Merge branch 'main' of https://github.com/huggingface/diffusers into main

Makefile

@@ -34,13 +34,9 @@ autogenerate_code: deps_table_update
 # Check that the repo is in a good state
 
 repo-consistency:
-	python utils/check_copies.py
-	python utils/check_table.py
 	python utils/check_dummies.py
 	python utils/check_repo.py
 	python utils/check_inits.py
-	python utils/check_config_docstrings.py
-	python utils/tests_fetcher.py --sanity_check
 
 # this target runs checks on all files
 
@@ -48,14 +44,13 @@ quality:
 	black --check --preview $(check_dirs)
 	isort --check-only $(check_dirs)
 	flake8 $(check_dirs)
-	doc-builder style src/transformers docs/source --max_len 119 --check_only --path_to_docs docs/source
+	doc-builder style src/diffusers docs/source --max_len 119 --check_only --path_to_docs docs/source
 
 # Format source code automatically and check is there are any problems left that need manual fixing
 
 extra_style_checks:
 	python utils/custom_init_isort.py
-	python utils/sort_auto_mappings.py
-	doc-builder style src/transformers docs/source --max_len 119 --path_to_docs docs/source
+	doc-builder style src/diffusers docs/source --max_len 119 --path_to_docs docs/source
 
 # this target runs checks on all files and potentially modifies some of them
 
@@ -73,8 +68,6 @@ fixup: modified_only_fixup extra_style_checks autogenerate_code repo-consistency
 
 fix-copies:
 	python utils/check_dummies.py --fix_and_overwrite
-	python utils/check_table.py --fix_and_overwrite
-	python utils/check_copies.py --fix_and_overwrite
 
 # Run tests for the library
 

examples/experimental/train_glide_text_to_image.py

+import argparse
+import os
+
+import torch
+import torch.nn.functional as F
+
+import bitsandbytes as bnb
+import PIL.Image
+from accelerate import Accelerator
+from datasets import load_dataset
+from diffusers import DDPMScheduler, Glide, GlideUNetModel
+from diffusers.hub_utils import init_git_repo, push_to_hub
+from diffusers.optimization import get_scheduler
+from diffusers.utils import logging
+from torchvision.transforms import (
+    CenterCrop,
+    Compose,
+    InterpolationMode,
+    Normalize,
+    RandomHorizontalFlip,
+    Resize,
+    ToTensor,
+)
+from tqdm.auto import tqdm
+
+
+logger = logging.get_logger(__name__)
+
+
+def main(args):
+    accelerator = Accelerator(mixed_precision=args.mixed_precision)
+
+    pipeline = Glide.from_pretrained("fusing/glide-base")
+    model = pipeline.text_unet
+    noise_scheduler = DDPMScheduler(timesteps=1000, tensor_format="pt")
+    optimizer = bnb.optim.Adam8bit(model.parameters(), lr=args.lr)
+
+    augmentations = Compose(
+        [
+            Resize(args.resolution, interpolation=InterpolationMode.BILINEAR),
+            CenterCrop(args.resolution),
+            RandomHorizontalFlip(),
+            ToTensor(),
+            Normalize([0.5], [0.5]),
+        ]
+    )
+    dataset = load_dataset(args.dataset, split="train")
+
+    text_encoder = pipeline.text_encoder.eval()
+
+    def transforms(examples):
+        images = [augmentations(image.convert("RGB")) for image in examples["image"]]
+        text_inputs = pipeline.tokenizer(examples["caption"], padding="max_length", max_length=77, return_tensors="pt")
+        text_inputs = text_inputs.input_ids.to(accelerator.device)
+        with torch.no_grad():
+            text_embeddings = accelerator.unwrap_model(text_encoder)(text_inputs).last_hidden_state
+        return {"images": images, "text_embeddings": text_embeddings}
+
+    dataset.set_transform(transforms)
+    train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, shuffle=True)
+
+    lr_scheduler = get_scheduler(
+        "linear",
+        optimizer=optimizer,
+        num_warmup_steps=args.warmup_steps,
+        num_training_steps=(len(train_dataloader) * args.num_epochs) // args.gradient_accumulation_steps,
+    )
+
+    model, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+        model, text_encoder, optimizer, train_dataloader, lr_scheduler
+    )
+
+    if args.push_to_hub:
+        repo = init_git_repo(args, at_init=True)
+
+    # Train!
+    is_distributed = torch.distributed.is_available() and torch.distributed.is_initialized()
+    world_size = torch.distributed.get_world_size() if is_distributed else 1
+    total_train_batch_size = args.batch_size * args.gradient_accumulation_steps * world_size
+    max_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_epochs
+    logger.info("***** Running training *****")
+    logger.info(f"  Num examples = {len(train_dataloader.dataset)}")
+    logger.info(f"  Num Epochs = {args.num_epochs}")
+    logger.info(f"  Instantaneous batch size per device = {args.batch_size}")
+    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size}")
+    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+    logger.info(f"  Total optimization steps = {max_steps}")
+
+    for epoch in range(args.num_epochs):
+        model.train()
+        with tqdm(total=len(train_dataloader), unit="ba") as pbar:
+            pbar.set_description(f"Epoch {epoch}")
+            for step, batch in enumerate(train_dataloader):
+                clean_images = batch["images"]
+                batch_size, n_channels, height, width = clean_images.shape
+                noise_samples = torch.randn(clean_images.shape).to(clean_images.device)
+                timesteps = torch.randint(
+                    0, noise_scheduler.timesteps, (batch_size,), device=clean_images.device
+                ).long()
+
+                # add noise onto the clean images according to the noise magnitude at each timestep
+                # (this is the forward diffusion process)
+                noisy_images = noise_scheduler.training_step(clean_images, noise_samples, timesteps)
+
+                if step % args.gradient_accumulation_steps != 0:
+                    with accelerator.no_sync(model):
+                        model_output = model(noisy_images, timesteps, batch["text_embeddings"])
+                        model_output, model_var_values = torch.split(model_output, n_channels, dim=1)
+                        # Learn the variance using the variational bound, but don't let
+                        # it affect our mean prediction.
+                        frozen_out = torch.cat([model_output.detach(), model_var_values], dim=1)
+
+                        # predict the noise residual
+                        loss = F.mse_loss(model_output, noise_samples)
+
+                        loss = loss / args.gradient_accumulation_steps
+
+                        accelerator.backward(loss)
+                        optimizer.step()
+                else:
+                    model_output = model(noisy_images, timesteps, batch["text_embeddings"])
+                    model_output, model_var_values = torch.split(model_output, n_channels, dim=1)
+                    # Learn the variance using the variational bound, but don't let
+                    # it affect our mean prediction.
+                    frozen_out = torch.cat([model_output.detach(), model_var_values], dim=1)
+
+                    # predict the noise residual
+                    loss = F.mse_loss(model_output, noise_samples)
+                    loss = loss / args.gradient_accumulation_steps
+                    accelerator.backward(loss)
+                    torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+                    optimizer.step()
+                    lr_scheduler.step()
+                    optimizer.zero_grad()
+                pbar.update(1)
+                pbar.set_postfix(loss=loss.detach().item(), lr=optimizer.param_groups[0]["lr"])
+
+        accelerator.wait_for_everyone()
+
+        # Generate a sample image for visual inspection
+        if accelerator.is_main_process:
+            model.eval()
+            with torch.no_grad():
+                pipeline.unet = accelerator.unwrap_model(model)
+
+                generator = torch.manual_seed(0)
+                # run pipeline in inference (sample random noise and denoise)
+                image = pipeline("a clip art of a corgi", generator=generator, num_upscale_inference_steps=50)
+
+            # process image to PIL
+            image_processed = image.squeeze(0)
+            image_processed = ((image_processed + 1) * 127.5).round().clamp(0, 255).to(torch.uint8).cpu().numpy()
+            image_pil = PIL.Image.fromarray(image_processed)
+
+            # save image
+            test_dir = os.path.join(args.output_dir, "test_samples")
+            os.makedirs(test_dir, exist_ok=True)
+            image_pil.save(f"{test_dir}/{epoch:04d}.png")
+
+            # save the model
+            if args.push_to_hub:
+                push_to_hub(args, pipeline, repo, commit_message=f"Epoch {epoch}", blocking=False)
+            else:
+                pipeline.save_pretrained(args.output_dir)
+        accelerator.wait_for_everyone()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument("--local_rank", type=int, default=-1)
+    parser.add_argument("--dataset", type=str, default="fusing/dog_captions")
+    parser.add_argument("--output_dir", type=str, default="glide-text2image")
+    parser.add_argument("--overwrite_output_dir", action="store_true")
+    parser.add_argument("--resolution", type=int, default=64)
+    parser.add_argument("--batch_size", type=int, default=4)
+    parser.add_argument("--num_epochs", type=int, default=100)
+    parser.add_argument("--gradient_accumulation_steps", type=int, default=4)
+    parser.add_argument("--lr", type=float, default=1e-4)
+    parser.add_argument("--warmup_steps", type=int, default=500)
+    parser.add_argument("--push_to_hub", action="store_true")
+    parser.add_argument("--hub_token", type=str, default=None)
+    parser.add_argument("--hub_model_id", type=str, default=None)
+    parser.add_argument("--hub_private_repo", action="store_true")
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default="no",
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose"
+            "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
+            "and an Nvidia Ampere GPU."
+        ),
+    )
+
+    args = parser.parse_args()
+    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    if env_local_rank != -1 and env_local_rank != args.local_rank:
+        args.local_rank = env_local_rank
+
+    main(args)

examples/train_latent_text_to_image.py

@@ -4,19 +4,19 @@ import os
 import torch
 import torch.nn.functional as F
 
+import bitsandbytes as bnb
 import PIL.Image
 from accelerate import Accelerator
 from datasets import load_dataset
-from diffusers import DDPM, DDPMScheduler, UNetLDMModel
+from diffusers import DDPMScheduler, LatentDiffusion, UNetLDMModel
 from diffusers.hub_utils import init_git_repo, push_to_hub
-from diffusers.modeling_utils import unwrap_model
 from diffusers.optimization import get_scheduler
 from diffusers.utils import logging
 from torchvision.transforms import (
     CenterCrop,
     Compose,
     InterpolationMode,
-    Lambda,
+    Normalize,
     RandomHorizontalFlip,
     Resize,
     ToTensor,
@@ -30,6 +30,8 @@ logger = logging.get_logger(__name__)
 def main(args):
     accelerator = Accelerator(mixed_precision=args.mixed_precision)
 
+    pipeline = LatentDiffusion.from_pretrained("fusing/latent-diffusion-text2im-large")
+    pipeline.unet = None  # this model will be trained from scratch now
     model = UNetLDMModel(
         attention_resolutions=[4, 2, 1],
         channel_mult=[1, 2, 4, 4],
@@ -37,7 +39,7 @@ def main(args):
         conv_resample=True,
         dims=2,
         dropout=0,
-        image_size=32,
+        image_size=8,
         in_channels=4,
         model_channels=320,
         num_heads=8,
@@ -51,7 +53,7 @@ def main(args):
         legacy=False,
     )
     noise_scheduler = DDPMScheduler(timesteps=1000, tensor_format="pt")
-    optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
+    optimizer = bnb.optim.Adam8bit(model.parameters(), lr=args.lr)
 
     augmentations = Compose(
         [
@@ -59,14 +61,22 @@ def main(args):
             CenterCrop(args.resolution),
             RandomHorizontalFlip(),
             ToTensor(),
-            Lambda(lambda x: x * 2 - 1),
+            Normalize([0.5], [0.5]),
         ]
     )
     dataset = load_dataset(args.dataset, split="train")
 
+    text_encoder = pipeline.bert.eval()
+    vqvae = pipeline.vqvae.eval()
+
     def transforms(examples):
         images = [augmentations(image.convert("RGB")) for image in examples["image"]]
-        return {"input": images}
+        text_inputs = pipeline.tokenizer(examples["caption"], padding="max_length", max_length=77, return_tensors="pt")
+        with torch.no_grad():
+            text_embeddings = accelerator.unwrap_model(text_encoder)(text_inputs.input_ids.cpu()).last_hidden_state
+            images = 1 / 0.18215 * torch.stack(images, dim=0)
+            latents = accelerator.unwrap_model(vqvae).encode(images.cpu()).mode()
+        return {"images": images, "text_embeddings": text_embeddings, "latents": latents}
 
     dataset.set_transform(transforms)
     train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, shuffle=True)
@@ -78,9 +88,11 @@ def main(args):
         num_training_steps=(len(train_dataloader) * args.num_epochs) // args.gradient_accumulation_steps,
     )
 
-    model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        model, optimizer, train_dataloader, lr_scheduler
+    model, text_encoder, vqvae, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+        model, text_encoder, vqvae, optimizer, train_dataloader, lr_scheduler
     )
+    text_encoder = text_encoder.cpu()
+    vqvae = vqvae.cpu()
 
     if args.push_to_hub:
         repo = init_git_repo(args, at_init=True)
@@ -98,29 +110,31 @@ def main(args):
     logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
     logger.info(f"  Total optimization steps = {max_steps}")
 
+    global_step = 0
     for epoch in range(args.num_epochs):
         model.train()
         with tqdm(total=len(train_dataloader), unit="ba") as pbar:
             pbar.set_description(f"Epoch {epoch}")
             for step, batch in enumerate(train_dataloader):
-                clean_images = batch["input"]
-                noise_samples = torch.randn(clean_images.shape).to(clean_images.device)
-                bsz = clean_images.shape[0]
-                timesteps = torch.randint(0, noise_scheduler.timesteps, (bsz,), device=clean_images.device).long()
+                clean_latents = batch["latents"]
+                noise_samples = torch.randn(clean_latents.shape).to(clean_latents.device)
+                bsz = clean_latents.shape[0]
+                timesteps = torch.randint(0, noise_scheduler.timesteps, (bsz,), device=clean_latents.device).long()
 
-                # add noise onto the clean images according to the noise magnitude at each timestep
+                # add noise onto the clean latents according to the noise magnitude at each timestep
                 # (this is the forward diffusion process)
-                noisy_images = noise_scheduler.training_step(clean_images, noise_samples, timesteps)
+                noisy_latents = noise_scheduler.training_step(clean_latents, noise_samples, timesteps)
 
                 if step % args.gradient_accumulation_steps != 0:
                     with accelerator.no_sync(model):
-                        output = model(noisy_images, timesteps)
+                        output = model(noisy_latents, timesteps, context=batch["text_embeddings"])
                         # predict the noise residual
                         loss = F.mse_loss(output, noise_samples)
                         loss = loss / args.gradient_accumulation_steps
                         accelerator.backward(loss)
+                        optimizer.step()
                 else:
-                    output = model(noisy_images, timesteps)
+                    output = model(noisy_latents, timesteps, context=batch["text_embeddings"])
                     # predict the noise residual
                     loss = F.mse_loss(output, noise_samples)
                     loss = loss / args.gradient_accumulation_steps
@@ -131,24 +145,25 @@ def main(args):
                     optimizer.zero_grad()
                 pbar.update(1)
                 pbar.set_postfix(loss=loss.detach().item(), lr=optimizer.param_groups[0]["lr"])
+                global_step += 1
 
-                optimizer.step()
-        if is_distributed:
-            torch.distributed.barrier()
+        accelerator.wait_for_everyone()
 
         # Generate a sample image for visual inspection
-        if args.local_rank in [-1, 0]:
+        if accelerator.is_main_process:
             model.eval()
             with torch.no_grad():
-                pipeline = DDPM(unet=unwrap_model(model), noise_scheduler=noise_scheduler)
+                pipeline.unet = accelerator.unwrap_model(model)
 
                 generator = torch.manual_seed(0)
                 # run pipeline in inference (sample random noise and denoise)
-                image = pipeline(generator=generator)
+                image = pipeline(
+                    ["a clip art of a corgi"], generator=generator, eta=0.3, guidance_scale=6.0, num_inference_steps=50
+                )
 
             # process image to PIL
             image_processed = image.cpu().permute(0, 2, 3, 1)
-            image_processed = (image_processed + 1.0) * 127.5
+            image_processed = image_processed * 255.0
             image_processed = image_processed.type(torch.uint8).numpy()
             image_pil = PIL.Image.fromarray(image_processed[0])
 
@@ -162,20 +177,19 @@ def main(args):
                 push_to_hub(args, pipeline, repo, commit_message=f"Epoch {epoch}", blocking=False)
             else:
                 pipeline.save_pretrained(args.output_dir)
-        if is_distributed:
-            torch.distributed.barrier()
+        accelerator.wait_for_everyone()
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Simple example of a training script.")
     parser.add_argument("--local_rank", type=int, default=-1)
-    parser.add_argument("--dataset", type=str, default="huggan/flowers-102-categories")
-    parser.add_argument("--output_dir", type=str, default="ddpm-model")
+    parser.add_argument("--dataset", type=str, default="fusing/dog_captions")
+    parser.add_argument("--output_dir", type=str, default="ldm-text2image")
     parser.add_argument("--overwrite_output_dir", action="store_true")
-    parser.add_argument("--resolution", type=int, default=64)
-    parser.add_argument("--batch_size", type=int, default=16)
+    parser.add_argument("--resolution", type=int, default=128)
+    parser.add_argument("--batch_size", type=int, default=1)
     parser.add_argument("--num_epochs", type=int, default=100)
-    parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
+    parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
     parser.add_argument("--lr", type=float, default=1e-4)
     parser.add_argument("--warmup_steps", type=int, default=500)
     parser.add_argument("--push_to_hub", action="store_true")

examples/train_unconditional.py

@@ -7,7 +7,7 @@ import torch.nn.functional as F
 import PIL.Image
 from accelerate import Accelerator
 from datasets import load_dataset
-from diffusers import DDPM, DDPMScheduler, UNetModel
+from diffusers import DDPMPipeline, DDPMScheduler, UNetModel
 from diffusers.hub_utils import init_git_repo, push_to_hub
 from diffusers.optimization import get_scheduler
 from diffusers.training_utils import EMAModel
@@ -71,7 +71,7 @@ def main(args):
         model, optimizer, train_dataloader, lr_scheduler
     )
 
-    ema_model = EMAModel(model, inv_gamma=1.0, power=3 / 4)
+    ema_model = EMAModel(model, inv_gamma=args.ema_inv_gamma, power=args.ema_power, max_value=args.ema_max_decay)
 
     if args.push_to_hub:
         repo = init_git_repo(args, at_init=True)
@@ -133,7 +133,7 @@ def main(args):
         # Generate a sample image for visual inspection
         if accelerator.is_main_process:
             with torch.no_grad():
-                pipeline = DDPM(
+                pipeline = DDPMPipeline(
                     unet=accelerator.unwrap_model(ema_model.averaged_model), noise_scheduler=noise_scheduler
                 )
 
@@ -172,6 +172,9 @@ if __name__ == "__main__":
     parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
     parser.add_argument("--lr", type=float, default=1e-4)
     parser.add_argument("--warmup_steps", type=int, default=500)
+    parser.add_argument("--ema_inv_gamma", type=float, default=1.0)
+    parser.add_argument("--ema_power", type=float, default=3 / 4)
+    parser.add_argument("--ema_max_decay", type=float, default=0.999)
     parser.add_argument("--push_to_hub", action="store_true")
     parser.add_argument("--hub_token", type=str, default=None)
     parser.add_argument("--hub_model_id", type=str, default=None)

src/diffusers/hub_utils.py

@@ -47,12 +47,11 @@ def get_full_repo_name(model_id: str, organization: Optional[str] = None, token:
 
 def init_git_repo(args, at_init: bool = False):
     """
-    Initializes a git repo in `args.hub_model_id`.
     Args:
+    Initializes a git repo in `args.hub_model_id`.
         at_init (`bool`, *optional*, defaults to `False`):
-            Whether this function is called before any training or not. If `self.args.overwrite_output_dir` is
-            `True` and `at_init` is `True`, the path to the repo (which is `self.args.output_dir`) might be wiped
-            out.
+            Whether this function is called before any training or not. If `self.args.overwrite_output_dir` is `True`
+            and `at_init` is `True`, the path to the repo (which is `self.args.output_dir`) might be wiped out.
     """
     if args.local_rank not in [-1, 0]:
         return
@@ -102,8 +101,8 @@ def push_to_hub(
     **kwargs,
 ) -> str:
     """
-    Upload *self.model* and *self.tokenizer* to the 🤗 model hub on the repo *self.args.hub_model_id*.
     Parameters:
+    Upload *self.model* and *self.tokenizer* to the 🤗 model hub on the repo *self.args.hub_model_id*.
         commit_message (`str`, *optional*, defaults to `"End of training"`):
             Message to commit while pushing.
         blocking (`bool`, *optional*, defaults to `True`):
@@ -111,8 +110,8 @@ def push_to_hub(
         kwargs:
             Additional keyword arguments passed along to [`create_model_card`].
     Returns:
-        The url of the commit of your model in the given repository if `blocking=False`, a tuple with the url of
-        the commit and an object to track the progress of the commit if `blocking=True`
+        The url of the commit of your model in the given repository if `blocking=False`, a tuple with the url of the
+        commit and an object to track the progress of the commit if `blocking=True`
     """
 
     if args.hub_model_id is None:

src/diffusers/modeling_utils.py

@@ -123,16 +123,16 @@ class ModelMixin(torch.nn.Module):
     r"""
     Base class for all models.
 
-    [`ModelMixin`] takes care of storing the configuration of the models and handles methods for loading,
-    downloading and saving models as well as a few methods common to all models to:
+    [`ModelMixin`] takes care of storing the configuration of the models and handles methods for loading, downloading
+    and saving models as well as a few methods common to all models to:
 
         - resize the input embeddings,
         - prune heads in the self-attention heads.
 
     Class attributes (overridden by derived classes):
 
-        - **config_class** ([`ConfigMixin`]) -- A subclass of [`ConfigMixin`] to use as configuration class
-          for this model architecture.
+        - **config_class** ([`ConfigMixin`]) -- A subclass of [`ConfigMixin`] to use as configuration class for this
+          model architecture.
         - **load_tf_weights** (`Callable`) -- A python *method* for loading a TensorFlow checkpoint in a PyTorch model,
           taking as arguments:
 
@@ -227,8 +227,8 @@ class ModelMixin(torch.nn.Module):
                     - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
                       Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
                       user or organization name, like `dbmdz/bert-base-german-cased`.
-                    - A path to a *directory* containing model weights saved using
-                      [`~ModelMixin.save_pretrained`], e.g., `./my_model_directory/`.
+                    - A path to a *directory* containing model weights saved using [`~ModelMixin.save_pretrained`],
+                      e.g., `./my_model_directory/`.
 
             config (`Union[ConfigMixin, str, os.PathLike]`, *optional*):
                 Can be either:
@@ -236,13 +236,13 @@ class ModelMixin(torch.nn.Module):
                     - an instance of a class derived from [`ConfigMixin`],
                     - a string or path valid as input to [`~ConfigMixin.from_pretrained`].
 
-                ConfigMixinuration for the model to use instead of an automatically loaded configuration. ConfigMixinuration can
-                be automatically loaded when:
+                ConfigMixinuration for the model to use instead of an automatically loaded configuration.
+                ConfigMixinuration can be automatically loaded when:
 
                     - The model is a model provided by the library (loaded with the *model id* string of a pretrained
                       model).
-                    - The model was saved using [`~ModelMixin.save_pretrained`] and is reloaded by supplying the
-                      save directory.
+                    - The model was saved using [`~ModelMixin.save_pretrained`] and is reloaded by supplying the save
+                      directory.
                     - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a
                       configuration JSON file named *config.json* is found in the directory.
             cache_dir (`Union[str, os.PathLike]`, *optional*):
@@ -292,10 +292,10 @@ class ModelMixin(torch.nn.Module):
                       underlying model's `__init__` method (we assume all relevant updates to the configuration have
                       already been done)
                     - If a configuration is not provided, `kwargs` will be first passed to the configuration class
-                      initialization function ([`~ConfigMixin.from_pretrained`]). Each key of `kwargs` that
-                      corresponds to a configuration attribute will be used to override said attribute with the
-                      supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute
-                      will be passed to the underlying model's `__init__` function.
+                      initialization function ([`~ConfigMixin.from_pretrained`]). Each key of `kwargs` that corresponds
+                      to a configuration attribute will be used to override said attribute with the supplied `kwargs`
+                      value. Remaining keys that do not correspond to any configuration attribute will be passed to the
+                      underlying model's `__init__` function.
 
         <Tip>
 

src/diffusers/models/embeddings.py

@@ -22,14 +22,12 @@ def get_timestep_embedding(
     timesteps, embedding_dim, flip_sin_to_cos=False, downscale_freq_shift=1, scale=1, max_period=10000
 ):
     """
-    This matches the implementation in Denoising Diffusion Probabilistic Models:
-    Create sinusoidal timestep embeddings.
+    This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
 
     :param timesteps: a 1-D Tensor of N indices, one per batch element.
                       These may be fractional.
-    :param embedding_dim: the dimension of the output.
-    :param max_period: controls the minimum frequency of the embeddings.
-    :return: an [N x dim] Tensor of positional embeddings.
+    :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the
+    embeddings. :return: an [N x dim] Tensor of positional embeddings.
     """
     assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
 

src/diffusers/models/resnet.py

@@ -58,9 +58,8 @@ class Upsample(nn.Module):
     """
     An upsampling layer with an optional convolution.
 
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+    :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is
+    applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
                  upsampling occurs in the inner-two dimensions.
     """
 
@@ -97,9 +96,8 @@ class Downsample(nn.Module):
     """
     A downsampling layer with an optional convolution.
 
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+    :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is
+    applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
                  downsampling occurs in the inner-two dimensions.
     """
 
@@ -136,6 +134,86 @@ class Downsample(nn.Module):
             return self.op(x)
 
 
+class UNetUpsample(nn.Module):
+    def __init__(self, in_channels, with_conv):
+        super().__init__()
+        self.with_conv = with_conv
+        if self.with_conv:
+            self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x):
+        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
+        if self.with_conv:
+            x = self.conv(x)
+        return x
+
+
+class GlideUpsample(nn.Module):
+    """
+    An upsampling layer with an optional convolution.
+
+    :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is
+    applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 upsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        if use_conv:
+            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        if self.dims == 3:
+            x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest")
+        else:
+            x = F.interpolate(x, scale_factor=2, mode="nearest")
+        if self.use_conv:
+            x = self.conv(x)
+        return x
+
+
+class LDMUpsample(nn.Module):
+    """
+    An upsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param
+    use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D.
+    If 3D, then
+                 upsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        if use_conv:
+            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        if self.dims == 3:
+            x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest")
+        else:
+            x = F.interpolate(x, scale_factor=2, mode="nearest")
+        if self.use_conv:
+            x = self.conv(x)
+        return x
+
+
+class GradTTSUpsample(torch.nn.Module):
+    def __init__(self, dim):
+        super(Upsample, self).__init__()
+        self.conv = torch.nn.ConvTranspose2d(dim, dim, 4, 2, 1)
+
+    def forward(self, x):
+        return self.conv(x)
+
+
 # TODO (patil-suraj): needs test
 class Upsample1d(nn.Module):
     def __init__(self, dim):

src/diffusers/models/unet_glide.py

@@ -82,8 +82,7 @@ def normalization(channels, swish=0.0):
     """
     Make a standard normalization layer, with an optional swish activation.
 
-    :param channels: number of input channels.
-    :return: an nn.Module for normalization.
+    :param channels: number of input channels. :return: an nn.Module for normalization.
     """
     return GroupNorm32(num_channels=channels, num_groups=32, swish=swish)
 
@@ -111,8 +110,7 @@ class TimestepBlock(nn.Module):
 
 class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
     """
-    A sequential module that passes timestep embeddings to the children that
-    support it as an extra input.
+    A sequential module that passes timestep embeddings to the children that support it as an extra input.
     """
 
     def forward(self, x, emb, encoder_out=None):
@@ -130,17 +128,13 @@ class ResBlock(TimestepBlock):
     """
     A residual block that can optionally change the number of channels.
 
-    :param channels: the number of input channels.
-    :param emb_channels: the number of timestep embedding channels.
-    :param dropout: the rate of dropout.
-    :param out_channels: if specified, the number of out channels.
-    :param use_conv: if True and out_channels is specified, use a spatial
-        convolution instead of a smaller 1x1 convolution to change the
-        channels in the skip connection.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param use_checkpoint: if True, use gradient checkpointing on this module.
-    :param up: if True, use this block for upsampling.
-    :param down: if True, use this block for downsampling.
+    :param channels: the number of input channels. :param emb_channels: the number of timestep embedding channels.
+    :param dropout: the rate of dropout. :param out_channels: if specified, the number of out channels. :param
+    use_conv: if True and out_channels is specified, use a spatial
+        convolution instead of a smaller 1x1 convolution to change the channels in the skip connection.
+    :param dims: determines if the signal is 1D, 2D, or 3D. :param use_checkpoint: if True, use gradient checkpointing
+    on this module. :param up: if True, use this block for upsampling. :param down: if True, use this block for
+    downsampling.
     """
 
     def __init__(
@@ -207,8 +201,7 @@ class ResBlock(TimestepBlock):
         """
         Apply the block to a Tensor, conditioned on a timestep embedding.
 
-        :param x: an [N x C x ...] Tensor of features.
-        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+        :param x: an [N x C x ...] Tensor of features. :param emb: an [N x emb_channels] Tensor of timestep embeddings.
         :return: an [N x C x ...] Tensor of outputs.
         """
         if self.updown:
@@ -292,8 +285,8 @@ class QKVAttention(nn.Module):
         """
         Apply QKV attention.
 
-        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
+        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. :return: an [N x (H * C) x T] tensor after
+        attention.
         """
         bs, width, length = qkv.shape
         assert width % (3 * self.n_heads) == 0
@@ -315,29 +308,24 @@ class GlideUNetModel(ModelMixin, ConfigMixin):
     """
     The full UNet model with attention and timestep embedding.
 
-    :param in_channels: channels in the input Tensor.
-    :param model_channels: base channel count for the model.
-    :param out_channels: channels in the output Tensor.
-    :param num_res_blocks: number of residual blocks per downsample.
+    :param in_channels: channels in the input Tensor. :param model_channels: base channel count for the model. :param
+    out_channels: channels in the output Tensor. :param num_res_blocks: number of residual blocks per downsample.
     :param attention_resolutions: a collection of downsample rates at which
-        attention will take place. May be a set, list, or tuple.
-        For example, if this contains 4, then at 4x downsampling, attention
-        will be used.
-    :param dropout: the dropout probability.
-    :param channel_mult: channel multiplier for each level of the UNet.
-    :param conv_resample: if True, use learned convolutions for upsampling and
+        attention will take place. May be a set, list, or tuple. For example, if this contains 4, then at 4x
+        downsampling, attention will be used.
+    :param dropout: the dropout probability. :param channel_mult: channel multiplier for each level of the UNet. :param
+    conv_resample: if True, use learned convolutions for upsampling and
         downsampling.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param num_classes: if specified (as an int), then this model will be
+    :param dims: determines if the signal is 1D, 2D, or 3D. :param num_classes: if specified (as an int), then this
+    model will be
         class-conditional with `num_classes` classes.
-    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
-    :param num_heads: the number of attention heads in each attention layer.
-    :param num_heads_channels: if specified, ignore num_heads and instead use
+    :param use_checkpoint: use gradient checkpointing to reduce memory usage. :param num_heads: the number of attention
+    heads in each attention layer. :param num_heads_channels: if specified, ignore num_heads and instead use
                                a fixed channel width per attention head.
     :param num_heads_upsample: works with num_heads to set a different number
                                of heads for upsampling. Deprecated.
-    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
-    :param resblock_updown: use residual blocks for up/downsampling.
+    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. :param resblock_updown: use residual blocks
+    for up/downsampling.
     """
 
     def __init__(
@@ -545,10 +533,8 @@ class GlideUNetModel(ModelMixin, ConfigMixin):
         """
         Apply the model to an input batch.
 
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :param y: an [N] Tensor of labels, if class-conditional.
-        :return: an [N x C x ...] Tensor of outputs.
+        :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps. :param y: an [N]
+        Tensor of labels, if class-conditional. :return: an [N x C x ...] Tensor of outputs.
         """
 
         hs = []

src/diffusers/models/unet_grad_tts.py

 import torch
 from numpy import pad
 
-
-try:
-    from einops import rearrange
-except:
-    print("Einops is not installed")
-    pass
-
 from ..configuration_utils import ConfigMixin
 from ..modeling_utils import ModelMixin
 from .embeddings import get_timestep_embedding
@@ -65,6 +58,7 @@ class LinearAttention(torch.nn.Module):
     def __init__(self, dim, heads=4, dim_head=32):
         super(LinearAttention, self).__init__()
         self.heads = heads
+        self.dim_head = dim_head
         hidden_dim = dim_head * heads
         self.to_qkv = torch.nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
         self.to_out = torch.nn.Conv2d(hidden_dim, dim, 1)
@@ -72,11 +66,17 @@ class LinearAttention(torch.nn.Module):
     def forward(self, x):
         b, c, h, w = x.shape
         qkv = self.to_qkv(x)
-        q, k, v = rearrange(qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3)
+        #        q, k, v = rearrange(qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3)
+        q, k, v = (
+            qkv.reshape(b, 3, self.heads, self.dim_head, h, w)
+            .permute(1, 0, 2, 3, 4, 5)
+            .reshape(3, b, self.heads, self.dim_head, -1)
+        )
         k = k.softmax(dim=-1)
         context = torch.einsum("bhdn,bhen->bhde", k, v)
         out = torch.einsum("bhde,bhdn->bhen", context, q)
-        out = rearrange(out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w)
+        #        out = rearrange(out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w)
+        out = out.reshape(b, self.heads, self.dim_head, h, w).reshape(b, self.heads * self.dim_head, h, w)
         return self.to_out(out)
 
 

src/diffusers/models/unet_ldm.py

@@ -7,13 +7,6 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-
-try:
-    from einops import rearrange, repeat
-except:
-    print("Einops is not installed")
-    pass
-
 from ..configuration_utils import ConfigMixin
 from ..modeling_utils import ModelMixin
 from .embeddings import get_timestep_embedding
@@ -155,7 +148,23 @@ class CrossAttention(nn.Module):
 
         self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
 
+    def reshape_heads_to_batch_dim(self, tensor):
+        batch_size, seq_len, dim = tensor.shape
+        head_size = self.heads
+        tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
+        tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size, seq_len, dim // head_size)
+        return tensor
+
+    def reshape_batch_dim_to_heads(self, tensor):
+        batch_size, seq_len, dim = tensor.shape
+        head_size = self.heads
+        tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
+        tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
+        return tensor
+
     def forward(self, x, context=None, mask=None):
+        batch_size, sequence_length, dim = x.shape
+
         h = self.heads
 
         q = self.to_q(x)
@@ -163,21 +172,29 @@ class CrossAttention(nn.Module):
         k = self.to_k(context)
         v = self.to_v(context)
 
-        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v))
+        #        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v))
+
+        q = self.reshape_heads_to_batch_dim(q)
+        k = self.reshape_heads_to_batch_dim(k)
+        v = self.reshape_heads_to_batch_dim(v)
 
         sim = torch.einsum("b i d, b j d -> b i j", q, k) * self.scale
 
         if exists(mask):
-            mask = rearrange(mask, "b ... -> b (...)")
+            #            mask = rearrange(mask, "b ... -> b (...)")
+            maks = mask.reshape(batch_size, -1)
             max_neg_value = -torch.finfo(sim.dtype).max
-            mask = repeat(mask, "b j -> (b h) () j", h=h)
+            #            mask = repeat(mask, "b j -> (b h) () j", h=h)
+            mask = mask[:, None, :].repeat(h, 1, 1)
+            #        x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
             sim.masked_fill_(~mask, max_neg_value)
 
         # attention, what we cannot get enough of
         attn = sim.softmax(dim=-1)
 
         out = torch.einsum("b i j, b j d -> b i d", attn, v)
-        out = rearrange(out, "(b h) n d -> b n (h d)", h=h)
+        out = self.reshape_batch_dim_to_heads(out)
+        #        out = rearrange(out, "(b h) n d -> b n (h d)", h=h)
         return self.to_out(out)
 
 
@@ -205,11 +222,8 @@ class BasicTransformerBlock(nn.Module):
 
 class SpatialTransformer(nn.Module):
     """
-    Transformer block for image-like data.
-    First, project the input (aka embedding)
-    and reshape to b, t, d.
-    Then apply standard transformer action.
-    Finally, reshape to image
+    Transformer block for image-like data. First, project the input (aka embedding) and reshape to b, t, d. Then apply
+    standard transformer action. Finally, reshape to image
     """
 
     def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0.0, context_dim=None):
@@ -235,10 +249,10 @@ class SpatialTransformer(nn.Module):
         x_in = x
         x = self.norm(x)
         x = self.proj_in(x)
-        x = rearrange(x, "b c h w -> b (h w) c")
+        x = x.permute(0, 2, 3, 1).reshape(b, h * w, c)
         for block in self.transformer_blocks:
             x = block(x, context=context)
-        x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
+        x = x.reshape(b, h, w, c).permute(0, 3, 1, 2)
         x = self.proj_out(x)
         return x + x_in
 
@@ -314,8 +328,7 @@ def normalization(channels, swish=0.0):
     """
     Make a standard normalization layer, with an optional swish activation.
 
-    :param channels: number of input channels.
-    :return: an nn.Module for normalization.
+    :param channels: number of input channels. :return: an nn.Module for normalization.
     """
     return GroupNorm32(num_channels=channels, num_groups=32, swish=swish)
 
@@ -365,8 +378,7 @@ class TimestepBlock(nn.Module):
 
 class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
     """
-    A sequential module that passes timestep embeddings to the children that
-    support it as an extra input.
+    A sequential module that passes timestep embeddings to the children that support it as an extra input.
     """
 
     def forward(self, x, emb, context=None):
@@ -382,18 +394,14 @@ class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
 
 class ResBlock(TimestepBlock):
     """
-    A residual block that can optionally change the number of channels.
-    :param channels: the number of input channels.
-    :param emb_channels: the number of timestep embedding channels.
-    :param dropout: the rate of dropout.
-    :param out_channels: if specified, the number of out channels.
-    :param use_conv: if True and out_channels is specified, use a spatial
-        convolution instead of a smaller 1x1 convolution to change the
-        channels in the skip connection.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param use_checkpoint: if True, use gradient checkpointing on this module.
-    :param up: if True, use this block for upsampling.
-    :param down: if True, use this block for downsampling.
+    A residual block that can optionally change the number of channels. :param channels: the number of input channels.
+    :param emb_channels: the number of timestep embedding channels. :param dropout: the rate of dropout. :param
+    out_channels: if specified, the number of out channels. :param use_conv: if True and out_channels is specified, use
+    a spatial
+        convolution instead of a smaller 1x1 convolution to change the channels in the skip connection.
+    :param dims: determines if the signal is 1D, 2D, or 3D. :param use_checkpoint: if True, use gradient checkpointing
+    on this module. :param up: if True, use this block for upsampling. :param down: if True, use this block for
+    downsampling.
     """
 
     def __init__(
@@ -481,8 +489,8 @@ class ResBlock(TimestepBlock):
 
 class AttentionBlock(nn.Module):
     """
-    An attention block that allows spatial positions to attend to each other.
-    Originally ported from here, but adapted to the N-d case.
+    An attention block that allows spatial positions to attend to each other. Originally ported from here, but adapted
+    to the N-d case.
     https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
     """
 
@@ -531,9 +539,8 @@ class QKVAttention(nn.Module):
 
     def forward(self, qkv):
         """
-        Apply QKV attention.
-        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
+        Apply QKV attention. :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. :return: an [N x (H * C) x
+        T] tensor after attention.
         """
         bs, width, length = qkv.shape
         assert width % (3 * self.n_heads) == 0
@@ -556,13 +563,9 @@ class QKVAttention(nn.Module):
 
 def count_flops_attn(model, _x, y):
     """
-    A counter for the `thop` package to count the operations in an
-    attention operation.
-    Meant to be used like:
+    A counter for the `thop` package to count the operations in an attention operation. Meant to be used like:
         macs, params = thop.profile(
-            model,
-            inputs=(inputs, timestamps),
-            custom_ops={QKVAttention: QKVAttention.count_flops},
+            model, inputs=(inputs, timestamps), custom_ops={QKVAttention: QKVAttention.count_flops},
         )
     """
     b, c, *spatial = y[0].shape
@@ -585,9 +588,8 @@ class QKVAttentionLegacy(nn.Module):
 
     def forward(self, qkv):
         """
-        Apply QKV attention.
-        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
+        Apply QKV attention. :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. :return: an [N x (H * C) x
+        T] tensor after attention.
         """
         bs, width, length = qkv.shape
         assert width % (3 * self.n_heads) == 0
@@ -606,31 +608,25 @@ class QKVAttentionLegacy(nn.Module):
 
 class UNetLDMModel(ModelMixin, ConfigMixin):
     """
-    The full UNet model with attention and timestep embedding.
-    :param in_channels: channels in the input Tensor.
-    :param model_channels: base channel count for the model.
-    :param out_channels: channels in the output Tensor.
-    :param num_res_blocks: number of residual blocks per downsample.
-    :param attention_resolutions: a collection of downsample rates at which
-        attention will take place. May be a set, list, or tuple.
-        For example, if this contains 4, then at 4x downsampling, attention
-        will be used.
-    :param dropout: the dropout probability.
-    :param channel_mult: channel multiplier for each level of the UNet.
-    :param conv_resample: if True, use learned convolutions for upsampling and
+    The full UNet model with attention and timestep embedding. :param in_channels: channels in the input Tensor. :param
+    model_channels: base channel count for the model. :param out_channels: channels in the output Tensor. :param
+    num_res_blocks: number of residual blocks per downsample. :param attention_resolutions: a collection of downsample
+    rates at which
+        attention will take place. May be a set, list, or tuple. For example, if this contains 4, then at 4x
+        downsampling, attention will be used.
+    :param dropout: the dropout probability. :param channel_mult: channel multiplier for each level of the UNet. :param
+    conv_resample: if True, use learned convolutions for upsampling and
         downsampling.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param num_classes: if specified (as an int), then this model will be
+    :param dims: determines if the signal is 1D, 2D, or 3D. :param num_classes: if specified (as an int), then this
+    model will be
         class-conditional with `num_classes` classes.
-    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
-    :param num_heads: the number of attention heads in each attention layer.
-    :param num_heads_channels: if specified, ignore num_heads and instead use
+    :param use_checkpoint: use gradient checkpointing to reduce memory usage. :param num_heads: the number of attention
+    heads in each attention layer. :param num_heads_channels: if specified, ignore num_heads and instead use
                                a fixed channel width per attention head.
     :param num_heads_upsample: works with num_heads to set a different number
                                of heads for upsampling. Deprecated.
-    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
-    :param resblock_updown: use residual blocks for up/downsampling.
-    :param use_new_attention_order: use a different attention pattern for potentially
+    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. :param resblock_updown: use residual blocks
+    for up/downsampling. :param use_new_attention_order: use a different attention pattern for potentially
                                     increased efficiency.
     """
 
@@ -933,12 +929,9 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
 
     def forward(self, x, timesteps=None, context=None, y=None, **kwargs):
         """
-        Apply the model to an input batch.
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :param context: conditioning plugged in via crossattn
-        :param y: an [N] Tensor of labels, if class-conditional.
-        :return: an [N x C x ...] Tensor of outputs.
+        Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch
+        of timesteps. :param context: conditioning plugged in via crossattn :param y: an [N] Tensor of labels, if
+        class-conditional. :return: an [N x C x ...] Tensor of outputs.
         """
         assert (y is not None) == (
             self.num_classes is not None
@@ -970,8 +963,7 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
 
 class EncoderUNetModel(nn.Module):
     """
-    The half UNet model with attention and timestep embedding.
-    For usage, see UNet.
+    The half UNet model with attention and timestep embedding. For usage, see UNet.
     """
 
     def __init__(
@@ -1157,10 +1149,8 @@ class EncoderUNetModel(nn.Module):
 
     def forward(self, x, timesteps):
         """
-        Apply the model to an input batch.
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :return: an [N x K] Tensor of outputs.
+        Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch
+        of timesteps. :return: an [N x K] Tensor of outputs.
         """
         emb = self.time_embed(
             get_timestep_embedding(timesteps, self.model_channels, flip_sin_to_cos=True, downscale_freq_shift=0)

src/diffusers/models/unet_rl.py

@@ -5,18 +5,18 @@ import math
 import torch
 import torch.nn as nn
 
-
-try:
-    import einops
-    from einops.layers.torch import Rearrange
-except:
-    print("Einops is not installed")
-    pass
-
 from ..configuration_utils import ConfigMixin
 from ..modeling_utils import ModelMixin
 
 
+# try:
+#    import einops
+#    from einops.layers.torch import Rearrange
+# except:
+#    print("Einops is not installed")
+#    pass
+
+
 class SinusoidalPosEmb(nn.Module):
     def __init__(self, dim):
         super().__init__()
@@ -50,6 +50,21 @@ class Upsample1d(nn.Module):
         return self.conv(x)
 
 
+class RearrangeDim(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, tensor):
+        if len(tensor.shape) == 2:
+            return tensor[:, :, None]
+        if len(tensor.shape) == 3:
+            return tensor[:, :, None, :]
+        elif len(tensor.shape) == 4:
+            return tensor[:, :, 0, :]
+        else:
+            raise ValueError(f"`len(tensor)`: {len(tensor)} has to be 2, 3 or 4.")
+
+
 class Conv1dBlock(nn.Module):
     """
     Conv1d --> GroupNorm --> Mish
@@ -60,9 +75,11 @@ class Conv1dBlock(nn.Module):
 
         self.block = nn.Sequential(
             nn.Conv1d(inp_channels, out_channels, kernel_size, padding=kernel_size // 2),
-            Rearrange("batch channels horizon -> batch channels 1 horizon"),
+            RearrangeDim(),
+            #            Rearrange("batch channels horizon -> batch channels 1 horizon"),
             nn.GroupNorm(n_groups, out_channels),
-            Rearrange("batch channels 1 horizon -> batch channels horizon"),
+            RearrangeDim(),
+            #            Rearrange("batch channels 1 horizon -> batch channels horizon"),
             nn.Mish(),
         )
 
@@ -84,7 +101,8 @@ class ResidualTemporalBlock(nn.Module):
         self.time_mlp = nn.Sequential(
             nn.Mish(),
             nn.Linear(embed_dim, out_channels),
-            Rearrange("batch t -> batch t 1"),
+            RearrangeDim(),
+            #            Rearrange("batch t -> batch t 1"),
         )
 
         self.residual_conv = (
@@ -93,10 +111,8 @@ class ResidualTemporalBlock(nn.Module):
 
     def forward(self, x, t):
         """
-        x : [ batch_size x inp_channels x horizon ]
-        t : [ batch_size x embed_dim ]
-        returns:
-        out : [ batch_size x out_channels x horizon ]
+        x : [ batch_size x inp_channels x horizon ] t : [ batch_size x embed_dim ] returns: out : [ batch_size x
+        out_channels x horizon ]
         """
         out = self.blocks[0](x) + self.time_mlp(t)
         out = self.blocks[1](out)
@@ -184,7 +200,8 @@ class TemporalUNet(ModelMixin, ConfigMixin):  # (nn.Module):
         x : [ batch x horizon x transition ]
         """
 
-        x = einops.rearrange(x, "b h t -> b t h")
+        #        x = einops.rearrange(x, "b h t -> b t h")
+        x = x.permute(0, 2, 1)
 
         t = self.time_mlp(time)
         h = []
@@ -206,7 +223,8 @@ class TemporalUNet(ModelMixin, ConfigMixin):  # (nn.Module):
 
         x = self.final_conv(x)
 
-        x = einops.rearrange(x, "b t h -> b h t")
+        #        x = einops.rearrange(x, "b t h -> b h t")
+        x = x.permute(0, 2, 1)
         return x
 
 
@@ -263,7 +281,8 @@ class TemporalValue(nn.Module):
         x : [ batch x horizon x transition ]
         """
 
-        x = einops.rearrange(x, "b h t -> b t h")
+        #        x = einops.rearrange(x, "b h t -> b t h")
+        x = x.permute(0, 2, 1)
 
         t = self.time_mlp(time)
 

src/diffusers/models/unet_sde_score_estimation.py

@@ -136,26 +136,21 @@ def naive_downsample_2d(x, factor=2):
 def upsample_conv_2d(x, w, k=None, factor=2, gain=1):
     """Fused `upsample_2d()` followed by `tf.nn.conv2d()`.
 
-    Padding is performed only once at the beginning, not between the
-    operations.
-    The fused op is considerably more efficient than performing the same
-    calculation
-    using standard TensorFlow ops. It supports gradients of arbitrary order.
     Args:
-      x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
+    Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
+    efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of arbitrary
+    order.
+      x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
         C]`.
-      w:            Weight tensor of the shape `[filterH, filterW, inChannels,
-        outChannels]`. Grouped convolution can be performed by `inChannels =
-        x.shape[0] // numGroups`.
-      k:            FIR filter of the shape `[firH, firW]` or `[firN]`
-        (separable). The default is `[1] * factor`, which corresponds to
-        nearest-neighbor upsampling.
-      factor:       Integer upsampling factor (default: 2).
-      gain:         Scaling factor for signal magnitude (default: 1.0).
+      w: Weight tensor of the shape `[filterH, filterW, inChannels,
+        outChannels]`. Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
+      k: FIR filter of the shape `[firH, firW]` or `[firN]`
+        (separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
+      factor: Integer upsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).
 
     Returns:
-      Tensor of the shape `[N, C, H * factor, W * factor]` or
-      `[N, H * factor, W * factor, C]`, and same datatype as `x`.
+      Tensor of the shape `[N, C, H * factor, W * factor]` or `[N, H * factor, W * factor, C]`, and same datatype as
+      `x`.
     """
 
     assert isinstance(factor, int) and factor >= 1
@@ -208,25 +203,21 @@ def upsample_conv_2d(x, w, k=None, factor=2, gain=1):
 def conv_downsample_2d(x, w, k=None, factor=2, gain=1):
     """Fused `tf.nn.conv2d()` followed by `downsample_2d()`.
 
-    Padding is performed only once at the beginning, not between the operations.
-    The fused op is considerably more efficient than performing the same
-    calculation
-    using standard TensorFlow ops. It supports gradients of arbitrary order.
     Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
+    Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
+    efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of arbitrary
+    order.
+        x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
           C]`.
-        w:            Weight tensor of the shape `[filterH, filterW, inChannels,
-          outChannels]`. Grouped convolution can be performed by `inChannels =
-          x.shape[0] // numGroups`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]`
-          (separable). The default is `[1] * factor`, which corresponds to
-          average pooling.
-        factor:       Integer downsampling factor (default: 2).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
+        w: Weight tensor of the shape `[filterH, filterW, inChannels,
+          outChannels]`. Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
+        k: FIR filter of the shape `[firH, firW]` or `[firN]`
+          (separable). The default is `[1] * factor`, which corresponds to average pooling.
+        factor: Integer downsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).
 
     Returns:
-        Tensor of the shape `[N, C, H // factor, W // factor]` or
-        `[N, H // factor, W // factor, C]`, and same datatype as `x`.
+        Tensor of the shape `[N, C, H // factor, W // factor]` or `[N, H // factor, W // factor, C]`, and same datatype
+        as `x`.
     """
 
     assert isinstance(factor, int) and factor >= 1
@@ -258,22 +249,16 @@ def _shape(x, dim):
 def upsample_2d(x, k=None, factor=2, gain=1):
     r"""Upsample a batch of 2D images with the given filter.
 
-    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
-    and upsamples each image with the given filter. The filter is normalized so
-    that
-    if the input pixels are constant, they will be scaled by the specified
-    `gain`.
-    Pixels outside the image are assumed to be zero, and the filter is padded
-    with
-    zeros so that its shape is a multiple of the upsampling factor.
     Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
+    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and upsamples each image with the given
+    filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the specified
+    `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its shape is a:
+    multiple of the upsampling factor.
+        x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
           C]`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]`
-          (separable). The default is `[1] * factor`, which corresponds to
-          nearest-neighbor upsampling.
-        factor:       Integer upsampling factor (default: 2).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
+        k: FIR filter of the shape `[firH, firW]` or `[firN]`
+          (separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
+        factor: Integer upsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).
 
     Returns:
         Tensor of the shape `[N, C, H * factor, W * factor]`
@@ -289,22 +274,16 @@ def upsample_2d(x, k=None, factor=2, gain=1):
 def downsample_2d(x, k=None, factor=2, gain=1):
     r"""Downsample a batch of 2D images with the given filter.
 
-    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
-    and downsamples each image with the given filter. The filter is normalized
-    so that
-    if the input pixels are constant, they will be scaled by the specified
-    `gain`.
-    Pixels outside the image are assumed to be zero, and the filter is padded
-    with
-    zeros so that its shape is a multiple of the downsampling factor.
     Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
+    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and downsamples each image with the
+    given filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the
+    specified `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its
+    shape is a multiple of the downsampling factor.
+        x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
           C]`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]`
-          (separable). The default is `[1] * factor`, which corresponds to
-          average pooling.
-        factor:       Integer downsampling factor (default: 2).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
+        k: FIR filter of the shape `[firH, firW]` or `[firN]`
+          (separable). The default is `[1] * factor`, which corresponds to average pooling.
+        factor: Integer downsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).
 
     Returns:
         Tensor of the shape `[N, C, H // factor, W // factor]`

src/diffusers/pipelines/grad_tts_utils.py

@@ -290,7 +290,7 @@ def normalize_numbers(text):
     return text
 
 
-""" from https://github.com/keithito/tacotron """
+""" from https://github.com/keithito/tacotron"""
 
 
 _pad = "_"
@@ -322,8 +322,8 @@ def get_arpabet(word, dictionary):
 def text_to_sequence(text, cleaner_names=[english_cleaners], dictionary=None):
     """Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
 
-    The text can optionally have ARPAbet sequences enclosed in curly braces embedded
-    in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
+    The text can optionally have ARPAbet sequences enclosed in curly braces embedded in it. For example, "Turn left on
+    {HH AW1 S S T AH0 N} Street."
 
     Args:
       text: string to convert to a sequence

src/diffusers/pipelines/pipeline_bddm.py

@@ -29,8 +29,7 @@ from ..pipeline_utils import DiffusionPipeline
 def calc_diffusion_step_embedding(diffusion_steps, diffusion_step_embed_dim_in):
     """
     Embed a diffusion step $t$ into a higher dimensional space
-        E.g. the embedding vector in the 128-dimensional space is
-        [sin(t * 10^(0*4/63)), ... , sin(t * 10^(63*4/63)),
+        E.g. the embedding vector in the 128-dimensional space is [sin(t * 10^(0*4/63)), ... , sin(t * 10^(63*4/63)),
          cos(t * 10^(0*4/63)), ... , cos(t * 10^(63*4/63))]
 
     Parameters:
@@ -53,8 +52,7 @@ def calc_diffusion_step_embedding(diffusion_steps, diffusion_step_embed_dim_in):
 
 
 """
-Below scripts were borrowed from
-https://github.com/philsyn/DiffWave-Vocoder/blob/master/WaveNet.py
+Below scripts were borrowed from https://github.com/philsyn/DiffWave-Vocoder/blob/master/WaveNet.py
 """
 
 

src/diffusers/pipelines/pipeline_glide.py

@@ -699,9 +699,8 @@ def _extract_into_tensor(arr, timesteps, broadcast_shape):
     """
     Extract values from a 1-D numpy array for a batch of indices.
 
-    :param arr: the 1-D numpy array.
-    :param timesteps: a tensor of indices into the array to extract.
-    :param broadcast_shape: a larger shape of K dimensions with the batch
+    :param arr: the 1-D numpy array. :param timesteps: a tensor of indices into the array to extract. :param
+    broadcast_shape: a larger shape of K dimensions with the batch
                             dimension equal to the length of timesteps.
     :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
     """

src/diffusers/pipelines/pipeline_grad_tts.py

-""" from https://github.com/jaywalnut310/glow-tts """
+""" from https://github.com/jaywalnut310/glow-tts"""
 
 import math
 

src/diffusers/pipelines/pipeline_latent_diffusion.py

@@ -554,11 +554,9 @@ class LDMBertModel(LDMBertPreTrainedModel):
 
 def get_timestep_embedding(timesteps, embedding_dim):
     """
-    This matches the implementation in Denoising Diffusion Probabilistic Models:
-    From Fairseq.
-    Build sinusoidal embeddings.
-    This matches the implementation in tensor2tensor, but differs slightly
-    from the description in Section 3.5 of "Attention Is All You Need".
+    This matches the implementation in Denoising Diffusion Probabilistic Models: From Fairseq. Build sinusoidal
+    embeddings. This matches the implementation in tensor2tensor, but differs slightly from the description in Section
+    3.5 of "Attention Is All You Need".
     """
     assert len(timesteps.shape) == 1
 
@@ -1055,8 +1053,8 @@ class Decoder(nn.Module):
 
 class VectorQuantizer(nn.Module):
     """
-    Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly
-    avoids costly matrix multiplications and allows for post-hoc remapping of indices.
+    Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly avoids costly matrix
+    multiplications and allows for post-hoc remapping of indices.
     """
 
     # NOTE: due to a bug the beta term was applied to the wrong term. for

src/diffusers/schedulers/scheduling_ddim.py

@@ -25,13 +25,12 @@ from .scheduling_utils import SchedulerMixin
 
 def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
     """
-    Create a beta schedule that discretizes the given alpha_t_bar function,
-    which defines the cumulative product of (1-beta) over time from t = [0,1].
+    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
+    (1-beta) over time from t = [0,1].
 
-    :param num_diffusion_timesteps: the number of betas to produce.
-    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
-                      produces the cumulative product of (1-beta) up to that
-                      part of the diffusion process.
+    :param num_diffusion_timesteps: the number of betas to produce. :param alpha_bar: a lambda that takes an argument t
+    from 0 to 1 and
+                      produces the cumulative product of (1-beta) up to that part of the diffusion process.
     :param max_beta: the maximum beta to use; use values lower than 1 to
                      prevent singularities.
     """

src/diffusers/schedulers/scheduling_ddpm.py

@@ -25,13 +25,12 @@ from .scheduling_utils import SchedulerMixin
 
 def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
     """
-    Create a beta schedule that discretizes the given alpha_t_bar function,
-    which defines the cumulative product of (1-beta) over time from t = [0,1].
+    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
+    (1-beta) over time from t = [0,1].
 
-    :param num_diffusion_timesteps: the number of betas to produce.
-    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
-                      produces the cumulative product of (1-beta) up to that
-                      part of the diffusion process.
+    :param num_diffusion_timesteps: the number of betas to produce. :param alpha_bar: a lambda that takes an argument t
+    from 0 to 1 and
+                      produces the cumulative product of (1-beta) up to that part of the diffusion process.
     :param max_beta: the maximum beta to use; use values lower than 1 to
                      prevent singularities.
     """
@@ -144,16 +143,12 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
         return pred_prev_sample
 
     def training_step(self, original_samples: torch.Tensor, noise: torch.Tensor, timesteps: torch.Tensor):
-        if timesteps.dim() != 1:
-            raise ValueError("`timesteps` must be a 1D tensor")
-
-        device = original_samples.device
-        batch_size = original_samples.shape[0]
-        timesteps = timesteps.reshape(batch_size, 1, 1, 1)
-
         sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
+        sqrt_alpha_prod = self.match_shape(sqrt_alpha_prod, original_samples)
         sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
-        noisy_samples = sqrt_alpha_prod.to(device) * original_samples + sqrt_one_minus_alpha_prod.to(device) * noise
+        sqrt_one_minus_alpha_prod = self.match_shape(sqrt_one_minus_alpha_prod, original_samples)
+
+        noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
         return noisy_samples
 
     def __len__(self):