589 lines
27 KiB
Python
589 lines
27 KiB
Python
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from typing import Callable, Dict, List, Optional, Union
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import torch
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from transformers import T5EncoderModel, T5Tokenizer
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from ...loaders import StableDiffusionLoraLoaderMixin
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from ...models import Kandinsky3UNet, VQModel
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from ...schedulers import DDPMScheduler
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from ...utils import (
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deprecate,
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is_torch_xla_available,
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logging,
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replace_example_docstring,
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)
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from ...utils.torch_utils import randn_tensor
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from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
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if is_torch_xla_available():
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import torch_xla.core.xla_model as xm
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XLA_AVAILABLE = True
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else:
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XLA_AVAILABLE = False
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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EXAMPLE_DOC_STRING = """
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Examples:
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```py
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>>> from diffusers import AutoPipelineForText2Image
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>>> import torch
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>>> pipe = AutoPipelineForText2Image.from_pretrained(
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... "kandinsky-community/kandinsky-3", variant="fp16", torch_dtype=torch.float16
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... )
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>>> pipe.enable_model_cpu_offload()
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>>> prompt = "A photograph of the inside of a subway train. There are raccoons sitting on the seats. One of them is reading a newspaper. The window shows the city in the background."
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>>> generator = torch.Generator(device="cpu").manual_seed(0)
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>>> image = pipe(prompt, num_inference_steps=25, generator=generator).images[0]
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```
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"""
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def downscale_height_and_width(height, width, scale_factor=8):
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new_height = height // scale_factor**2
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if height % scale_factor**2 != 0:
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new_height += 1
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new_width = width // scale_factor**2
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if width % scale_factor**2 != 0:
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new_width += 1
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return new_height * scale_factor, new_width * scale_factor
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class Kandinsky3Pipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin):
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model_cpu_offload_seq = "text_encoder->unet->movq"
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_callback_tensor_inputs = [
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"latents",
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"prompt_embeds",
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"negative_prompt_embeds",
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"negative_attention_mask",
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"attention_mask",
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]
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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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unet: Kandinsky3UNet,
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scheduler: DDPMScheduler,
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movq: VQModel,
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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, unet=unet, scheduler=scheduler, movq=movq
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)
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def process_embeds(self, embeddings, attention_mask, cut_context):
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if cut_context:
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embeddings[attention_mask == 0] = torch.zeros_like(embeddings[attention_mask == 0])
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max_seq_length = attention_mask.sum(-1).max() + 1
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embeddings = embeddings[:, :max_seq_length]
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attention_mask = attention_mask[:, :max_seq_length]
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return embeddings, attention_mask
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@torch.no_grad()
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def encode_prompt(
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self,
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prompt,
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do_classifier_free_guidance=True,
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num_images_per_prompt=1,
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device=None,
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negative_prompt=None,
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prompt_embeds: Optional[torch.Tensor] = None,
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negative_prompt_embeds: Optional[torch.Tensor] = None,
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_cut_context=False,
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attention_mask: Optional[torch.Tensor] = None,
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negative_attention_mask: Optional[torch.Tensor] = None,
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):
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r"""
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Encodes the prompt into text encoder hidden states.
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Args:
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prompt (`str` or `List[str]`, *optional*):
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prompt to be encoded
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device: (`torch.device`, *optional*):
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torch device to place the resulting embeddings on
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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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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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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. If not defined, one has to pass `negative_prompt_embeds`. instead.
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Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
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prompt_embeds (`torch.Tensor`, *optional*):
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Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
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provided, text embeddings will be generated from `prompt` input argument.
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negative_prompt_embeds (`torch.Tensor`, *optional*):
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Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
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weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
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argument.
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attention_mask (`torch.Tensor`, *optional*):
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Pre-generated attention mask. Must provide if passing `prompt_embeds` directly.
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negative_attention_mask (`torch.Tensor`, *optional*):
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Pre-generated negative attention mask. Must provide if passing `negative_prompt_embeds` directly.
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"""
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if prompt is not None and negative_prompt is not None:
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if type(prompt) is not type(negative_prompt):
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raise TypeError(
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f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
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f" {type(prompt)}."
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)
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if device is None:
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device = 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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max_length = 128
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if prompt_embeds is None:
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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_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids.to(device)
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attention_mask = text_inputs.attention_mask.to(device)
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prompt_embeds = self.text_encoder(
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text_input_ids,
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attention_mask=attention_mask,
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)
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prompt_embeds = prompt_embeds[0]
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prompt_embeds, attention_mask = self.process_embeds(prompt_embeds, attention_mask, _cut_context)
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prompt_embeds = prompt_embeds * attention_mask.unsqueeze(2)
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if self.text_encoder is not None:
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dtype = self.text_encoder.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 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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attention_mask = 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: List[str]
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if negative_prompt is None:
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uncond_tokens = [""] * batch_size
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elif isinstance(negative_prompt, str):
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uncond_tokens = [negative_prompt]
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elif batch_size != len(negative_prompt):
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raise ValueError(
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f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
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f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
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" the batch size of `prompt`."
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)
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else:
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uncond_tokens = negative_prompt
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if negative_prompt is not None:
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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=128,
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truncation=True,
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return_attention_mask=True,
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return_tensors="pt",
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)
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text_input_ids = uncond_input.input_ids.to(device)
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negative_attention_mask = uncond_input.attention_mask.to(device)
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negative_prompt_embeds = self.text_encoder(
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text_input_ids,
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attention_mask=negative_attention_mask,
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)
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negative_prompt_embeds = negative_prompt_embeds[0]
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negative_prompt_embeds = negative_prompt_embeds[:, : prompt_embeds.shape[1]]
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negative_attention_mask = negative_attention_mask[:, : prompt_embeds.shape[1]]
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negative_prompt_embeds = negative_prompt_embeds * negative_attention_mask.unsqueeze(2)
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else:
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negative_prompt_embeds = torch.zeros_like(prompt_embeds)
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negative_attention_mask = torch.zeros_like(attention_mask)
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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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if negative_prompt_embeds.shape != prompt_embeds.shape:
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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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negative_attention_mask = negative_attention_mask.repeat(num_images_per_prompt, 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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negative_attention_mask = None
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return prompt_embeds, negative_prompt_embeds, attention_mask, negative_attention_mask
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def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
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if latents is None:
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latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
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else:
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if latents.shape != shape:
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raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
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latents = latents.to(device)
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latents = latents * scheduler.init_noise_sigma
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return latents
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def check_inputs(
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self,
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prompt,
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callback_steps,
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negative_prompt=None,
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prompt_embeds=None,
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negative_prompt_embeds=None,
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callback_on_step_end_tensor_inputs=None,
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attention_mask=None,
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negative_attention_mask=None,
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):
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if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
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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 callback_on_step_end_tensor_inputs is not None and not all(
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k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
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):
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raise ValueError(
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f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
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)
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if prompt is not None and prompt_embeds is not None:
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raise ValueError(
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f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
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" only forward one of the two."
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)
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elif prompt is None and prompt_embeds is None:
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raise ValueError(
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"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
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)
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elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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if 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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if negative_prompt_embeds is not None and negative_attention_mask is None:
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raise ValueError("Please provide `negative_attention_mask` along with `negative_prompt_embeds`")
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if negative_prompt_embeds is not None and negative_attention_mask is not None:
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if negative_prompt_embeds.shape[:2] != negative_attention_mask.shape:
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raise ValueError(
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"`negative_prompt_embeds` and `negative_attention_mask` must have the same batch_size and token length when passed directly, but"
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f" got: `negative_prompt_embeds` {negative_prompt_embeds.shape[:2]} != `negative_attention_mask`"
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f" {negative_attention_mask.shape}."
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)
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if prompt_embeds is not None and attention_mask is None:
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raise ValueError("Please provide `attention_mask` along with `prompt_embeds`")
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if prompt_embeds is not None and attention_mask is not None:
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if prompt_embeds.shape[:2] != attention_mask.shape:
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raise ValueError(
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"`prompt_embeds` and `attention_mask` must have the same batch_size and token length when passed directly, but"
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f" got: `prompt_embeds` {prompt_embeds.shape[:2]} != `attention_mask`"
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f" {attention_mask.shape}."
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)
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@property
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def guidance_scale(self):
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return self._guidance_scale
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@property
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def do_classifier_free_guidance(self):
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return self._guidance_scale > 1
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@property
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def num_timesteps(self):
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return self._num_timesteps
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@torch.no_grad()
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@replace_example_docstring(EXAMPLE_DOC_STRING)
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def __call__(
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self,
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prompt: Union[str, List[str]] = None,
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num_inference_steps: int = 25,
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guidance_scale: float = 3.0,
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negative_prompt: Optional[Union[str, List[str]]] = None,
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num_images_per_prompt: Optional[int] = 1,
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height: Optional[int] = 1024,
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width: Optional[int] = 1024,
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
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prompt_embeds: Optional[torch.Tensor] = None,
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negative_prompt_embeds: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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negative_attention_mask: Optional[torch.Tensor] = None,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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latents=None,
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callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
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callback_on_step_end_tensor_inputs: List[str] = ["latents"],
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**kwargs,
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):
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"""
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Function invoked when calling the pipeline for generation.
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Args:
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prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
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instead.
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num_inference_steps (`int`, *optional*, defaults to 25):
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference.
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timesteps (`List[int]`, *optional*):
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Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
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timesteps are used. Must be in descending order.
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guidance_scale (`float`, *optional*, defaults to 3.0):
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Guidance scale as defined in [Classifier-Free Diffusion
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Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
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of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
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`guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
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the text `prompt`, usually at the expense of lower image quality.
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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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num_images_per_prompt (`int`, *optional*, defaults to 1):
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The number of images to generate per prompt.
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height (`int`, *optional*, defaults to self.unet.config.sample_size):
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|
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://huggingface.co/papers/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.
|
||
|
prompt_embeds (`torch.Tensor`, *optional*):
|
||
|
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
||
|
provided, text embeddings will be generated from `prompt` input argument.
|
||
|
negative_prompt_embeds (`torch.Tensor`, *optional*):
|
||
|
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
||
|
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
||
|
argument.
|
||
|
attention_mask (`torch.Tensor`, *optional*):
|
||
|
Pre-generated attention mask. Must provide if passing `prompt_embeds` directly.
|
||
|
negative_attention_mask (`torch.Tensor`, *optional*):
|
||
|
Pre-generated negative attention mask. Must provide if passing `negative_prompt_embeds` directly.
|
||
|
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.Tensor)`.
|
||
|
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.
|
||
|
cross_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).
|
||
|
|
||
|
Examples:
|
||
|
|
||
|
Returns:
|
||
|
[`~pipelines.ImagePipelineOutput`] or `tuple`
|
||
|
|
||
|
"""
|
||
|
|
||
|
callback = kwargs.pop("callback", None)
|
||
|
callback_steps = kwargs.pop("callback_steps", None)
|
||
|
|
||
|
if callback is not None:
|
||
|
deprecate(
|
||
|
"callback",
|
||
|
"1.0.0",
|
||
|
"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
|
||
|
)
|
||
|
if callback_steps is not None:
|
||
|
deprecate(
|
||
|
"callback_steps",
|
||
|
"1.0.0",
|
||
|
"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
|
||
|
)
|
||
|
|
||
|
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]}"
|
||
|
)
|
||
|
|
||
|
cut_context = True
|
||
|
device = self._execution_device
|
||
|
|
||
|
# 1. Check inputs. Raise error if not correct
|
||
|
self.check_inputs(
|
||
|
prompt,
|
||
|
callback_steps,
|
||
|
negative_prompt,
|
||
|
prompt_embeds,
|
||
|
negative_prompt_embeds,
|
||
|
callback_on_step_end_tensor_inputs,
|
||
|
attention_mask,
|
||
|
negative_attention_mask,
|
||
|
)
|
||
|
|
||
|
self._guidance_scale = guidance_scale
|
||
|
|
||
|
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]
|
||
|
|
||
|
# 3. Encode input prompt
|
||
|
prompt_embeds, negative_prompt_embeds, attention_mask, negative_attention_mask = self.encode_prompt(
|
||
|
prompt,
|
||
|
self.do_classifier_free_guidance,
|
||
|
num_images_per_prompt=num_images_per_prompt,
|
||
|
device=device,
|
||
|
negative_prompt=negative_prompt,
|
||
|
prompt_embeds=prompt_embeds,
|
||
|
negative_prompt_embeds=negative_prompt_embeds,
|
||
|
_cut_context=cut_context,
|
||
|
attention_mask=attention_mask,
|
||
|
negative_attention_mask=negative_attention_mask,
|
||
|
)
|
||
|
|
||
|
if self.do_classifier_free_guidance:
|
||
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
||
|
attention_mask = torch.cat([negative_attention_mask, attention_mask]).bool()
|
||
|
# 4. Prepare timesteps
|
||
|
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
||
|
timesteps = self.scheduler.timesteps
|
||
|
|
||
|
# 5. Prepare latents
|
||
|
height, width = downscale_height_and_width(height, width, 8)
|
||
|
|
||
|
latents = self.prepare_latents(
|
||
|
(batch_size * num_images_per_prompt, 4, height, width),
|
||
|
prompt_embeds.dtype,
|
||
|
device,
|
||
|
generator,
|
||
|
latents,
|
||
|
self.scheduler,
|
||
|
)
|
||
|
|
||
|
if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None:
|
||
|
self.text_encoder_offload_hook.offload()
|
||
|
|
||
|
# 7. Denoising loop
|
||
|
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
||
|
self._num_timesteps = len(timesteps)
|
||
|
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 self.do_classifier_free_guidance else latents
|
||
|
|
||
|
# predict the noise residual
|
||
|
noise_pred = self.unet(
|
||
|
latent_model_input,
|
||
|
t,
|
||
|
encoder_hidden_states=prompt_embeds,
|
||
|
encoder_attention_mask=attention_mask,
|
||
|
return_dict=False,
|
||
|
)[0]
|
||
|
|
||
|
if self.do_classifier_free_guidance:
|
||
|
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
||
|
|
||
|
noise_pred = (guidance_scale + 1.0) * noise_pred_text - guidance_scale * noise_pred_uncond
|
||
|
# noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
||
|
|
||
|
# compute the previous noisy sample x_t -> x_t-1
|
||
|
latents = self.scheduler.step(
|
||
|
noise_pred,
|
||
|
t,
|
||
|
latents,
|
||
|
generator=generator,
|
||
|
).prev_sample
|
||
|
|
||
|
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)
|
||
|
attention_mask = callback_outputs.pop("attention_mask", attention_mask)
|
||
|
negative_attention_mask = callback_outputs.pop("negative_attention_mask", negative_attention_mask)
|
||
|
|
||
|
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 XLA_AVAILABLE:
|
||
|
xm.mark_step()
|
||
|
|
||
|
# post-processing
|
||
|
if output_type not in ["pt", "np", "pil", "latent"]:
|
||
|
raise ValueError(
|
||
|
f"Only the output types `pt`, `pil`, `np` and `latent` are supported not output_type={output_type}"
|
||
|
)
|
||
|
|
||
|
if not output_type == "latent":
|
||
|
image = self.movq.decode(latents, force_not_quantize=True)["sample"]
|
||
|
|
||
|
if output_type in ["np", "pil"]:
|
||
|
image = image * 0.5 + 0.5
|
||
|
image = image.clamp(0, 1)
|
||
|
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
|
||
|
|
||
|
if output_type == "pil":
|
||
|
image = self.numpy_to_pil(image)
|
||
|
else:
|
||
|
image = latents
|
||
|
|
||
|
self.maybe_free_model_hooks()
|
||
|
|
||
|
if not return_dict:
|
||
|
return (image,)
|
||
|
|
||
|
return ImagePipelineOutput(images=image)
|