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from typing import Optional
import numpy as np
import torch
from diffusers import (
AutoencoderKL,
DDIMScheduler,
SchedulerMixin,
UNet2DConditionModel,
UniPCMultistepScheduler,
)
from diffusers.models.attention_processor import AttnProcessor2_0
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer
from interpolation import (
InnerInterpolatedAttnProcessor,
OuterInterpolatedAttnProcessor,
generate_beta_tensor,
linear_interpolation,
slerp,
spherical_interpolation,
)
class InterpolationStableDiffusionPipeline:
"""
Diffusion Pipeline that generates interpolated images
"""
def __init__(
self,
repo_name: str = "CompVis/stable-diffusion-v1-4",
scheduler_name: str = "ddim",
frozen: bool = True,
guidance_scale: float = 7.5,
scheduler: Optional[SchedulerMixin] = None,
cache_dir: Optional[str] = None,
):
# Initialize the generator
self.vae = AutoencoderKL.from_pretrained(
repo_name, subfolder="vae", use_safetensors=True, cache_dir=cache_dir
)
self.tokenizer = CLIPTokenizer.from_pretrained(
repo_name, subfolder="tokenizer", cache_dir=cache_dir
)
self.text_encoder = CLIPTextModel.from_pretrained(
repo_name,
subfolder="text_encoder",
use_safetensors=True,
cache_dir=cache_dir,
)
self.unet = UNet2DConditionModel.from_pretrained(
repo_name, subfolder="unet", use_safetensors=True, cache_dir=cache_dir
)
# Initialize the scheduler
if scheduler is not None:
self.scheduler = scheduler
elif scheduler_name == "ddim":
self.scheduler = DDIMScheduler.from_pretrained(
repo_name, subfolder="scheduler", cache_dir=cache_dir
)
elif scheduler_name == "unipc":
self.scheduler = UniPCMultistepScheduler.from_pretrained(
repo_name, subfolder="scheduler", cache_dir=cache_dir
)
else:
raise ValueError(
"Invalid scheduler name (ddim, unipc) and not specify scheduler."
)
# Setup device
self.guidance_scale = guidance_scale # Scale for classifier-free guidance
if frozen:
for param in self.unet.parameters():
param.requires_grad = False
for param in self.text_encoder.parameters():
param.requires_grad = False
for param in self.vae.parameters():
param.requires_grad = False
def to(self, *args, **kwargs):
self.vae.to(*args, **kwargs)
self.text_encoder.to(*args, **kwargs)
self.unet.to(*args, **kwargs)
def generate_latent(
self, generator: Optional[torch.Generator] = None, torch_device: str = "cpu"
) -> torch.FloatTensor:
"""
Generates a random latent tensor.
Args:
generator (Optional[torch.Generator], optional): Generator for random number generation. Defaults to None.
torch_device (str, optional): Device to store the tensor. Defaults to "cpu".
Returns:
torch.FloatTensor: Random latent tensor.
"""
channel = self.unet.config.in_channels
height = self.unet.config.sample_size
width = self.unet.config.sample_size
if generator is None:
latent = torch.randn(
(1, channel, height, width),
device=torch_device,
)
else:
latent = torch.randn(
(1, channel, height, width),
generator=generator,
device=torch_device,
)
return latent
@torch.no_grad()
def prompt_to_embedding(
self, prompt: str, negative_prompt: str = ""
) -> torch.FloatTensor:
"""
Prepare the text prompt for the diffusion process
Args:
prompt: str, text prompt
negative_prompt: str, negative text prompt
Returns:
FloatTensor, text embeddings
"""
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = self.text_encoder(text_input.input_ids.to(self.torch_device))[
0
]
uncond_input = self.tokenizer(
negative_prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
uncond_embeddings = self.text_encoder(
uncond_input.input_ids.to(self.torch_device)
)[0]
text_embeddings = torch.cat([text_embeddings, uncond_embeddings])
return text_embeddings
@torch.no_grad()
def interpolate(
self,
latent_start: torch.FloatTensor,
latent_end: torch.FloatTensor,
prompt_start: str,
prompt_end: str,
guide_prompt: Optional[str] = None,
negative_prompt: str = "",
size: int = 7,
num_inference_steps: int = 25,
warmup_ratio: float = 0.5,
early: str = "fused_outer",
late: str = "self",
alpha: Optional[float] = None,
beta: Optional[float] = None,
guidance_scale: Optional[float] = None,
) -> np.ndarray:
"""
Interpolate between two generation
Args:
latent_start: FloatTensor, latent vector of the first image
latent_end: FloatTensor, latent vector of the second image
prompt_start: str, text prompt of the first image
prompt_end: str, text prompt of the second image
guide_prompt: str, text prompt for the interpolation
negative_prompt: str, negative text prompt
size: int, number of interpolations including starting and ending points
num_inference_steps: int, number of inference steps in scheduler
warmup_ratio: float, ratio of warmup steps
early: str, warmup interpolation methods
late: str, late interpolation methods
alpha: float, alpha parameter for beta distribution
beta: float, beta parameter for beta distribution
guidance_scale: Optional[float], scale for classifier-free guidance
Returns:
Numpy array of interpolated images, shape (size, H, W, 3)
"""
# Specify alpha and beta
self.torch_device = self.unet.device
if alpha is None:
alpha = num_inference_steps
if beta is None:
beta = num_inference_steps
if guidance_scale is None:
guidance_scale = self.guidance_scale
self.scheduler.set_timesteps(num_inference_steps)
# Prepare interpolated latents and embeddings
latents = spherical_interpolation(latent_start, latent_end, size)
embs_start = self.prompt_to_embedding(prompt_start, negative_prompt)
emb_start = embs_start[0:1]
uncond_emb_start = embs_start[1:2]
embs_end = self.prompt_to_embedding(prompt_end, negative_prompt)
emb_end = embs_end[0:1]
uncond_emb_end = embs_end[1:2]
# Perform prompt guidance if it is specified
if guide_prompt is not None:
guide_embs = self.prompt_to_embedding(guide_prompt, negative_prompt)
guide_emb = guide_embs[0:1]
uncond_guide_emb = guide_embs[1:2]
embs = torch.cat([emb_start] + [guide_emb] * (size - 2) + [emb_end], dim=0)
uncond_embs = torch.cat(
[uncond_emb_start] + [uncond_guide_emb] * (size - 2) + [uncond_emb_end],
dim=0,
)
else:
embs = linear_interpolation(emb_start, emb_end, size=size)
uncond_embs = linear_interpolation(
uncond_emb_start, uncond_emb_end, size=size
)
# Specify the interpolation methods
pure_inner_attn_proc = InnerInterpolatedAttnProcessor(
size=size,
is_fused=False,
alpha=alpha,
beta=beta,
)
fused_inner_attn_proc = InnerInterpolatedAttnProcessor(
size=size,
is_fused=True,
alpha=alpha,
beta=beta,
)
pure_outer_attn_proc = OuterInterpolatedAttnProcessor(
size=size,
is_fused=False,
alpha=alpha,
beta=beta,
)
fused_outer_attn_proc = OuterInterpolatedAttnProcessor(
size=size,
is_fused=True,
alpha=alpha,
beta=beta,
)
self_attn_proc = AttnProcessor2_0()
procs_dict = {
"pure_inner": pure_inner_attn_proc,
"fused_inner": fused_inner_attn_proc,
"pure_outer": pure_outer_attn_proc,
"fused_outer": fused_outer_attn_proc,
"self": self_attn_proc,
}
# Denoising process
i = 0
warmup_step = int(num_inference_steps * warmup_ratio)
for t in tqdm(self.scheduler.timesteps):
i += 1
latent_model_input = self.scheduler.scale_model_input(latents, timestep=t)
with torch.no_grad():
# Change attention module
if i < warmup_step:
interpolate_attn_proc = procs_dict[early]
else:
interpolate_attn_proc = procs_dict[late]
self.unet.set_attn_processor(processor=interpolate_attn_proc)
# Predict the noise residual
noise_pred = self.unet(
latent_model_input, t, encoder_hidden_states=embs
).sample
attn_proc = AttnProcessor2_0()
self.unet.set_attn_processor(processor=attn_proc)
noise_uncond = self.unet(
latent_model_input, t, encoder_hidden_states=uncond_embs
).sample
# perform guidance
noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents).prev_sample
# Decode the images
latents = 1 / 0.18215 * latents
with torch.no_grad():
image = self.vae.decode(latents).sample
images = (image / 2 + 0.5).clamp(0, 1)
images = (images.permute(0, 2, 3, 1) * 255).to(torch.uint8).cpu().numpy()
return images
@torch.no_grad()
def interpolate_save_gpu(
self,
latent_start: torch.FloatTensor,
latent_end: torch.FloatTensor,
prompt_start: str,
prompt_end: str,
guide_prompt: Optional[str] = None,
negative_prompt: str = "",
size: int = 7,
num_inference_steps: int = 25,
warmup_ratio: float = 0.5,
early: str = "fused_outer",
late: str = "self",
alpha: Optional[float] = None,
beta: Optional[float] = None,
init: str = "linear",
guidance_scale: Optional[float] = None,
) -> np.ndarray:
"""
Interpolate between two generation
Args:
latent_start: FloatTensor, latent vector of the first image
latent_end: FloatTensor, latent vector of the second image
prompt_start: str, text prompt of the first image
prompt_end: str, text prompt of the second image
guide_prompt: str, text prompt for the interpolation
negative_prompt: str, negative text prompt
size: int, number of interpolations including starting and ending points
num_inference_steps: int, number of inference steps in scheduler
warmup_ratio: float, ratio of warmup steps
early: str, warmup interpolation methods
late: str, late interpolation methods
alpha: float, alpha parameter for beta distribution
beta: float, beta parameter for beta distribution
init: str, interpolation initialization methods
Returns:
Numpy array of interpolated images, shape (size, H, W, 3)
"""
self.torch_device = self.unet.device
# Specify alpha and beta
if alpha is None:
alpha = num_inference_steps
if beta is None:
beta = num_inference_steps
betas = generate_beta_tensor(size, alpha=alpha, beta=beta)
final_images = None
# Generate interpolated images one by one
for i in range(size - 2):
it = betas[i + 1].item()
if init == "denoising":
images = self.denoising_interpolate(
latent_start,
prompt_start,
prompt_end,
negative_prompt,
interpolated_ratio=it,
timesteps=num_inference_steps,
)
else:
images = self.interpolate_single(
it,
latent_start,
latent_end,
prompt_start,
prompt_end,
guide_prompt=guide_prompt,
num_inference_steps=num_inference_steps,
warmup_ratio=warmup_ratio,
early=early,
late=late,
negative_prompt=negative_prompt,
init=init,
guidance_scale=guidance_scale,
)
if size == 3:
return images
if i == 0:
final_images = images[:2]
elif i == size - 3:
final_images = np.concatenate([final_images, images[1:]], axis=0)
else:
final_images = np.concatenate([final_images, images[1:2]], axis=0)
return final_images
def interpolate_single(
self,
it,
latent_start: torch.FloatTensor,
latent_end: torch.FloatTensor,
prompt_start: str,
prompt_end: str,
guide_prompt: str = None,
negative_prompt: str = "",
num_inference_steps: int = 25,
warmup_ratio: float = 0.5,
early: str = "fused_outer",
late: str = "self",
init="linear",
guidance_scale: Optional[float] = None,
) -> np.ndarray:
"""
Interpolates between two latent vectors and generates a sequence of images.
Args:
it (float): Interpolation factor between latent_start and latent_end.
latent_start (torch.FloatTensor): Starting latent vector.
latent_end (torch.FloatTensor): Ending latent vector.
prompt_start (str): Starting prompt for text conditioning.
prompt_end (str): Ending prompt for text conditioning.
guide_prompt (str, optional): Guiding prompt for text conditioning. Defaults to None.
negative_prompt (str, optional): Negative prompt for text conditioning. Defaults to "".
num_inference_steps (int, optional): Number of inference steps. Defaults to 25.
warmup_ratio (float, optional): Ratio of warm-up steps. Defaults to 0.5.
early (str, optional): Early attention processing method. Defaults to "fused_outer".
late (str, optional): Late attention processing method. Defaults to "self".
init (str, optional): Initialization method for interpolation. Defaults to "linear".
guidance_scale (Optional[float], optional): Scale for classifier-free guidance. Defaults to None.
Returns:
numpy.ndarray: Sequence of generated images.
"""
self.torch_device = self.unet.device
if guidance_scale is None:
guidance_scale = self.guidance_scale
# Prepare interpolated inputs
self.scheduler.set_timesteps(num_inference_steps)
embs_start = self.prompt_to_embedding(prompt_start, negative_prompt)
emb_start = embs_start[0:1]
uncond_emb_start = embs_start[1:2]
embs_end = self.prompt_to_embedding(prompt_end, negative_prompt)
emb_end = embs_end[0:1]
uncond_emb_end = embs_end[1:2]
latent_t = slerp(latent_start, latent_end, it)
if guide_prompt is not None:
embs_guide = self.prompt_to_embedding(guide_prompt, negative_prompt)
emb_t = embs_guide[0:1]
else:
if init == "linear":
emb_t = torch.lerp(emb_start, emb_end, it)
else:
emb_t = slerp(emb_start, emb_end, it)
if init == "linear":
uncond_emb_t = torch.lerp(uncond_emb_start, uncond_emb_end, it)
else:
uncond_emb_t = slerp(uncond_emb_start, uncond_emb_end, it)
latents = torch.cat([latent_start, latent_t, latent_end], dim=0)
embs = torch.cat([emb_start, emb_t, emb_end], dim=0)
uncond_embs = torch.cat([uncond_emb_start, uncond_emb_t, uncond_emb_end], dim=0)
# Specifiy the attention processors
pure_inner_attn_proc = InnerInterpolatedAttnProcessor(
t=it,
is_fused=False,
)
fused_inner_attn_proc = InnerInterpolatedAttnProcessor(
t=it,
is_fused=True,
)
pure_outer_attn_proc = OuterInterpolatedAttnProcessor(
t=it,
is_fused=False,
)
fused_outer_attn_proc = OuterInterpolatedAttnProcessor(
t=it,
is_fused=True,
)
self_attn_proc = AttnProcessor2_0()
procs_dict = {
"pure_inner": pure_inner_attn_proc,
"fused_inner": fused_inner_attn_proc,
"pure_outer": pure_outer_attn_proc,
"fused_outer": fused_outer_attn_proc,
"self": self_attn_proc,
}
i = 0
warmup_step = int(num_inference_steps * warmup_ratio)
for t in tqdm(self.scheduler.timesteps):
i += 1
latent_model_input = self.scheduler.scale_model_input(latents, timestep=t)
# predict the noise residual
with torch.no_grad():
# Warmup
if i < warmup_step:
interpolate_attn_proc = procs_dict[early]
else:
interpolate_attn_proc = procs_dict[late]
self.unet.set_attn_processor(processor=interpolate_attn_proc)
# predict the noise residual
noise_pred = self.unet(
latent_model_input, t, encoder_hidden_states=embs
).sample
attn_proc = AttnProcessor2_0()
self.unet.set_attn_processor(processor=attn_proc)
noise_uncond = self.unet(
latent_model_input, t, encoder_hidden_states=uncond_embs
).sample
# perform guidance
noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents).prev_sample
# Decode the images
latents = 1 / 0.18215 * latents
with torch.no_grad():
image = self.vae.decode(latents).sample
images = (image / 2 + 0.5).clamp(0, 1)
images = (images.permute(0, 2, 3, 1) * 255).to(torch.uint8).cpu().numpy()
return images
def denoising_interpolate(
self,
latents: torch.FloatTensor,
text_1: str,
text_2: str,
negative_prompt: str = "",
interpolated_ratio: float = 1,
timesteps: int = 25,
) -> np.ndarray:
"""
Performs denoising interpolation on the given latents.
Args:
latents (torch.Tensor): The input latents.
text_1 (str): The first text prompt.
text_2 (str): The second text prompt.
negative_prompt (str, optional): The negative text prompt. Defaults to "".
interpolated_ratio (int, optional): The ratio of interpolation between text_1 and text_2. Defaults to 1.
timesteps (int, optional): The number of timesteps for diffusion. Defaults to 25.
Returns:
numpy.ndarray: The interpolated images.
"""
self.unet.set_attn_processor(processor=AttnProcessor2_0())
start_emb = self.prompt_to_embedding(text_1)
end_emb = self.prompt_to_embedding(text_2)
neg_emb = self.prompt_to_embedding(negative_prompt)
uncond_emb = neg_emb[0:1]
emb_1 = start_emb[0:1]
emb_2 = end_emb[0:1]
self.scheduler.set_timesteps(timesteps)
i = 0
for t in tqdm(self.scheduler.timesteps):
i += 1
# expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
latent_model_input = self.scheduler.scale_model_input(latents, timestep=t)
# predict the noise residual
with torch.no_grad():
if i < timesteps * interpolated_ratio:
noise_pred = self.unet(
latent_model_input, t, encoder_hidden_states=emb_1
).sample
else:
noise_pred = self.unet(
latent_model_input, t, encoder_hidden_states=emb_2
).sample
noise_uncond = self.unet(
latent_model_input, t, encoder_hidden_states=uncond_emb
).sample
# perform guidance
noise_pred = noise_uncond + self.guidance_scale * (
noise_pred - noise_uncond
)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents).prev_sample
latents = 1 / 0.18215 * latents
with torch.no_grad():
image = self.vae.decode(latents).sample
images = (image / 2 + 0.5).clamp(0, 1)
images = (images.permute(0, 2, 3, 1) * 255).to(torch.uint8).cpu().numpy()
return images
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