(unittest.TestCase):
         for param_name, param_value in single_file_pipe.unet.config.items():
             if param_name in PARAMS_TO_IGNORE:
                 continue
+
+            # Upcast attention might be set to None in a config file, which is incorrect. It should default to False in the model
+            if param_name == "upcast_attention" and pipe.unet.config[param_name] is None:
+                pipe.unet.config[param_name] = False
+
             assert (
                 pipe.unet.config[param_name] == param_value
             ), f"{param_name} differs between single file loading and pretrained loading"

tests/pipelines/controlnet/test_controlnet_sdxl_img2img.py

@@ -163,7 +163,7 @@ class ControlNetPipelineSDXLImg2ImgFastTests(
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
             "image": image,
             "control_image": image,
         }

tests/pipelines/ddim/test_ddim.py

@@ -63,7 +63,7 @@ class DDIMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             "batch_size": 1,
             "generator": generator,
             "num_inference_steps": 2,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -113,7 +113,7 @@ class DDIMPipelineIntegrationTests(unittest.TestCase):
         ddim.set_progress_bar_config(disable=None)
 
         generator = torch.manual_seed(0)
-        image = ddim(generator=generator, eta=0.0, output_type="numpy").images
+        image = ddim(generator=generator, eta=0.0, output_type="np").images
 
         image_slice = image[0, -3:, -3:, -1]
 
@@ -133,7 +133,7 @@ class DDIMPipelineIntegrationTests(unittest.TestCase):
         ddpm.set_progress_bar_config(disable=None)
 
         generator = torch.manual_seed(0)
-        image = ddpm(generator=generator, output_type="numpy").images
+        image = ddpm(generator=generator, output_type="np").images
 
         image_slice = image[0, -3:, -3:, -1]
 

tests/pipelines/ddpm/test_ddpm.py

@@ -50,10 +50,10 @@ class DDPMPipelineFastTests(unittest.TestCase):
         ddpm.set_progress_bar_config(disable=None)
 
         generator = torch.Generator(device=device).manual_seed(0)
-        image = ddpm(generator=generator, num_inference_steps=2, output_type="numpy").images
+        image = ddpm(generator=generator, num_inference_steps=2, output_type="np").images
 
         generator = torch.Generator(device=device).manual_seed(0)
-        image_from_tuple = ddpm(generator=generator, num_inference_steps=2, output_type="numpy", return_dict=False)[0]
+        image_from_tuple = ddpm(generator=generator, num_inference_steps=2, output_type="np", return_dict=False)[0]
 
         image_slice = image[0, -3:, -3:, -1]
         image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
@@ -75,10 +75,10 @@ class DDPMPipelineFastTests(unittest.TestCase):
         ddpm.set_progress_bar_config(disable=None)
 
         generator = torch.manual_seed(0)
-        image = ddpm(generator=generator, num_inference_steps=2, output_type="numpy").images
+        image = ddpm(generator=generator, num_inference_steps=2, output_type="np").images
 
         generator = torch.manual_seed(0)
-        image_eps = ddpm(generator=generator, num_inference_steps=2, output_type="numpy")[0]
+        image_eps = ddpm(generator=generator, num_inference_steps=2, output_type="np")[0]
 
         image_slice = image[0, -3:, -3:, -1]
         image_eps_slice = image_eps[0, -3:, -3:, -1]
@@ -102,7 +102,7 @@ class DDPMPipelineIntegrationTests(unittest.TestCase):
         ddpm.set_progress_bar_config(disable=None)
 
         generator = torch.manual_seed(0)
-        image = ddpm(generator=generator, output_type="numpy").images
+        image = ddpm(generator=generator, output_type="np").images
 
         image_slice = image[0, -3:, -3:, -1]
 

tests/pipelines/deepfloyd_if/test_if.py

@@ -50,7 +50,7 @@ class IFPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin, unittest.T
             "prompt": "A painting of a squirrel eating a burger",
             "generator": generator,
             "num_inference_steps": 2,
-            "output_type": "numpy",
+            "output_type": "np",
         }
 
         return inputs

tests/pipelines/deepfloyd_if/test_if_img2img.py

@@ -55,7 +55,7 @@ class IFImg2ImgPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin, uni
             "image": image,
             "generator": generator,
             "num_inference_steps": 2,
-            "output_type": "numpy",
+            "output_type": "np",
         }
 
         return inputs

tests/pipelines/deepfloyd_if/test_if_img2img_superresolution.py

@@ -57,7 +57,7 @@ class IFImg2ImgSuperResolutionPipelineFastTests(PipelineTesterMixin, IFPipelineT
             "original_image": original_image,
             "generator": generator,
             "num_inference_steps": 2,
-            "output_type": "numpy",
+            "output_type": "np",
         }
 
         return inputs

tests/pipelines/deepfloyd_if/test_if_inpainting.py

@@ -57,7 +57,7 @@ class IFInpaintingPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin,
             "mask_image": mask_image,
             "generator": generator,
             "num_inference_steps": 2,
-            "output_type": "numpy",
+            "output_type": "np",
         }
 
         return inputs

tests/pipelines/deepfloyd_if/test_if_inpainting_superresolution.py

@@ -59,7 +59,7 @@ class IFInpaintingSuperResolutionPipelineFastTests(PipelineTesterMixin, IFPipeli
             "mask_image": mask_image,
             "generator": generator,
             "num_inference_steps": 2,
-            "output_type": "numpy",
+            "output_type": "np",
         }
 
         return inputs

tests/pipelines/deepfloyd_if/test_if_superresolution.py

@@ -52,7 +52,7 @@ class IFSuperResolutionPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMi
             "image": image,
             "generator": generator,
             "num_inference_steps": 2,
-            "output_type": "numpy",
+            "output_type": "np",
         }
 
         return inputs

tests/pipelines/dit/test_dit.py

@@ -74,7 +74,7 @@ class DiTPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             "class_labels": [1],
             "generator": generator,
             "num_inference_steps": 2,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 

tests/pipelines/latent_diffusion/test_latent_diffusion.py

@@ -113,7 +113,7 @@ class LDMTextToImagePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -153,7 +153,7 @@ class LDMTextToImagePipelineSlowTests(unittest.TestCase):
             "generator": generator,
             "num_inference_steps": 3,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -189,7 +189,7 @@ class LDMTextToImagePipelineNightlyTests(unittest.TestCase):
             "generator": generator,
             "num_inference_steps": 50,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 

tests/pipelines/latent_diffusion/test_latent_diffusion_superresolution.py

@@ -84,7 +84,7 @@ class LDMSuperResolutionPipelineFastTests(unittest.TestCase):
         init_image = self.dummy_image.to(device)
 
         generator = torch.Generator(device=device).manual_seed(0)
-        image = ldm(image=init_image, generator=generator, num_inference_steps=2, output_type="numpy").images
+        image = ldm(image=init_image, generator=generator, num_inference_steps=2, output_type="np").images
 
         image_slice = image[0, -3:, -3:, -1]
 
@@ -109,7 +109,7 @@ class LDMSuperResolutionPipelineFastTests(unittest.TestCase):
 
         init_image = self.dummy_image.to(torch_device)
 
-        image = ldm(init_image, num_inference_steps=2, output_type="numpy").images
+        image = ldm(init_image, num_inference_steps=2, output_type="np").images
 
         assert image.shape == (1, 64, 64, 3)
 
@@ -128,7 +128,7 @@ class LDMSuperResolutionPipelineIntegrationTests(unittest.TestCase):
         ldm.set_progress_bar_config(disable=None)
 
         generator = torch.manual_seed(0)
-        image = ldm(image=init_image, generator=generator, num_inference_steps=20, output_type="numpy").images
+        image = ldm(image=init_image, generator=generator, num_inference_steps=20, output_type="np").images
 
         image_slice = image[0, -3:, -3:, -1]
 

tests/pipelines/paint_by_example/test_paint_by_example.py

@@ -117,7 +117,7 @@ class PaintByExamplePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 

tests/pipelines/pndm/test_pndm.py

@@ -49,10 +49,10 @@ class PNDMPipelineFastTests(unittest.TestCase):
         pndm.set_progress_bar_config(disable=None)
 
         generator = torch.manual_seed(0)
-        image = pndm(generator=generator, num_inference_steps=20, output_type="numpy").images
+        image = pndm(generator=generator, num_inference_steps=20, output_type="np").images
 
         generator = torch.manual_seed(0)
-        image_from_tuple = pndm(generator=generator, num_inference_steps=20, output_type="numpy", return_dict=False)[0]
+        image_from_tuple = pndm(generator=generator, num_inference_steps=20, output_type="np", return_dict=False)[0]
 
         image_slice = image[0, -3:, -3:, -1]
         image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
@@ -77,7 +77,7 @@ class PNDMPipelineIntegrationTests(unittest.TestCase):
         pndm.to(torch_device)
         pndm.set_progress_bar_config(disable=None)
         generator = torch.manual_seed(0)
-        image = pndm(generator=generator, output_type="numpy").images
+        image = pndm(generator=generator, output_type="np").images
 
         image_slice = image[0, -3:, -3:, -1]
 

tests/pipelines/stable_cascade/test_stable_cascade_decoder.py

@@ -21,13 +21,13 @@ import torch
 from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
 
 from diffusers import DDPMWuerstchenScheduler, StableCascadeDecoderPipeline
-from diffusers.image_processor import VaeImageProcessor
 from diffusers.models import StableCascadeUNet
 from diffusers.pipelines.wuerstchen import PaellaVQModel
 from diffusers.utils.testing_utils import (
     enable_full_determinism,
-    load_image,
+    load_numpy,
     load_pt,
+    numpy_cosine_similarity_distance,
     require_torch_gpu,
     skip_mps,
     slow,
@@ -258,7 +258,7 @@ class StableCascadeDecoderPipelineIntegrationTests(unittest.TestCase):
 
     def test_stable_cascade_decoder(self):
         pipe = StableCascadeDecoderPipeline.from_pretrained(
-            "diffusers/StableCascade-decoder", torch_dtype=torch.bfloat16
+            "stabilityai/stable-cascade", variant="bf16", torch_dtype=torch.bfloat16
         )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=None)
@@ -271,18 +271,16 @@ class StableCascadeDecoderPipelineIntegrationTests(unittest.TestCase):
         )
 
         image = pipe(
-            prompt=prompt, image_embeddings=image_embedding, num_inference_steps=10, generator=generator
+            prompt=prompt,
+            image_embeddings=image_embedding,
+            output_type="np",
+            num_inference_steps=2,
+            generator=generator,
         ).images[0]
 
-        assert image.size == (1024, 1024)
-
-        expected_image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_cascade/t2i.png"
+        assert image.shape == (1024, 1024, 3)
+        expected_image = load_numpy(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_cascade/stable_cascade_decoder_image.npy"
         )
-
-        image_processor = VaeImageProcessor()
-
-        image_np = image_processor.pil_to_numpy(image)
-        expected_image_np = image_processor.pil_to_numpy(expected_image)
-
-        self.assertTrue(np.allclose(image_np, expected_image_np, atol=53e-2))
+        max_diff = numpy_cosine_similarity_distance(image.flatten(), expected_image.flatten())
+        assert max_diff < 1e-4

tests/pipelines/stable_cascade/test_stable_cascade_prior.py

@@ -29,7 +29,8 @@ from diffusers.models.attention_processor import LoRAAttnProcessor, LoRAAttnProc
 from diffusers.utils.import_utils import is_peft_available
 from diffusers.utils.testing_utils import (
     enable_full_determinism,
-    load_pt,
+    load_numpy,
+    numpy_cosine_similarity_distance,
     require_peft_backend,
     require_torch_gpu,
     skip_mps,
@@ -319,7 +320,9 @@ class StableCascadePriorPipelineIntegrationTests(unittest.TestCase):
         torch.cuda.empty_cache()
 
     def test_stable_cascade_prior(self):
-        pipe = StableCascadePriorPipeline.from_pretrained("diffusers/StableCascade-prior", torch_dtype=torch.bfloat16)
+        pipe = StableCascadePriorPipeline.from_pretrained(
+            "stabilityai/stable-cascade-prior", variant="bf16", torch_dtype=torch.bfloat16
+        )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=None)
 
@@ -327,17 +330,12 @@ class StableCascadePriorPipelineIntegrationTests(unittest.TestCase):
 
         generator = torch.Generator(device="cpu").manual_seed(0)
 
-        output = pipe(prompt, num_inference_steps=10, generator=generator)
+        output = pipe(prompt, num_inference_steps=2, output_type="np", generator=generator)
         image_embedding = output.image_embeddings
-
-        expected_image_embedding = load_pt(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_cascade/image_embedding.pt"
+        expected_image_embedding = load_numpy(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_cascade/stable_cascade_prior_image_embeddings.npy"
         )
-
         assert image_embedding.shape == (1, 16, 24, 24)
 
-        self.assertTrue(
-            np.allclose(
-                image_embedding.cpu().float().numpy(), expected_image_embedding.cpu().float().numpy(), atol=5e-2
-            )
-        )
+        max_diff = numpy_cosine_similarity_distance(image_embedding.flatten(), expected_image_embedding.flatten())
+        assert max_diff < 1e-4

tests/pipelines/stable_diffusion/test_onnx_stable_diffusion.py

@@ -46,7 +46,7 @@ class OnnxStableDiffusionPipelineFastTests(OnnxPipelineTesterMixin, unittest.Tes
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 7.5,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 

tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py

@@ -55,7 +55,7 @@ class OnnxStableDiffusionImg2ImgPipelineFastTests(OnnxPipelineTesterMixin, unitt
             "num_inference_steps": 3,
             "strength": 0.75,
             "guidance_scale": 7.5,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 

tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_upscale.py

@@ -55,7 +55,7 @@ class OnnxStableDiffusionUpscalePipelineFastTests(OnnxPipelineTesterMixin, unitt
             "generator": generator,
             "num_inference_steps": 3,
             "guidance_scale": 7.5,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 

tests/pipelines/stable_diffusion/test_stable_diffusion.py

@@ -775,7 +775,7 @@ class StableDiffusionPipelineSlowTests(unittest.TestCase):
             "generator": generator,
             "num_inference_steps": 3,
             "guidance_scale": 7.5,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -950,7 +950,7 @@ class StableDiffusionPipelineSlowTests(unittest.TestCase):
             generator=generator,
             guidance_scale=7.5,
             num_inference_steps=2,
-            output_type="numpy",
+            output_type="np",
         )
         image_chunked = output_chunked.images
 
@@ -966,7 +966,7 @@ class StableDiffusionPipelineSlowTests(unittest.TestCase):
             generator=generator,
             guidance_scale=7.5,
             num_inference_steps=2,
-            output_type="numpy",
+            output_type="np",
         )
         image = output.images
 

tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py

@@ -179,7 +179,7 @@ class StableDiffusionImg2ImgPipelineFastTests(
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py

@@ -199,7 +199,7 @@ class StableDiffusionInpaintPipelineFastTests(
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -470,7 +470,7 @@ class StableDiffusionSimpleInpaintPipelineFastTests(StableDiffusionInpaintPipeli
             "generator": [generator1, generator2],
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -586,7 +586,7 @@ class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase):
             "generator": generator,
             "num_inference_steps": 3,
             "guidance_scale": 7.5,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -847,7 +847,7 @@ class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.Te
             "generator": generator,
             "num_inference_steps": 3,
             "guidance_scale": 7.5,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -1072,7 +1072,7 @@ class StableDiffusionInpaintPipelineNightlyTests(unittest.TestCase):
             "generator": generator,
             "num_inference_steps": 50,
             "guidance_scale": 7.5,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs