LEDTISをgoogle colabで試してみた。
LEDTISとは
LEDTISは、PromptベースでImg2Imgを可能にしたstable diffusionモデルです。
リンク
準備
Google Colabを開き、メニューから「ランタイム→ランタイムのタイプを変更」でランタイムを「GPU」に変更します。
環境構築
インストール手順です。
!pip install -q transformers diffusers accelerate
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")推論
(1)Utils関数の定義
import torch
# import os
from tqdm import tqdm
from PIL import Image, ImageDraw ,ImageFont
# from matplotlib import pyplot as plt
import torchvision.transforms as T
# import os
# import yaml
# import numpy as np
def load_512(image_path, left=0, right=0, top=0, bottom=0, device=None):
if type(image_path) is str:
image = np.array(Image.open(image_path).convert('RGB'))[:, :, :3]
else:
image = image_path
h, w, c = image.shape
left = min(left, w-1)
right = min(right, w - left - 1)
top = min(top, h - left - 1)
bottom = min(bottom, h - top - 1)
image = image[top:h-bottom, left:w-right]
h, w, c = image.shape
if h < w:
offset = (w - h) // 2
image = image[:, offset:offset + h]
elif w < h:
offset = (h - w) // 2
image = image[offset:offset + w]
image = np.array(Image.fromarray(image).resize((512, 512)))
image = torch.from_numpy(image).float() / 127.5 - 1
image = image.permute(2, 0, 1).unsqueeze(0).to(device)
return image
def load_real_image(folder = "data/", img_name = None, idx = 0, img_size=512, device='cuda'):
from PIL import Image
from glob import glob
if img_name is not None:
path = os.path.join(folder, img_name)
else:
path = glob(folder + "*")[idx]
img = Image.open(path).resize((img_size,
img_size))
img = pil_to_tensor(img).to(device)
if img.shape[1]== 4:
img = img[:,:3,:,:]
return img
def mu_tilde(model, xt,x0, timestep):
"mu_tilde(x_t, x_0) DDPM paper eq. 7"
prev_timestep = timestep - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps
alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod
alpha_t = model.scheduler.alphas[timestep]
beta_t = 1 - alpha_t
alpha_bar = model.scheduler.alphas_cumprod[timestep]
return ((alpha_prod_t_prev ** 0.5 * beta_t) / (1-alpha_bar)) * x0 + ((alpha_t**0.5 *(1-alpha_prod_t_prev)) / (1- alpha_bar))*xt
def sample_xts_from_x0(model, x0, num_inference_steps=50):
"""
Samples from P(x_1:T|x_0)
"""
# torch.manual_seed(43256465436)
alpha_bar = model.scheduler.alphas_cumprod
sqrt_one_minus_alpha_bar = (1-alpha_bar) ** 0.5
alphas = model.scheduler.alphas
betas = 1 - alphas
variance_noise_shape = (
num_inference_steps,
model.unet.in_channels,
model.unet.sample_size,
model.unet.sample_size)
timesteps = model.scheduler.timesteps.to(model.device)
t_to_idx = {int(v):k for k,v in enumerate(timesteps)}
xts = torch.zeros(variance_noise_shape).to(x0.device)
for t in reversed(timesteps):
idx = t_to_idx[int(t)]
xts[idx] = x0 * (alpha_bar[t] ** 0.5) + torch.randn_like(x0) * sqrt_one_minus_alpha_bar[t]
xts = torch.cat([xts, x0 ],dim = 0)
return xts
def encode_text(model, prompts):
text_input = model.tokenizer(
prompts,
padding="max_length",
max_length=model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
with torch.no_grad():
text_encoding = model.text_encoder(text_input.input_ids.to(model.device))[0]
return text_encoding
def forward_step(model, model_output, timestep, sample):
next_timestep = min(model.scheduler.config.num_train_timesteps - 2,
timestep + model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps)
# 2. compute alphas, betas
alpha_prod_t = model.scheduler.alphas_cumprod[timestep]
# alpha_prod_t_next = self.scheduler.alphas_cumprod[next_timestep] if next_ltimestep >= 0 else self.scheduler.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
# 5. TODO: simple noising implementatiom
next_sample = model.scheduler.add_noise(pred_original_sample,
model_output,
torch.LongTensor([next_timestep]))
return next_sample
def get_variance(model, timestep): #, prev_timestep):
prev_timestep = timestep - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps
alpha_prod_t = model.scheduler.alphas_cumprod[timestep]
alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev
variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
return variance
def inversion_forward_process(model, x0,
etas = None,
prog_bar = False,
prompt = "",
cfg_scale = 3.5,
num_inference_steps=50, eps = None):
if not prompt=="":
text_embeddings = encode_text(model, prompt)
uncond_embedding = encode_text(model, "")
timesteps = model.scheduler.timesteps.to(model.device)
variance_noise_shape = (
num_inference_steps,
model.unet.in_channels,
model.unet.sample_size,
model.unet.sample_size)
if etas is None or (type(etas) in [int, float] and etas == 0):
eta_is_zero = True
zs = None
else:
eta_is_zero = False
if type(etas) in [int, float]: etas = [etas]*model.scheduler.num_inference_steps
xts = sample_xts_from_x0(model, x0, num_inference_steps=num_inference_steps)
alpha_bar = model.scheduler.alphas_cumprod
zs = torch.zeros(size=variance_noise_shape, device=model.device)
t_to_idx = {int(v):k for k,v in enumerate(timesteps)}
xt = x0
op = tqdm(reversed(timesteps)) if prog_bar else reversed(timesteps)
for t in op:
idx = t_to_idx[int(t)]
# 1. predict noise residual
if not eta_is_zero:
xt = xts[idx][None]
with torch.no_grad():
out = model.unet.forward(xt, timestep = t, encoder_hidden_states = uncond_embedding)
if not prompt=="":
cond_out = model.unet.forward(xt, timestep=t, encoder_hidden_states = text_embeddings)
if not prompt=="":
## classifier free guidance
noise_pred = out.sample + cfg_scale * (cond_out.sample - out.sample)
else:
noise_pred = out.sample
if eta_is_zero:
# 2. compute more noisy image and set x_t -> x_t+1
xt = forward_step(model, noise_pred, t, xt)
else:
xtm1 = xts[idx+1][None]
# pred of x0
pred_original_sample = (xt - (1-alpha_bar[t]) ** 0.5 * noise_pred ) / alpha_bar[t] ** 0.5
# direction to xt
prev_timestep = t - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps
alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod
variance = get_variance(model, t)
pred_sample_direction = (1 - alpha_prod_t_prev - etas[idx] * variance ) ** (0.5) * noise_pred
mu_xt = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
z = (xtm1 - mu_xt ) / ( etas[idx] * variance ** 0.5 )
zs[idx] = z
# correction to avoid error accumulation
xtm1 = mu_xt + ( etas[idx] * variance ** 0.5 )*z
xts[idx+1] = xtm1
if not zs is None:
zs[-1] = torch.zeros_like(zs[-1])
return xt, zs, xts
def reverse_step(model, model_output, timestep, sample, eta = 0, variance_noise=None):
# 1. get previous step value (=t-1)
prev_timestep = timestep - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps
# 2. compute alphas, betas
alpha_prod_t = model.scheduler.alphas_cumprod[timestep]
alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
# variance = self.scheduler._get_variance(timestep, prev_timestep)
variance = get_variance(model, timestep) #, prev_timestep)
std_dev_t = eta * variance ** (0.5)
# Take care of asymetric reverse process (asyrp)
model_output_direction = model_output
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
# pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output_direction
pred_sample_direction = (1 - alpha_prod_t_prev - eta * variance) ** (0.5) * model_output_direction
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
# 8. Add noice if eta > 0
if eta > 0:
if variance_noise is None:
variance_noise = torch.randn(model_output.shape, device=model.device)
sigma_z = eta * variance ** (0.5) * variance_noise
prev_sample = prev_sample + sigma_z
return prev_sample
def inversion_reverse_process(model,
xT,
etas = 0,
prompts = "",
cfg_scales = None,
prog_bar = False,
zs = None,
controller=None,
asyrp = False):
batch_size = len(prompts)
cfg_scales_tensor = torch.Tensor(cfg_scales).view(-1,1,1,1).to(model.device)
text_embeddings = encode_text(model, prompts)
uncond_embedding = encode_text(model, [""] * batch_size)
if etas is None: etas = 0
if type(etas) in [int, float]: etas = [etas]*model.scheduler.num_inference_steps
assert len(etas) == model.scheduler.num_inference_steps
timesteps = model.scheduler.timesteps.to(model.device)
xt = xT.expand(batch_size, -1, -1, -1)
op = tqdm(timesteps[-zs.shape[0]:]) if prog_bar else timesteps[-zs.shape[0]:]
t_to_idx = {int(v):k for k,v in enumerate(timesteps[-zs.shape[0]:])}
for t in op:
idx = t_to_idx[int(t)]
## Unconditional embedding
with torch.no_grad():
uncond_out = model.unet.forward(xt, timestep = t,
encoder_hidden_states = uncond_embedding)
## Conditional embedding
if prompts:
with torch.no_grad():
cond_out = model.unet.forward(xt, timestep = t,
encoder_hidden_states = text_embeddings)
z = zs[idx] if not zs is None else None
z = z.expand(batch_size, -1, -1, -1)
if prompts:
## classifier free guidance
noise_pred = uncond_out.sample + cfg_scales_tensor * (cond_out.sample - uncond_out.sample)
else:
noise_pred = uncond_out.sample
# 2. compute less noisy image and set x_t -> x_t-1
xt = reverse_step(model, noise_pred, t, xt, eta = etas[idx], variance_noise = z)
if controller is not None:
xt = controller.step_callback(xt)
return xt, zs
import PIL
from PIL import Image, ImageDraw ,ImageFont
from matplotlib import pyplot as plt
import torchvision.transforms as T
import os
import torch
import yaml
def show_torch_img(img):
img = to_np_image(img)
plt.imshow(img)
plt.axis("off")
def to_np_image(all_images):
all_images = (all_images.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).cpu().numpy()[0]
return all_images
def tensor_to_pil(tensor_imgs):
if type(tensor_imgs) == list:
tensor_imgs = torch.cat(tensor_imgs)
tensor_imgs = (tensor_imgs / 2 + 0.5).clamp(0, 1)
to_pil = T.ToPILImage()
pil_imgs = [to_pil(img) for img in tensor_imgs]
return pil_imgs
def pil_to_tensor(pil_imgs):
to_torch = T.ToTensor()
if type(pil_imgs) == PIL.Image.Image:
tensor_imgs = to_torch(pil_imgs).unsqueeze(0)*2-1
elif type(pil_imgs) == list:
tensor_imgs = torch.cat([to_torch(pil_imgs).unsqueeze(0)*2-1 for img in pil_imgs]).to(device)
else:
raise Exception("Input need to be PIL.Image or list of PIL.Image")
return tensor_imgs
## TODO implement this
# n = 10
# num_rows = 4
# num_col = n // num_rows
# num_col = num_col + 1 if n % num_rows else num_col
# num_col
def add_margin(pil_img, top = 0, right = 0, bottom = 0,
left = 0, color = (255,255,255)):
width, height = pil_img.size
new_width = width + right + left
new_height = height + top + bottom
result = Image.new(pil_img.mode, (new_width, new_height), color)
result.paste(pil_img, (left, top))
return result
def image_grid(imgs, rows = 1, cols = None,
size = None,
titles = None, text_pos = (0, 0)):
if type(imgs) == list and type(imgs[0]) == torch.Tensor:
imgs = torch.cat(imgs)
if type(imgs) == torch.Tensor:
imgs = tensor_to_pil(imgs)
if not size is None:
imgs = [img.resize((size,size)) for img in imgs]
if cols is None:
cols = len(imgs)
assert len(imgs) >= rows*cols
top=20
w, h = imgs[0].size
delta = 0
if len(imgs)> 1 and not imgs[1].size[1] == h:
delta = top
h = imgs[1].size[1]
if not titles is None:
font = ImageFont.truetype("/usr/share/fonts/truetype/freefont/FreeMono.ttf",
size = 20, encoding="unic")
h = top + h
grid = Image.new('RGB', size=(cols*w, rows*h+delta))
for i, img in enumerate(imgs):
if not titles is None:
img = add_margin(img, top = top, bottom = 0,left=0)
draw = ImageDraw.Draw(img)
draw.text(text_pos, titles[i],(0,0,0),
font = font)
if not delta == 0 and i > 0:
grid.paste(img, box=(i%cols*w, i//cols*h+delta))
else:
grid.paste(img, box=(i%cols*w, i//cols*h))
return grid
"""
input_folder - dataset folder
"""
def load_dataset(input_folder):
# full_file_names = glob.glob(input_folder)
# class_names = [x[0] for x in os.walk(input_folder)]
class_names = next(os.walk(input_folder))[1]
class_names[:] = [d for d in class_names if not d[0] == '.']
file_names=[]
for class_name in class_names:
cur_path = os.path.join(input_folder, class_name)
filenames = next(os.walk(cur_path), (None, None, []))[2]
filenames = [f for f in filenames if not f[0] == '.']
file_names.append(filenames)
return class_names, file_names
def dataset_from_yaml(yaml_location):
with open(yaml_location, 'r') as stream:
data_loaded = yaml.safe_load(stream)
return data_loaded
def invert(x0:torch.FloatTensor, prompt_src:str ="", num_inference_steps=100, cfg_scale_src = 3.5, eta = 1):
# inverts a real image according to Algorihm 1 in https://arxiv.org/pdf/2304.06140.pdf,
# based on the code in https://github.com/inbarhub/DDPM_inversion
# returns wt, zs, wts:
# wt - inverted latent
# wts - intermediate inverted latents
# zs - noise maps
sd_pipe.scheduler.set_timesteps(num_diffusion_steps)
# vae encode image
with autocast("cuda"), inference_mode():
w0 = (sd_pipe.vae.encode(x0).latent_dist.mode() * 0.18215).float()
# find Zs and wts - forward process
wt, zs, wts = inversion_forward_process(sd_pipe, w0, etas=eta, prompt=prompt_src, cfg_scale=cfg_scale_src, prog_bar=True, num_inference_steps=num_diffusion_steps)
return zs, wts
def sample(zs, wts, prompt_tar="", cfg_scale_tar=15, skip=36, eta = 1):
# reverse process (via Zs and wT)
w0, _ = inversion_reverse_process(sd_pipe, xT=wts[skip], etas=eta, prompts=[prompt_tar], cfg_scales=[cfg_scale_tar], prog_bar=True, zs=zs[skip:])
# vae decode image
with autocast("cuda"), inference_mode():
x0_dec = sd_pipe.vae.decode(1 / 0.18215 * w0).sample
if x0_dec.dim()<4:
x0_dec = x0_dec[None,:,:,:]
img = image_grid(x0_dec)
return img
def foo(a:str = "l") -> int:
print("a")
foo(3)(2) SEGA Pipelineの構築
import inspect
import warnings
from itertools import repeat
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers.image_processor import VaeImageProcessor
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import logging, randn_tensor
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
# from . import SemanticStableDiffusionPipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class SemanticStableDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation with latent editing.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
This model builds on the implementation of ['StableDiffusionPipeline']
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`Q16SafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
feature_extractor ([`CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
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)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
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
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
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
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
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 (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
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}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
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
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: int = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
editing_prompt: Optional[Union[str, List[str]]] = None,
editing_prompt_embeddings: Optional[torch.Tensor] = None,
reverse_editing_direction: Optional[Union[bool, List[bool]]] = False,
edit_guidance_scale: Optional[Union[float, List[float]]] = 5,
edit_warmup_steps: Optional[Union[int, List[int]]] = 10,
edit_cooldown_steps: Optional[Union[int, List[int]]] = None,
edit_threshold: Optional[Union[float, List[float]]] = 0.9,
edit_momentum_scale: Optional[float] = 0.1,
edit_mom_beta: Optional[float] = 0.4,
edit_weights: Optional[List[float]] = None,
sem_guidance: Optional[List[torch.Tensor]] = None,
# DDPM additions
use_ddpm: bool = False,
wts: Optional[List[torch.Tensor]] = None,
zs: Optional[List[torch.Tensor]] = None
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
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.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `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) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
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.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
editing_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to use for Semantic guidance. Semantic guidance is disabled by setting
`editing_prompt = None`. Guidance direction of prompt should be specified via
`reverse_editing_direction`.
editing_prompt_embeddings (`torch.Tensor>`, *optional*):
Pre-computed embeddings to use for semantic guidance. Guidance direction of embedding should be
specified via `reverse_editing_direction`.
reverse_editing_direction (`bool` or `List[bool]`, *optional*, defaults to `False`):
Whether the corresponding prompt in `editing_prompt` should be increased or decreased.
edit_guidance_scale (`float` or `List[float]`, *optional*, defaults to 5):
Guidance scale for semantic guidance. If provided as list values should correspond to `editing_prompt`.
`edit_guidance_scale` is defined as `s_e` of equation 6 of [SEGA
Paper](https://arxiv.org/pdf/2301.12247.pdf).
edit_warmup_steps (`float` or `List[float]`, *optional*, defaults to 10):
Number of diffusion steps (for each prompt) for which semantic guidance will not be applied. Momentum
will still be calculated for those steps and applied once all warmup periods are over.
`edit_warmup_steps` is defined as `delta` (δ) of [SEGA Paper](https://arxiv.org/pdf/2301.12247.pdf).
edit_cooldown_steps (`float` or `List[float]`, *optional*, defaults to `None`):
Number of diffusion steps (for each prompt) after which semantic guidance will no longer be applied.
edit_threshold (`float` or `List[float]`, *optional*, defaults to 0.9):
Threshold of semantic guidance.
edit_momentum_scale (`float`, *optional*, defaults to 0.1):
Scale of the momentum to be added to the semantic guidance at each diffusion step. If set to 0.0
momentum will be disabled. Momentum is already built up during warmup, i.e. for diffusion steps smaller
than `sld_warmup_steps`. Momentum will only be added to latent guidance once all warmup periods are
finished. `edit_momentum_scale` is defined as `s_m` of equation 7 of [SEGA
Paper](https://arxiv.org/pdf/2301.12247.pdf).
edit_mom_beta (`float`, *optional*, defaults to 0.4):
Defines how semantic guidance momentum builds up. `edit_mom_beta` indicates how much of the previous
momentum will be kept. Momentum is already built up during warmup, i.e. for diffusion steps smaller
than `edit_warmup_steps`. `edit_mom_beta` is defined as `beta_m` (β) of equation 8 of [SEGA
Paper](https://arxiv.org/pdf/2301.12247.pdf).
edit_weights (`List[float]`, *optional*, defaults to `None`):
Indicates how much each individual concept should influence the overall guidance. If no weights are
provided all concepts are applied equally. `edit_mom_beta` is defined as `g_i` of equation 9 of [SEGA
Paper](https://arxiv.org/pdf/2301.12247.pdf).
sem_guidance (`List[torch.Tensor]`, *optional*):
List of pre-generated guidance vectors to be applied at generation. Length of the list has to
correspond to `num_inference_steps`.
Returns:
[`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] if `return_dict` is True,
otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the
second element is a list of `bool`s denoting whether the corresponding generated image likely represents
"not-safe-for-work" (nsfw) content, according to the `safety_checker`.
"""
# 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
# 1. Check inputs. Raise error if not correct
self.check_inputs(prompt, height, width, callback_steps)
# 2. Define call parameters
batch_size = 1 if isinstance(prompt, str) else len(prompt)
if editing_prompt:
enable_edit_guidance = True
if isinstance(editing_prompt, str):
editing_prompt = [editing_prompt]
enabled_editing_prompts = len(editing_prompt)
elif editing_prompt_embeddings is not None:
enable_edit_guidance = True
enabled_editing_prompts = editing_prompt_embeddings.shape[0]
else:
enabled_editing_prompts = 0
enable_edit_guidance = False
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
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}"
)
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
if enable_edit_guidance:
# get safety text embeddings
if editing_prompt_embeddings is None:
edit_concepts_input = self.tokenizer(
[x for item in editing_prompt for x in repeat(item, batch_size)],
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
)
edit_concepts_input_ids = edit_concepts_input.input_ids
if edit_concepts_input_ids.shape[-1] > self.tokenizer.model_max_length:
removed_text = self.tokenizer.batch_decode(
edit_concepts_input_ids[:, self.tokenizer.model_max_length :]
)
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}"
)
edit_concepts_input_ids = edit_concepts_input_ids[:, : self.tokenizer.model_max_length]
edit_concepts = self.text_encoder(edit_concepts_input_ids.to(self.device))[0]
else:
edit_concepts = editing_prompt_embeddings.to(self.device).repeat(batch_size, 1, 1)
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed_edit, seq_len_edit, _ = edit_concepts.shape
edit_concepts = edit_concepts.repeat(1, num_images_per_prompt, 1)
edit_concepts = edit_concepts.view(bs_embed_edit * num_images_per_prompt, seq_len_edit, -1)
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""]
elif 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 = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
# 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 enable_edit_guidance:
text_embeddings = torch.cat([uncond_embeddings, text_embeddings, edit_concepts])
else:
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
# get the initial random noise unless the user supplied it
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=self.device)
timesteps = self.scheduler.timesteps
if use_ddpm:
t_to_idx = {int(v):k for k,v in enumerate(timesteps[-zs.shape[0]:])}
timesteps = timesteps[-zs.shape[0]:]
# 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,
text_embeddings.dtype,
self.device,
generator,
latents,
)
# 6. Prepare extra step kwargs.
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# Initialize edit_momentum to None
edit_momentum = None
self.uncond_estimates = None
self.text_estimates = None
self.edit_estimates = None
self.sem_guidance = None
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = (
torch.cat([latents] * (2 + enabled_editing_prompts)) if 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=text_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_out = noise_pred.chunk(2 + enabled_editing_prompts) # [b,4, 64, 64]
noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1]
noise_pred_edit_concepts = noise_pred_out[2:]
# default text guidance
noise_guidance = guidance_scale * (noise_pred_text - noise_pred_uncond)
# noise_guidance = (noise_pred_text - noise_pred_edit_concepts[0])
if self.uncond_estimates is None:
self.uncond_estimates = torch.zeros((num_inference_steps + 1, *noise_pred_uncond.shape))
self.uncond_estimates[i] = noise_pred_uncond.detach().cpu()
if self.text_estimates is None:
self.text_estimates = torch.zeros((num_inference_steps + 1, *noise_pred_text.shape))
self.text_estimates[i] = noise_pred_text.detach().cpu()
if self.edit_estimates is None and enable_edit_guidance:
self.edit_estimates = torch.zeros(
(num_inference_steps + 1, len(noise_pred_edit_concepts), *noise_pred_edit_concepts[0].shape)
)
if self.sem_guidance is None:
self.sem_guidance = torch.zeros((num_inference_steps + 1, *noise_pred_text.shape))
if edit_momentum is None:
edit_momentum = torch.zeros_like(noise_guidance)
if enable_edit_guidance:
concept_weights = torch.zeros(
(len(noise_pred_edit_concepts), noise_guidance.shape[0]),
device=self.device,
dtype=noise_guidance.dtype,
)
noise_guidance_edit = torch.zeros(
(len(noise_pred_edit_concepts), *noise_guidance.shape),
device=self.device,
dtype=noise_guidance.dtype,
)
# noise_guidance_edit = torch.zeros_like(noise_guidance)
warmup_inds = []
for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts):
self.edit_estimates[i, c] = noise_pred_edit_concept
if isinstance(edit_guidance_scale, list):
edit_guidance_scale_c = edit_guidance_scale[c]
else:
edit_guidance_scale_c = edit_guidance_scale
if isinstance(edit_threshold, list):
edit_threshold_c = edit_threshold[c]
else:
edit_threshold_c = edit_threshold
if isinstance(reverse_editing_direction, list):
reverse_editing_direction_c = reverse_editing_direction[c]
else:
reverse_editing_direction_c = reverse_editing_direction
if edit_weights:
edit_weight_c = edit_weights[c]
else:
edit_weight_c = 1.0
if isinstance(edit_warmup_steps, list):
edit_warmup_steps_c = edit_warmup_steps[c]
else:
edit_warmup_steps_c = edit_warmup_steps
if isinstance(edit_cooldown_steps, list):
edit_cooldown_steps_c = edit_cooldown_steps[c]
elif edit_cooldown_steps is None:
edit_cooldown_steps_c = i + 1
else:
edit_cooldown_steps_c = edit_cooldown_steps
if i >= edit_warmup_steps_c:
warmup_inds.append(c)
if i >= edit_cooldown_steps_c:
noise_guidance_edit[c, :, :, :, :] = torch.zeros_like(noise_pred_edit_concept)
continue
noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred_uncond
# tmp_weights = (noise_pred_text - noise_pred_edit_concept).sum(dim=(1, 2, 3))
tmp_weights = (noise_guidance - noise_pred_edit_concept).sum(dim=(1, 2, 3))
tmp_weights = torch.full_like(tmp_weights, edit_weight_c) # * (1 / enabled_editing_prompts)
if reverse_editing_direction_c:
noise_guidance_edit_tmp = noise_guidance_edit_tmp * -1
concept_weights[c, :] = tmp_weights
noise_guidance_edit_tmp = noise_guidance_edit_tmp * edit_guidance_scale_c
# torch.quantile function expects float32
if noise_guidance_edit_tmp.dtype == torch.float32:
tmp = torch.quantile(
torch.abs(noise_guidance_edit_tmp).flatten(start_dim=2),
edit_threshold_c,
dim=2,
keepdim=False,
)
else:
tmp = torch.quantile(
torch.abs(noise_guidance_edit_tmp).flatten(start_dim=2).to(torch.float32),
edit_threshold_c,
dim=2,
keepdim=False,
).to(noise_guidance_edit_tmp.dtype)
noise_guidance_edit_tmp = torch.where(
torch.abs(noise_guidance_edit_tmp) >= tmp[:, :, None, None],
noise_guidance_edit_tmp,
torch.zeros_like(noise_guidance_edit_tmp),
)
noise_guidance_edit[c, :, :, :, :] = noise_guidance_edit_tmp
# noise_guidance_edit = noise_guidance_edit + noise_guidance_edit_tmp
warmup_inds = torch.tensor(warmup_inds).to(self.device)
if len(noise_pred_edit_concepts) > warmup_inds.shape[0] > 0:
concept_weights = concept_weights.to("cpu") # Offload to cpu
noise_guidance_edit = noise_guidance_edit.to("cpu")
concept_weights_tmp = torch.index_select(concept_weights.to(self.device), 0, warmup_inds)
concept_weights_tmp = torch.where(
concept_weights_tmp < 0, torch.zeros_like(concept_weights_tmp), concept_weights_tmp
)
concept_weights_tmp = concept_weights_tmp / concept_weights_tmp.sum(dim=0)
# concept_weights_tmp = torch.nan_to_num(concept_weights_tmp)
noise_guidance_edit_tmp = torch.index_select(
noise_guidance_edit.to(self.device), 0, warmup_inds
)
noise_guidance_edit_tmp = torch.einsum(
"cb,cbijk->bijk", concept_weights_tmp, noise_guidance_edit_tmp
)
noise_guidance_edit_tmp = noise_guidance_edit_tmp
noise_guidance = noise_guidance + noise_guidance_edit_tmp
self.sem_guidance[i] = noise_guidance_edit_tmp.detach().cpu()
del noise_guidance_edit_tmp
del concept_weights_tmp
concept_weights = concept_weights.to(self.device)
noise_guidance_edit = noise_guidance_edit.to(self.device)
concept_weights = torch.where(
concept_weights < 0, torch.zeros_like(concept_weights), concept_weights
)
concept_weights = torch.nan_to_num(concept_weights)
noise_guidance_edit = torch.einsum("cb,cbijk->bijk", concept_weights, noise_guidance_edit)
noise_guidance_edit = noise_guidance_edit + edit_momentum_scale * edit_momentum
edit_momentum = edit_mom_beta * edit_momentum + (1 - edit_mom_beta) * noise_guidance_edit
if warmup_inds.shape[0] == len(noise_pred_edit_concepts):
noise_guidance = noise_guidance + noise_guidance_edit
self.sem_guidance[i] = noise_guidance_edit.detach().cpu()
if sem_guidance is not None:
edit_guidance = sem_guidance[i].to(self.device)
noise_guidance = noise_guidance + edit_guidance
noise_pred = noise_pred_uncond + noise_guidance
## ddpm ###########################################################
if use_ddpm:
idx = t_to_idx[int(t)]
z = zs[idx] if not zs is None else None
# 1. get previous step value (=t-1)
prev_timestep = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
# 2. compute alphas, betas
alpha_prod_t = self.scheduler.alphas_cumprod[t]
alpha_prod_t_prev = self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
# variance = self.scheduler._get_variance(timestep, prev_timestep)
# variance = get_variance(model, t) #, prev_timestep)
beta_prod_t_prev = 1 - alpha_prod_t_prev
variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
std_dev_t = eta * variance ** (0.5)
# Take care of asymetric reverse process (asyrp)
noise_pred_direction = noise_pred
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
# pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output_direction
pred_sample_direction = (1 - alpha_prod_t_prev - eta * variance) ** (0.5) * noise_pred_direction
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
# 8. Add noice if eta > 0
if eta > 0:
if z is None:
z = torch.randn(noise_pred.shape, device=self.device)
sigma_z = eta * variance ** (0.5) * z
latents = prev_sample + sigma_z
## ddpm ##########################################################
# compute the previous noisy sample x_t -> x_t-1
else: #if not use_ddpm:
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
# 8. Post-processing
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, self.device, text_embeddings.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)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)(3)Load Pipeline
import torch
import numpy as np
import requests
import random
from io import BytesIO
from diffusers import StableDiffusionPipeline
from diffusers import DDIMScheduler
from torch import autocast, inference_mode
import re
# load pipelines
sd_model_id = "runwayml/stable-diffusion-v1-5"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
sd_pipe = StableDiffusionPipeline.from_pretrained(sd_model_id).to(device)
sd_pipe.scheduler = DDIMScheduler.from_config(sd_model_id, subfolder = "scheduler")
sega_pipe = SemanticStableDiffusionPipeline.from_pretrained(sd_model_id).to(device)
def edit(wts, zs,
tar_prompt = "",
steps = 100,
skip = 36,
tar_cfg_scale =15,
edit_concept = "",
guidnace_scale = 7,
warmup = 1,
neg_guidance=False,
threshold=0.95
):
# SEGA
# parse concepts and neg guidance
editing_args = dict(
editing_prompt = edit_concept,
reverse_editing_direction = neg_guidance,
edit_warmup_steps=warmup,
edit_guidance_scale=guidnace_scale,
edit_threshold=threshold,
edit_momentum_scale=0.5,
edit_mom_beta=0.6,
eta=1,
)
latnets = wts[skip].expand(1, -1, -1, -1)
sega_out = sega_pipe(prompt=tar_prompt, latents=latnets, guidance_scale = tar_cfg_scale,
num_images_per_prompt=1,
num_inference_steps=steps,
use_ddpm=True, wts=wts, zs=zs[skip:], **editing_args)
return sega_out.images[0](4)デモデータの準備
%cd /content
!git clone https://huggingface.co/spaces/editing-images/ledits(5)実行
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
input_image = "/content/ledits/examples/flower_field_input.jpg" #@param
source_prompt ="" #@param
target_prompt ="oil painting" #@param
num_diffusion_steps =100 #@param
source_guidance_scale = 3.5 #@param
reconstruct = True #@param
skip_steps =36 #@param
target_guidance_scale=20 #@param
# SEGA only params
edit_concepts=["red flowers", "wheat"]#@param
edit_guidance_scales=[7,15] #@param
warmup_steps=[1,1] #@param
reverse_editing=[True, False] #@param
thresholds = [ 0.95,0.95] #@param
# uncomment for reproducabilty
import torch
seed = 36478574352 #@param
# torch.manual_seed(seed)
# Invert with ddpm
x0 = load_512(input_image, device=device)
# noise maps and latents
zs, wts = invert(x0 =x0 , prompt_src=source_prompt, num_inference_steps=num_diffusion_steps, cfg_scale_src=source_guidance_scale)
if reconstruct:
ddpm_out_img = sample(zs, wts, prompt_tar=target_prompt, skip=skip_steps, cfg_scale_tar=target_guidance_scale)
# edit with the pre-computed latents and noise maps
sega_ddpm_edited_img =edit(wts, zs,
tar_prompt = target_prompt,
steps = num_diffusion_steps,
skip = skip_steps,
tar_cfg_scale =target_guidance_scale,
edit_concept = edit_concepts,
guidnace_scale = edit_guidance_scales,
warmup = warmup_steps,
neg_guidance=reverse_editing,
threshold=thresholds)(6)結果の確認
# Show results
def display(show_reconstruction):
orig_img_pt = load_512(input_image)
orig_img = tensor_to_pil(orig_img_pt)[0]
if show_reconstruction:
return image_grid([orig_img, ddpm_out_img, sega_ddpm_edited_img], rows=1, cols=3)
else:
return image_grid([orig_img, sega_ddpm_edited_img], rows=1, cols=2)
display(show_reconstruction=True)
あー素晴らしきこの世界。
Advanced Application
蝶の画像を蜂に変えてみました。
rom PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
input_image = "/content/ledits/examples/butterfly_input.jpg" #@param
source_prompt ="" #@param
target_prompt ="oil painting" #@param
num_diffusion_steps =100 #@param
source_guidance_scale = 3.5 #@param
reconstruct = True #@param
skip_steps =36 #@param
target_guidance_scale=20 #@param
# SEGA only params
edit_concepts=["buttefly", "bee"]#@param
edit_guidance_scales=[7,15] #@param
warmup_steps=[1,1] #@param
reverse_editing=[True, False] #@param
thresholds = [ 0.95,0.95] #@param
# uncomment for reproducabilty
import torch
seed = 36478574352 #@param
# torch.manual_seed(seed)
最後に
今回は、新しいimg2imgのdiffusion modelであるLEDTISをgoogle colabで試してみました。instructPix2Pixよりもはるかに使いやすいですし、スタイル変換まで対応しているとは。。
画像のあれこれを全て含んだdiffusion modelがだんだんと出てきているのでしっかり追いついていかないと。
今後ともLLM, Diffusion model, Image Analysis, 3Dに関連する試した記事を投稿していく予定なのでよろしくお願いします。
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