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https://git.datalinker.icu/ali-vilab/TeaCache
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838 lines
38 KiB
Python
838 lines
38 KiB
Python
# Adapted from CogVideo
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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# --------------------------------------------------------
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# References:
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# CogVideo: https://github.com/THUDM/CogVideo
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# diffusers: https://github.com/huggingface/diffusers
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# --------------------------------------------------------
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import inspect
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import math
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from typing import Callable, Dict, List, Optional, Tuple, Union
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import torch
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import torch.distributed as dist
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from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
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from diffusers.utils.torch_utils import randn_tensor
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from diffusers.video_processor import VideoProcessor
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from transformers import T5EncoderModel, T5Tokenizer
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from videosys.core.pab_mgr import PABConfig, set_pab_manager, update_steps
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from videosys.core.pipeline import VideoSysPipeline, VideoSysPipelineOutput
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from videosys.models.autoencoders.autoencoder_kl_cogvideox import AutoencoderKLCogVideoX
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from videosys.models.modules.embeddings import get_3d_rotary_pos_embed
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from videosys.models.transformers.cogvideox_transformer_3d import CogVideoXTransformer3DModel
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from videosys.schedulers.scheduling_ddim_cogvideox import CogVideoXDDIMScheduler
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from videosys.schedulers.scheduling_dpm_cogvideox import CogVideoXDPMScheduler
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from videosys.utils.logging import logger
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from videosys.utils.utils import save_video, set_seed
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import tqdm
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class CogVideoXPABConfig(PABConfig):
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def __init__(
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self,
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spatial_broadcast: bool = True,
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spatial_threshold: list = [100, 850],
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spatial_range: int = 2,
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):
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super().__init__(
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spatial_broadcast=spatial_broadcast,
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spatial_threshold=spatial_threshold,
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spatial_range=spatial_range,
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)
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class CogVideoXConfig:
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"""
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This config is to instantiate a `CogVideoXPipeline` class for video generation.
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To be specific, this config will be passed to engine by `VideoSysEngine(config)`.
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In the engine, it will be used to instantiate the corresponding pipeline class.
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And the engine will call the `generate` function of the pipeline to generate the video.
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If you want to explore the detail of generation, please refer to the pipeline class below.
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Args:
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model_path (str):
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A path to the pretrained pipeline. Defaults to "THUDM/CogVideoX-2b".
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num_gpus (int):
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The number of GPUs to use. Defaults to 1.
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cpu_offload (bool):
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Whether to enable CPU offload. Defaults to False.
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vae_tiling (bool):
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Whether to enable tiling for the VAE. Defaults to True.
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enable_pab (bool):
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Whether to enable Pyramid Attention Broadcast. Defaults to False.
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pab_config (CogVideoXPABConfig):
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The configuration for Pyramid Attention Broadcast. Defaults to `CogVideoXPABConfig()`.
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Examples:
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```python
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from videosys import CogVideoXConfig, VideoSysEngine
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# models: "THUDM/CogVideoX-2b" or "THUDM/CogVideoX-5b"
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# change num_gpus for multi-gpu inference
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config = CogVideoXConfig("THUDM/CogVideoX-2b", num_gpus=1)
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engine = VideoSysEngine(config)
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prompt = "Sunset over the sea."
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# num frames should be <= 49. resolution is fixed to 720p.
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video = engine.generate(
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prompt=prompt,
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guidance_scale=6,
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num_inference_steps=50,
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num_frames=49,
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).video[0]
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engine.save_video(video, f"./outputs/{prompt}.mp4")
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```
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"""
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def __init__(
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self,
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model_path: str = "THUDM/CogVideoX-2b",
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# ======= distributed ========
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num_gpus: int = 1,
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# ======= memory =======
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cpu_offload: bool = False,
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vae_tiling: bool = True,
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# ======= pab ========
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enable_pab: bool = False,
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pab_config=CogVideoXPABConfig(),
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):
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self.model_path = model_path
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self.pipeline_cls = CogVideoXPipeline
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# ======= distributed ========
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self.num_gpus = num_gpus
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# ======= memory ========
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self.cpu_offload = cpu_offload
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self.vae_tiling = vae_tiling
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# ======= pab ========
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self.enable_pab = enable_pab
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self.pab_config = pab_config
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class CogVideoXPipeline(VideoSysPipeline):
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_optional_components = ["tokenizer", "text_encoder", "vae", "transformer", "scheduler"]
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model_cpu_offload_seq = "text_encoder->transformer->vae"
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_callback_tensor_inputs = [
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"latents",
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"prompt_embeds",
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"negative_prompt_embeds",
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]
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def __init__(
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self,
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config: CogVideoXConfig,
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tokenizer: Optional[T5Tokenizer] = None,
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text_encoder: Optional[T5EncoderModel] = None,
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vae: Optional[AutoencoderKLCogVideoX] = None,
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transformer: Optional[CogVideoXTransformer3DModel] = None,
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scheduler: Optional[CogVideoXDDIMScheduler] = None,
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device: torch.device = torch.device("cuda"),
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dtype: torch.dtype = torch.bfloat16,
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):
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super().__init__()
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self._config = config
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self._device = device
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if config.model_path == "THUDM/CogVideoX-2b":
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dtype = torch.float16
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self._dtype = dtype
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if transformer is None:
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transformer = CogVideoXTransformer3DModel.from_pretrained(
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config.model_path, subfolder="transformer", torch_dtype=self._dtype
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)
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if vae is None:
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vae = AutoencoderKLCogVideoX.from_pretrained(config.model_path, subfolder="vae", torch_dtype=self._dtype)
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if tokenizer is None:
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tokenizer = T5Tokenizer.from_pretrained(config.model_path, subfolder="tokenizer")
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if text_encoder is None:
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text_encoder = T5EncoderModel.from_pretrained(
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config.model_path, subfolder="text_encoder", torch_dtype=self._dtype
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)
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if scheduler is None:
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scheduler = CogVideoXDDIMScheduler.from_pretrained(
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config.model_path,
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subfolder="scheduler",
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)
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self.register_modules(
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tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler
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)
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# cpu offload
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if config.cpu_offload:
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self.enable_model_cpu_offload()
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else:
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self.set_eval_and_device(self._device, text_encoder, vae, transformer)
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# vae tiling
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if config.vae_tiling:
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vae.enable_tiling()
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# pab
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if config.enable_pab:
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set_pab_manager(config.pab_config)
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self.vae_scale_factor_spatial = (
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2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
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)
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self.vae_scale_factor_temporal = (
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self.vae.config.temporal_compression_ratio if hasattr(self, "vae") and self.vae is not None else 4
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)
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self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
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# parallel
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self._set_parallel()
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def _set_seed(self, seed):
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if dist.get_world_size() == 1:
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set_seed(seed)
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else:
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set_seed(seed, self.transformer.parallel_manager.dp_rank)
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def _set_parallel(
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self, dp_size: Optional[int] = None, sp_size: Optional[int] = None, enable_cp: Optional[bool] = False
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):
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# init sequence parallel
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if sp_size is None:
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sp_size = dist.get_world_size()
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dp_size = 1
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else:
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assert (
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dist.get_world_size() % sp_size == 0
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), f"world_size {dist.get_world_size()} must be divisible by sp_size"
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dp_size = dist.get_world_size() // sp_size
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# transformer parallel
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self.transformer.enable_parallel(dp_size, sp_size, enable_cp)
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def _get_t5_prompt_embeds(
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self,
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prompt: Union[str, List[str]] = None,
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num_videos_per_prompt: int = 1,
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max_sequence_length: int = 226,
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = None,
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):
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device = device or self._execution_device
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dtype = dtype or self.text_encoder.dtype
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prompt = [prompt] if isinstance(prompt, str) else prompt
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batch_size = len(prompt)
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text_inputs = self.tokenizer(
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prompt,
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padding="max_length",
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max_length=max_sequence_length,
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truncation=True,
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add_special_tokens=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
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if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
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removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
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logger.warning(
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"The following part of your input was truncated because `max_sequence_length` is set to "
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f" {max_sequence_length} tokens: {removed_text}"
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)
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prompt_embeds = self.text_encoder(text_input_ids.to(device))[0]
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prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
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# duplicate text embeddings for each generation per prompt, using mps friendly method
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_, seq_len, _ = prompt_embeds.shape
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prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
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prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
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return prompt_embeds
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def encode_prompt(
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self,
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prompt: Union[str, List[str]],
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negative_prompt: Optional[Union[str, List[str]]] = None,
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do_classifier_free_guidance: bool = True,
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num_videos_per_prompt: int = 1,
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prompt_embeds: Optional[torch.Tensor] = None,
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negative_prompt_embeds: Optional[torch.Tensor] = None,
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max_sequence_length: int = 226,
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = None,
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):
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r"""
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Encodes the prompt into text encoder hidden states.
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Args:
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prompt (`str` or `List[str]`, *optional*):
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prompt to be encoded
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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the image generation. If not defined, one has to pass
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`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
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less than `1`).
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do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
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Whether to use classifier free guidance or not.
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num_videos_per_prompt (`int`, *optional*, defaults to 1):
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Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
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prompt_embeds (`torch.Tensor`, *optional*):
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Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
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provided, text embeddings will be generated from `prompt` input argument.
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negative_prompt_embeds (`torch.Tensor`, *optional*):
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Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
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weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
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argument.
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device: (`torch.device`, *optional*):
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torch device
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dtype: (`torch.dtype`, *optional*):
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torch dtype
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"""
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device = device or self._execution_device
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prompt = [prompt] if isinstance(prompt, str) else prompt
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if prompt is not None:
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batch_size = len(prompt)
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else:
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batch_size = prompt_embeds.shape[0]
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if prompt_embeds is None:
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prompt_embeds = self._get_t5_prompt_embeds(
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prompt=prompt,
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num_videos_per_prompt=num_videos_per_prompt,
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max_sequence_length=max_sequence_length,
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device=device,
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dtype=dtype,
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)
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if do_classifier_free_guidance and negative_prompt_embeds is None:
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negative_prompt = negative_prompt or ""
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negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
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if prompt is not None and type(prompt) is not type(negative_prompt):
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raise TypeError(
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f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
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f" {type(prompt)}."
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)
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elif batch_size != len(negative_prompt):
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raise ValueError(
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f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
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f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
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" the batch size of `prompt`."
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)
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negative_prompt_embeds = self._get_t5_prompt_embeds(
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prompt=negative_prompt,
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num_videos_per_prompt=num_videos_per_prompt,
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max_sequence_length=max_sequence_length,
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device=device,
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dtype=dtype,
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)
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return prompt_embeds, negative_prompt_embeds
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def prepare_latents(
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self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
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):
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shape = (
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batch_size,
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(num_frames - 1) // self.vae_scale_factor_temporal + 1,
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num_channels_latents,
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height // self.vae_scale_factor_spatial,
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width // self.vae_scale_factor_spatial,
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)
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if isinstance(generator, list) and len(generator) != batch_size:
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raise ValueError(
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f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
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f" size of {batch_size}. Make sure the batch size matches the length of the generators."
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)
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if latents is None:
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latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
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else:
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latents = latents.to(device)
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# scale the initial noise by the standard deviation required by the scheduler
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latents = latents * self.scheduler.init_noise_sigma
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return latents
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def decode_latents(self, latents: torch.Tensor) -> torch.Tensor:
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latents = latents.permute(0, 2, 1, 3, 4) # [batch_size, num_channels, num_frames, height, width]
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latents = 1 / self.vae.config.scaling_factor * latents
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frames = self.vae.decode(latents).sample
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return frames
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
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def prepare_extra_step_kwargs(self, generator, eta):
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# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
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# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
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# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
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# and should be between [0, 1]
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accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
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extra_step_kwargs = {}
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if accepts_eta:
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extra_step_kwargs["eta"] = eta
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# check if the scheduler accepts generator
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accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
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if accepts_generator:
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extra_step_kwargs["generator"] = generator
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return extra_step_kwargs
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# Copied from diffusers.pipelines.latte.pipeline_latte.LattePipeline.check_inputs
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def check_inputs(
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self,
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prompt,
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height,
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width,
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negative_prompt,
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callback_on_step_end_tensor_inputs,
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prompt_embeds=None,
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negative_prompt_embeds=None,
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):
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if height % 8 != 0 or width % 8 != 0:
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raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
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if callback_on_step_end_tensor_inputs is not None and not all(
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k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
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):
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raise ValueError(
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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]}"
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)
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if prompt is not None and prompt_embeds is not None:
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raise ValueError(
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f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
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" only forward one of the two."
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)
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elif prompt is None and prompt_embeds is None:
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raise ValueError(
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"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
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)
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elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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if prompt is not None and negative_prompt_embeds is not None:
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raise ValueError(
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f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:"
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f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
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)
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if negative_prompt is not None and negative_prompt_embeds is not None:
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raise ValueError(
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f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
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f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
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)
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if prompt_embeds is not None and negative_prompt_embeds is not None:
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if prompt_embeds.shape != negative_prompt_embeds.shape:
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raise ValueError(
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"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
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f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
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f" {negative_prompt_embeds.shape}."
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)
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def fuse_qkv_projections(self) -> None:
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r"""Enables fused QKV projections."""
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self.fusing_transformer = True
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self.transformer.fuse_qkv_projections()
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def unfuse_qkv_projections(self) -> None:
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r"""Disable QKV projection fusion if enabled."""
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if not self.fusing_transformer:
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logger.warning("The Transformer was not initially fused for QKV projections. Doing nothing.")
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else:
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self.transformer.unfuse_qkv_projections()
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self.fusing_transformer = False
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def _prepare_rotary_positional_embeddings(
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self,
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height: int,
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width: int,
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num_frames: int,
|
|
device: torch.device,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
|
|
grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
|
|
base_size_width = 720 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
|
|
base_size_height = 480 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
|
|
|
|
grid_crops_coords = get_resize_crop_region_for_grid(
|
|
(grid_height, grid_width), base_size_width, base_size_height
|
|
)
|
|
freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
|
|
embed_dim=self.transformer.config.attention_head_dim,
|
|
crops_coords=grid_crops_coords,
|
|
grid_size=(grid_height, grid_width),
|
|
temporal_size=num_frames,
|
|
use_real=True,
|
|
)
|
|
|
|
freqs_cos = freqs_cos.to(device=device)
|
|
freqs_sin = freqs_sin.to(device=device)
|
|
return freqs_cos, freqs_sin
|
|
|
|
@property
|
|
def guidance_scale(self):
|
|
return self._guidance_scale
|
|
|
|
@property
|
|
def num_timesteps(self):
|
|
return self._num_timesteps
|
|
|
|
@property
|
|
def interrupt(self):
|
|
return self._interrupt
|
|
|
|
@torch.no_grad()
|
|
def generate(
|
|
self,
|
|
prompt: Optional[Union[str, List[str]]] = None,
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
|
height: int = 480,
|
|
width: int = 720,
|
|
num_frames: int = 49,
|
|
num_inference_steps: int = 50,
|
|
timesteps: Optional[List[int]] = None,
|
|
seed: int = -1,
|
|
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,
|
|
verbose=True
|
|
) -> Union[VideoSysPipelineOutput, Tuple]:
|
|
"""
|
|
Function invoked when calling the pipeline for generation.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
|
|
instead.
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
|
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
|
|
less than `1`).
|
|
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
|
The height in pixels of the generated image. This is set to 1024 by default for the best results.
|
|
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
|
The width in pixels of the generated image. This is set to 1024 by default for the best results.
|
|
num_frames (`int`, defaults to `48`):
|
|
Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will
|
|
contain 1 extra frame because CogVideoX is conditioned with (num_seconds * fps + 1) frames where
|
|
num_seconds is 6 and fps is 4. However, since videos can be saved at any fps, the only condition that
|
|
needs to be satisfied is that of divisibility mentioned above.
|
|
num_inference_steps (`int`, *optional*, defaults to 50):
|
|
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
|
expense of slower inference.
|
|
timesteps (`List[int]`, *optional*):
|
|
Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
|
|
in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
|
|
passed will be used. Must be in descending order.
|
|
guidance_scale (`float`, *optional*, defaults to 7.0):
|
|
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
|
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
|
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
|
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
|
usually at the expense of lower image quality.
|
|
num_videos_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of videos to generate per prompt.
|
|
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
|
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
|
|
to make generation deterministic.
|
|
latents (`torch.FloatTensor`, *optional*):
|
|
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
|
|
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
|
tensor will ge generated by sampling using the supplied random `generator`.
|
|
prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
|
provided, text embeddings will be generated from `prompt` input argument.
|
|
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
|
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
|
argument.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generate image. Choose between
|
|
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
|
|
of a plain tuple.
|
|
callback_on_step_end (`Callable`, *optional*):
|
|
A function that calls at the end of each denoising steps during the inference. The function is called
|
|
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
|
|
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
|
|
`callback_on_step_end_tensor_inputs`.
|
|
callback_on_step_end_tensor_inputs (`List`, *optional*):
|
|
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
|
|
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
|
|
`._callback_tensor_inputs` attribute of your pipeline class.
|
|
max_sequence_length (`int`, defaults to `226`):
|
|
Maximum sequence length in encoded prompt. Must be consistent with
|
|
`self.transformer.config.max_text_seq_length` otherwise may lead to poor results.
|
|
|
|
Examples:
|
|
|
|
Returns:
|
|
[`~pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput`] or `tuple`:
|
|
[`~pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput`] if `return_dict` is True, otherwise a
|
|
`tuple`. When returning a tuple, the first element is a list with the generated images.
|
|
"""
|
|
|
|
if num_frames > 49:
|
|
raise ValueError(
|
|
"The number of frames must be less than 49 for now due to static positional embeddings. This will be updated in the future to remove this limitation."
|
|
)
|
|
update_steps(num_inference_steps)
|
|
self._set_seed(seed)
|
|
|
|
if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
|
|
callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
|
|
|
|
height = height or self.transformer.config.sample_size * self.vae_scale_factor_spatial
|
|
width = width or self.transformer.config.sample_size * self.vae_scale_factor_spatial
|
|
num_videos_per_prompt = 1
|
|
|
|
# 1. Check inputs. Raise error if not correct
|
|
self.check_inputs(
|
|
prompt,
|
|
height,
|
|
width,
|
|
negative_prompt,
|
|
callback_on_step_end_tensor_inputs,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
)
|
|
self._guidance_scale = guidance_scale
|
|
self._interrupt = False
|
|
|
|
# 2. Default call parameters
|
|
if prompt is not None and isinstance(prompt, str):
|
|
batch_size = 1
|
|
elif prompt is not None and isinstance(prompt, list):
|
|
batch_size = len(prompt)
|
|
else:
|
|
batch_size = prompt_embeds.shape[0]
|
|
|
|
device = self._device
|
|
|
|
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
|
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
do_classifier_free_guidance = guidance_scale > 1.0
|
|
|
|
# 3. Encode input prompt
|
|
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
|
|
prompt,
|
|
negative_prompt,
|
|
do_classifier_free_guidance,
|
|
num_videos_per_prompt=num_videos_per_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
max_sequence_length=max_sequence_length,
|
|
device=device,
|
|
)
|
|
if do_classifier_free_guidance:
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
|
|
|
|
# 4. Prepare timesteps
|
|
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
|
|
self._num_timesteps = len(timesteps)
|
|
|
|
# 5. Prepare latents.
|
|
latent_channels = self.transformer.config.in_channels
|
|
latents = self.prepare_latents(
|
|
batch_size * num_videos_per_prompt,
|
|
latent_channels,
|
|
num_frames,
|
|
height,
|
|
width,
|
|
prompt_embeds.dtype,
|
|
device,
|
|
generator,
|
|
latents,
|
|
)
|
|
|
|
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
|
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
|
|
|
# 7. Create rotary embeds if required
|
|
image_rotary_emb = (
|
|
self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device)
|
|
if self.transformer.config.use_rotary_positional_embeddings
|
|
else None
|
|
)
|
|
|
|
# 8. Denoising loop
|
|
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
|
|
|
|
# with self.progress_bar(total=num_inference_steps) as progress_bar:
|
|
progress_wrap = tqdm.tqdm if verbose and dist.get_rank() == 0 else (lambda x: x)
|
|
# for DPM-solver++
|
|
old_pred_original_sample = None
|
|
for i, t in progress_wrap(list(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,
|
|
all_timesteps=timesteps,
|
|
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 VideoSysPipelineOutput(video=video)
|
|
|
|
def save_video(self, video, output_path):
|
|
save_video(video, output_path, 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
|