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import torch
from diffusers import AutoencoderKL, LMSDiscreteScheduler, UNet2DConditionModel
from transformers import CLIPTextModel, CLIPTokenizer
from PIL import Image
from tqdm import tqdm
class StableDiffusion:
def __init__(
self,
vae_arch="CompVis/stable-diffusion-v1-4",
tokenizer_arch="openai/clip-vit-large-patch14",
encoder_arch="openai/clip-vit-large-patch14",
unet_arch="CompVis/stable-diffusion-v1-4",
device="cpu",
height=512,
width=512,
num_inference_steps=30,
guidance_scale=7.5,
manual_seed=1,
) -> None:
self.height = height # default height of Stable Diffusion
self.width = width # default width of Stable Diffusion
self.num_inference_steps = num_inference_steps # Number of denoising steps
self.guidance_scale = guidance_scale # Scale for classifier-free guidance
self.device = device
self.manual_seed = manual_seed
vae = AutoencoderKL.from_pretrained(vae_arch, subfolder="vae")
# Load the tokenizer and text encoder to tokenize and encode the text.
self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_arch)
text_encoder = CLIPTextModel.from_pretrained(encoder_arch)
# The UNet model for generating the latents.
unet = UNet2DConditionModel.from_pretrained(unet_arch, subfolder="unet")
# The noise scheduler
self.scheduler = LMSDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
num_train_timesteps=1000,
)
# To the GPU we go!
self.vae = vae.to(self.device)
self.text_encoder = text_encoder.to(self.device)
self.unet = unet.to(self.device)
self.token_emb_layer = text_encoder.text_model.embeddings.token_embedding
pos_emb_layer = text_encoder.text_model.embeddings.position_embedding
position_ids = text_encoder.text_model.embeddings.position_ids[:, :77]
self.position_embeddings = pos_emb_layer(position_ids)
def get_output_embeds(self, input_embeddings):
# CLIP's text model uses causal mask, so we prepare it here:
bsz, seq_len = input_embeddings.shape[:2]
causal_attention_mask = (
self.text_encoder.text_model._build_causal_attention_mask(
bsz, seq_len, dtype=input_embeddings.dtype
)
)
# Getting the output embeddings involves calling the model with passing output_hidden_states=True
# so that it doesn't just return the pooled final predictions:
encoder_outputs = self.text_encoder.text_model.encoder(
inputs_embeds=input_embeddings,
attention_mask=None, # We aren't using an attention mask so that can be None
causal_attention_mask=causal_attention_mask.to(self.device),
output_attentions=None,
output_hidden_states=True, # We want the output embs not the final output
return_dict=None,
)
# We're interested in the output hidden state only
output = encoder_outputs[0]
# There is a final layer norm we need to pass these through
output = self.text_encoder.text_model.final_layer_norm(output)
# And now they're ready!
return output
def set_timesteps(self, scheduler, num_inference_steps):
scheduler.set_timesteps(num_inference_steps)
scheduler.timesteps = scheduler.timesteps.to(torch.float32)
def latents_to_pil(self, latents):
# bath of latents -> list of images
latents = (1 / 0.18215) * latents
with torch.no_grad():
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.detach().cpu().permute(0, 2, 3, 1).numpy()
images = (image * 255).round().astype("uint8")
pil_images = [Image.fromarray(image) for image in images]
return pil_images
def generate_with_embs(self, text_embeddings, text_input, loss_fn, loss_scale):
generator = torch.manual_seed(
self.manual_seed
) # Seed generator to create the inital latent noise
batch_size = 1
max_length = text_input.input_ids.shape[-1]
uncond_input = self.tokenizer(
[""] * batch_size,
padding="max_length",
max_length=max_length,
return_tensors="pt",
)
with torch.no_grad():
uncond_embeddings = self.text_encoder(
uncond_input.input_ids.to(self.device)
)[0]
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
# Prep Scheduler
self.set_timesteps(self.scheduler, self.num_inference_steps)
# Prep latents
latents = torch.randn(
(batch_size, self.unet.in_channels, self.height // 8, self.width // 8),
generator=generator,
)
latents = latents.to(self.device)
latents = latents * self.scheduler.init_noise_sigma
# Loop
for i, t in tqdm(
enumerate(self.scheduler.timesteps), total=len(self.scheduler.timesteps)
):
# expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
latent_model_input = torch.cat([latents] * 2)
sigma = self.scheduler.sigmas[i]
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
with torch.no_grad():
noise_pred = self.unet(
latent_model_input, t, encoder_hidden_states=text_embeddings
)["sample"]
# perform 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 i % 5 == 0:
# Requires grad on the latents
latents = latents.detach().requires_grad_()
# Get the predicted x0:
# latents_x0 = latents - sigma * noise_pred
latents_x0 = self.scheduler.step(
noise_pred, t, latents
).pred_original_sample
# Decode to image space
denoised_images = (
self.vae.decode((1 / 0.18215) * latents_x0).sample / 2 + 0.5
) # range (0, 1)
# Calculate loss
loss = loss_fn(denoised_images) * loss_scale
# Occasionally print it out
# if i % 10 == 0:
# print(i, "loss:", loss.item())
# Get gradient
cond_grad = torch.autograd.grad(loss, latents)[0]
# Modify the latents based on this gradient
latents = latents.detach() - cond_grad * sigma**2
self.scheduler._step_index = self.scheduler._step_index - 1
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents).prev_sample
return self.latents_to_pil(latents)[0]
def generate_image(
self,
prompt="A campfire (oil on canvas)",
loss_fn=None,
loss_scale=200,
concept_embed=None, # birb_embed["<birb-style>"]
):
prompt += " in the style of cs"
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
input_ids = text_input.input_ids.to(self.device)
custom_style_token = self.tokenizer.encode("cs", add_special_tokens=False)[0]
# Get token embeddings
token_embeddings = self.token_emb_layer(input_ids)
# The new embedding - our special birb word
embed_key = list(concept_embed.keys())[0]
replacement_token_embedding = concept_embed[embed_key]
# Insert this into the token embeddings
token_embeddings[
0, torch.where(input_ids[0] == custom_style_token)
] = replacement_token_embedding.to(self.device)
# token_embeddings = token_embeddings + (replacement_token_embedding * 0.9)
# Combine with pos embs
input_embeddings = token_embeddings + self.position_embeddings
# Feed through to get final output embs
modified_output_embeddings = self.get_output_embeds(input_embeddings)
# And generate an image with this:
generated_image = self.generate_with_embs(
modified_output_embeddings, text_input, loss_fn, loss_scale
)
return generated_image
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