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import math
import random
import torch
from diffusers import DiffusionPipeline, DDPMScheduler
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker, StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
from diffusers.image_processor import VaeImageProcessor
from huggingface_hub import PyTorchModelHubMixin
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer, CLIPImageProcessor
class CombinedStableDiffusion(
DiffusionPipeline,
PyTorchModelHubMixin
):
"""
A Stable Diffusion model wrapper that provides functionality for text-to-image synthesis,
noise scheduling, latent space manipulation, and image decoding.
"""
def __init__(
self,
original_unet: torch.nn.Module,
fine_tuned_unet: torch.nn.Module,
scheduler: DDPMScheduler,
vae: torch.nn.Module,
tokenizer: CLIPTextModel,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
text_encoder: CLIPTokenizer,
) -> None:
super().__init__()
self.register_modules(
tokenizer=tokenizer,
text_encoder=text_encoder,
original_unet=original_unet,
fine_tuned_unet=fine_tuned_unet,
scheduler=scheduler,
vae=vae,
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 _get_negative_prompts(self, batch_size: int) -> torch.Tensor:
return self.tokenizer(
[""] * batch_size,
max_length=self.tokenizer.model_max_length,
padding="max_length",
truncation=True,
return_tensors="pt",
).input_ids
def _get_encoder_hidden_states(
self, tokenized_prompts: torch.Tensor, do_classifier_free_guidance: bool = False
) -> torch.Tensor:
if do_classifier_free_guidance:
tokenized_prompts = torch.cat(
[
self._get_negative_prompts(tokenized_prompts.shape[0]).to(
tokenized_prompts.device
),
tokenized_prompts,
]
)
return self.text_encoder(tokenized_prompts)[0]
def _get_unet_prediction(
self,
latent_model_input: torch.Tensor,
timestep: int,
encoder_hidden_states: torch.Tensor,
) -> torch.Tensor:
"""
Return unet noise prediction
Args:
latent_model_input (torch.Tensor): Unet latents input
timestep (int): noise scheduler timestep
encoder_hidden_states (torch.Tensor): Text encoder hidden states
Returns:
torch.Tensor: noise prediction
"""
unet = self.original_unet if self._use_original_unet else self.fine_tuned_unet
return unet(
latent_model_input,
timestep=timestep,
encoder_hidden_states=encoder_hidden_states,
).sample
def get_noise_prediction(
self,
latents: torch.Tensor,
timestep_index: int,
encoder_hidden_states: torch.Tensor,
do_classifier_free_guidance: bool = False,
detach_main_path: bool = False,
):
"""
Return noise prediction
Args:
latents (torch.Tensor): Image latents
timestep_index (int): noise scheduler timestep index
encoder_hidden_states (torch.Tensor): Text encoder hidden states
do_classifier_free_guidance (bool) Whether to do classifier free guidance
detach_main_path (bool): Detach gradient
Returns:
torch.Tensor: noise prediction
"""
timestep = self.scheduler.timesteps[timestep_index]
latent_model_input = self.scheduler.scale_model_input(
sample=torch.cat([latents] * 2) if do_classifier_free_guidance else latents,
timestep=timestep,
)
noise_pred = self._get_unet_prediction(
latent_model_input=latent_model_input,
timestep=timestep,
encoder_hidden_states=encoder_hidden_states,
)
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
if detach_main_path:
noise_pred_text = noise_pred_text.detach()
noise_pred = noise_pred_uncond + self.guidance_scale * (
noise_pred_text - noise_pred_uncond
)
return noise_pred
def sample_next_latents(
self,
latents: torch.Tensor,
timestep_index: int,
noise_pred: torch.Tensor,
return_pred_original: bool = False,
) -> torch.Tensor:
"""
Return next latents prediction
Args:
latents (torch.Tensor): Image latents
timestep_index (int): noise scheduler timestep index
noise_pred (torch.Tensor): noise prediction
return_pred_original (bool) Whether to sample original sample
Returns:
torch.Tensor: latent prediction
"""
timestep = self.scheduler.timesteps[timestep_index]
sample = self.scheduler.step(
model_output=noise_pred, timestep=timestep, sample=latents
)
return (
sample.pred_original_sample if return_pred_original else sample.prev_sample
)
def predict_next_latents(
self,
latents: torch.Tensor,
timestep_index: int,
encoder_hidden_states: torch.Tensor,
return_pred_original: bool = False,
do_classifier_free_guidance: bool = False,
detach_main_path: bool = False,
) -> tuple[torch.Tensor, torch.Tensor]:
"""
Predicts the next latent states during the diffusion process.
Args:
latents (torch.Tensor): Current latent states.
timestep_index (int): Index of the current timestep.
encoder_hidden_states (torch.Tensor): Encoder hidden states from the text encoder.
return_pred_original (bool): Whether to return the predicted original sample.
do_classifier_free_guidance (bool) Whether to do classifier free guidance
detach_main_path (bool): Detach gradient
Returns:
tuple: Next latents and predicted noise tensor.
"""
noise_pred = self.get_noise_prediction(
latents=latents,
timestep_index=timestep_index,
encoder_hidden_states=encoder_hidden_states,
do_classifier_free_guidance=do_classifier_free_guidance,
detach_main_path=detach_main_path,
)
latents = self.sample_next_latents(
latents=latents,
noise_pred=noise_pred,
timestep_index=timestep_index,
return_pred_original=return_pred_original,
)
return latents, noise_pred
def get_latents(self, batch_size: int, device: torch.device) -> torch.Tensor:
latent_resolution = int(self.resolution) // self.vae_scale_factor
return torch.randn(
(
batch_size,
self.original_unet.config.in_channels,
latent_resolution,
latent_resolution,
),
device=device,
)
def do_k_diffusion_steps(
self,
start_timestep_index: int,
end_timestep_index: int,
latents: torch.Tensor,
encoder_hidden_states: torch.Tensor,
return_pred_original: bool = False,
do_classifier_free_guidance: bool = False,
detach_main_path: bool = False,
) -> tuple[torch.Tensor, torch.Tensor]:
"""
Performs multiple diffusion steps between specified timesteps.
Args:
start_timestep_index (int): Starting timestep index.
end_timestep_index (int): Ending timestep index.
latents (torch.Tensor): Initial latents.
encoder_hidden_states (torch.Tensor): Encoder hidden states.
return_pred_original (bool): Whether to return the predicted original sample.
do_classifier_free_guidance (bool) Whether to do classifier free guidance
detach_main_path (bool): Detach gradient
Returns:
tuple: Resulting latents and encoder hidden states.
"""
assert start_timestep_index <= end_timestep_index
for timestep_index in range(start_timestep_index, end_timestep_index - 1):
latents, _ = self.predict_next_latents(
latents=latents,
timestep_index=timestep_index,
encoder_hidden_states=encoder_hidden_states,
return_pred_original=False,
do_classifier_free_guidance=do_classifier_free_guidance,
detach_main_path=detach_main_path,
)
res, _ = self.predict_next_latents(
latents=latents,
timestep_index=end_timestep_index - 1,
encoder_hidden_states=encoder_hidden_states,
return_pred_original=return_pred_original,
do_classifier_free_guidance=do_classifier_free_guidance,
)
return res, encoder_hidden_states
def get_pil_image(self, raw_images: torch.Tensor) -> list[Image]:
do_denormalize = [True] * raw_images.shape[0]
images = self.inference_image_processor.postprocess(
raw_images, output_type="pil", do_denormalize=do_denormalize
)
return images
def get_reward_image(self, raw_images: torch.Tensor) -> torch.Tensor:
reward_images = (raw_images / 2 + 0.5).clamp(0, 1)
if self.use_image_shifting:
self._shift_tensor_batch(
reward_images,
dx=random.randint(0, math.ceil(self.resolution / 224)),
dy=random.randint(0, math.ceil(self.resolution / 224)),
)
return self.reward_image_processor(reward_images)
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
@torch.no_grad()
def __call__(
self,
prompt: str | list[str],
num_inference_steps=40,
original_unet_steps=30,
resolution=512,
guidance_scale=7.5,
output_type: str = "pil",
return_dict: bool = True,
generator=None,
):
self.guidance_scale = guidance_scale
batch_size = 1 if isinstance(prompt, str) else len(prompt)
tokenized_prompts = self.tokenizer(
prompt,
return_tensors="pt",
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True
).input_ids.to(self.device)
original_encoder_hidden_states = self._get_encoder_hidden_states(
tokenized_prompts=tokenized_prompts,
do_classifier_free_guidance=True
)
fine_tuned_encoder_hidden_states = self._get_encoder_hidden_states(
tokenized_prompts=tokenized_prompts,
do_classifier_free_guidance=False
)
latent_resolution = int(resolution) // self.vae_scale_factor
latents = torch.randn(
(
batch_size,
self.original_unet.config.in_channels,
latent_resolution,
latent_resolution,
),
device=self.device,
)
self.scheduler.set_timesteps(
num_inference_steps,
device=self.device
)
self._use_original_unet = True
latents, _ = self.do_k_diffusion_steps(
start_timestep_index=0,
end_timestep_index=original_unet_steps,
latents=latents,
encoder_hidden_states=original_encoder_hidden_states,
return_pred_original=False,
do_classifier_free_guidance=True,
)
self._use_original_unet = False
latents, _ = self.do_k_diffusion_steps(
start_timestep_index=original_unet_steps,
end_timestep_index=num_inference_steps,
latents=latents,
encoder_hidden_states=fine_tuned_encoder_hidden_states,
return_pred_original=False,
do_classifier_free_guidance=False,
)
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, self.device, original_encoder_hidden_states.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)
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