760 lines
35 KiB
Python
760 lines
35 KiB
Python
# All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import html
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import inspect
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import re
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import urllib.parse as ul
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from typing import Callable, List, Optional, Tuple, Union
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import torch
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import einops
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from einops import rearrange
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from transformers import T5EncoderModel, T5Tokenizer
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from diffusers.image_processor import VaeImageProcessor
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from diffusers.models import AutoencoderKL, Transformer2DModel
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from diffusers.schedulers import DPMSolverMultistepScheduler
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from diffusers.utils import (
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BACKENDS_MAPPING,
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is_bs4_available,
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is_ftfy_available,
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logging,
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replace_example_docstring,
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)
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from diffusers.utils.torch_utils import randn_tensor
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from diffusers.pipelines.pipeline_utils import DiffusionPipeline
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from diffusers.utils import BaseOutput
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from dataclasses import dataclass
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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if is_bs4_available():
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from bs4 import BeautifulSoup
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if is_ftfy_available():
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import ftfy
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EXAMPLE_DOC_STRING = """
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Examples:
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```py
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>>> import torch
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>>> from diffusers import PixArtAlphaPipeline
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>>> # You can replace the checkpoint id with "PixArt-alpha/PixArt-XL-2-512x512" too.
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>>> pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", torch_dtype=torch.float16)
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>>> # Enable memory optimizations.
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>>> pipe.enable_model_cpu_offload()
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>>> prompt = "A small cactus with a happy face in the Sahara desert."
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>>> image = pipe(prompt).images[0]
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```
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"""
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@dataclass
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class VideoPipelineOutput(BaseOutput):
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video: torch.Tensor
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class VideoGenPipeline(DiffusionPipeline):
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r"""
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Pipeline for text-to-image generation using PixArt-Alpha.
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
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library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
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Args:
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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 ([`T5EncoderModel`]):
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Frozen text-encoder. PixArt-Alpha 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/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
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tokenizer (`T5Tokenizer`):
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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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transformer ([`Transformer2DModel`]):
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A text conditioned `Transformer2DModel` to denoise the encoded image latents.
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scheduler ([`SchedulerMixin`]):
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A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
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"""
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bad_punct_regex = re.compile(
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r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
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) # noqa
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_optional_components = ["tokenizer", "text_encoder"]
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model_cpu_offload_seq = "text_encoder->transformer->vae"
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def __init__(
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self,
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tokenizer: T5Tokenizer,
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text_encoder: T5EncoderModel,
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vae: AutoencoderKL,
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transformer: Transformer2DModel,
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scheduler: DPMSolverMultistepScheduler,
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):
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super().__init__()
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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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# self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
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# Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/utils.py
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def mask_text_embeddings(self, emb, mask):
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if emb.shape[0] == 1:
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keep_index = mask.sum().item()
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return emb[:, :, :keep_index, :], keep_index # 1, 120, 4096 -> 1 7 4096
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else:
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masked_feature = emb * mask[:, None, :, None] # 1 120 4096
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return masked_feature, emb.shape[2]
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# Adapted from diffusers.pipelines.deepfloyd_if.pipeline_if.encode_prompt
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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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do_classifier_free_guidance: bool = True,
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negative_prompt: str = "",
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num_images_per_prompt: int = 1,
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device: Optional[torch.device] = 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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clean_caption: bool = False,
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mask_feature: bool = True,
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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 not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`
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instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). For
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PixArt-Alpha, this should be "".
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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_images_per_prompt (`int`, *optional*, defaults to 1):
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number of images that should be generated per prompt
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device: (`torch.device`, *optional*):
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torch device to place the resulting embeddings on
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prompt_embeds (`torch.FloatTensor`, *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.FloatTensor`, *optional*):
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Pre-generated negative text embeddings. For PixArt-Alpha, it's should be the embeddings of the ""
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string.
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clean_caption (bool, defaults to `False`):
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If `True`, the function will preprocess and clean the provided caption before encoding.
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mask_feature: (bool, defaults to `True`):
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If `True`, the function will mask the text embeddings.
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"""
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embeds_initially_provided = prompt_embeds is not None and negative_prompt_embeds is not None
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if device is None:
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device = self.text_encoder.device or self._execution_device
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if prompt is not None and isinstance(prompt, str):
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batch_size = 1
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elif prompt is not None and isinstance(prompt, list):
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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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# See Section 3.1. of the paper.
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max_length = 300
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if prompt_embeds is None:
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prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
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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_length,
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truncation=True,
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return_attention_mask=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(
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text_input_ids, untruncated_ids
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):
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removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1: -1])
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logger.warning(
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"The following part of your input was truncated because the model can only handle sequences up to"
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f" {max_length} tokens: {removed_text}"
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)
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attention_mask = text_inputs.attention_mask.to(device)
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prompt_embeds_attention_mask = attention_mask
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prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
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prompt_embeds = prompt_embeds[0]
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else:
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prompt_embeds_attention_mask = torch.ones_like(prompt_embeds)
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if self.text_encoder is not None:
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dtype = self.text_encoder.dtype
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elif self.transformer is not None:
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dtype = self.transformer.dtype
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else:
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dtype = None
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prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
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bs_embed, 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(bs_embed * num_images_per_prompt, seq_len, -1)
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prompt_embeds_attention_mask = prompt_embeds_attention_mask.view(bs_embed, -1)
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prompt_embeds_attention_mask = prompt_embeds_attention_mask.repeat(num_images_per_prompt, 1)
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# get unconditional embeddings for classifier free guidance
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if do_classifier_free_guidance and negative_prompt_embeds is None:
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uncond_tokens = [negative_prompt] * batch_size
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uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)
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max_length = prompt_embeds.shape[1]
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uncond_input = self.tokenizer(
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uncond_tokens,
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padding="max_length",
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max_length=max_length,
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truncation=True,
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return_attention_mask=True,
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add_special_tokens=True,
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return_tensors="pt",
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)
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attention_mask = uncond_input.attention_mask.to(device)
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negative_prompt_embeds = self.text_encoder(
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uncond_input.input_ids.to(device),
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attention_mask=attention_mask,
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)
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negative_prompt_embeds = negative_prompt_embeds[0]
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if do_classifier_free_guidance:
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# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
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seq_len = negative_prompt_embeds.shape[1]
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negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)
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negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
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negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
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# For classifier free guidance, we need to do two forward passes.
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# Here we concatenate the unconditional and text embeddings into a single batch
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# to avoid doing two forward passes
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else:
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negative_prompt_embeds = None
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# print(prompt_embeds.shape) # 1 120 4096
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# print(negative_prompt_embeds.shape) # 1 120 4096
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# Perform additional masking.
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if mask_feature and not embeds_initially_provided:
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prompt_embeds = prompt_embeds.unsqueeze(1)
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masked_prompt_embeds, keep_indices = self.mask_text_embeddings(prompt_embeds, prompt_embeds_attention_mask)
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masked_prompt_embeds = masked_prompt_embeds.squeeze(1)
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masked_negative_prompt_embeds = (
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negative_prompt_embeds[:, :keep_indices, :] if negative_prompt_embeds is not None else None
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)
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# import torch.nn.functional as F
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# padding = (0, 0, 0, 113) # (左, 右, 下, 上)
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# masked_prompt_embeds_ = F.pad(masked_prompt_embeds, padding, "constant", 0)
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# masked_negative_prompt_embeds_ = F.pad(masked_negative_prompt_embeds, padding, "constant", 0)
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# print(masked_prompt_embeds == masked_prompt_embeds_[:, :masked_negative_prompt_embeds.shape[1], ...])
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return masked_prompt_embeds, masked_negative_prompt_embeds
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# return masked_prompt_embeds_, masked_negative_prompt_embeds_
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return prompt_embeds, negative_prompt_embeds
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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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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_steps,
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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_steps is None) or (
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callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
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):
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raise ValueError(
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f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
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f" {type(callback_steps)}."
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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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# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
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def _text_preprocessing(self, text, clean_caption=False):
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if clean_caption and not is_bs4_available():
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logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
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logger.warn("Setting `clean_caption` to False...")
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clean_caption = False
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if clean_caption and not is_ftfy_available():
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logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
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logger.warn("Setting `clean_caption` to False...")
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clean_caption = False
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if not isinstance(text, (tuple, list)):
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text = [text]
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def process(text: str):
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if clean_caption:
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text = self._clean_caption(text)
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text = self._clean_caption(text)
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else:
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text = text.lower().strip()
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return text
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return [process(t) for t in text]
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# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
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def _clean_caption(self, caption):
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caption = str(caption)
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caption = ul.unquote_plus(caption)
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caption = caption.strip().lower()
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caption = re.sub("<person>", "person", caption)
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# urls:
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caption = re.sub(
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r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",
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# noqa
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"",
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caption,
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) # regex for urls
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caption = re.sub(
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r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",
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# noqa
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"",
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caption,
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) # regex for urls
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# html:
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caption = BeautifulSoup(caption, features="html.parser").text
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# @<nickname>
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caption = re.sub(r"@[\w\d]+\b", "", caption)
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# 31C0—31EF CJK Strokes
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# 31F0—31FF Katakana Phonetic Extensions
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# 3200—32FF Enclosed CJK Letters and Months
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# 3300—33FF CJK Compatibility
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# 3400—4DBF CJK Unified Ideographs Extension A
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# 4DC0—4DFF Yijing Hexagram Symbols
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# 4E00—9FFF CJK Unified Ideographs
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caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
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caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
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caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
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caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
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caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
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caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
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caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
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#######################################################
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# все виды тире / all types of dash --> "-"
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caption = re.sub(
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r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+",
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# noqa
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"-",
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caption,
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)
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# кавычки к одному стандарту
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caption = re.sub(r"[`´«»“”¨]", '"', caption)
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caption = re.sub(r"[‘’]", "'", caption)
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# "
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caption = re.sub(r""?", "", caption)
|
|
# &
|
|
caption = re.sub(r"&", "", caption)
|
|
|
|
# ip adresses:
|
|
caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
|
|
|
|
# article ids:
|
|
caption = re.sub(r"\d:\d\d\s+$", "", caption)
|
|
|
|
# \n
|
|
caption = re.sub(r"\\n", " ", caption)
|
|
|
|
# "#123"
|
|
caption = re.sub(r"#\d{1,3}\b", "", caption)
|
|
# "#12345.."
|
|
caption = re.sub(r"#\d{5,}\b", "", caption)
|
|
# "123456.."
|
|
caption = re.sub(r"\b\d{6,}\b", "", caption)
|
|
# filenames:
|
|
caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)
|
|
|
|
#
|
|
caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT"""
|
|
caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT"""
|
|
|
|
caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT
|
|
caption = re.sub(r"\s+\.\s+", r" ", caption) # " . "
|
|
|
|
# this-is-my-cute-cat / this_is_my_cute_cat
|
|
regex2 = re.compile(r"(?:\-|\_)")
|
|
if len(re.findall(regex2, caption)) > 3:
|
|
caption = re.sub(regex2, " ", caption)
|
|
|
|
caption = ftfy.fix_text(caption)
|
|
caption = html.unescape(html.unescape(caption))
|
|
|
|
caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640
|
|
caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc
|
|
caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231
|
|
|
|
caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
|
|
caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
|
|
caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
|
|
caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
|
|
caption = re.sub(r"\bpage\s+\d+\b", "", caption)
|
|
|
|
caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a...
|
|
|
|
caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
|
|
|
|
caption = re.sub(r"\b\s+\:\s+", r": ", caption)
|
|
caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
|
|
caption = re.sub(r"\s+", " ", caption)
|
|
|
|
caption.strip()
|
|
|
|
caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
|
|
caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
|
|
caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
|
|
caption = re.sub(r"^\.\S+$", "", caption)
|
|
|
|
return caption.strip()
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
|
|
def prepare_latents(self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator,
|
|
latents=None):
|
|
shape = (
|
|
batch_size, num_channels_latents, video_length, self.vae.latent_size[0], self.vae.latent_size[1])
|
|
if isinstance(generator, list) and len(generator) != batch_size:
|
|
raise ValueError(
|
|
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
|
|
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
|
|
)
|
|
|
|
if latents is None:
|
|
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
|
else:
|
|
latents = latents.to(device)
|
|
|
|
# scale the initial noise by the standard deviation required by the scheduler
|
|
latents = latents * self.scheduler.init_noise_sigma
|
|
return latents
|
|
|
|
@torch.no_grad()
|
|
@replace_example_docstring(EXAMPLE_DOC_STRING)
|
|
def __call__(
|
|
self,
|
|
prompt: Union[str, List[str]] = None,
|
|
negative_prompt: str = "",
|
|
num_inference_steps: int = 20,
|
|
timesteps: List[int] = None,
|
|
guidance_scale: float = 4.5,
|
|
num_images_per_prompt: Optional[int] = 1,
|
|
video_length: Optional[int] = None,
|
|
height: Optional[int] = None,
|
|
width: Optional[int] = None,
|
|
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: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
|
|
callback_steps: int = 1,
|
|
clean_caption: bool = True,
|
|
mask_feature: bool = True,
|
|
enable_temporal_attentions: bool = True,
|
|
) -> Union[VideoPipelineOutput, 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`).
|
|
num_inference_steps (`int`, *optional*, defaults to 100):
|
|
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. If not defined, equal spaced `num_inference_steps`
|
|
timesteps are 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_images_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of images to generate per prompt.
|
|
height (`int`, *optional*, defaults to self.unet.config.sample_size):
|
|
The height in pixels of the generated image.
|
|
width (`int`, *optional*, defaults to self.unet.config.sample_size):
|
|
The width in pixels of the generated image.
|
|
eta (`float`, *optional*, defaults to 0.0):
|
|
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
|
[`schedulers.DDIMScheduler`], will be ignored for others.
|
|
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. For PixArt-Alpha this negative prompt should be "". 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.IFPipelineOutput`] instead of a plain tuple.
|
|
callback (`Callable`, *optional*):
|
|
A function that will be called every `callback_steps` steps during inference. The function will be
|
|
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
|
callback_steps (`int`, *optional*, defaults to 1):
|
|
The frequency at which the `callback` function will be called. If not specified, the callback will be
|
|
called at every step.
|
|
clean_caption (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
|
|
be installed. If the dependencies are not installed, the embeddings will be created from the raw
|
|
prompt.
|
|
mask_feature (`bool` defaults to `True`): If set to `True`, the text embeddings will be masked.
|
|
|
|
Examples:
|
|
|
|
Returns:
|
|
[`~pipelines.ImagePipelineOutput`] or `tuple`:
|
|
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
|
|
returned where the first element is a list with the generated images
|
|
"""
|
|
# 1. Check inputs. Raise error if not correct
|
|
# height = height or self.transformer.config.sample_size * self.vae_scale_factor
|
|
# width = width or self.transformer.config.sample_size * self.vae_scale_factor
|
|
self.check_inputs(
|
|
prompt, height, width, negative_prompt, callback_steps, prompt_embeds, negative_prompt_embeds
|
|
)
|
|
|
|
# 2. Default height and width to transformer
|
|
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.text_encoder.device or self._execution_device
|
|
|
|
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
|
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
do_classifier_free_guidance = guidance_scale > 1.0
|
|
|
|
# 3. Encode input prompt
|
|
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
|
|
prompt,
|
|
do_classifier_free_guidance,
|
|
negative_prompt=negative_prompt,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
device=device,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
clean_caption=clean_caption,
|
|
mask_feature=mask_feature,
|
|
)
|
|
if do_classifier_free_guidance:
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
|
|
|
|
# 4. Prepare timesteps
|
|
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
|
timesteps = self.scheduler.timesteps
|
|
|
|
# 5. Prepare latents.
|
|
latent_channels = self.transformer.config.in_channels
|
|
latents = self.prepare_latents(
|
|
batch_size * num_images_per_prompt,
|
|
latent_channels,
|
|
video_length,
|
|
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)
|
|
|
|
# 6.1 Prepare micro-conditions.
|
|
added_cond_kwargs = {"resolution": None, "aspect_ratio": None}
|
|
# if self.transformer.config.sample_size == 128:
|
|
# resolution = torch.tensor([height, width]).repeat(batch_size * num_images_per_prompt, 1)
|
|
# aspect_ratio = torch.tensor([float(height / width)]).repeat(batch_size * num_images_per_prompt, 1)
|
|
# resolution = resolution.to(dtype=prompt_embeds.dtype, device=device)
|
|
# aspect_ratio = aspect_ratio.to(dtype=prompt_embeds.dtype, device=device)
|
|
# added_cond_kwargs = {"resolution": resolution, "aspect_ratio": aspect_ratio}
|
|
|
|
# 7. Denoising loop
|
|
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
|
|
|
|
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
|
for i, t in enumerate(timesteps):
|
|
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)
|
|
|
|
current_timestep = t
|
|
if not torch.is_tensor(current_timestep):
|
|
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
|
|
# This would be a good case for the `match` statement (Python 3.10+)
|
|
is_mps = latent_model_input.device.type == "mps"
|
|
if isinstance(current_timestep, float):
|
|
dtype = torch.float32 if is_mps else torch.float64
|
|
else:
|
|
dtype = torch.int32 if is_mps else torch.int64
|
|
current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device)
|
|
elif len(current_timestep.shape) == 0:
|
|
current_timestep = current_timestep[None].to(latent_model_input.device)
|
|
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
|
current_timestep = current_timestep.expand(latent_model_input.shape[0])
|
|
|
|
# predict noise model_output
|
|
noise_pred = self.transformer(
|
|
latent_model_input,
|
|
encoder_hidden_states=prompt_embeds,
|
|
timestep=current_timestep,
|
|
added_cond_kwargs=added_cond_kwargs,
|
|
enable_temporal_attentions=enable_temporal_attentions,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
# perform guidance
|
|
if do_classifier_free_guidance:
|
|
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
|
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
|
|
|
# learned sigma
|
|
if self.transformer.config.out_channels // 2 == latent_channels:
|
|
noise_pred = noise_pred.chunk(2, dim=1)[0]
|
|
else:
|
|
noise_pred = noise_pred
|
|
|
|
# compute previous image: x_t -> x_t-1
|
|
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
|
|
|
# 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 callback is not None and i % callback_steps == 0:
|
|
step_idx = i // getattr(self.scheduler, "order", 1)
|
|
callback(step_idx, t, latents)
|
|
|
|
if not output_type == 'latents':
|
|
video = self.decode_latents(latents)
|
|
else:
|
|
video = latents
|
|
return VideoPipelineOutput(video=video)
|
|
|
|
# Offload all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return (video,)
|
|
|
|
return VideoPipelineOutput(video=video)
|
|
|
|
def decode_latents(self, latents):
|
|
video = self.vae.decode(latents)
|
|
# video = self.vae.decode(latents / 0.18215)
|
|
# video = rearrange(video, 'b c t h w -> b t c h w').contiguous()
|
|
video = ((video / 2.0 + 0.5).clamp(0, 1) * 255).to(dtype=torch.uint8).cpu().permute(0, 1, 3, 4, 2).contiguous()
|
|
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
|
|
return video
|