Merge branch 'main' into cogvideox-lora-and-training

.github/workflows/nightly_tests.yml

@@ -79,7 +79,7 @@ jobs:
           python utils/print_env.py
       - name: Pipeline CUDA Test
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
           # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
@@ -139,7 +139,7 @@ jobs:
     - name: Run nightly PyTorch CUDA tests for non-pipeline modules
       if: ${{ matrix.module != 'examples'}}
       env:
-        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
         # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |
@@ -152,7 +152,7 @@ jobs:
     - name: Run nightly example tests with Torch
       if: ${{ matrix.module == 'examples' }}
       env:
-        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
         # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |
@@ -209,7 +209,7 @@ jobs:
 
     - name: Run nightly Flax TPU tests
       env:
-        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
       run: |
         python -m pytest -n 0 \
           -s -v -k "Flax" \
@@ -264,7 +264,7 @@ jobs:
 
     - name: Run Nightly ONNXRuntime CUDA tests
       env:
-        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
       run: |
         python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
           -s -v -k "Onnx" \

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

@@ -77,16 +77,33 @@ CogVideoX-2b requires about 19 GB of GPU memory to decode 49 frames (6 seconds o
 - `pipe.enable_model_cpu_offload()`:
   - Without enabling cpu offloading, memory usage is `33 GB`
   - With enabling cpu offloading, memory usage is `19 GB`
+- `pipe.enable_sequential_cpu_offload()`:
+  - Similar to `enable_model_cpu_offload` but can significantly reduce memory usage at the cost of slow inference
+  - When enabled, memory usage is under `4 GB`
 - `pipe.vae.enable_tiling()`:
   - With enabling cpu offloading and tiling, memory usage is `11 GB`
 - `pipe.vae.enable_slicing()`
 
+### Quantized inference
+
+[torchao](https://github.com/pytorch/ao) and [optimum-quanto](https://github.com/huggingface/optimum-quanto/) can be used to quantize the text encoder, transformer and VAE modules to lower the memory requirements. This makes it possible to run the model on a free-tier T4 Colab or lower VRAM GPUs!
+
+It is also worth noting that torchao quantization is fully compatible with [torch.compile](/optimization/torch2.0#torchcompile), which allows for much faster inference speed. Additionally, models can be serialized and stored in a quantized datatype to save disk space with torchao. Find examples and benchmarks in the gists below.
+- [torchao](https://gist.github.com/a-r-r-o-w/4d9732d17412888c885480c6521a9897)
+- [quanto](https://gist.github.com/a-r-r-o-w/31be62828b00a9292821b85c1017effa)
+
 ## CogVideoXPipeline
 
 [[autodoc]] CogVideoXPipeline
   - all
   - __call__
 
+## CogVideoXVideoToVideoPipeline
+
+[[autodoc]] CogVideoXVideoToVideoPipeline
+  - all
+  - __call__
+
 ## CogVideoXPipelineOutput
 
-[[autodoc]] pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput
+[[autodoc]] pipelines.cogvideo.pipeline_output.CogVideoXPipelineOutput

examples/custom_diffusion/train_custom_diffusion.py

@@ -314,11 +314,12 @@ def save_new_embed(text_encoder, modifier_token_id, accelerator, args, output_di
     for x, y in zip(modifier_token_id, args.modifier_token):
         learned_embeds_dict = {}
         learned_embeds_dict[y] = learned_embeds[x]
-        filename = f"{output_dir}/{y}.bin"
 
         if safe_serialization:
+            filename = f"{output_dir}/{y}.safetensors"
             safetensors.torch.save_file(learned_embeds_dict, filename, metadata={"format": "pt"})
         else:
+            filename = f"{output_dir}/{y}.bin"
             torch.save(learned_embeds_dict, filename)
 
 
@@ -1040,17 +1041,22 @@ def main(args):
     )
 
     # Scheduler and math around the number of training steps.
-    overrode_max_train_steps = False
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    # Check the PR https://github.com/huggingface/diffusers/pull/8312 for detailed explanation.
+    num_warmup_steps_for_scheduler = args.lr_warmup_steps * accelerator.num_processes
     if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-        overrode_max_train_steps = True
+        len_train_dataloader_after_sharding = math.ceil(len(train_dataloader) / accelerator.num_processes)
+        num_update_steps_per_epoch = math.ceil(len_train_dataloader_after_sharding / args.gradient_accumulation_steps)
+        num_training_steps_for_scheduler = (
+            args.num_train_epochs * num_update_steps_per_epoch * accelerator.num_processes
+        )
+    else:
+        num_training_steps_for_scheduler = args.max_train_steps * accelerator.num_processes
 
     lr_scheduler = get_scheduler(
         args.lr_scheduler,
         optimizer=optimizer,
-        num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
-        num_training_steps=args.max_train_steps * accelerator.num_processes,
+        num_warmup_steps=num_warmup_steps_for_scheduler,
+        num_training_steps=num_training_steps_for_scheduler,
     )
 
     # Prepare everything with our `accelerator`.
@@ -1065,8 +1071,14 @@ def main(args):
 
     # We need to recalculate our total training steps as the size of the training dataloader may have changed.
     num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if overrode_max_train_steps:
+    if args.max_train_steps is None:
         args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+        if num_training_steps_for_scheduler != args.max_train_steps * accelerator.num_processes:
+            logger.warning(
+                f"The length of the 'train_dataloader' after 'accelerator.prepare' ({len(train_dataloader)}) does not match "
+                f"the expected length ({len_train_dataloader_after_sharding}) when the learning rate scheduler was created. "
+                f"This inconsistency may result in the learning rate scheduler not functioning properly."
+            )
     # Afterwards we recalculate our number of training epochs
     args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
 

src/diffusers/__init__.py

@@ -255,6 +255,7 @@ else:
             "BlipDiffusionPipeline",
             "CLIPImageProjection",
             "CogVideoXPipeline",
+            "CogVideoXVideoToVideoPipeline",
             "CycleDiffusionPipeline",
             "FluxControlNetPipeline",
             "FluxPipeline",
@@ -699,6 +700,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AuraFlowPipeline,
             CLIPImageProjection,
             CogVideoXPipeline,
+            CogVideoXVideoToVideoPipeline,
             CycleDiffusionPipeline,
             FluxControlNetPipeline,
             FluxPipeline,

src/diffusers/loaders/lora_conversion_utils.py

@@ -14,6 +14,8 @@
 
 import re
 
+import torch
+
 from ..utils import is_peft_version, logging
 
 
@@ -326,3 +328,294 @@ def _get_alpha_name(lora_name_alpha, diffusers_name, alpha):
         prefix = "text_encoder_2."
     new_name = prefix + diffusers_name.split(".lora.")[0] + ".alpha"
     return {new_name: alpha}
+
+
+# The utilities under `_convert_kohya_flux_lora_to_diffusers()`
+# are taken from https://github.com/kohya-ss/sd-scripts/blob/a61cf73a5cb5209c3f4d1a3688dd276a4dfd1ecb/networks/convert_flux_lora.py
+# All credits go to `kohya-ss`.
+def _convert_kohya_flux_lora_to_diffusers(state_dict):
+    def _convert_to_ai_toolkit(sds_sd, ait_sd, sds_key, ait_key):
+        if sds_key + ".lora_down.weight" not in sds_sd:
+            return
+        down_weight = sds_sd.pop(sds_key + ".lora_down.weight")
+
+        # scale weight by alpha and dim
+        rank = down_weight.shape[0]
+        alpha = sds_sd.pop(sds_key + ".alpha").item()  # alpha is scalar
+        scale = alpha / rank  # LoRA is scaled by 'alpha / rank' in forward pass, so we need to scale it back here
+
+        # calculate scale_down and scale_up to keep the same value. if scale is 4, scale_down is 2 and scale_up is 2
+        scale_down = scale
+        scale_up = 1.0
+        while scale_down * 2 < scale_up:
+            scale_down *= 2
+            scale_up /= 2
+
+        ait_sd[ait_key + ".lora_A.weight"] = down_weight * scale_down
+        ait_sd[ait_key + ".lora_B.weight"] = sds_sd.pop(sds_key + ".lora_up.weight") * scale_up
+
+    def _convert_to_ai_toolkit_cat(sds_sd, ait_sd, sds_key, ait_keys, dims=None):
+        if sds_key + ".lora_down.weight" not in sds_sd:
+            return
+        down_weight = sds_sd.pop(sds_key + ".lora_down.weight")
+        up_weight = sds_sd.pop(sds_key + ".lora_up.weight")
+        sd_lora_rank = down_weight.shape[0]
+
+        # scale weight by alpha and dim
+        alpha = sds_sd.pop(sds_key + ".alpha")
+        scale = alpha / sd_lora_rank
+
+        # calculate scale_down and scale_up
+        scale_down = scale
+        scale_up = 1.0
+        while scale_down * 2 < scale_up:
+            scale_down *= 2
+            scale_up /= 2
+
+        down_weight = down_weight * scale_down
+        up_weight = up_weight * scale_up
+
+        # calculate dims if not provided
+        num_splits = len(ait_keys)
+        if dims is None:
+            dims = [up_weight.shape[0] // num_splits] * num_splits
+        else:
+            assert sum(dims) == up_weight.shape[0]
+
+        # check upweight is sparse or not
+        is_sparse = False
+        if sd_lora_rank % num_splits == 0:
+            ait_rank = sd_lora_rank // num_splits
+            is_sparse = True
+            i = 0
+            for j in range(len(dims)):
+                for k in range(len(dims)):
+                    if j == k:
+                        continue
+                    is_sparse = is_sparse and torch.all(
+                        up_weight[i : i + dims[j], k * ait_rank : (k + 1) * ait_rank] == 0
+                    )
+                i += dims[j]
+            if is_sparse:
+                logger.info(f"weight is sparse: {sds_key}")
+
+        # make ai-toolkit weight
+        ait_down_keys = [k + ".lora_A.weight" for k in ait_keys]
+        ait_up_keys = [k + ".lora_B.weight" for k in ait_keys]
+        if not is_sparse:
+            # down_weight is copied to each split
+            ait_sd.update({k: down_weight for k in ait_down_keys})
+
+            # up_weight is split to each split
+            ait_sd.update({k: v for k, v in zip(ait_up_keys, torch.split(up_weight, dims, dim=0))})  # noqa: C416
+        else:
+            # down_weight is chunked to each split
+            ait_sd.update({k: v for k, v in zip(ait_down_keys, torch.chunk(down_weight, num_splits, dim=0))})  # noqa: C416
+
+            # up_weight is sparse: only non-zero values are copied to each split
+            i = 0
+            for j in range(len(dims)):
+                ait_sd[ait_up_keys[j]] = up_weight[i : i + dims[j], j * ait_rank : (j + 1) * ait_rank].contiguous()
+                i += dims[j]
+
+    def _convert_sd_scripts_to_ai_toolkit(sds_sd):
+        ait_sd = {}
+        for i in range(19):
+            _convert_to_ai_toolkit(
+                sds_sd,
+                ait_sd,
+                f"lora_unet_double_blocks_{i}_img_attn_proj",
+                f"transformer.transformer_blocks.{i}.attn.to_out.0",
+            )
+            _convert_to_ai_toolkit_cat(
+                sds_sd,
+                ait_sd,
+                f"lora_unet_double_blocks_{i}_img_attn_qkv",
+                [
+                    f"transformer.transformer_blocks.{i}.attn.to_q",
+                    f"transformer.transformer_blocks.{i}.attn.to_k",
+                    f"transformer.transformer_blocks.{i}.attn.to_v",
+                ],
+            )
+            _convert_to_ai_toolkit(
+                sds_sd,
+                ait_sd,
+                f"lora_unet_double_blocks_{i}_img_mlp_0",
+                f"transformer.transformer_blocks.{i}.ff.net.0.proj",
+            )
+            _convert_to_ai_toolkit(
+                sds_sd,
+                ait_sd,
+                f"lora_unet_double_blocks_{i}_img_mlp_2",
+                f"transformer.transformer_blocks.{i}.ff.net.2",
+            )
+            _convert_to_ai_toolkit(
+                sds_sd,
+                ait_sd,
+                f"lora_unet_double_blocks_{i}_img_mod_lin",
+                f"transformer.transformer_blocks.{i}.norm1.linear",
+            )
+            _convert_to_ai_toolkit(
+                sds_sd,
+                ait_sd,
+                f"lora_unet_double_blocks_{i}_txt_attn_proj",
+                f"transformer.transformer_blocks.{i}.attn.to_add_out",
+            )
+            _convert_to_ai_toolkit_cat(
+                sds_sd,
+                ait_sd,
+                f"lora_unet_double_blocks_{i}_txt_attn_qkv",
+                [
+                    f"transformer.transformer_blocks.{i}.attn.add_q_proj",
+                    f"transformer.transformer_blocks.{i}.attn.add_k_proj",
+                    f"transformer.transformer_blocks.{i}.attn.add_v_proj",
+                ],
+            )
+            _convert_to_ai_toolkit(
+                sds_sd,
+                ait_sd,
+                f"lora_unet_double_blocks_{i}_txt_mlp_0",
+                f"transformer.transformer_blocks.{i}.ff_context.net.0.proj",
+            )
+            _convert_to_ai_toolkit(
+                sds_sd,
+                ait_sd,
+                f"lora_unet_double_blocks_{i}_txt_mlp_2",
+                f"transformer.transformer_blocks.{i}.ff_context.net.2",
+            )
+            _convert_to_ai_toolkit(
+                sds_sd,
+                ait_sd,
+                f"lora_unet_double_blocks_{i}_txt_mod_lin",
+                f"transformer.transformer_blocks.{i}.norm1_context.linear",
+            )
+
+        for i in range(38):
+            _convert_to_ai_toolkit_cat(
+                sds_sd,
+                ait_sd,
+                f"lora_unet_single_blocks_{i}_linear1",
+                [
+                    f"transformer.single_transformer_blocks.{i}.attn.to_q",
+                    f"transformer.single_transformer_blocks.{i}.attn.to_k",
+                    f"transformer.single_transformer_blocks.{i}.attn.to_v",
+                    f"transformer.single_transformer_blocks.{i}.proj_mlp",
+                ],
+                dims=[3072, 3072, 3072, 12288],
+            )
+            _convert_to_ai_toolkit(
+                sds_sd,
+                ait_sd,
+                f"lora_unet_single_blocks_{i}_linear2",
+                f"transformer.single_transformer_blocks.{i}.proj_out",
+            )
+            _convert_to_ai_toolkit(
+                sds_sd,
+                ait_sd,
+                f"lora_unet_single_blocks_{i}_modulation_lin",
+                f"transformer.single_transformer_blocks.{i}.norm.linear",
+            )
+
+        if len(sds_sd) > 0:
+            logger.warning(f"Unsuppored keys for ai-toolkit: {sds_sd.keys()}")
+
+        return ait_sd
+
+    return _convert_sd_scripts_to_ai_toolkit(state_dict)
+
+
+# Adapted from https://gist.github.com/Leommm-byte/6b331a1e9bd53271210b26543a7065d6
+# Some utilities were reused from
+# https://github.com/kohya-ss/sd-scripts/blob/a61cf73a5cb5209c3f4d1a3688dd276a4dfd1ecb/networks/convert_flux_lora.py
+def _convert_xlabs_flux_lora_to_diffusers(old_state_dict):
+    new_state_dict = {}
+    orig_keys = list(old_state_dict.keys())
+
+    def handle_qkv(sds_sd, ait_sd, sds_key, ait_keys, dims=None):
+        down_weight = sds_sd.pop(sds_key)
+        up_weight = sds_sd.pop(sds_key.replace(".down.weight", ".up.weight"))
+
+        # calculate dims if not provided
+        num_splits = len(ait_keys)
+        if dims is None:
+            dims = [up_weight.shape[0] // num_splits] * num_splits
+        else:
+            assert sum(dims) == up_weight.shape[0]
+
+        # make ai-toolkit weight
+        ait_down_keys = [k + ".lora_A.weight" for k in ait_keys]
+        ait_up_keys = [k + ".lora_B.weight" for k in ait_keys]
+
+        # down_weight is copied to each split
+        ait_sd.update({k: down_weight for k in ait_down_keys})
+
+        # up_weight is split to each split
+        ait_sd.update({k: v for k, v in zip(ait_up_keys, torch.split(up_weight, dims, dim=0))})  # noqa: C416
+
+    for old_key in orig_keys:
+        # Handle double_blocks
+        if old_key.startswith(("diffusion_model.double_blocks", "double_blocks")):
+            block_num = re.search(r"double_blocks\.(\d+)", old_key).group(1)
+            new_key = f"transformer.transformer_blocks.{block_num}"
+
+            if "processor.proj_lora1" in old_key:
+                new_key += ".attn.to_out.0"
+            elif "processor.proj_lora2" in old_key:
+                new_key += ".attn.to_add_out"
+            elif "processor.qkv_lora1" in old_key and "up" not in old_key:
+                handle_qkv(
+                    old_state_dict,
+                    new_state_dict,
+                    old_key,
+                    [
+                        f"transformer.transformer_blocks.{block_num}.attn.add_q_proj",
+                        f"transformer.transformer_blocks.{block_num}.attn.add_k_proj",
+                        f"transformer.transformer_blocks.{block_num}.attn.add_v_proj",
+                    ],
+                )
+                # continue
+            elif "processor.qkv_lora2" in old_key and "up" not in old_key:
+                handle_qkv(
+                    old_state_dict,
+                    new_state_dict,
+                    old_key,
+                    [
+                        f"transformer.transformer_blocks.{block_num}.attn.to_q",
+                        f"transformer.transformer_blocks.{block_num}.attn.to_k",
+                        f"transformer.transformer_blocks.{block_num}.attn.to_v",
+                    ],
+                )
+                # continue
+
+            if "down" in old_key:
+                new_key += ".lora_A.weight"
+            elif "up" in old_key:
+                new_key += ".lora_B.weight"
+
+        # Handle single_blocks
+        elif old_key.startswith("diffusion_model.single_blocks", "single_blocks"):
+            block_num = re.search(r"single_blocks\.(\d+)", old_key).group(1)
+            new_key = f"transformer.single_transformer_blocks.{block_num}"
+
+            if "proj_lora1" in old_key or "proj_lora2" in old_key:
+                new_key += ".proj_out"
+            elif "qkv_lora1" in old_key or "qkv_lora2" in old_key:
+                new_key += ".norm.linear"
+
+            if "down" in old_key:
+                new_key += ".lora_A.weight"
+            elif "up" in old_key:
+                new_key += ".lora_B.weight"
+
+        else:
+            # Handle other potential key patterns here
+            new_key = old_key
+
+        # Since we already handle qkv above.
+        if "qkv" not in old_key:
+            new_state_dict[new_key] = old_state_dict.pop(old_key)
+
+    if len(old_state_dict) > 0:
+        raise ValueError(f"`old_state_dict` should be at this point but has: {list(old_state_dict.keys())}.")
+
+    return new_state_dict

src/diffusers/loaders/lora_pipeline.py

@@ -31,7 +31,12 @@ from ..utils import (
     scale_lora_layers,
 )
 from .lora_base import LoraBaseMixin
-from .lora_conversion_utils import _convert_non_diffusers_lora_to_diffusers, _maybe_map_sgm_blocks_to_diffusers
+from .lora_conversion_utils import (
+    _convert_kohya_flux_lora_to_diffusers,
+    _convert_non_diffusers_lora_to_diffusers,
+    _convert_xlabs_flux_lora_to_diffusers,
+    _maybe_map_sgm_blocks_to_diffusers,
+)
 
 
 if is_transformers_available():
@@ -1583,6 +1588,20 @@ class FluxLoraLoaderMixin(LoraBaseMixin):
             allow_pickle=allow_pickle,
         )
 
+        # TODO (sayakpaul): to a follow-up to clean and try to unify the conditions.
+
+        is_kohya = any(".lora_down.weight" in k for k in state_dict)
+        if is_kohya:
+            state_dict = _convert_kohya_flux_lora_to_diffusers(state_dict)
+            # Kohya already takes care of scaling the LoRA parameters with alpha.
+            return (state_dict, None) if return_alphas else state_dict
+
+        is_xlabs = any("processor" in k for k in state_dict)
+        if is_xlabs:
+            state_dict = _convert_xlabs_flux_lora_to_diffusers(state_dict)
+            # xlabs doesn't use `alpha`.
+            return (state_dict, None) if return_alphas else state_dict
+
         # For state dicts like
         # https://huggingface.co/TheLastBen/Jon_Snow_Flux_LoRA
         keys = list(state_dict.keys())

src/diffusers/loaders/single_file_utils.py

@@ -91,11 +91,11 @@ DIFFUSERS_DEFAULT_PIPELINE_PATHS = {
     "xl_inpaint": {"pretrained_model_name_or_path": "diffusers/stable-diffusion-xl-1.0-inpainting-0.1"},
     "playground-v2-5": {"pretrained_model_name_or_path": "playgroundai/playground-v2.5-1024px-aesthetic"},
     "upscale": {"pretrained_model_name_or_path": "stabilityai/stable-diffusion-x4-upscaler"},
-    "inpainting": {"pretrained_model_name_or_path": "runwayml/stable-diffusion-inpainting"},
+    "inpainting": {"pretrained_model_name_or_path": "Lykon/dreamshaper-8-inpainting"},
     "inpainting_v2": {"pretrained_model_name_or_path": "stabilityai/stable-diffusion-2-inpainting"},
     "controlnet": {"pretrained_model_name_or_path": "lllyasviel/control_v11p_sd15_canny"},
     "v2": {"pretrained_model_name_or_path": "stabilityai/stable-diffusion-2-1"},
-    "v1": {"pretrained_model_name_or_path": "runwayml/stable-diffusion-v1-5"},
+    "v1": {"pretrained_model_name_or_path": "Lykon/dreamshaper-8"},
     "stable_cascade_stage_b": {"pretrained_model_name_or_path": "stabilityai/stable-cascade", "subfolder": "decoder"},
     "stable_cascade_stage_b_lite": {
         "pretrained_model_name_or_path": "stabilityai/stable-cascade",

src/diffusers/models/attention.py

@@ -972,15 +972,32 @@ class FreeNoiseTransformerBlock(nn.Module):
         return frame_indices
 
     def _get_frame_weights(self, num_frames: int, weighting_scheme: str = "pyramid") -> List[float]:
-        if weighting_scheme == "pyramid":
+        if weighting_scheme == "flat":
+            weights = [1.0] * num_frames
+
+        elif weighting_scheme == "pyramid":
             if num_frames % 2 == 0:
                 # num_frames = 4 => [1, 2, 2, 1]
-                weights = list(range(1, num_frames // 2 + 1))
+                mid = num_frames // 2
+                weights = list(range(1, mid + 1))
                 weights = weights + weights[::-1]
             else:
                 # num_frames = 5 => [1, 2, 3, 2, 1]
-                weights = list(range(1, num_frames // 2 + 1))
-                weights = weights + [num_frames // 2 + 1] + weights[::-1]
+                mid = (num_frames + 1) // 2
+                weights = list(range(1, mid))
+                weights = weights + [mid] + weights[::-1]
+
+        elif weighting_scheme == "delayed_reverse_sawtooth":
+            if num_frames % 2 == 0:
+                # num_frames = 4 => [0.01, 2, 2, 1]
+                mid = num_frames // 2
+                weights = [0.01] * (mid - 1) + [mid]
+                weights = weights + list(range(mid, 0, -1))
+            else:
+                # num_frames = 5 => [0.01, 0.01, 3, 2, 1]
+                mid = (num_frames + 1) // 2
+                weights = [0.01] * mid
+                weights = weights + list(range(mid, 0, -1))
         else:
             raise ValueError(f"Unsupported value for weighting_scheme={weighting_scheme}")
 

src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py

@@ -999,6 +999,7 @@ class AutoencoderKLCogVideoX(ModelMixin, ConfigMixin, FromOriginalModelMixin):
         # setting it to anything other than 2 would give poor results because the VAE hasn't been trained to be adaptive with different
         # number of temporal frames.
         self.num_latent_frames_batch_size = 2
+        self.num_sample_frames_batch_size = 8
 
         # We make the minimum height and width of sample for tiling half that of the generally supported
         self.tile_sample_min_height = sample_height // 2
@@ -1082,12 +1083,27 @@ class AutoencoderKLCogVideoX(ModelMixin, ConfigMixin, FromOriginalModelMixin):
         self.use_slicing = False
 
     def _encode(self, x: torch.Tensor) -> torch.Tensor:
-        # TODO: Implement context parallel cache
-        # TODO: Implement tiled encoding
-        h = self.encoder(x)
-        if self.quant_conv is not None:
-            h = self.quant_conv(h)
-        return h
+        batch_size, num_channels, num_frames, height, width = x.shape
+
+        if self.use_tiling and (width > self.tile_sample_min_width or height > self.tile_sample_min_height):
+            return self.tiled_encode(x)
+
+        frame_batch_size = self.num_sample_frames_batch_size
+        enc = []
+        for i in range(num_frames // frame_batch_size):
+            remaining_frames = num_frames % frame_batch_size
+            start_frame = frame_batch_size * i + (0 if i == 0 else remaining_frames)
+            end_frame = frame_batch_size * (i + 1) + remaining_frames
+            x_intermediate = x[:, :, start_frame:end_frame]
+            x_intermediate = self.encoder(x_intermediate)
+            if self.quant_conv is not None:
+                x_intermediate = self.quant_conv(x_intermediate)
+            enc.append(x_intermediate)
+
+        self._clear_fake_context_parallel_cache()
+        enc = torch.cat(enc, dim=2)
+
+        return enc
 
     @apply_forward_hook
     def encode(
@@ -1102,7 +1118,7 @@ class AutoencoderKLCogVideoX(ModelMixin, ConfigMixin, FromOriginalModelMixin):
                 Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
 
         Returns:
-                The latent representations of the encoded images. If `return_dict` is True, a
+                The latent representations of the encoded videos. If `return_dict` is True, a
                 [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
         """
         if self.use_slicing and x.shape[0] > 1:
@@ -1112,6 +1128,7 @@ class AutoencoderKLCogVideoX(ModelMixin, ConfigMixin, FromOriginalModelMixin):
             h = self._encode(x)
 
         posterior = DiagonalGaussianDistribution(h)
+
         if not return_dict:
             return (posterior,)
         return AutoencoderKLOutput(latent_dist=posterior)
@@ -1183,6 +1200,75 @@ class AutoencoderKLCogVideoX(ModelMixin, ConfigMixin, FromOriginalModelMixin):
             )
         return b
 
+    def tiled_encode(self, x: torch.Tensor) -> torch.Tensor:
+        r"""Encode a batch of images using a tiled encoder.
+
+        When this option is enabled, the VAE will split the input tensor into tiles to compute encoding in several
+        steps. This is useful to keep memory use constant regardless of image size. The end result of tiled encoding is
+        different from non-tiled encoding because each tile uses a different encoder. To avoid tiling artifacts, the
+        tiles overlap and are blended together to form a smooth output. You may still see tile-sized changes in the
+        output, but they should be much less noticeable.
+
+        Args:
+            x (`torch.Tensor`): Input batch of videos.
+
+        Returns:
+            `torch.Tensor`:
+                The latent representation of the encoded videos.
+        """
+        # For a rough memory estimate, take a look at the `tiled_decode` method.
+        batch_size, num_channels, num_frames, height, width = x.shape
+
+        overlap_height = int(self.tile_sample_min_height * (1 - self.tile_overlap_factor_height))
+        overlap_width = int(self.tile_sample_min_width * (1 - self.tile_overlap_factor_width))
+        blend_extent_height = int(self.tile_latent_min_height * self.tile_overlap_factor_height)
+        blend_extent_width = int(self.tile_latent_min_width * self.tile_overlap_factor_width)
+        row_limit_height = self.tile_latent_min_height - blend_extent_height
+        row_limit_width = self.tile_latent_min_width - blend_extent_width
+        frame_batch_size = self.num_sample_frames_batch_size
+
+        # Split x into overlapping tiles and encode them separately.
+        # The tiles have an overlap to avoid seams between tiles.
+        rows = []
+        for i in range(0, height, overlap_height):
+            row = []
+            for j in range(0, width, overlap_width):
+                time = []
+                for k in range(num_frames // frame_batch_size):
+                    remaining_frames = num_frames % frame_batch_size
+                    start_frame = frame_batch_size * k + (0 if k == 0 else remaining_frames)
+                    end_frame = frame_batch_size * (k + 1) + remaining_frames
+                    tile = x[
+                        :,
+                        :,
+                        start_frame:end_frame,
+                        i : i + self.tile_sample_min_height,
+                        j : j + self.tile_sample_min_width,
+                    ]
+                    tile = self.encoder(tile)
+                    if self.quant_conv is not None:
+                        tile = self.quant_conv(tile)
+                    time.append(tile)
+                self._clear_fake_context_parallel_cache()
+                row.append(torch.cat(time, dim=2))
+            rows.append(row)
+
+        result_rows = []
+        for i, row in enumerate(rows):
+            result_row = []
+            for j, tile in enumerate(row):
+                # blend the above tile and the left tile
+                # to the current tile and add the current tile to the result row
+                if i > 0:
+                    tile = self.blend_v(rows[i - 1][j], tile, blend_extent_height)
+                if j > 0:
+                    tile = self.blend_h(row[j - 1], tile, blend_extent_width)
+                result_row.append(tile[:, :, :, :row_limit_height, :row_limit_width])
+            result_rows.append(torch.cat(result_row, dim=4))
+
+        enc = torch.cat(result_rows, dim=3)
+        return enc
+
     def tiled_decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
         r"""
         Decode a batch of images using a tiled decoder.

src/diffusers/models/controlnet_sparsectrl.py

@@ -691,7 +691,6 @@ class SparseControlNetModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
 
         emb = self.time_embedding(t_emb, timestep_cond)
         emb = emb.repeat_interleave(sample_num_frames, dim=0)
-        encoder_hidden_states = encoder_hidden_states.repeat_interleave(sample_num_frames, dim=0)
 
         # 2. pre-process
         batch_size, channels, num_frames, height, width = sample.shape

src/diffusers/models/embeddings.py

@@ -514,7 +514,7 @@ def get_1d_rotary_pos_embed(
     linear_factor=1.0,
     ntk_factor=1.0,
     repeat_interleave_real=True,
-    freqs_dtype=torch.float32,  # torch.float32 (hunyuan, stable audio), torch.float64 (flux)
+    freqs_dtype=torch.float32,  #  torch.float32, torch.float64 (flux)
 ):
     """
     Precompute the frequency tensor for complex exponentials (cis) with given dimensions.
@@ -545,21 +545,27 @@ def get_1d_rotary_pos_embed(
     assert dim % 2 == 0
 
     if isinstance(pos, int):
-        pos = np.arange(pos)
+        pos = torch.arange(pos)
+    if isinstance(pos, np.ndarray):
+        pos = torch.from_numpy(pos)  # type: ignore  # [S]
+
     theta = theta * ntk_factor
     freqs = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=freqs_dtype)[: (dim // 2)] / dim)) / linear_factor  # [D/2]
-    t = torch.from_numpy(pos).to(freqs.device)  # type: ignore  # [S]
-    freqs = torch.outer(t, freqs)  # type: ignore   # [S, D/2]
+    freqs = freqs.to(pos.device)
+    freqs = torch.outer(pos, freqs)  # type: ignore   # [S, D/2]
     if use_real and repeat_interleave_real:
+        # flux, hunyuan-dit, cogvideox
         freqs_cos = freqs.cos().repeat_interleave(2, dim=1).float()  # [S, D]
         freqs_sin = freqs.sin().repeat_interleave(2, dim=1).float()  # [S, D]
         return freqs_cos, freqs_sin
     elif use_real:
+        # stable audio
         freqs_cos = torch.cat([freqs.cos(), freqs.cos()], dim=-1).float()  # [S, D]
         freqs_sin = torch.cat([freqs.sin(), freqs.sin()], dim=-1).float()  # [S, D]
         return freqs_cos, freqs_sin
     else:
-        freqs_cis = torch.polar(torch.ones_like(freqs), freqs).float()  # complex64     # [S, D/2]
+        # lumina
+        freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64     # [S, D/2]
         return freqs_cis
 
 
@@ -590,11 +596,11 @@ def apply_rotary_emb(
         cos, sin = cos.to(x.device), sin.to(x.device)
 
         if use_real_unbind_dim == -1:
-            # Use for example in Lumina
+            # Used for flux, cogvideox, hunyuan-dit
             x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1)  # [B, S, H, D//2]
             x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)
         elif use_real_unbind_dim == -2:
-            # Use for example in Stable Audio
+            # Used for Stable Audio
             x_real, x_imag = x.reshape(*x.shape[:-1], 2, -1).unbind(-2)  # [B, S, H, D//2]
             x_rotated = torch.cat([-x_imag, x_real], dim=-1)
         else:
@@ -604,6 +610,7 @@ def apply_rotary_emb(
 
         return out
     else:
+        # used for lumina
         x_rotated = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
         freqs_cis = freqs_cis.unsqueeze(2)
         x_out = torch.view_as_real(x_rotated * freqs_cis).flatten(3)
@@ -622,7 +629,7 @@ class FluxPosEmbed(nn.Module):
         n_axes = ids.shape[-1]
         cos_out = []
         sin_out = []
-        pos = ids.squeeze().float().cpu().numpy()
+        pos = ids.squeeze().float()
         is_mps = ids.device.type == "mps"
         freqs_dtype = torch.float32 if is_mps else torch.float64
         for i in range(n_axes):

src/diffusers/models/unets/unet_motion_model.py

@@ -116,7 +116,7 @@ class AnimateDiffTransformer3D(nn.Module):
 
         self.in_channels = in_channels
 
-        self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+        self.norm = nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
         self.proj_in = nn.Linear(in_channels, inner_dim)
 
         # 3. Define transformers blocks
@@ -2178,7 +2178,6 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, Peft
 
         emb = emb if aug_emb is None else emb + aug_emb
         emb = emb.repeat_interleave(repeats=num_frames, dim=0)
-        encoder_hidden_states = encoder_hidden_states.repeat_interleave(repeats=num_frames, dim=0)
 
         if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj":
             if "image_embeds" not in added_cond_kwargs:

src/diffusers/pipelines/__init__.py

@@ -132,7 +132,7 @@ else:
         "AudioLDM2UNet2DConditionModel",
     ]
     _import_structure["blip_diffusion"] = ["BlipDiffusionPipeline"]
-    _import_structure["cogvideo"] = ["CogVideoXPipeline"]
+    _import_structure["cogvideo"] = ["CogVideoXPipeline", "CogVideoXVideoToVideoPipeline"]
     _import_structure["controlnet"].extend(
         [
             "BlipDiffusionControlNetPipeline",
@@ -454,7 +454,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         )
         from .aura_flow import AuraFlowPipeline
         from .blip_diffusion import BlipDiffusionPipeline
-        from .cogvideo import CogVideoXPipeline
+        from .cogvideo import CogVideoXPipeline, CogVideoXVideoToVideoPipeline
         from .controlnet import (
             BlipDiffusionControlNetPipeline,
             StableDiffusionControlNetImg2ImgPipeline,

src/diffusers/pipelines/animatediff/pipeline_animatediff.py

@@ -432,7 +432,6 @@ class AnimateDiffPipeline(
             extra_step_kwargs["generator"] = generator
         return extra_step_kwargs
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
     def check_inputs(
         self,
         prompt,
@@ -470,8 +469,8 @@ class AnimateDiffPipeline(
             raise ValueError(
                 "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
             )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+        elif prompt is not None and not isinstance(prompt, (str, list, dict)):
+            raise ValueError(f"`prompt` has to be of type `str`, `list` or `dict` but is {type(prompt)=}")
 
         if negative_prompt is not None and negative_prompt_embeds is not None:
             raise ValueError(
@@ -557,11 +556,15 @@ class AnimateDiffPipeline(
     def num_timesteps(self):
         return self._num_timesteps
 
+    @property
+    def interrupt(self):
+        return self._interrupt
+
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
     def __call__(
         self,
-        prompt: Union[str, List[str]] = None,
+        prompt: Optional[Union[str, List[str]]] = None,
         num_frames: Optional[int] = 16,
         height: Optional[int] = None,
         width: Optional[int] = None,
@@ -701,9 +704,10 @@ class AnimateDiffPipeline(
         self._guidance_scale = guidance_scale
         self._clip_skip = clip_skip
         self._cross_attention_kwargs = cross_attention_kwargs
+        self._interrupt = False
 
         # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
+        if prompt is not None and isinstance(prompt, (str, dict)):
             batch_size = 1
         elif prompt is not None and isinstance(prompt, list):
             batch_size = len(prompt)
@@ -716,22 +720,39 @@ class AnimateDiffPipeline(
         text_encoder_lora_scale = (
             self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
         )
-        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
-            prompt,
-            device,
-            num_videos_per_prompt,
-            self.do_classifier_free_guidance,
-            negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            lora_scale=text_encoder_lora_scale,
-            clip_skip=self.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
-        # to avoid doing two forward passes
-        if self.do_classifier_free_guidance:
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+        if self.free_noise_enabled:
+            prompt_embeds, negative_prompt_embeds = self._encode_prompt_free_noise(
+                prompt=prompt,
+                num_frames=num_frames,
+                device=device,
+                num_videos_per_prompt=num_videos_per_prompt,
+                do_classifier_free_guidance=self.do_classifier_free_guidance,
+                negative_prompt=negative_prompt,
+                prompt_embeds=prompt_embeds,
+                negative_prompt_embeds=negative_prompt_embeds,
+                lora_scale=text_encoder_lora_scale,
+                clip_skip=self.clip_skip,
+            )
+        else:
+            prompt_embeds, negative_prompt_embeds = self.encode_prompt(
+                prompt,
+                device,
+                num_videos_per_prompt,
+                self.do_classifier_free_guidance,
+                negative_prompt,
+                prompt_embeds=prompt_embeds,
+                negative_prompt_embeds=negative_prompt_embeds,
+                lora_scale=text_encoder_lora_scale,
+                clip_skip=self.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
+            # to avoid doing two forward passes
+            if self.do_classifier_free_guidance:
+                prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+
+            prompt_embeds = prompt_embeds.repeat_interleave(repeats=num_frames, dim=0)
 
         if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
             image_embeds = self.prepare_ip_adapter_image_embeds(
@@ -783,6 +804,9 @@ class AnimateDiffPipeline(
             # 8. Denoising loop
             with self.progress_bar(total=self._num_timesteps) as progress_bar:
                 for i, t in enumerate(timesteps):
+                    if self.interrupt:
+                        continue
+
                     # expand the latents if we are doing classifier free guidance
                     latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
                     latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py

@@ -505,8 +505,8 @@ class AnimateDiffControlNetPipeline(
             raise ValueError(
                 "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
             )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+        elif prompt is not None and not isinstance(prompt, (str, list, dict)):
+            raise ValueError(f"`prompt` has to be of type `str`, `list` or `dict` but is {type(prompt)}")
 
         if negative_prompt is not None and negative_prompt_embeds is not None:
             raise ValueError(
@@ -699,6 +699,10 @@ class AnimateDiffControlNetPipeline(
     def num_timesteps(self):
         return self._num_timesteps
 
+    @property
+    def interrupt(self):
+        return self._interrupt
+
     @torch.no_grad()
     def __call__(
         self,
@@ -858,9 +862,10 @@ class AnimateDiffControlNetPipeline(
         self._guidance_scale = guidance_scale
         self._clip_skip = clip_skip
         self._cross_attention_kwargs = cross_attention_kwargs
+        self._interrupt = False
 
         # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
+        if prompt is not None and isinstance(prompt, (str, dict)):
             batch_size = 1
         elif prompt is not None and isinstance(prompt, list):
             batch_size = len(prompt)
@@ -883,22 +888,39 @@ class AnimateDiffControlNetPipeline(
         text_encoder_lora_scale = (
             cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
         )
-        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
-            prompt,
-            device,
-            num_videos_per_prompt,
-            self.do_classifier_free_guidance,
-            negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            lora_scale=text_encoder_lora_scale,
-            clip_skip=self.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
-        # to avoid doing two forward passes
-        if self.do_classifier_free_guidance:
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+        if self.free_noise_enabled:
+            prompt_embeds, negative_prompt_embeds = self._encode_prompt_free_noise(
+                prompt=prompt,
+                num_frames=num_frames,
+                device=device,
+                num_videos_per_prompt=num_videos_per_prompt,
+                do_classifier_free_guidance=self.do_classifier_free_guidance,
+                negative_prompt=negative_prompt,
+                prompt_embeds=prompt_embeds,
+                negative_prompt_embeds=negative_prompt_embeds,
+                lora_scale=text_encoder_lora_scale,
+                clip_skip=self.clip_skip,
+            )
+        else:
+            prompt_embeds, negative_prompt_embeds = self.encode_prompt(
+                prompt,
+                device,
+                num_videos_per_prompt,
+                self.do_classifier_free_guidance,
+                negative_prompt,
+                prompt_embeds=prompt_embeds,
+                negative_prompt_embeds=negative_prompt_embeds,
+                lora_scale=text_encoder_lora_scale,
+                clip_skip=self.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
+            # to avoid doing two forward passes
+            if self.do_classifier_free_guidance:
+                prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+
+            prompt_embeds = prompt_embeds.repeat_interleave(repeats=num_frames, dim=0)
 
         if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
             image_embeds = self.prepare_ip_adapter_image_embeds(
@@ -990,6 +1012,9 @@ class AnimateDiffControlNetPipeline(
             # 8. Denoising loop
             with self.progress_bar(total=self._num_timesteps) as progress_bar:
                 for i, t in enumerate(timesteps):
+                    if self.interrupt:
+                        continue
+
                     # expand the latents if we are doing classifier free guidance
                     latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
                     latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
@@ -1002,7 +1027,6 @@ class AnimateDiffControlNetPipeline(
                     else:
                         control_model_input = latent_model_input
                         controlnet_prompt_embeds = prompt_embeds
-                    controlnet_prompt_embeds = controlnet_prompt_embeds.repeat_interleave(num_frames, dim=0)
 
                     if isinstance(controlnet_keep[i], list):
                         cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]

src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py

@@ -1143,6 +1143,8 @@ class AnimateDiffSDXLPipeline(
             add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
             add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
 
+        prompt_embeds = prompt_embeds.repeat_interleave(repeats=num_frames, dim=0)
+
         prompt_embeds = prompt_embeds.to(device)
         add_text_embeds = add_text_embeds.to(device)
         add_time_ids = add_time_ids.to(device).repeat(batch_size * num_videos_per_prompt, 1)

src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py

@@ -878,6 +878,8 @@ class AnimateDiffSparseControlNetPipeline(
         if self.do_classifier_free_guidance:
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
 
+        prompt_embeds = prompt_embeds.repeat_interleave(repeats=num_frames, dim=0)
+
         # 4. Prepare IP-Adapter embeddings
         if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
             image_embeds = self.prepare_ip_adapter_image_embeds(

src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py

@@ -246,7 +246,6 @@ class AnimateDiffVideoToVideoPipeline(
         self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
         self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor)
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt
     def encode_prompt(
         self,
         prompt,
@@ -299,7 +298,7 @@ class AnimateDiffVideoToVideoPipeline(
             else:
                 scale_lora_layers(self.text_encoder, lora_scale)
 
-        if prompt is not None and isinstance(prompt, str):
+        if prompt is not None and isinstance(prompt, (str, dict)):
             batch_size = 1
         elif prompt is not None and isinstance(prompt, list):
             batch_size = len(prompt)
@@ -582,8 +581,8 @@ class AnimateDiffVideoToVideoPipeline(
             raise ValueError(
                 "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
             )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+        elif prompt is not None and not isinstance(prompt, (str, list, dict)):
+            raise ValueError(f"`prompt` has to be of type `str`, `list` or `dict` but is {type(prompt)}")
 
         if negative_prompt is not None and negative_prompt_embeds is not None:
             raise ValueError(
@@ -628,23 +627,20 @@ class AnimateDiffVideoToVideoPipeline(
 
     def prepare_latents(
         self,
-        video,
-        height,
-        width,
-        num_channels_latents,
-        batch_size,
-        timestep,
-        dtype,
-        device,
-        generator,
-        latents=None,
+        video: Optional[torch.Tensor] = None,
+        height: int = 64,
+        width: int = 64,
+        num_channels_latents: int = 4,
+        batch_size: int = 1,
+        timestep: Optional[int] = None,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
         decode_chunk_size: int = 16,
-    ):
-        if latents is None:
-            num_frames = video.shape[1]
-        else:
-            num_frames = latents.shape[2]
-
+        add_noise: bool = False,
+    ) -> torch.Tensor:
+        num_frames = video.shape[1] if latents is None else latents.shape[2]
         shape = (
             batch_size,
             num_channels_latents,
@@ -708,8 +704,13 @@ class AnimateDiffVideoToVideoPipeline(
             if shape != latents.shape:
                 # [B, C, F, H, W]
                 raise ValueError(f"`latents` expected to have {shape=}, but found {latents.shape=}")
+
             latents = latents.to(device, dtype=dtype)
 
+            if add_noise:
+                noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+                latents = self.scheduler.add_noise(latents, noise, timestep)
+
         return latents
 
     @property
@@ -735,6 +736,10 @@ class AnimateDiffVideoToVideoPipeline(
     def num_timesteps(self):
         return self._num_timesteps
 
+    @property
+    def interrupt(self):
+        return self._interrupt
+
     @torch.no_grad()
     def __call__(
         self,
@@ -743,6 +748,7 @@ class AnimateDiffVideoToVideoPipeline(
         height: Optional[int] = None,
         width: Optional[int] = None,
         num_inference_steps: int = 50,
+        enforce_inference_steps: bool = False,
         timesteps: Optional[List[int]] = None,
         sigmas: Optional[List[float]] = None,
         guidance_scale: float = 7.5,
@@ -874,9 +880,10 @@ class AnimateDiffVideoToVideoPipeline(
         self._guidance_scale = guidance_scale
         self._clip_skip = clip_skip
         self._cross_attention_kwargs = cross_attention_kwargs
+        self._interrupt = False
 
         # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
+        if prompt is not None and isinstance(prompt, (str, dict)):
             batch_size = 1
         elif prompt is not None and isinstance(prompt, list):
             batch_size = len(prompt)
@@ -884,29 +891,85 @@ class AnimateDiffVideoToVideoPipeline(
             batch_size = prompt_embeds.shape[0]
 
         device = self._execution_device
+        dtype = self.dtype
 
-        # 3. Encode input prompt
+        # 3. Prepare timesteps
+        if not enforce_inference_steps:
+            timesteps, num_inference_steps = retrieve_timesteps(
+                self.scheduler, num_inference_steps, device, timesteps, sigmas
+            )
+            timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, timesteps, strength, device)
+            latent_timestep = timesteps[:1].repeat(batch_size * num_videos_per_prompt)
+        else:
+            denoising_inference_steps = int(num_inference_steps / strength)
+            timesteps, denoising_inference_steps = retrieve_timesteps(
+                self.scheduler, denoising_inference_steps, device, timesteps, sigmas
+            )
+            timesteps = timesteps[-num_inference_steps:]
+            latent_timestep = timesteps[:1].repeat(batch_size * num_videos_per_prompt)
+
+        # 4. Prepare latent variables
+        if latents is None:
+            video = self.video_processor.preprocess_video(video, height=height, width=width)
+            # Move the number of frames before the number of channels.
+            video = video.permute(0, 2, 1, 3, 4)
+            video = video.to(device=device, dtype=dtype)
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            video=video,
+            height=height,
+            width=width,
+            num_channels_latents=num_channels_latents,
+            batch_size=batch_size * num_videos_per_prompt,
+            timestep=latent_timestep,
+            dtype=dtype,
+            device=device,
+            generator=generator,
+            latents=latents,
+            decode_chunk_size=decode_chunk_size,
+            add_noise=enforce_inference_steps,
+        )
+
+        # 5. Encode input prompt
         text_encoder_lora_scale = (
             self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
         )
-        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
-            prompt,
-            device,
-            num_videos_per_prompt,
-            self.do_classifier_free_guidance,
-            negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            lora_scale=text_encoder_lora_scale,
-            clip_skip=self.clip_skip,
-        )
+        num_frames = latents.shape[2]
+        if self.free_noise_enabled:
+            prompt_embeds, negative_prompt_embeds = self._encode_prompt_free_noise(
+                prompt=prompt,
+                num_frames=num_frames,
+                device=device,
+                num_videos_per_prompt=num_videos_per_prompt,
+                do_classifier_free_guidance=self.do_classifier_free_guidance,
+                negative_prompt=negative_prompt,
+                prompt_embeds=prompt_embeds,
+                negative_prompt_embeds=negative_prompt_embeds,
+                lora_scale=text_encoder_lora_scale,
+                clip_skip=self.clip_skip,
+            )
+        else:
+            prompt_embeds, negative_prompt_embeds = self.encode_prompt(
+                prompt,
+                device,
+                num_videos_per_prompt,
+                self.do_classifier_free_guidance,
+                negative_prompt,
+                prompt_embeds=prompt_embeds,
+                negative_prompt_embeds=negative_prompt_embeds,
+                lora_scale=text_encoder_lora_scale,
+                clip_skip=self.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
-        # to avoid doing two forward passes
-        if self.do_classifier_free_guidance:
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            if self.do_classifier_free_guidance:
+                prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
 
+            prompt_embeds = prompt_embeds.repeat_interleave(repeats=num_frames, dim=0)
+
+        # 6. Prepare IP-Adapter embeddings
         if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
             image_embeds = self.prepare_ip_adapter_image_embeds(
                 ip_adapter_image,
@@ -916,38 +979,10 @@ class AnimateDiffVideoToVideoPipeline(
                 self.do_classifier_free_guidance,
             )
 
-        # 4. Prepare timesteps
-        timesteps, num_inference_steps = retrieve_timesteps(
-            self.scheduler, num_inference_steps, device, timesteps, sigmas
-        )
-        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, timesteps, strength, device)
-        latent_timestep = timesteps[:1].repeat(batch_size * num_videos_per_prompt)
-
-        # 5. Prepare latent variables
-        if latents is None:
-            video = self.video_processor.preprocess_video(video, height=height, width=width)
-            # Move the number of frames before the number of channels.
-            video = video.permute(0, 2, 1, 3, 4)
-            video = video.to(device=device, dtype=prompt_embeds.dtype)
-        num_channels_latents = self.unet.config.in_channels
-        latents = self.prepare_latents(
-            video=video,
-            height=height,
-            width=width,
-            num_channels_latents=num_channels_latents,
-            batch_size=batch_size * num_videos_per_prompt,
-            timestep=latent_timestep,
-            dtype=prompt_embeds.dtype,
-            device=device,
-            generator=generator,
-            latents=latents,
-            decode_chunk_size=decode_chunk_size,
-        )
-
-        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
-        # 7. Add image embeds for IP-Adapter
+        # 8. Add image embeds for IP-Adapter
         added_cond_kwargs = (
             {"image_embeds": image_embeds}
             if ip_adapter_image is not None or ip_adapter_image_embeds is not None
@@ -967,9 +1002,12 @@ class AnimateDiffVideoToVideoPipeline(
             self._num_timesteps = len(timesteps)
             num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
 
-            # 8. Denoising loop
+            # 9. Denoising loop
             with self.progress_bar(total=self._num_timesteps) as progress_bar:
                 for i, t in enumerate(timesteps):
+                    if self.interrupt:
+                        continue
+
                     # expand the latents if we are doing classifier free guidance
                     latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
                     latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
@@ -1005,14 +1043,14 @@ class AnimateDiffVideoToVideoPipeline(
                     if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                         progress_bar.update()
 
-        # 9. Post-processing
+        # 10. Post-processing
         if output_type == "latent":
             video = latents
         else:
             video_tensor = self.decode_latents(latents, decode_chunk_size)
             video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)
 
-        # 10. Offload all models
+        # 11. Offload all models
         self.maybe_free_model_hooks()
 
         if not return_dict:

src/diffusers/pipelines/cogvideo/__init__.py

@@ -23,6 +23,7 @@ except OptionalDependencyNotAvailable:
     _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
 else:
     _import_structure["pipeline_cogvideox"] = ["CogVideoXPipeline"]
+    _import_structure["pipeline_cogvideox_video2video"] = ["CogVideoXVideoToVideoPipeline"]
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     try:
@@ -33,6 +34,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from ...utils.dummy_torch_and_transformers_objects import *
     else:
         from .pipeline_cogvideox import CogVideoXPipeline
+        from .pipeline_cogvideox_video2video import CogVideoXVideoToVideoPipeline
 
 else:
     import sys

src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py

@@ -15,7 +15,6 @@
 
 import inspect
 import math
-from dataclasses import dataclass
 from typing import Callable, Dict, List, Optional, Tuple, Union
 
 import torch
@@ -37,6 +36,7 @@ from ...utils import (
 )
 from ...utils.torch_utils import randn_tensor
 from ...video_processor import VideoProcessor
+from .pipeline_output import CogVideoXPipelineOutput
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -144,21 +144,6 @@ def retrieve_timesteps(
     return timesteps, num_inference_steps
 
 
-@dataclass
-class CogVideoXPipelineOutput(BaseOutput):
-    r"""
-    Output class for CogVideo pipelines.
-
-    Args:
-        frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
-            List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
-            denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape
-            `(batch_size, num_frames, channels, height, width)`.
-    """
-
-    frames: torch.Tensor
-
-
 class CogVideoXPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
     r"""
     Pipeline for text-to-video generation using CogVideoX.

src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py

@@ -0,0 +1,812 @@
+# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI and The HuggingFace Team.
+# All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+import math
+from typing import Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+from PIL import Image
+from transformers import T5EncoderModel, T5Tokenizer
+
+from ...callbacks import MultiPipelineCallbacks, PipelineCallback
+from ...models import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel
+from ...models.embeddings import get_3d_rotary_pos_embed
+from ...pipelines.pipeline_utils import DiffusionPipeline
+from ...schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler
+from ...utils import (
+    logging,
+    replace_example_docstring,
+)
+from ...utils.torch_utils import randn_tensor
+from ...video_processor import VideoProcessor
+from .pipeline_output import CogVideoXPipelineOutput
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```python
+        >>> import torch
+        >>> from diffusers import CogVideoXDPMScheduler, CogVideoXVideoToVideoPipeline
+        >>> from diffusers.utils import export_to_video, load_video
+
+        >>> # Models: "THUDM/CogVideoX-2b" or "THUDM/CogVideoX-5b"
+        >>> pipe = CogVideoXVideoToVideoPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16)
+        >>> pipe.to("cuda")
+        >>> pipe.scheduler = CogVideoXDPMScheduler.from_config(pipe.scheduler.config)
+
+        >>> input_video = load_video(
+        ...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/hiker.mp4"
+        ... )
+        >>> prompt = (
+        ...     "An astronaut stands triumphantly at the peak of a towering mountain. Panorama of rugged peaks and "
+        ...     "valleys. Very futuristic vibe and animated aesthetic. Highlights of purple and golden colors in "
+        ...     "the scene. The sky is looks like an animated/cartoonish dream of galaxies, nebulae, stars, planets, "
+        ...     "moons, but the remainder of the scene is mostly realistic."
+        ... )
+
+        >>> video = pipe(
+        ...     video=input_video, prompt=prompt, strength=0.8, guidance_scale=6, num_inference_steps=50
+        ... ).frames[0]
+        >>> export_to_video(video, "output.mp4", fps=8)
+        ```
+"""
+
+
+# Similar to diffusers.pipelines.hunyuandit.pipeline_hunyuandit.get_resize_crop_region_for_grid
+def get_resize_crop_region_for_grid(src, tgt_width, tgt_height):
+    tw = tgt_width
+    th = tgt_height
+    h, w = src
+    r = h / w
+    if r > (th / tw):
+        resize_height = th
+        resize_width = int(round(th / h * w))
+    else:
+        resize_width = tw
+        resize_height = int(round(tw / w * h))
+
+    crop_top = int(round((th - resize_height) / 2.0))
+    crop_left = int(round((tw - resize_width) / 2.0))
+
+    return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(
+    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
+):
+    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
+        return encoder_output.latent_dist.mode()
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
+class CogVideoXVideoToVideoPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for video-to-video generation using CogVideoX.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
+        text_encoder ([`T5EncoderModel`]):
+            Frozen text-encoder. CogVideoX uses
+            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel); specifically the
+            [t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
+        tokenizer (`T5Tokenizer`):
+            Tokenizer of class
+            [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
+        transformer ([`CogVideoXTransformer3DModel`]):
+            A text conditioned `CogVideoXTransformer3DModel` to denoise the encoded video latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded video latents.
+    """
+
+    _optional_components = []
+    model_cpu_offload_seq = "text_encoder->transformer->vae"
+
+    _callback_tensor_inputs = [
+        "latents",
+        "prompt_embeds",
+        "negative_prompt_embeds",
+    ]
+
+    def __init__(
+        self,
+        tokenizer: T5Tokenizer,
+        text_encoder: T5EncoderModel,
+        vae: AutoencoderKLCogVideoX,
+        transformer: CogVideoXTransformer3DModel,
+        scheduler: Union[CogVideoXDDIMScheduler, CogVideoXDPMScheduler],
+    ):
+        super().__init__()
+
+        self.register_modules(
+            tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler
+        )
+        self.vae_scale_factor_spatial = (
+            2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
+        )
+        self.vae_scale_factor_temporal = (
+            self.vae.config.temporal_compression_ratio if hasattr(self, "vae") and self.vae is not None else 4
+        )
+
+        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
+
+    # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline._get_t5_prompt_embeds
+    def _get_t5_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]] = None,
+        num_videos_per_prompt: int = 1,
+        max_sequence_length: int = 226,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or self.text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        batch_size = len(prompt)
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            add_special_tokens=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
+            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
+            logger.warning(
+                "The following part of your input was truncated because `max_sequence_length` is set to "
+                f" {max_sequence_length} tokens: {removed_text}"
+            )
+
+        prompt_embeds = self.text_encoder(text_input_ids.to(device))[0]
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        _, seq_len, _ = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
+
+        return prompt_embeds
+
+    # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        do_classifier_free_guidance: bool = True,
+        num_videos_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        max_sequence_length: int = 226,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
+                Whether to use classifier free guidance or not.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            device: (`torch.device`, *optional*):
+                torch device
+            dtype: (`torch.dtype`, *optional*):
+                torch dtype
+        """
+        device = device or self._execution_device
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            prompt_embeds = self._get_t5_prompt_embeds(
+                prompt=prompt,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+                dtype=dtype,
+            )
+
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+
+            negative_prompt_embeds = self._get_t5_prompt_embeds(
+                prompt=negative_prompt,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+                dtype=dtype,
+            )
+
+        return prompt_embeds, negative_prompt_embeds
+
+    def prepare_latents(
+        self,
+        video: Optional[torch.Tensor] = None,
+        batch_size: int = 1,
+        num_channels_latents: int = 16,
+        height: int = 60,
+        width: int = 90,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.Tensor] = None,
+        timestep: Optional[torch.Tensor] = None,
+    ):
+        num_frames = (video.size(2) - 1) // self.vae_scale_factor_temporal + 1 if latents is None else latents.size(1)
+
+        shape = (
+            batch_size,
+            num_frames,
+            num_channels_latents,
+            height // self.vae_scale_factor_spatial,
+            width // self.vae_scale_factor_spatial,
+        )
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            if isinstance(generator, list):
+                if len(generator) != batch_size:
+                    raise ValueError(
+                        f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                        f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+                    )
+
+                init_latents = [
+                    retrieve_latents(self.vae.encode(video[i].unsqueeze(0)), generator[i]) for i in range(batch_size)
+                ]
+            else:
+                init_latents = [retrieve_latents(self.vae.encode(vid.unsqueeze(0)), generator) for vid in video]
+
+            init_latents = torch.cat(init_latents, dim=0).to(dtype).permute(0, 2, 1, 3, 4)  # [B, F, C, H, W]
+            init_latents = self.vae.config.scaling_factor * init_latents
+
+            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            latents = self.scheduler.add_noise(init_latents, noise, timestep)
+        else:
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.decode_latents
+    def decode_latents(self, latents: torch.Tensor) -> torch.Tensor:
+        latents = latents.permute(0, 2, 1, 3, 4)  # [batch_size, num_channels, num_frames, height, width]
+        latents = 1 / self.vae.config.scaling_factor * latents
+
+        frames = self.vae.decode(latents).sample
+        return frames
+
+    # Copied from diffusers.pipelines.animatediff.pipeline_animatediff_video2video.AnimateDiffVideoToVideoPipeline.get_timesteps
+    def get_timesteps(self, num_inference_steps, timesteps, strength, device):
+        # get the original timestep using init_timestep
+        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+
+        t_start = max(num_inference_steps - init_timestep, 0)
+        timesteps = timesteps[t_start * self.scheduler.order :]
+
+        return timesteps, num_inference_steps - t_start
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        strength,
+        negative_prompt,
+        callback_on_step_end_tensor_inputs,
+        video=None,
+        latents=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+    ):
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if strength < 0 or strength > 1:
+            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
+
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+        if video is not None and latents is not None:
+            raise ValueError("Only one of `video` or `latents` should be provided")
+
+    # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.fuse_qkv_projections
+    def fuse_qkv_projections(self) -> None:
+        r"""Enables fused QKV projections."""
+        self.fusing_transformer = True
+        self.transformer.fuse_qkv_projections()
+
+    # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.unfuse_qkv_projections
+    def unfuse_qkv_projections(self) -> None:
+        r"""Disable QKV projection fusion if enabled."""
+        if not self.fusing_transformer:
+            logger.warning("The Transformer was not initially fused for QKV projections. Doing nothing.")
+        else:
+            self.transformer.unfuse_qkv_projections()
+            self.fusing_transformer = False
+
+    # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline._prepare_rotary_positional_embeddings
+    def _prepare_rotary_positional_embeddings(
+        self,
+        height: int,
+        width: int,
+        num_frames: int,
+        device: torch.device,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
+        grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
+        base_size_width = 720 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
+        base_size_height = 480 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
+
+        grid_crops_coords = get_resize_crop_region_for_grid(
+            (grid_height, grid_width), base_size_width, base_size_height
+        )
+        freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
+            embed_dim=self.transformer.config.attention_head_dim,
+            crops_coords=grid_crops_coords,
+            grid_size=(grid_height, grid_width),
+            temporal_size=num_frames,
+        )
+
+        freqs_cos = freqs_cos.to(device=device)
+        freqs_sin = freqs_sin.to(device=device)
+        return freqs_cos, freqs_sin
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        video: List[Image.Image] = None,
+        prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        height: int = 480,
+        width: int = 720,
+        num_inference_steps: int = 50,
+        timesteps: Optional[List[int]] = None,
+        strength: float = 0.8,
+        guidance_scale: float = 6,
+        use_dynamic_cfg: bool = False,
+        num_videos_per_prompt: int = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: str = "pil",
+        return_dict: bool = True,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 226,
+    ) -> Union[CogVideoXPipelineOutput, Tuple]:
+        """
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            video (`List[PIL.Image.Image]`):
+                The input video to condition the generation on. Must be a list of images/frames of the video.
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            strength (`float`, *optional*, defaults to 0.8):
+                Higher strength leads to more differences between original video and generated video.
+            guidance_scale (`float`, *optional*, defaults to 7.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                The number of videos to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+                of a plain tuple.
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int`, defaults to `226`):
+                Maximum sequence length in encoded prompt. Must be consistent with
+                `self.transformer.config.max_text_seq_length` otherwise may lead to poor results.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.cogvideo.pipeline_output.CogVideoXPipelineOutput`] or `tuple`:
+            [`~pipelines.cogvideo.pipeline_output.CogVideoXPipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple`. When returning a tuple, the first element is a list with the generated images.
+        """
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        height = height or self.transformer.config.sample_size * self.vae_scale_factor_spatial
+        width = width or self.transformer.config.sample_size * self.vae_scale_factor_spatial
+        num_videos_per_prompt = 1
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            strength,
+            negative_prompt,
+            callback_on_step_end_tensor_inputs,
+            prompt_embeds,
+            negative_prompt_embeds,
+        )
+        self._guidance_scale = guidance_scale
+        self._interrupt = False
+
+        # 2. Default call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
+            prompt,
+            negative_prompt,
+            do_classifier_free_guidance,
+            num_videos_per_prompt=num_videos_per_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            max_sequence_length=max_sequence_length,
+            device=device,
+        )
+        if do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+
+        # 4. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, timesteps, strength, device)
+        latent_timestep = timesteps[:1].repeat(batch_size * num_videos_per_prompt)
+        self._num_timesteps = len(timesteps)
+
+        # 5. Prepare latents
+        if latents is None:
+            video = self.video_processor.preprocess_video(video, height=height, width=width)
+            video = video.to(device=device, dtype=prompt_embeds.dtype)
+
+        latent_channels = self.transformer.config.in_channels
+        latents = self.prepare_latents(
+            video,
+            batch_size * num_videos_per_prompt,
+            latent_channels,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+            latent_timestep,
+        )
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7. Create rotary embeds if required
+        image_rotary_emb = (
+            self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device)
+            if self.transformer.config.use_rotary_positional_embeddings
+            else None
+        )
+
+        # 8. Denoising loop
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            # for DPM-solver++
+            old_pred_original_sample = None
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latent_model_input.shape[0])
+
+                # predict noise model_output
+                noise_pred = self.transformer(
+                    hidden_states=latent_model_input,
+                    encoder_hidden_states=prompt_embeds,
+                    timestep=timestep,
+                    image_rotary_emb=image_rotary_emb,
+                    return_dict=False,
+                )[0]
+                noise_pred = noise_pred.float()
+
+                # perform guidance
+                if use_dynamic_cfg:
+                    self._guidance_scale = 1 + guidance_scale * (
+                        (1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2
+                    )
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                if not isinstance(self.scheduler, CogVideoXDPMScheduler):
+                    latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+                else:
+                    latents, old_pred_original_sample = self.scheduler.step(
+                        noise_pred,
+                        old_pred_original_sample,
+                        t,
+                        timesteps[i - 1] if i > 0 else None,
+                        latents,
+                        **extra_step_kwargs,
+                        return_dict=False,
+                    )
+                latents = latents.to(prompt_embeds.dtype)
+
+                # call the callback, if provided
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+        if not output_type == "latent":
+            video = self.decode_latents(latents)
+            video = self.video_processor.postprocess_video(video=video, output_type=output_type)
+        else:
+            video = latents
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (video,)
+
+        return CogVideoXPipelineOutput(frames=video)

src/diffusers/pipelines/cogvideo/pipeline_output.py

@@ -0,0 +1,20 @@
+from dataclasses import dataclass
+
+import torch
+
+from diffusers.utils import BaseOutput
+
+
+@dataclass
+class CogVideoXPipelineOutput(BaseOutput):
+    r"""
+    Output class for CogVideo pipelines.
+
+    Args:
+        frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
+            List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
+            denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape
+            `(batch_size, num_frames, channels, height, width)`.
+    """
+
+    frames: torch.Tensor

src/diffusers/pipelines/flux/pipeline_flux.py

@@ -536,7 +536,7 @@ class FluxPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFileMixin):
         width: Optional[int] = None,
         num_inference_steps: int = 28,
         timesteps: List[int] = None,
-        guidance_scale: float = 7.0,
+        guidance_scale: float = 3.5,
         num_images_per_prompt: Optional[int] = 1,
         generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
         latents: Optional[torch.FloatTensor] = None,

src/diffusers/pipelines/free_noise_utils.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Optional, Union
+from typing import Callable, Dict, Optional, Union
 
 import torch
 
@@ -22,6 +22,7 @@ from ..models.unets.unet_motion_model import (
     DownBlockMotion,
     UpBlockMotion,
 )
+from ..pipelines.pipeline_utils import DiffusionPipeline
 from ..utils import logging
 from ..utils.torch_utils import randn_tensor
 
@@ -98,6 +99,142 @@ class AnimateDiffFreeNoiseMixin:
                         free_noise_transfomer_block.state_dict(), strict=True
                     )
 
+    def _check_inputs_free_noise(
+        self,
+        prompt,
+        negative_prompt,
+        prompt_embeds,
+        negative_prompt_embeds,
+        num_frames,
+    ) -> None:
+        if not isinstance(prompt, (str, dict)):
+            raise ValueError(f"Expected `prompt` to have type `str` or `dict` but found {type(prompt)=}")
+
+        if negative_prompt is not None:
+            if not isinstance(negative_prompt, (str, dict)):
+                raise ValueError(
+                    f"Expected `negative_prompt` to have type `str` or `dict` but found {type(negative_prompt)=}"
+                )
+
+        if prompt_embeds is not None or negative_prompt_embeds is not None:
+            raise ValueError("`prompt_embeds` and `negative_prompt_embeds` is not supported in FreeNoise yet.")
+
+        frame_indices = [isinstance(x, int) for x in prompt.keys()]
+        frame_prompts = [isinstance(x, str) for x in prompt.values()]
+        min_frame = min(list(prompt.keys()))
+        max_frame = max(list(prompt.keys()))
+
+        if not all(frame_indices):
+            raise ValueError("Expected integer keys in `prompt` dict for FreeNoise.")
+        if not all(frame_prompts):
+            raise ValueError("Expected str values in `prompt` dict for FreeNoise.")
+        if min_frame != 0:
+            raise ValueError("The minimum frame index in `prompt` dict must be 0 as a starting prompt is necessary.")
+        if max_frame >= num_frames:
+            raise ValueError(
+                f"The maximum frame index in `prompt` dict must be lesser than {num_frames=} and follow 0-based indexing."
+            )
+
+    def _encode_prompt_free_noise(
+        self,
+        prompt: Union[str, Dict[int, str]],
+        num_frames: int,
+        device: torch.device,
+        num_videos_per_prompt: int,
+        do_classifier_free_guidance: bool,
+        negative_prompt: Optional[Union[str, Dict[int, str]]] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        lora_scale: Optional[float] = None,
+        clip_skip: Optional[int] = None,
+    ) -> torch.Tensor:
+        if negative_prompt is None:
+            negative_prompt = ""
+
+        # Ensure that we have a dictionary of prompts
+        if isinstance(prompt, str):
+            prompt = {0: prompt}
+        if isinstance(negative_prompt, str):
+            negative_prompt = {0: negative_prompt}
+
+        self._check_inputs_free_noise(prompt, negative_prompt, prompt_embeds, negative_prompt_embeds, num_frames)
+
+        # Sort the prompts based on frame indices
+        prompt = dict(sorted(prompt.items()))
+        negative_prompt = dict(sorted(negative_prompt.items()))
+
+        # Ensure that we have a prompt for the last frame index
+        prompt[num_frames - 1] = prompt[list(prompt.keys())[-1]]
+        negative_prompt[num_frames - 1] = negative_prompt[list(negative_prompt.keys())[-1]]
+
+        frame_indices = list(prompt.keys())
+        frame_prompts = list(prompt.values())
+        frame_negative_indices = list(negative_prompt.keys())
+        frame_negative_prompts = list(negative_prompt.values())
+
+        # Generate and interpolate positive prompts
+        prompt_embeds, _ = self.encode_prompt(
+            prompt=frame_prompts,
+            device=device,
+            num_images_per_prompt=num_videos_per_prompt,
+            do_classifier_free_guidance=False,
+            negative_prompt=None,
+            prompt_embeds=None,
+            negative_prompt_embeds=None,
+            lora_scale=lora_scale,
+            clip_skip=clip_skip,
+        )
+
+        shape = (num_frames, *prompt_embeds.shape[1:])
+        prompt_interpolation_embeds = prompt_embeds.new_zeros(shape)
+
+        for i in range(len(frame_indices) - 1):
+            start_frame = frame_indices[i]
+            end_frame = frame_indices[i + 1]
+            start_tensor = prompt_embeds[i].unsqueeze(0)
+            end_tensor = prompt_embeds[i + 1].unsqueeze(0)
+
+            prompt_interpolation_embeds[start_frame : end_frame + 1] = self._free_noise_prompt_interpolation_callback(
+                start_frame, end_frame, start_tensor, end_tensor
+            )
+
+        # Generate and interpolate negative prompts
+        negative_prompt_embeds = None
+        negative_prompt_interpolation_embeds = None
+
+        if do_classifier_free_guidance:
+            _, negative_prompt_embeds = self.encode_prompt(
+                prompt=[""] * len(frame_negative_prompts),
+                device=device,
+                num_images_per_prompt=num_videos_per_prompt,
+                do_classifier_free_guidance=True,
+                negative_prompt=frame_negative_prompts,
+                prompt_embeds=None,
+                negative_prompt_embeds=None,
+                lora_scale=lora_scale,
+                clip_skip=clip_skip,
+            )
+
+            negative_prompt_interpolation_embeds = negative_prompt_embeds.new_zeros(shape)
+
+            for i in range(len(frame_negative_indices) - 1):
+                start_frame = frame_negative_indices[i]
+                end_frame = frame_negative_indices[i + 1]
+                start_tensor = negative_prompt_embeds[i].unsqueeze(0)
+                end_tensor = negative_prompt_embeds[i + 1].unsqueeze(0)
+
+                negative_prompt_interpolation_embeds[
+                    start_frame : end_frame + 1
+                ] = self._free_noise_prompt_interpolation_callback(start_frame, end_frame, start_tensor, end_tensor)
+
+        prompt_embeds = prompt_interpolation_embeds
+        negative_prompt_embeds = negative_prompt_interpolation_embeds
+
+        if do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+
+        return prompt_embeds, negative_prompt_embeds
+
     def _prepare_latents_free_noise(
         self,
         batch_size: int,
@@ -172,12 +309,29 @@ class AnimateDiffFreeNoiseMixin:
         latents = latents[:, :, :num_frames]
         return latents
 
+    def _lerp(
+        self, start_index: int, end_index: int, start_tensor: torch.Tensor, end_tensor: torch.Tensor
+    ) -> torch.Tensor:
+        num_indices = end_index - start_index + 1
+        interpolated_tensors = []
+
+        for i in range(num_indices):
+            alpha = i / (num_indices - 1)
+            interpolated_tensor = (1 - alpha) * start_tensor + alpha * end_tensor
+            interpolated_tensors.append(interpolated_tensor)
+
+        interpolated_tensors = torch.cat(interpolated_tensors)
+        return interpolated_tensors
+
     def enable_free_noise(
         self,
         context_length: Optional[int] = 16,
         context_stride: int = 4,
         weighting_scheme: str = "pyramid",
         noise_type: str = "shuffle_context",
+        prompt_interpolation_callback: Optional[
+            Callable[[DiffusionPipeline, int, int, torch.Tensor, torch.Tensor], torch.Tensor]
+        ] = None,
     ) -> None:
         r"""
         Enable long video generation using FreeNoise.
@@ -195,13 +349,27 @@ class AnimateDiffFreeNoiseMixin:
             weighting_scheme (`str`, defaults to `pyramid`):
                 Weighting scheme for averaging latents after accumulation in FreeNoise blocks. The following weighting
                 schemes are supported currently:
+                    - "flat"
+                       Performs weighting averaging with a flat weight pattern: [1, 1, 1, 1, 1].
                     - "pyramid"
-                        Peforms weighted averaging with a pyramid like weight pattern: [1, 2, 3, 2, 1].
+                        Performs weighted averaging with a pyramid like weight pattern: [1, 2, 3, 2, 1].
+                    - "delayed_reverse_sawtooth"
+                        Performs weighted averaging with low weights for earlier frames and high-to-low weights for
+                        later frames: [0.01, 0.01, 3, 2, 1].
             noise_type (`str`, defaults to "shuffle_context"):
-                TODO
+                Must be one of ["shuffle_context", "repeat_context", "random"].
+                    - "shuffle_context"
+                        Shuffles a fixed batch of `context_length` latents to create a final latent of size
+                        `num_frames`. This is usually the best setting for most generation scenarious. However, there
+                        might be visible repetition noticeable in the kinds of motion/animation generated.
+                    - "repeated_context"
+                        Repeats a fixed batch of `context_length` latents to create a final latent of size
+                        `num_frames`.
+                    - "random"
+                        The final latents are random without any repetition.
         """
 
-        allowed_weighting_scheme = ["pyramid"]
+        allowed_weighting_scheme = ["flat", "pyramid", "delayed_reverse_sawtooth"]
         allowed_noise_type = ["shuffle_context", "repeat_context", "random"]
 
         if context_length > self.motion_adapter.config.motion_max_seq_length:
@@ -219,6 +387,7 @@ class AnimateDiffFreeNoiseMixin:
         self._free_noise_context_stride = context_stride
         self._free_noise_weighting_scheme = weighting_scheme
         self._free_noise_noise_type = noise_type
+        self._free_noise_prompt_interpolation_callback = prompt_interpolation_callback or self._lerp
 
         if hasattr(self.unet.mid_block, "motion_modules"):
             blocks = [*self.unet.down_blocks, self.unet.mid_block, *self.unet.up_blocks]
@@ -229,6 +398,7 @@ class AnimateDiffFreeNoiseMixin:
             self._enable_free_noise_in_block(block)
 
     def disable_free_noise(self) -> None:
+        r"""Disable the FreeNoise sampling mechanism."""
         self._free_noise_context_length = None
 
         if hasattr(self.unet.mid_block, "motion_modules"):

src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py

@@ -734,6 +734,8 @@ class AnimateDiffPAGPipeline(
         elif self.do_classifier_free_guidance:
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
 
+        prompt_embeds = prompt_embeds.repeat_interleave(repeats=num_frames, dim=0)
+
         if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
             ip_adapter_image_embeds = self.prepare_ip_adapter_image_embeds(
                 ip_adapter_image,
@@ -805,7 +807,9 @@ class AnimateDiffPAGPipeline(
             with self.progress_bar(total=self._num_timesteps) as progress_bar:
                 for i, t in enumerate(timesteps):
                     # expand the latents if we are doing classifier free guidance
-                    latent_model_input = torch.cat([latents] * (prompt_embeds.shape[0] // latents.shape[0]))
+                    latent_model_input = torch.cat(
+                        [latents] * (prompt_embeds.shape[0] // num_frames // latents.shape[0])
+                    )
                     latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
 
                     # predict the noise residual

src/diffusers/pipelines/pia/pipeline_pia.py

@@ -824,6 +824,8 @@ class PIAPipeline(
         if self.do_classifier_free_guidance:
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
 
+        prompt_embeds = prompt_embeds.repeat_interleave(repeats=num_frames, dim=0)
+
         if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
             image_embeds = self.prepare_ip_adapter_image_embeds(
                 ip_adapter_image,

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -272,6 +272,21 @@ class CogVideoXPipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class CogVideoXVideoToVideoPipeline(metaclass=DummyObject):
+    _backends = ["torch", "transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+
 class CycleDiffusionPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

tests/models/test_modeling_common.py

@@ -417,6 +417,9 @@ class ModelTesterMixin:
 
     @require_torch_gpu
     def test_set_attn_processor_for_determinism(self):
+        if self.uses_custom_attn_processor:
+            return
+
         torch.use_deterministic_algorithms(False)
         if self.forward_requires_fresh_args:
             model = self.model_class(**self.init_dict)

tests/models/transformers/test_models_transformer_flux.py

@@ -32,6 +32,9 @@ class FluxTransformerTests(ModelTesterMixin, unittest.TestCase):
     # We override the items here because the transformer under consideration is small.
     model_split_percents = [0.7, 0.6, 0.6]
 
+    # Skip setting testing with default: AttnProcessor
+    uses_custom_attn_processor = True
+
     @property
     def dummy_input(self):
         batch_size = 1

tests/models/unets/test_models_unet_motion.py

@@ -51,7 +51,7 @@ class UNetMotionModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase)
 
         noise = floats_tensor((batch_size, num_channels, num_frames) + sizes).to(torch_device)
         time_step = torch.tensor([10]).to(torch_device)
-        encoder_hidden_states = floats_tensor((batch_size, 4, 16)).to(torch_device)
+        encoder_hidden_states = floats_tensor((batch_size * num_frames, 4, 16)).to(torch_device)
 
         return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
 

tests/pipelines/animatediff/test_animatediff.py

@@ -460,6 +460,29 @@ class AnimateDiffPipelineFastTests(
                     "Disabling of FreeNoise should lead to results similar to the default pipeline results",
                 )
 
+    def test_free_noise_multi_prompt(self):
+        components = self.get_dummy_components()
+        pipe: AnimateDiffPipeline = self.pipeline_class(**components)
+        pipe.set_progress_bar_config(disable=None)
+        pipe.to(torch_device)
+
+        context_length = 8
+        context_stride = 4
+        pipe.enable_free_noise(context_length, context_stride)
+
+        # Make sure that pipeline works when prompt indices are within num_frames bounds
+        inputs = self.get_dummy_inputs(torch_device)
+        inputs["prompt"] = {0: "Caterpillar on a leaf", 10: "Butterfly on a leaf"}
+        inputs["num_frames"] = 16
+        pipe(**inputs).frames[0]
+
+        with self.assertRaises(ValueError):
+            # Ensure that prompt indices are within bounds
+            inputs = self.get_dummy_inputs(torch_device)
+            inputs["num_frames"] = 16
+            inputs["prompt"] = {0: "Caterpillar on a leaf", 10: "Butterfly on a leaf", 42: "Error on a leaf"}
+            pipe(**inputs).frames[0]
+
     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available(),
         reason="XFormers attention is only available with CUDA and `xformers` installed",

tests/pipelines/animatediff/test_animatediff_controlnet.py

@@ -476,6 +476,27 @@ class AnimateDiffControlNetPipelineFastTests(
                     "Disabling of FreeNoise should lead to results similar to the default pipeline results",
                 )
 
+    def test_free_noise_multi_prompt(self):
+        components = self.get_dummy_components()
+        pipe: AnimateDiffControlNetPipeline = self.pipeline_class(**components)
+        pipe.set_progress_bar_config(disable=None)
+        pipe.to(torch_device)
+
+        context_length = 8
+        context_stride = 4
+        pipe.enable_free_noise(context_length, context_stride)
+
+        # Make sure that pipeline works when prompt indices are within num_frames bounds
+        inputs = self.get_dummy_inputs(torch_device, num_frames=16)
+        inputs["prompt"] = {0: "Caterpillar on a leaf", 10: "Butterfly on a leaf"}
+        pipe(**inputs).frames[0]
+
+        with self.assertRaises(ValueError):
+            # Ensure that prompt indices are within bounds
+            inputs = self.get_dummy_inputs(torch_device, num_frames=16)
+            inputs["prompt"] = {0: "Caterpillar on a leaf", 10: "Butterfly on a leaf", 42: "Error on a leaf"}
+            pipe(**inputs).frames[0]
+
     def test_vae_slicing(self, video_count=2):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
         components = self.get_dummy_components()

tests/pipelines/animatediff/test_animatediff_video2video.py

@@ -491,3 +491,28 @@ class AnimateDiffVideoToVideoPipelineFastTests(
                     1e-4,
                     "Disabling of FreeNoise should lead to results similar to the default pipeline results",
                 )
+
+    def test_free_noise_multi_prompt(self):
+        components = self.get_dummy_components()
+        pipe: AnimateDiffVideoToVideoPipeline = self.pipeline_class(**components)
+        pipe.set_progress_bar_config(disable=None)
+        pipe.to(torch_device)
+
+        context_length = 8
+        context_stride = 4
+        pipe.enable_free_noise(context_length, context_stride)
+
+        # Make sure that pipeline works when prompt indices are within num_frames bounds
+        inputs = self.get_dummy_inputs(torch_device, num_frames=16)
+        inputs["prompt"] = {0: "Caterpillar on a leaf", 10: "Butterfly on a leaf"}
+        inputs["num_inference_steps"] = 2
+        inputs["strength"] = 0.5
+        pipe(**inputs).frames[0]
+
+        with self.assertRaises(ValueError):
+            # Ensure that prompt indices are within bounds
+            inputs = self.get_dummy_inputs(torch_device, num_frames=16)
+            inputs["num_inference_steps"] = 2
+            inputs["strength"] = 0.5
+            inputs["prompt"] = {0: "Caterpillar on a leaf", 10: "Butterfly on a leaf", 42: "Error on a leaf"}
+            pipe(**inputs).frames[0]

tests/pipelines/cogvideox/test_cogvideox_video2video.py

@@ -0,0 +1,328 @@
+# Copyright 2024 The HuggingFace Team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+import unittest
+
+import numpy as np
+import torch
+from PIL import Image
+from transformers import AutoTokenizer, T5EncoderModel
+
+from diffusers import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel, CogVideoXVideoToVideoPipeline, DDIMScheduler
+from diffusers.utils.testing_utils import enable_full_determinism, torch_device
+
+from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
+from ..test_pipelines_common import (
+    PipelineTesterMixin,
+    check_qkv_fusion_matches_attn_procs_length,
+    check_qkv_fusion_processors_exist,
+    to_np,
+)
+
+
+enable_full_determinism()
+
+
+class CogVideoXVideoToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+    pipeline_class = CogVideoXVideoToVideoPipeline
+    params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
+    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS.union({"video"})
+    image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
+    image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
+    required_optional_params = frozenset(
+        [
+            "num_inference_steps",
+            "generator",
+            "latents",
+            "return_dict",
+            "callback_on_step_end",
+            "callback_on_step_end_tensor_inputs",
+        ]
+    )
+
+    def get_dummy_components(self):
+        torch.manual_seed(0)
+        transformer = CogVideoXTransformer3DModel(
+            # Product of num_attention_heads * attention_head_dim must be divisible by 16 for 3D positional embeddings
+            # But, since we are using tiny-random-t5 here, we need the internal dim of CogVideoXTransformer3DModel
+            # to be 32. The internal dim is product of num_attention_heads and attention_head_dim
+            num_attention_heads=4,
+            attention_head_dim=8,
+            in_channels=4,
+            out_channels=4,
+            time_embed_dim=2,
+            text_embed_dim=32,  # Must match with tiny-random-t5
+            num_layers=1,
+            sample_width=16,  # latent width: 2 -> final width: 16
+            sample_height=16,  # latent height: 2 -> final height: 16
+            sample_frames=9,  # latent frames: (9 - 1) / 4 + 1 = 3 -> final frames: 9
+            patch_size=2,
+            temporal_compression_ratio=4,
+            max_text_seq_length=16,
+        )
+
+        torch.manual_seed(0)
+        vae = AutoencoderKLCogVideoX(
+            in_channels=3,
+            out_channels=3,
+            down_block_types=(
+                "CogVideoXDownBlock3D",
+                "CogVideoXDownBlock3D",
+                "CogVideoXDownBlock3D",
+                "CogVideoXDownBlock3D",
+            ),
+            up_block_types=(
+                "CogVideoXUpBlock3D",
+                "CogVideoXUpBlock3D",
+                "CogVideoXUpBlock3D",
+                "CogVideoXUpBlock3D",
+            ),
+            block_out_channels=(8, 8, 8, 8),
+            latent_channels=4,
+            layers_per_block=1,
+            norm_num_groups=2,
+            temporal_compression_ratio=4,
+        )
+
+        torch.manual_seed(0)
+        scheduler = DDIMScheduler()
+        text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
+
+        components = {
+            "transformer": transformer,
+            "vae": vae,
+            "scheduler": scheduler,
+            "text_encoder": text_encoder,
+            "tokenizer": tokenizer,
+        }
+        return components
+
+    def get_dummy_inputs(self, device, seed: int = 0, num_frames: int = 8):
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device=device).manual_seed(seed)
+
+        video_height = 16
+        video_width = 16
+        video = [Image.new("RGB", (video_width, video_height))] * num_frames
+
+        inputs = {
+            "video": video,
+            "prompt": "dance monkey",
+            "negative_prompt": "",
+            "generator": generator,
+            "num_inference_steps": 2,
+            "strength": 0.5,
+            "guidance_scale": 6.0,
+            # Cannot reduce because convolution kernel becomes bigger than sample
+            "height": video_height,
+            "width": video_width,
+            "max_sequence_length": 16,
+            "output_type": "pt",
+        }
+        return inputs
+
+    def test_inference(self):
+        device = "cpu"
+
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        video = pipe(**inputs).frames
+        generated_video = video[0]
+
+        self.assertEqual(generated_video.shape, (8, 3, 16, 16))
+        expected_video = torch.randn(8, 3, 16, 16)
+        max_diff = np.abs(generated_video - expected_video).max()
+        self.assertLessEqual(max_diff, 1e10)
+
+    def test_callback_inputs(self):
+        sig = inspect.signature(self.pipeline_class.__call__)
+        has_callback_tensor_inputs = "callback_on_step_end_tensor_inputs" in sig.parameters
+        has_callback_step_end = "callback_on_step_end" in sig.parameters
+
+        if not (has_callback_tensor_inputs and has_callback_step_end):
+            return
+
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        self.assertTrue(
+            hasattr(pipe, "_callback_tensor_inputs"),
+            f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs",
+        )
+
+        def callback_inputs_subset(pipe, i, t, callback_kwargs):
+            # iterate over callback args
+            for tensor_name, tensor_value in callback_kwargs.items():
+                # check that we're only passing in allowed tensor inputs
+                assert tensor_name in pipe._callback_tensor_inputs
+
+            return callback_kwargs
+
+        def callback_inputs_all(pipe, i, t, callback_kwargs):
+            for tensor_name in pipe._callback_tensor_inputs:
+                assert tensor_name in callback_kwargs
+
+            # iterate over callback args
+            for tensor_name, tensor_value in callback_kwargs.items():
+                # check that we're only passing in allowed tensor inputs
+                assert tensor_name in pipe._callback_tensor_inputs
+
+            return callback_kwargs
+
+        inputs = self.get_dummy_inputs(torch_device)
+
+        # Test passing in a subset
+        inputs["callback_on_step_end"] = callback_inputs_subset
+        inputs["callback_on_step_end_tensor_inputs"] = ["latents"]
+        output = pipe(**inputs)[0]
+
+        # Test passing in a everything
+        inputs["callback_on_step_end"] = callback_inputs_all
+        inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
+        output = pipe(**inputs)[0]
+
+        def callback_inputs_change_tensor(pipe, i, t, callback_kwargs):
+            is_last = i == (pipe.num_timesteps - 1)
+            if is_last:
+                callback_kwargs["latents"] = torch.zeros_like(callback_kwargs["latents"])
+            return callback_kwargs
+
+        inputs["callback_on_step_end"] = callback_inputs_change_tensor
+        inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
+        output = pipe(**inputs)[0]
+        assert output.abs().sum() < 1e10
+
+    def test_inference_batch_single_identical(self):
+        self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-3)
+
+    def test_attention_slicing_forward_pass(
+        self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3
+    ):
+        if not self.test_attention_slicing:
+            return
+
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        for component in pipe.components.values():
+            if hasattr(component, "set_default_attn_processor"):
+                component.set_default_attn_processor()
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        generator_device = "cpu"
+        inputs = self.get_dummy_inputs(generator_device)
+        output_without_slicing = pipe(**inputs)[0]
+
+        pipe.enable_attention_slicing(slice_size=1)
+        inputs = self.get_dummy_inputs(generator_device)
+        output_with_slicing1 = pipe(**inputs)[0]
+
+        pipe.enable_attention_slicing(slice_size=2)
+        inputs = self.get_dummy_inputs(generator_device)
+        output_with_slicing2 = pipe(**inputs)[0]
+
+        if test_max_difference:
+            max_diff1 = np.abs(to_np(output_with_slicing1) - to_np(output_without_slicing)).max()
+            max_diff2 = np.abs(to_np(output_with_slicing2) - to_np(output_without_slicing)).max()
+            self.assertLess(
+                max(max_diff1, max_diff2),
+                expected_max_diff,
+                "Attention slicing should not affect the inference results",
+            )
+
+    def test_vae_tiling(self, expected_diff_max: float = 0.2):
+        # Since VideoToVideo uses both encoder and decoder tiling, there seems to be much more numerical
+        # difference. We seem to need a higher tolerance here...
+        # TODO(aryan): Look into this more deeply
+        expected_diff_max = 0.4
+
+        generator_device = "cpu"
+        components = self.get_dummy_components()
+
+        pipe = self.pipeline_class(**components)
+        pipe.to("cpu")
+        pipe.set_progress_bar_config(disable=None)
+
+        # Without tiling
+        inputs = self.get_dummy_inputs(generator_device)
+        inputs["height"] = inputs["width"] = 128
+        output_without_tiling = pipe(**inputs)[0]
+
+        # With tiling
+        pipe.vae.enable_tiling(
+            tile_sample_min_height=96,
+            tile_sample_min_width=96,
+            tile_overlap_factor_height=1 / 12,
+            tile_overlap_factor_width=1 / 12,
+        )
+        inputs = self.get_dummy_inputs(generator_device)
+        inputs["height"] = inputs["width"] = 128
+        output_with_tiling = pipe(**inputs)[0]
+
+        self.assertLess(
+            (to_np(output_without_tiling) - to_np(output_with_tiling)).max(),
+            expected_diff_max,
+            "VAE tiling should not affect the inference results",
+        )
+
+    @unittest.skip("xformers attention processor does not exist for CogVideoX")
+    def test_xformers_attention_forwardGenerator_pass(self):
+        pass
+
+    def test_fused_qkv_projections(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        frames = pipe(**inputs).frames  # [B, F, C, H, W]
+        original_image_slice = frames[0, -2:, -1, -3:, -3:]
+
+        pipe.fuse_qkv_projections()
+        assert check_qkv_fusion_processors_exist(
+            pipe.transformer
+        ), "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
+        assert check_qkv_fusion_matches_attn_procs_length(
+            pipe.transformer, pipe.transformer.original_attn_processors
+        ), "Something wrong with the attention processors concerning the fused QKV projections."
+
+        inputs = self.get_dummy_inputs(device)
+        frames = pipe(**inputs).frames
+        image_slice_fused = frames[0, -2:, -1, -3:, -3:]
+
+        pipe.transformer.unfuse_qkv_projections()
+        inputs = self.get_dummy_inputs(device)
+        frames = pipe(**inputs).frames
+        image_slice_disabled = frames[0, -2:, -1, -3:, -3:]
+
+        assert np.allclose(
+            original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3
+        ), "Fusion of QKV projections shouldn't affect the outputs."
+        assert np.allclose(
+            image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3
+        ), "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
+        assert np.allclose(
+            original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2
+        ), "Original outputs should match when fused QKV projections are disabled."

tests/pipelines/flux/test_pipeline_flux.py

@@ -25,6 +25,9 @@ class FluxPipelineFastTests(unittest.TestCase, PipelineTesterMixin):
     params = frozenset(["prompt", "height", "width", "guidance_scale", "prompt_embeds", "pooled_prompt_embeds"])
     batch_params = frozenset(["prompt"])
 
+    # there is no xformers processor for Flux
+    test_xformers_attention = False
+
     def get_dummy_components(self):
         torch.manual_seed(0)
         transformer = FluxTransformer2DModel(

tests/pipelines/pag/test_pag_sd3.py

@@ -37,6 +37,7 @@ class StableDiffusion3PAGPipelineFastTests(unittest.TestCase, PipelineTesterMixi
         ]
     )
     batch_params = frozenset(["prompt", "negative_prompt"])
+    test_xformers_attention = False
 
     def get_dummy_components(self):
         torch.manual_seed(0)

tests/pipelines/stable_audio/test_stable_audio.py

@@ -68,6 +68,8 @@ class StableAudioPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             "callback_steps",
         ]
     )
+    # There is not xformers version of the StableAudioPipeline custom attention processor
+    test_xformers_attention = False
 
     def get_dummy_components(self):
         torch.manual_seed(0)