255 lines
12 KiB
Python
255 lines
12 KiB
Python
# Copied from https://github.com/huggingface/diffusers/blob/fc6acb6b97e93d58cb22b5fee52d884d77ce84d8/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
|
|
# with the following modifications:
|
|
# - It uses the patched version of `ddim_step_with_logprob` from `ddim_with_logprob.py`. As such, it only supports the
|
|
# `ddim` scheduler.
|
|
# - It returns all the intermediate latents of the denoising process as well as the log probs of each denoising step.
|
|
|
|
from typing import Any, Callable, Dict, List, Optional, Union
|
|
|
|
import torch
|
|
|
|
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import (
|
|
StableDiffusionPipeline,
|
|
rescale_noise_cfg,
|
|
)
|
|
from .ddim_with_logprob import ddim_step_with_logprob
|
|
|
|
|
|
@torch.no_grad()
|
|
def pipeline_with_logprob(
|
|
self: StableDiffusionPipeline,
|
|
prompt: Union[str, List[str]] = None,
|
|
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: Optional[int] = 1,
|
|
eta: float = 0.0,
|
|
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
|
latents: Optional[torch.FloatTensor] = None,
|
|
prompt_embeds: Optional[torch.FloatTensor] = None,
|
|
negative_prompt_embeds: 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,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
guidance_rescale: float = 0.0,
|
|
):
|
|
r"""
|
|
Function invoked when calling the pipeline for generation.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
|
|
instead.
|
|
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. If not defined, one has to pass
|
|
`negative_prompt_embeds` instead. 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` or `List[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`.
|
|
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.
|
|
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
|
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
|
argument.
|
|
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.
|
|
cross_attention_kwargs (`dict`, *optional*):
|
|
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
|
`self.processor` in
|
|
[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
|
|
guidance_rescale (`float`, *optional*, defaults to 0.7):
|
|
Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
|
|
Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
|
|
[Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
|
|
Guidance rescale factor should fix overexposure when using zero terminal SNR.
|
|
|
|
Examples:
|
|
|
|
Returns:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] 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,
|
|
negative_prompt,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
)
|
|
|
|
# 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]
|
|
|
|
device = self._execution_device
|
|
# 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
|
|
|
|
# 3. Encode input prompt
|
|
text_encoder_lora_scale = (
|
|
cross_attention_kwargs.get("scale", None)
|
|
if cross_attention_kwargs is not None
|
|
else None
|
|
)
|
|
prompt_embeds = self._encode_prompt(
|
|
prompt,
|
|
device,
|
|
num_images_per_prompt,
|
|
do_classifier_free_guidance,
|
|
negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
lora_scale=text_encoder_lora_scale,
|
|
)
|
|
|
|
# 4. Prepare timesteps
|
|
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
|
timesteps = self.scheduler.timesteps
|
|
|
|
# 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,
|
|
prompt_embeds.dtype,
|
|
device,
|
|
generator,
|
|
latents,
|
|
)
|
|
|
|
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
|
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
|
|
|
# 7. Denoising loop
|
|
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
|
all_latents = [latents]
|
|
all_log_probs = []
|
|
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
|
for i, t in enumerate(timesteps):
|
|
# expand the latents if we are doing classifier free guidance
|
|
latent_model_input = (
|
|
torch.cat([latents] * 2) 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=prompt_embeds,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
# perform guidance
|
|
if do_classifier_free_guidance:
|
|
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
|
noise_pred = noise_pred_uncond + guidance_scale * (
|
|
noise_pred_text - noise_pred_uncond
|
|
)
|
|
|
|
if do_classifier_free_guidance and guidance_rescale > 0.0:
|
|
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
|
|
noise_pred = rescale_noise_cfg(
|
|
noise_pred, noise_pred_text, guidance_rescale=guidance_rescale
|
|
)
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
latents, log_prob = ddim_step_with_logprob(
|
|
self.scheduler, noise_pred, t, latents, **extra_step_kwargs
|
|
)
|
|
|
|
all_latents.append(latents)
|
|
all_log_probs.append(log_prob)
|
|
|
|
# call the callback, if provided
|
|
if i == len(timesteps) - 1 or (
|
|
(i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
|
|
):
|
|
progress_bar.update()
|
|
if callback is not None and i % callback_steps == 0:
|
|
callback(i, t, latents)
|
|
|
|
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, device, prompt_embeds.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 last model to CPU
|
|
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
|
self.final_offload_hook.offload()
|
|
|
|
return image, has_nsfw_concept, all_latents, all_log_probs
|