mirror of
https://github.com/huggingface/diffusers.git
synced 2026-01-27 17:22:53 +03:00
fa633ed6de
* move model helper function in pipeline to EfficiencyMixin --------- Co-authored-by: YiYi Xu <yixu310@gmail.com> Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
812 lines
36 KiB
Python
812 lines
36 KiB
Python
import inspect
|
|
from typing import Any, Dict, List, Optional, Union
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
from transformers import AutoModel, AutoTokenizer, CLIPImageProcessor
|
|
|
|
from diffusers import DiffusionPipeline
|
|
from diffusers.image_processor import VaeImageProcessor
|
|
from diffusers.loaders import LoraLoaderMixin
|
|
from diffusers.models import AutoencoderKL, UNet2DConditionModel
|
|
from diffusers.models.lora import adjust_lora_scale_text_encoder
|
|
from diffusers.pipelines.pipeline_utils import StableDiffusionMixin
|
|
from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
|
|
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
|
|
from diffusers.schedulers import KarrasDiffusionSchedulers
|
|
from diffusers.utils import (
|
|
USE_PEFT_BACKEND,
|
|
logging,
|
|
scale_lora_layers,
|
|
unscale_lora_layers,
|
|
)
|
|
from diffusers.utils.torch_utils import randn_tensor
|
|
|
|
|
|
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
|
|
|
|
|
class TranslatorBase(nn.Module):
|
|
def __init__(self, num_tok, dim, dim_out, mult=2):
|
|
super().__init__()
|
|
|
|
self.dim_in = dim
|
|
self.dim_out = dim_out
|
|
|
|
self.net_tok = nn.Sequential(
|
|
nn.Linear(num_tok, int(num_tok * mult)),
|
|
nn.LayerNorm(int(num_tok * mult)),
|
|
nn.GELU(),
|
|
nn.Linear(int(num_tok * mult), int(num_tok * mult)),
|
|
nn.LayerNorm(int(num_tok * mult)),
|
|
nn.GELU(),
|
|
nn.Linear(int(num_tok * mult), num_tok),
|
|
nn.LayerNorm(num_tok),
|
|
)
|
|
|
|
self.net_sen = nn.Sequential(
|
|
nn.Linear(dim, int(dim * mult)),
|
|
nn.LayerNorm(int(dim * mult)),
|
|
nn.GELU(),
|
|
nn.Linear(int(dim * mult), int(dim * mult)),
|
|
nn.LayerNorm(int(dim * mult)),
|
|
nn.GELU(),
|
|
nn.Linear(int(dim * mult), dim_out),
|
|
nn.LayerNorm(dim_out),
|
|
)
|
|
|
|
def forward(self, x):
|
|
if self.dim_in == self.dim_out:
|
|
indentity_0 = x
|
|
x = self.net_sen(x)
|
|
x += indentity_0
|
|
x = x.transpose(1, 2)
|
|
|
|
indentity_1 = x
|
|
x = self.net_tok(x)
|
|
x += indentity_1
|
|
x = x.transpose(1, 2)
|
|
else:
|
|
x = self.net_sen(x)
|
|
x = x.transpose(1, 2)
|
|
|
|
x = self.net_tok(x)
|
|
x = x.transpose(1, 2)
|
|
return x
|
|
|
|
|
|
class TranslatorBaseNoLN(nn.Module):
|
|
def __init__(self, num_tok, dim, dim_out, mult=2):
|
|
super().__init__()
|
|
|
|
self.dim_in = dim
|
|
self.dim_out = dim_out
|
|
|
|
self.net_tok = nn.Sequential(
|
|
nn.Linear(num_tok, int(num_tok * mult)),
|
|
nn.GELU(),
|
|
nn.Linear(int(num_tok * mult), int(num_tok * mult)),
|
|
nn.GELU(),
|
|
nn.Linear(int(num_tok * mult), num_tok),
|
|
)
|
|
|
|
self.net_sen = nn.Sequential(
|
|
nn.Linear(dim, int(dim * mult)),
|
|
nn.GELU(),
|
|
nn.Linear(int(dim * mult), int(dim * mult)),
|
|
nn.GELU(),
|
|
nn.Linear(int(dim * mult), dim_out),
|
|
)
|
|
|
|
def forward(self, x):
|
|
if self.dim_in == self.dim_out:
|
|
indentity_0 = x
|
|
x = self.net_sen(x)
|
|
x += indentity_0
|
|
x = x.transpose(1, 2)
|
|
|
|
indentity_1 = x
|
|
x = self.net_tok(x)
|
|
x += indentity_1
|
|
x = x.transpose(1, 2)
|
|
else:
|
|
x = self.net_sen(x)
|
|
x = x.transpose(1, 2)
|
|
|
|
x = self.net_tok(x)
|
|
x = x.transpose(1, 2)
|
|
return x
|
|
|
|
|
|
class TranslatorNoLN(nn.Module):
|
|
def __init__(self, num_tok, dim, dim_out, mult=2, depth=5):
|
|
super().__init__()
|
|
|
|
self.blocks = nn.ModuleList([TranslatorBase(num_tok, dim, dim, mult=2) for d in range(depth)])
|
|
self.gelu = nn.GELU()
|
|
|
|
self.tail = TranslatorBaseNoLN(num_tok, dim, dim_out, mult=2)
|
|
|
|
def forward(self, x):
|
|
for block in self.blocks:
|
|
x = block(x) + x
|
|
x = self.gelu(x)
|
|
|
|
x = self.tail(x)
|
|
return x
|
|
|
|
|
|
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
|
|
"""
|
|
Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
|
|
Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
|
|
"""
|
|
std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
|
|
std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
|
|
# rescale the results from guidance (fixes overexposure)
|
|
noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
|
|
# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
|
|
noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
|
|
return noise_cfg
|
|
|
|
|
|
def retrieve_timesteps(
|
|
scheduler,
|
|
num_inference_steps: Optional[int] = None,
|
|
device: Optional[Union[str, torch.device]] = None,
|
|
timesteps: Optional[List[int]] = 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 support arbitrary spacing between timesteps. If `None`, then the default
|
|
timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
|
|
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:
|
|
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)
|
|
else:
|
|
scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
|
|
timesteps = scheduler.timesteps
|
|
return timesteps, num_inference_steps
|
|
|
|
|
|
class GlueGenStableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin, LoraLoaderMixin):
|
|
def __init__(
|
|
self,
|
|
vae: AutoencoderKL,
|
|
text_encoder: AutoModel,
|
|
tokenizer: AutoTokenizer,
|
|
unet: UNet2DConditionModel,
|
|
scheduler: KarrasDiffusionSchedulers,
|
|
safety_checker: StableDiffusionSafetyChecker,
|
|
feature_extractor: CLIPImageProcessor,
|
|
language_adapter: TranslatorNoLN = None,
|
|
tensor_norm: torch.FloatTensor = None,
|
|
requires_safety_checker: bool = True,
|
|
):
|
|
super().__init__()
|
|
|
|
self.register_modules(
|
|
vae=vae,
|
|
text_encoder=text_encoder,
|
|
tokenizer=tokenizer,
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
safety_checker=safety_checker,
|
|
feature_extractor=feature_extractor,
|
|
language_adapter=language_adapter,
|
|
tensor_norm=tensor_norm,
|
|
)
|
|
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
|
|
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
|
|
self.register_to_config(requires_safety_checker=requires_safety_checker)
|
|
|
|
def load_language_adapter(
|
|
self,
|
|
model_path: str,
|
|
num_token: int,
|
|
dim: int,
|
|
dim_out: int,
|
|
tensor_norm: torch.FloatTensor,
|
|
mult: int = 2,
|
|
depth: int = 5,
|
|
):
|
|
device = self._execution_device
|
|
self.tensor_norm = tensor_norm.to(device)
|
|
self.language_adapter = TranslatorNoLN(num_tok=num_token, dim=dim, dim_out=dim_out, mult=mult, depth=depth).to(
|
|
device
|
|
)
|
|
self.language_adapter.load_state_dict(torch.load(model_path))
|
|
|
|
def _adapt_language(self, prompt_embeds: torch.FloatTensor):
|
|
prompt_embeds = prompt_embeds / 3
|
|
prompt_embeds = self.language_adapter(prompt_embeds) * (self.tensor_norm / 2)
|
|
return prompt_embeds
|
|
|
|
def encode_prompt(
|
|
self,
|
|
prompt,
|
|
device,
|
|
num_images_per_prompt,
|
|
do_classifier_free_guidance,
|
|
negative_prompt=None,
|
|
prompt_embeds: Optional[torch.FloatTensor] = None,
|
|
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
|
lora_scale: Optional[float] = None,
|
|
clip_skip: Optional[int] = None,
|
|
):
|
|
r"""
|
|
Encodes the prompt into text encoder hidden states.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
prompt to be encoded
|
|
device: (`torch.device`):
|
|
torch device
|
|
num_images_per_prompt (`int`):
|
|
number of images that should be generated per prompt
|
|
do_classifier_free_guidance (`bool`):
|
|
whether to use classifier free guidance or not
|
|
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`).
|
|
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.
|
|
lora_scale (`float`, *optional*):
|
|
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
|
|
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.
|
|
"""
|
|
# set lora scale so that monkey patched LoRA
|
|
# function of text encoder can correctly access it
|
|
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
|
|
self._lora_scale = lora_scale
|
|
|
|
# dynamically adjust the LoRA scale
|
|
if not USE_PEFT_BACKEND:
|
|
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
|
|
else:
|
|
scale_lora_layers(self.text_encoder, lora_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]
|
|
|
|
if prompt_embeds is None:
|
|
text_inputs = self.tokenizer(
|
|
prompt,
|
|
padding="max_length",
|
|
max_length=self.tokenizer.model_max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
text_input_ids = text_inputs.input_ids
|
|
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
|
|
|
|
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
|
|
text_input_ids, untruncated_ids
|
|
):
|
|
removed_text = self.tokenizer.batch_decode(
|
|
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
|
|
)
|
|
logger.warning(
|
|
"The following part of your input was truncated because CLIP can only handle sequences up to"
|
|
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
|
|
)
|
|
|
|
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
|
|
attention_mask = text_inputs.attention_mask.to(device)
|
|
elif self.language_adapter is not None:
|
|
attention_mask = text_inputs.attention_mask.to(device)
|
|
else:
|
|
attention_mask = None
|
|
|
|
if clip_skip is None:
|
|
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
|
|
prompt_embeds = prompt_embeds[0]
|
|
|
|
else:
|
|
prompt_embeds = self.text_encoder(
|
|
text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
|
|
)
|
|
# Access the `hidden_states` first, that contains a tuple of
|
|
# all the hidden states from the encoder layers. Then index into
|
|
# the tuple to access the hidden states from the desired layer.
|
|
prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
|
|
# We also need to apply the final LayerNorm here to not mess with the
|
|
# representations. The `last_hidden_states` that we typically use for
|
|
# obtaining the final prompt representations passes through the LayerNorm
|
|
# layer.
|
|
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
|
|
|
|
# Run prompt language adapter
|
|
if self.language_adapter is not None:
|
|
prompt_embeds = self._adapt_language(prompt_embeds)
|
|
|
|
if self.text_encoder is not None:
|
|
prompt_embeds_dtype = self.text_encoder.dtype
|
|
elif self.unet is not None:
|
|
prompt_embeds_dtype = self.unet.dtype
|
|
else:
|
|
prompt_embeds_dtype = prompt_embeds.dtype
|
|
|
|
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
|
|
|
|
bs_embed, seq_len, _ = prompt_embeds.shape
|
|
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
|
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
|
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
|
|
|
|
# get unconditional embeddings for classifier free guidance
|
|
if do_classifier_free_guidance and negative_prompt_embeds is None:
|
|
uncond_tokens: List[str]
|
|
if negative_prompt is None:
|
|
uncond_tokens = [""] * batch_size
|
|
elif 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 isinstance(negative_prompt, str):
|
|
uncond_tokens = [negative_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`."
|
|
)
|
|
else:
|
|
uncond_tokens = negative_prompt
|
|
|
|
max_length = prompt_embeds.shape[1]
|
|
uncond_input = self.tokenizer(
|
|
uncond_tokens,
|
|
padding="max_length",
|
|
max_length=max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
|
|
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
|
|
attention_mask = uncond_input.attention_mask.to(device)
|
|
else:
|
|
attention_mask = None
|
|
|
|
negative_prompt_embeds = self.text_encoder(
|
|
uncond_input.input_ids.to(device),
|
|
attention_mask=attention_mask,
|
|
)
|
|
negative_prompt_embeds = negative_prompt_embeds[0]
|
|
# Run negative prompt language adapter
|
|
if self.language_adapter is not None:
|
|
negative_prompt_embeds = self._adapt_language(negative_prompt_embeds)
|
|
|
|
if do_classifier_free_guidance:
|
|
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
|
seq_len = negative_prompt_embeds.shape[1]
|
|
|
|
negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
|
|
|
|
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
|
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
|
|
|
|
if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
|
|
# Retrieve the original scale by scaling back the LoRA layers
|
|
unscale_lora_layers(self.text_encoder, lora_scale)
|
|
|
|
return prompt_embeds, negative_prompt_embeds
|
|
|
|
def run_safety_checker(self, image, device, dtype):
|
|
if self.safety_checker is None:
|
|
has_nsfw_concept = None
|
|
else:
|
|
if torch.is_tensor(image):
|
|
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
|
|
else:
|
|
feature_extractor_input = self.image_processor.numpy_to_pil(image)
|
|
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
|
|
image, has_nsfw_concept = self.safety_checker(
|
|
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
|
|
)
|
|
return image, has_nsfw_concept
|
|
|
|
def prepare_extra_step_kwargs(self, generator, eta):
|
|
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
|
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
|
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
|
|
# and should be between [0, 1]
|
|
|
|
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
|
extra_step_kwargs = {}
|
|
if accepts_eta:
|
|
extra_step_kwargs["eta"] = eta
|
|
|
|
# check if the scheduler accepts generator
|
|
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
|
if accepts_generator:
|
|
extra_step_kwargs["generator"] = generator
|
|
return extra_step_kwargs
|
|
|
|
def check_inputs(
|
|
self,
|
|
prompt,
|
|
height,
|
|
width,
|
|
negative_prompt=None,
|
|
prompt_embeds=None,
|
|
negative_prompt_embeds=None,
|
|
):
|
|
if height % 8 != 0 or width % 8 != 0:
|
|
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
|
|
|
|
if prompt is not None and prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
|
|
" only forward one of the two."
|
|
)
|
|
elif prompt is None and prompt_embeds is None:
|
|
raise ValueError(
|
|
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
|
|
)
|
|
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
|
|
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
|
|
|
if negative_prompt is not None and negative_prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
|
|
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
|
)
|
|
|
|
if prompt_embeds is not None and negative_prompt_embeds is not None:
|
|
if prompt_embeds.shape != negative_prompt_embeds.shape:
|
|
raise ValueError(
|
|
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
|
|
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
|
|
f" {negative_prompt_embeds.shape}."
|
|
)
|
|
|
|
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
|
|
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, 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."
|
|
)
|
|
|
|
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
|
|
|
|
# Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
|
|
def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
|
|
"""
|
|
See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
|
|
|
|
Args:
|
|
timesteps (`torch.Tensor`):
|
|
generate embedding vectors at these timesteps
|
|
embedding_dim (`int`, *optional*, defaults to 512):
|
|
dimension of the embeddings to generate
|
|
dtype:
|
|
data type of the generated embeddings
|
|
|
|
Returns:
|
|
`torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
|
|
"""
|
|
assert len(w.shape) == 1
|
|
w = w * 1000.0
|
|
|
|
half_dim = embedding_dim // 2
|
|
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
|
|
emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
|
|
emb = w.to(dtype)[:, None] * emb[None, :]
|
|
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
|
|
if embedding_dim % 2 == 1: # zero pad
|
|
emb = torch.nn.functional.pad(emb, (0, 1))
|
|
assert emb.shape == (w.shape[0], embedding_dim)
|
|
return emb
|
|
|
|
@property
|
|
def guidance_scale(self):
|
|
return self._guidance_scale
|
|
|
|
@property
|
|
def guidance_rescale(self):
|
|
return self._guidance_rescale
|
|
|
|
@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 and self.unet.config.time_cond_proj_dim is None
|
|
|
|
@property
|
|
def cross_attention_kwargs(self):
|
|
return self._cross_attention_kwargs
|
|
|
|
@property
|
|
def num_timesteps(self):
|
|
return self._num_timesteps
|
|
|
|
@property
|
|
def interrupt(self):
|
|
return self._interrupt
|
|
|
|
@torch.no_grad()
|
|
def __call__(
|
|
self,
|
|
prompt: Union[str, List[str]] = None,
|
|
height: Optional[int] = None,
|
|
width: Optional[int] = None,
|
|
num_inference_steps: int = 50,
|
|
timesteps: List[int] = None,
|
|
guidance_scale: float = 7.5,
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
|
num_images_per_prompt: Optional[int] = 1,
|
|
eta: float = 0.0,
|
|
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
|
latents: Optional[torch.FloatTensor] = None,
|
|
prompt_embeds: Optional[torch.FloatTensor] = None,
|
|
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
|
output_type: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
guidance_rescale: float = 0.0,
|
|
clip_skip: Optional[int] = None,
|
|
**kwargs,
|
|
):
|
|
r"""
|
|
The call function to the pipeline for generation.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
|
|
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
|
The height in pixels of the generated image.
|
|
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
|
The width in pixels of the generated image.
|
|
num_inference_steps (`int`, *optional*, defaults to 50):
|
|
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
|
expense of slower inference.
|
|
timesteps (`List[int]`, *optional*):
|
|
Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
|
|
in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
|
|
passed will be used. Must be in descending order.
|
|
guidance_scale (`float`, *optional*, defaults to 7.5):
|
|
A higher guidance scale value encourages the model to generate images closely linked to the text
|
|
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
|
|
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
|
|
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of images to generate per prompt.
|
|
eta (`float`, *optional*, defaults to 0.0):
|
|
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
|
|
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
|
|
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
|
A [`torch.Generator`](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 is 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 (prompt weighting). If not
|
|
provided, text embeddings are generated from the `prompt` input argument.
|
|
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
|
|
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
|
|
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
|
plain tuple.
|
|
cross_attention_kwargs (`dict`, *optional*):
|
|
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
|
|
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
|
guidance_rescale (`float`, *optional*, defaults to 0.0):
|
|
Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are
|
|
Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when
|
|
using zero terminal SNR.
|
|
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.
|
|
|
|
Examples:
|
|
|
|
Returns:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
|
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
|
|
otherwise a `tuple` is returned where the first element is a list with the generated images and the
|
|
second element is a list of `bool`s indicating whether the corresponding generated image contains
|
|
"not-safe-for-work" (nsfw) content.
|
|
"""
|
|
|
|
# 0. Default height and width to unet
|
|
height = height or self.unet.config.sample_size * self.vae_scale_factor
|
|
width = width or self.unet.config.sample_size * self.vae_scale_factor
|
|
# to deal with lora scaling and other possible forward hooks
|
|
|
|
# 1. Check inputs. Raise error if not correct
|
|
self.check_inputs(
|
|
prompt,
|
|
height,
|
|
width,
|
|
negative_prompt,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
)
|
|
|
|
self._guidance_scale = guidance_scale
|
|
self._guidance_rescale = guidance_rescale
|
|
self._clip_skip = clip_skip
|
|
self._cross_attention_kwargs = cross_attention_kwargs
|
|
self._interrupt = False
|
|
|
|
# 2. Define call parameters
|
|
if prompt is not None and isinstance(prompt, str):
|
|
batch_size = 1
|
|
elif prompt is not None and isinstance(prompt, list):
|
|
batch_size = len(prompt)
|
|
else:
|
|
batch_size = prompt_embeds.shape[0]
|
|
|
|
device = self._execution_device
|
|
|
|
# 3. Encode input prompt
|
|
lora_scale = (
|
|
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
|
|
)
|
|
|
|
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
|
|
prompt,
|
|
device,
|
|
num_images_per_prompt,
|
|
self.do_classifier_free_guidance,
|
|
negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
lora_scale=lora_scale,
|
|
clip_skip=self.clip_skip,
|
|
)
|
|
|
|
# For classifier free guidance, we need to do two forward passes.
|
|
# Here we concatenate the unconditional and text embeddings into a single batch
|
|
# to avoid doing two forward passes
|
|
if self.do_classifier_free_guidance:
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
|
|
|
# 4. Prepare timesteps
|
|
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
|
|
|
|
# 5. Prepare latent variables
|
|
num_channels_latents = self.unet.config.in_channels
|
|
latents = self.prepare_latents(
|
|
batch_size * num_images_per_prompt,
|
|
num_channels_latents,
|
|
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)
|
|
|
|
# 6.2 Optionally get Guidance Scale Embedding
|
|
timestep_cond = None
|
|
if self.unet.config.time_cond_proj_dim is not None:
|
|
guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
|
|
timestep_cond = self.get_guidance_scale_embedding(
|
|
guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
|
|
).to(device=device, dtype=latents.dtype)
|
|
|
|
# 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):
|
|
if self.interrupt:
|
|
continue
|
|
|
|
# expand the latents if we are doing classifier free guidance
|
|
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
|
|
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
|
|
|
# predict the noise residual
|
|
noise_pred = self.unet(
|
|
latent_model_input,
|
|
t,
|
|
encoder_hidden_states=prompt_embeds,
|
|
timestep_cond=timestep_cond,
|
|
cross_attention_kwargs=self.cross_attention_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
# 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)
|
|
|
|
if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
|
|
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
|
|
noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
|
|
|
|
# compute the previous noisy sample 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 not output_type == "latent":
|
|
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
|
|
0
|
|
]
|
|
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
|
|
else:
|
|
image = latents
|
|
has_nsfw_concept = None
|
|
|
|
if has_nsfw_concept is None:
|
|
do_denormalize = [True] * image.shape[0]
|
|
else:
|
|
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
|
|
|
|
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
|
|
|
|
# Offload all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return (image, has_nsfw_concept)
|
|
|
|
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|