import torch
from diffusers import DiffusionPipeline, AutoencoderKL, UNet2DConditionModel
from transformers import CLIPTokenizer, CLIPTextModel, CLIPImageProcessor
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.image_processor import VaeImageProcessor
# import modules.shared
from typing import List, Optional, Union, Dict, Any
from diffusers import logging
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class LatentConsistencyModelPipeline(DiffusionPipeline):
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: None,
safety_checker: None,
feature_extractor: CLIPImageProcessor
):
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,
)
self.vae_scale_factor = 2 ** (
len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(
vae_scale_factor=self.vae_scale_factor)
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
prompt_embeds: 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
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.
"""
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)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
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)
# Don't need to get uncond prompt embedding because of LCM Guided Distillation
return prompt_embeds
# ¯\_(ツ)_/¯
def run_safety_checker(self, image, device, dtype):
return image, None
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, latents=None):
shape = (batch_size, num_channels_latents, height //
self.vae_scale_factor, width // self.vae_scale_factor)
if latents is None:
latents = torch.randn(shape, dtype=dtype).to(device)
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
def get_w_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: dimension of the embeddings to generate
dtype: data type of the generated embeddings
Returns:
embedding vectors with shape `(len(timesteps), embedding_dim)`
"""
assert len(w.shape) == 1
w = w * 1000.
half_dim = embedding_dim // 2
emb = torch.log(torch.tensor(10000.)) / (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
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = 768,
width: Optional[int] = 768,
guidance_scale: float = 7.5,
num_images_per_prompt: Optional[int] = 1,
latents: Optional[torch.FloatTensor] = None,
num_inference_steps: int = 4,
original_inference_steps: int = 50,
prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
device: Optional[Union[str, torch.device]] = None,
):
# 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
# 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]
# do_classifier_free_guidance = guidance_scale > 0.0 # In LCM Implementation: cfg_noise = noise_cond + cfg_scale * (noise_cond - noise_uncond) , (cfg_scale > 0.0 using CFG)
# 3. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
prompt_embeds=prompt_embeds,
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, original_inference_steps)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variable
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,
latents,
)
bs = batch_size * num_images_per_prompt
# 6. Get Guidance Scale Embedding
w = torch.tensor(guidance_scale).repeat(bs)
w_embedding = self.get_w_embedding(w, embedding_dim=256).to(
device=device, dtype=latents.dtype)
# 7. LCM MultiStep Sampling Loop:
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
ts = torch.full((bs,), t, device=device, dtype=torch.long)
latents = latents.to(prompt_embeds.dtype)
# model prediction (v-prediction, eps, x)
model_pred = self.unet(
latents,
ts,
timestep_cond=w_embedding,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False)[0]
# compute the previous noisy sample x_t -> x_t-1
latents, denoised = self.scheduler.step(
model_pred, i, t, latents, return_dict=False)
# # call the callback, if provided
# if i == len(timesteps) - 1:
progress_bar.update()
denoised = denoised.to(prompt_embeds.dtype)
if not output_type == "latent":
image = self.vae.decode(
denoised / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(
image, device, prompt_embeds.dtype)
else:
image = denoised
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)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)