mirror of
https://git.datalinker.icu/ali-vilab/TeaCache
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1058 lines
50 KiB
Python
1058 lines
50 KiB
Python
# Adapted from Vchitect
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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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# Vchitect: https://github.com/Vchitect/Vchitect-2.0
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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 Any, Callable, Dict, List, Optional, Union
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import torch
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import torch.distributed as dist
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from diffusers.image_processor import VaeImageProcessor
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from diffusers.models.autoencoders import AutoencoderKL
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from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
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from diffusers.utils.torch_utils import randn_tensor
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from torch.amp import autocast
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from tqdm import tqdm
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from transformers import CLIPTextModelWithProjection, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
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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.transformers.vchitect_transformer_3d import VchitectXLTransformerModel
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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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class VchitectPABConfig(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, 800],
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spatial_range: int = 2,
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temporal_broadcast: bool = True,
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temporal_threshold: list = [100, 800],
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temporal_range: int = 4,
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cross_broadcast: bool = True,
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cross_threshold: list = [100, 800],
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cross_range: int = 6,
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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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temporal_broadcast=temporal_broadcast,
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temporal_threshold=temporal_threshold,
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temporal_range=temporal_range,
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cross_broadcast=cross_broadcast,
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cross_threshold=cross_threshold,
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cross_range=cross_range,
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)
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class VchitectConfig:
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"""
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This config is to instantiate a `VchitectXLPipeline` 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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The model path to use. Defaults to "Vchitect/Vchitect-2.0-2B".
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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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enable_pab (bool):
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Whether to enable Pyramid Attention Broadcast. Defaults to False.
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pab_config (VchitectPABConfig):
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The configuration for Pyramid Attention Broadcast. Defaults to `VchitectPABConfig()`.
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Examples:
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```python
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from videosys import OpenSoraPlanConfig, VideoSysEngine
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# change num_gpus for multi-gpu inference
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config = VchitectConfig("Vchitect/Vchitect-2.0-2B", num_gpus=1)
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engine = VideoSysEngine(config)
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prompt = "Sunset over the sea."
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# seed=-1 means random seed. >0 means fixed seed.
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# WxH: 480x288 624x352 432x240 768x432
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video = engine.generate(
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prompt=prompt,
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negative_prompt="",
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num_inference_steps=100,
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guidance_scale=7.5,
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width=480,
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height=288,
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frames=40,
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seed=0,
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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 = "Vchitect/Vchitect-2.0-2B",
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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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# ======= pab ========
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enable_pab: bool = False,
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pab_config: VchitectPABConfig = VchitectPABConfig(),
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):
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self.model_path = model_path
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self.pipeline_cls = VchitectXLPipeline
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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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# ======= pab ========
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self.enable_pab = enable_pab
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self.pab_config = pab_config
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class VchitectXLPipeline(VideoSysPipeline):
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r"""
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Args:
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transformer ([`VchitectXLTransformerModel`]):
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Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
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scheduler ([`FlowMatchEulerDiscreteScheduler`]):
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A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
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vae ([`AutoencoderKL`]):
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Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
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text_encoder ([`CLIPTextModelWithProjection`]):
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[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
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specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant,
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with an additional added projection layer that is initialized with a diagonal matrix with the `hidden_size`
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as its dimension.
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text_encoder_2 ([`CLIPTextModelWithProjection`]):
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[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
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specifically the
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[laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
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variant.
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text_encoder_3 ([`T5EncoderModel`]):
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Frozen text-encoder. Stable Diffusion 3 uses
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[T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
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[t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
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tokenizer (`CLIPTokenizer`):
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Tokenizer of class
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[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
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tokenizer_2 (`CLIPTokenizer`):
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Second Tokenizer of class
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[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
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tokenizer_3 (`T5TokenizerFast`):
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Tokenizer of class
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[T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
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"""
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model_cpu_offload_seq = "text_encoder->text_encoder_2->text_encoder_3->transformer->vae"
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_optional_components = [
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"text_encoder",
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"text_encoder_2",
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"text_encoder_3",
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"tokenizer",
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"tokenizer_2",
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"tokenizer_3",
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"vae",
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"transformer",
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"scheduler",
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]
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_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds", "negative_pooled_prompt_embeds"]
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def __init__(
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self,
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config: VchitectConfig,
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text_encoder: Optional[CLIPTextModelWithProjection] = None,
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text_encoder_2: Optional[CLIPTextModelWithProjection] = None,
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text_encoder_3: Optional[T5EncoderModel] = None,
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tokenizer: Optional[CLIPTokenizer] = None,
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tokenizer_2: Optional[CLIPTokenizer] = None,
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tokenizer_3: Optional[T5TokenizerFast] = None,
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vae: Optional[AutoencoderKL] = None,
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transformer: Optional[VchitectXLTransformerModel] = None,
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scheduler: Optional[FlowMatchEulerDiscreteScheduler] = 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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self._dtype = dtype
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if text_encoder is None:
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self.text_encoder = CLIPTextModelWithProjection.from_pretrained(
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config.model_path, subfolder="text_encoder", torch_dtype=dtype
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)
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if text_encoder_2 is None:
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self.text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(
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config.model_path, subfolder="text_encoder_2", torch_dtype=dtype
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)
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if text_encoder_3 is None:
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self.text_encoder_3 = T5EncoderModel.from_pretrained(
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config.model_path, subfolder="text_encoder_3", torch_dtype=dtype
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)
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if tokenizer is None:
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self.tokenizer = CLIPTokenizer.from_pretrained(config.model_path, subfolder="tokenizer")
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if tokenizer_2 is None:
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self.tokenizer_2 = CLIPTokenizer.from_pretrained(config.model_path, subfolder="tokenizer_2")
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if tokenizer_3 is None:
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self.tokenizer_3 = T5TokenizerFast.from_pretrained(config.model_path, subfolder="tokenizer_3")
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if vae is None:
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self.vae = AutoencoderKL.from_pretrained(config.model_path, subfolder="vae", torch_dtype=dtype)
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if transformer is None:
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self.transformer = VchitectXLTransformerModel.from_pretrained(
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config.model_path, subfolder="transformer", torch_dtype=dtype
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)
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if scheduler is None:
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self.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(config.model_path, subfolder="scheduler")
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self.register_modules(
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tokenizer=self.tokenizer,
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tokenizer_2=self.tokenizer_2,
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tokenizer_3=self.tokenizer_3,
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text_encoder=self.text_encoder,
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text_encoder_3=self.text_encoder_3,
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text_encoder_2=self.text_encoder_2,
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vae=self.vae,
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transformer=self.transformer,
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scheduler=self.scheduler,
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)
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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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# 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(
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device, self.text_encoder, self.text_encoder_2, self.text_encoder_3, self.transformer, self.vae
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)
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self.vae_scale_factor = (
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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.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
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self.tokenizer_max_length = (
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self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
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)
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self.max_sequence_length_t5 = 256
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self.default_sample_size = (
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self.transformer.config.sample_size
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if hasattr(self, "transformer") and self.transformer is not None
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else 128
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)
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# parallel
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self._set_parallel()
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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_images_per_prompt: int = 1,
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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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if self.text_encoder_3 is None:
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return torch.zeros(
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(batch_size, self.max_sequence_length_t5, self.transformer.config.joint_attention_dim),
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device=device,
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dtype=dtype,
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)
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text_inputs = self.tokenizer_3(
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prompt,
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padding="max_length",
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max_length=self.max_sequence_length_t5,
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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_3(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_3.batch_decode(untruncated_ids[:, self.max_sequence_length_t5 - 1 : -1])
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logger.warning(
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"The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.max_sequence_length_t5} tokens: {removed_text}"
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)
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prompt_embeds = self.text_encoder_3(text_input_ids.to(device))[0]
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dtype = self.text_encoder_3.dtype
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prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
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_, seq_len, _ = prompt_embeds.shape
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# duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
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prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
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prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
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return prompt_embeds
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def _get_clip_prompt_embeds(
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self,
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prompt: Union[str, List[str]],
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num_images_per_prompt: int = 1,
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device: Optional[torch.device] = None,
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clip_skip: Optional[int] = None,
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clip_model_index: int = 0,
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):
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device = device or self.execution_device
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clip_tokenizers = [self.tokenizer, self.tokenizer_2]
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clip_text_encoders = [self.text_encoder, self.text_encoder_2]
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tokenizer = clip_tokenizers[clip_model_index]
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text_encoder = clip_text_encoders[clip_model_index]
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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 = tokenizer(
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prompt,
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padding="max_length",
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max_length=self.tokenizer_max_length,
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truncation=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 = 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 = tokenizer.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1])
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logger.warning(
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"The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.tokenizer_max_length} tokens: {removed_text}"
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)
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prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
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pooled_prompt_embeds = prompt_embeds[0]
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if clip_skip is None:
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prompt_embeds = prompt_embeds.hidden_states[-2]
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else:
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prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
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prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
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_, seq_len, _ = prompt_embeds.shape
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# duplicate text embeddings for each generation per prompt, using mps friendly method
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prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
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prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
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pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt, 1)
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pooled_prompt_embeds = pooled_prompt_embeds.view(batch_size * num_images_per_prompt, -1)
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return prompt_embeds, pooled_prompt_embeds
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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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@autocast("cuda", enabled=False)
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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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prompt_2: Union[str, List[str]],
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prompt_3: Union[str, List[str]],
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device: Optional[torch.device] = None,
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num_images_per_prompt: int = 1,
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do_classifier_free_guidance: bool = True,
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negative_prompt: Optional[Union[str, List[str]]] = None,
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negative_prompt_2: Optional[Union[str, List[str]]] = None,
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negative_prompt_3: Optional[Union[str, List[str]]] = None,
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prompt_embeds: Optional[torch.FloatTensor] = None,
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negative_prompt_embeds: Optional[torch.FloatTensor] = None,
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pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
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negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
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clip_skip: Optional[int] = None,
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):
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r"""
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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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prompt_2 (`str` or `List[str]`, *optional*):
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The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
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used in all text-encoders
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prompt_3 (`str` or `List[str]`, *optional*):
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The prompt or prompts to be sent to the `tokenizer_3` and `text_encoder_3`. If not defined, `prompt` is
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used in all text-encoders
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device: (`torch.device`):
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torch device
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num_images_per_prompt (`int`):
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number of images that should be generated per prompt
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do_classifier_free_guidance (`bool`):
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whether to use classifier free guidance or not
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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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negative_prompt_2 (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
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`text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders.
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negative_prompt_2 (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and
|
|
`text_encoder_3`. If not defined, `negative_prompt` is used in both text-encoders
|
|
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.
|
|
pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
|
|
If not provided, pooled text embeddings will be generated from `prompt` input argument.
|
|
negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
|
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
|
|
input argument.
|
|
clip_skip (`int`, *optional*):
|
|
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
|
|
the output of the pre-final layer will be used for computing the prompt embeddings.
|
|
"""
|
|
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_2 = prompt_2 or prompt
|
|
prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
|
|
|
|
prompt_3 = prompt_3 or prompt
|
|
prompt_3 = [prompt_3] if isinstance(prompt_3, str) else prompt_3
|
|
|
|
prompt_embed, pooled_prompt_embed = self._get_clip_prompt_embeds(
|
|
prompt=prompt,
|
|
device=device,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
clip_skip=clip_skip,
|
|
clip_model_index=0,
|
|
)
|
|
prompt_2_embed, pooled_prompt_2_embed = self._get_clip_prompt_embeds(
|
|
prompt=prompt_2,
|
|
device=device,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
clip_skip=clip_skip,
|
|
clip_model_index=1,
|
|
)
|
|
clip_prompt_embeds = torch.cat([prompt_embed, prompt_2_embed], dim=-1)
|
|
|
|
t5_prompt_embed = self._get_t5_prompt_embeds(
|
|
prompt=prompt_3,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
device=device,
|
|
)
|
|
|
|
clip_prompt_embeds = torch.nn.functional.pad(
|
|
clip_prompt_embeds, (0, t5_prompt_embed.shape[-1] - clip_prompt_embeds.shape[-1])
|
|
)
|
|
|
|
prompt_embeds = torch.cat([clip_prompt_embeds, t5_prompt_embed], dim=-2)
|
|
pooled_prompt_embeds = torch.cat([pooled_prompt_embed, pooled_prompt_2_embed], dim=-1)
|
|
|
|
if do_classifier_free_guidance and negative_prompt_embeds is None:
|
|
negative_prompt = negative_prompt or ""
|
|
negative_prompt_2 = negative_prompt_2 or negative_prompt
|
|
negative_prompt_3 = negative_prompt_3 or negative_prompt
|
|
|
|
# normalize str to list
|
|
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
|
|
negative_prompt_2 = (
|
|
batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
|
|
)
|
|
negative_prompt_3 = (
|
|
batch_size * [negative_prompt_3] if isinstance(negative_prompt_3, str) else negative_prompt_3
|
|
)
|
|
|
|
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_embed, negative_pooled_prompt_embed = self._get_clip_prompt_embeds(
|
|
negative_prompt,
|
|
device=device,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
clip_skip=None,
|
|
clip_model_index=0,
|
|
)
|
|
negative_prompt_2_embed, negative_pooled_prompt_2_embed = self._get_clip_prompt_embeds(
|
|
negative_prompt_2,
|
|
device=device,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
clip_skip=None,
|
|
clip_model_index=1,
|
|
)
|
|
negative_clip_prompt_embeds = torch.cat([negative_prompt_embed, negative_prompt_2_embed], dim=-1)
|
|
|
|
t5_negative_prompt_embed = self._get_t5_prompt_embeds(
|
|
prompt=negative_prompt_3, num_images_per_prompt=num_images_per_prompt, device=device
|
|
)
|
|
|
|
negative_clip_prompt_embeds = torch.nn.functional.pad(
|
|
negative_clip_prompt_embeds,
|
|
(0, t5_negative_prompt_embed.shape[-1] - negative_clip_prompt_embeds.shape[-1]),
|
|
)
|
|
|
|
negative_prompt_embeds = torch.cat([negative_clip_prompt_embeds, t5_negative_prompt_embed], dim=-2)
|
|
negative_pooled_prompt_embeds = torch.cat(
|
|
[negative_pooled_prompt_embed, negative_pooled_prompt_2_embed], dim=-1
|
|
)
|
|
prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = (
|
|
prompt_embeds.to(self._device),
|
|
negative_prompt_embeds.to(self._device),
|
|
pooled_prompt_embeds.to(self._device),
|
|
negative_pooled_prompt_embeds.to(self._device),
|
|
)
|
|
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
|
|
|
|
def check_inputs(
|
|
self,
|
|
prompt,
|
|
prompt_2,
|
|
prompt_3,
|
|
height,
|
|
width,
|
|
negative_prompt=None,
|
|
negative_prompt_2=None,
|
|
negative_prompt_3=None,
|
|
prompt_embeds=None,
|
|
negative_prompt_embeds=None,
|
|
pooled_prompt_embeds=None,
|
|
negative_pooled_prompt_embeds=None,
|
|
callback_on_step_end_tensor_inputs=None,
|
|
):
|
|
if height % 8 != 0 or width % 8 != 0:
|
|
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
|
|
|
|
if callback_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_2 is not None and prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
|
|
" only forward one of the two."
|
|
)
|
|
elif prompt_3 is not None and prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `prompt_3`: {prompt_2} 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)}")
|
|
elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
|
|
raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
|
|
elif prompt_3 is not None and (not isinstance(prompt_3, str) and not isinstance(prompt_3, list)):
|
|
raise ValueError(f"`prompt_3` has to be of type `str` or `list` but is {type(prompt_3)}")
|
|
|
|
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."
|
|
)
|
|
elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
|
|
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
|
)
|
|
elif negative_prompt_3 is not None and negative_prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `negative_prompt_3`: {negative_prompt_3} 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 prompt_embeds is not None and pooled_prompt_embeds is None:
|
|
raise ValueError(
|
|
"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
|
|
)
|
|
|
|
if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
|
|
raise ValueError(
|
|
"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
|
|
)
|
|
|
|
def prepare_latents(
|
|
self,
|
|
batch_size,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
frames,
|
|
dtype,
|
|
device,
|
|
generator,
|
|
latents=None,
|
|
):
|
|
if latents is not None:
|
|
return latents.to(device=device, dtype=dtype)
|
|
# 1, 60, 16, 32, 32
|
|
shape = (
|
|
batch_size,
|
|
frames,
|
|
num_channels_latents,
|
|
int(height) // self.vae_scale_factor,
|
|
int(width) // self.vae_scale_factor,
|
|
)
|
|
|
|
if isinstance(generator, list) and len(generator) != batch_size:
|
|
raise ValueError(
|
|
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
|
|
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
|
|
)
|
|
|
|
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
|
|
|
return latents
|
|
|
|
@property
|
|
def guidance_scale(self):
|
|
return self._guidance_scale
|
|
|
|
@property
|
|
def clip_skip(self):
|
|
return self._clip_skip
|
|
|
|
# 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.
|
|
@property
|
|
def do_classifier_free_guidance(self):
|
|
return self._guidance_scale > 1
|
|
|
|
@property
|
|
def joint_attention_kwargs(self):
|
|
return self._joint_attention_kwargs
|
|
|
|
@property
|
|
def num_timesteps(self):
|
|
return self._num_timesteps
|
|
|
|
@property
|
|
def interrupt(self):
|
|
return self._interrupt
|
|
|
|
@torch.no_grad()
|
|
@autocast("cuda", dtype=torch.bfloat16)
|
|
def generate(
|
|
self,
|
|
prompt: Union[str, List[str]] = None,
|
|
prompt_2: Optional[Union[str, List[str]]] = None,
|
|
prompt_3: Optional[Union[str, List[str]]] = None,
|
|
height: int = 288,
|
|
width: int = 480,
|
|
frames: int = 40,
|
|
num_inference_steps: int = 100,
|
|
timesteps: List[int] = None,
|
|
guidance_scale: float = 7.5,
|
|
seed: int = -1,
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
|
negative_prompt_2: Optional[Union[str, List[str]]] = None,
|
|
negative_prompt_3: Optional[Union[str, List[str]]] = None,
|
|
num_images_per_prompt: Optional[int] = 1,
|
|
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,
|
|
pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
|
|
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
|
|
output_type: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
clip_skip: Optional[int] = None,
|
|
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
|
|
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
|
|
):
|
|
r"""
|
|
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.
|
|
prompt_2 (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
|
|
will be used instead
|
|
prompt_3 (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to be sent to `tokenizer_3` and `text_encoder_3`. If not defined, `prompt` is
|
|
will be used instead
|
|
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.
|
|
guidance_scale (`float`, *optional*, defaults to 5.0):
|
|
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.
|
|
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`).
|
|
negative_prompt_2 (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
|
|
`text_encoder_2`. If not defined, `negative_prompt` is used instead
|
|
negative_prompt_3 (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and
|
|
`text_encoder_3`. If not defined, `negative_prompt` is used instead
|
|
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of images 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.
|
|
pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
|
|
If not provided, pooled text embeddings will be generated from `prompt` input argument.
|
|
negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
|
weighting. If not provided, pooled 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.
|
|
joint_attention_kwargs (`dict`, *optional*):
|
|
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
|
`self.processor` in
|
|
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
|
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.
|
|
|
|
Examples:
|
|
|
|
Returns:
|
|
[`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
|
|
[`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
|
|
`tuple`. When returning a tuple, the first element is a list with the generated images.
|
|
"""
|
|
|
|
height = height or self.default_sample_size * self.vae_scale_factor
|
|
width = width or self.default_sample_size * self.vae_scale_factor
|
|
frames = frames or 24
|
|
self._set_seed(seed)
|
|
update_steps(num_inference_steps)
|
|
|
|
# 1. Check inputs. Raise error if not correct
|
|
self.check_inputs(
|
|
prompt,
|
|
prompt_2,
|
|
prompt_3,
|
|
height,
|
|
width,
|
|
negative_prompt=negative_prompt,
|
|
negative_prompt_2=negative_prompt_2,
|
|
negative_prompt_3=negative_prompt_3,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
pooled_prompt_embeds=pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
|
|
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
|
|
)
|
|
|
|
self._guidance_scale = guidance_scale
|
|
self._clip_skip = clip_skip
|
|
self._joint_attention_kwargs = joint_attention_kwargs
|
|
self._interrupt = False
|
|
|
|
# 2. Define 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]
|
|
|
|
(
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds,
|
|
) = self.encode_prompt(
|
|
prompt=prompt,
|
|
prompt_2=prompt_2,
|
|
prompt_3=prompt_3,
|
|
negative_prompt=negative_prompt,
|
|
negative_prompt_2=negative_prompt_2,
|
|
negative_prompt_3=negative_prompt_3,
|
|
do_classifier_free_guidance=self.do_classifier_free_guidance,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
pooled_prompt_embeds=pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
|
|
device=self.text_encoder.device,
|
|
clip_skip=self.clip_skip,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
)
|
|
|
|
if self.do_classifier_free_guidance:
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
|
|
pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
|
|
|
|
# 4. Prepare timesteps
|
|
timesteps, num_inference_steps = retrieve_timesteps(
|
|
self.scheduler, num_inference_steps, self._device, timesteps
|
|
)
|
|
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
|
|
self._num_timesteps = len(timesteps)
|
|
|
|
# 5. Prepare latent variables
|
|
num_channels_latents = self.transformer.config.in_channels
|
|
latents = self.prepare_latents(
|
|
batch_size * num_images_per_prompt,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
frames,
|
|
prompt_embeds.dtype,
|
|
self._device,
|
|
generator,
|
|
latents,
|
|
)
|
|
|
|
# 6. Denoising loop
|
|
# with self.progress_bar(total=num_inference_steps) as progress_bar:
|
|
for i, t in tqdm(enumerate(timesteps), total=len(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
|
|
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
|
timestep = t.expand(latents.shape[0])
|
|
noise_pred_uncond = self.transformer(
|
|
hidden_states=latent_model_input[0, :].unsqueeze(0),
|
|
timestep=timestep,
|
|
encoder_hidden_states=prompt_embeds[0, :].unsqueeze(0),
|
|
pooled_projections=pooled_prompt_embeds[0, :].unsqueeze(0),
|
|
joint_attention_kwargs=self.joint_attention_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
noise_pred_text = self.transformer(
|
|
hidden_states=latent_model_input[1, :].unsqueeze(0),
|
|
timestep=timestep,
|
|
encoder_hidden_states=prompt_embeds[1, :].unsqueeze(0),
|
|
pooled_projections=pooled_prompt_embeds[1, :].unsqueeze(0),
|
|
joint_attention_kwargs=self.joint_attention_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
self._guidance_scale = 1 + guidance_scale * (
|
|
(1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2
|
|
)
|
|
# perform guidance
|
|
if self.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
|
|
latents_dtype = latents.dtype
|
|
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
|
|
|
|
if latents.dtype != latents_dtype:
|
|
if torch.backends.mps.is_available():
|
|
# some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
|
|
latents = latents.to(latents_dtype)
|
|
|
|
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)
|
|
negative_pooled_prompt_embeds = callback_outputs.pop(
|
|
"negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
|
|
)
|
|
|
|
# call the callback, if provided
|
|
# if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
|
# progress_bar.update()
|
|
|
|
# if output_type == "latent":
|
|
# image = latents
|
|
|
|
# else:
|
|
latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
|
|
videos = []
|
|
for v_idx in range(latents.shape[1]):
|
|
image = self.vae.decode(latents[:, v_idx], return_dict=False)[0]
|
|
image = self.image_processor.postprocess(image, output_type=output_type)
|
|
videos.append(image[0])
|
|
videos = [videos]
|
|
|
|
# Offload all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return (videos,)
|
|
|
|
return VideoSysPipelineOutput(video=videos)
|
|
|
|
def save_video(self, video, output_path):
|
|
save_video(video, output_path, fps=8)
|
|
|
|
|
|
# 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
|