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pipeline_stable_diffusion_xl_img2img.py
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pipeline_stable_diffusion_xl_img2img.py
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# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import logging
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
from .pipeline_utils import DiffusionPipelineMixin, rescale_noise_cfg
logger = logging.getLogger(__name__)
class StableDiffusionXLImg2ImgPipelineMixin(DiffusionPipelineMixin):
# Adapted from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
def _encode_prompt(
self,
prompt: Union[str, List[str]],
num_images_per_prompt: int,
do_classifier_free_guidance: bool,
negative_prompt: Optional[Union[str, list]],
prompt_embeds: Optional[np.ndarray] = None,
negative_prompt_embeds: Optional[np.ndarray] = None,
pooled_prompt_embeds: Optional[np.ndarray] = None,
negative_pooled_prompt_embeds: Optional[np.ndarray] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`Union[str, List[str]]`):
prompt to be encoded
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`Optional[Union[str, list]]`):
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`).
prompt_embeds (`Optional[np.ndarray]`, defaults to `None`):
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 (`Optional[np.ndarray]`, defaults to `None`):
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.
pooled_prompt_embeds (`Optional[np.ndarray]`, defaults to `None`):
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
If not provided, pooled text embeddings will be generated from `prompt` input argument.
negative_pooled_prompt_embeds (`Optional[np.ndarray]`, defaults to `None`):
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
input argument.
"""
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
# Define tokenizers and text encoders
tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
text_encoders = (
[self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
)
if prompt_embeds is None:
prompt_embeds_list = []
for tokenizer, text_encoder in zip(tokenizers, text_encoders):
# get prompt text embeddings
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="np").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not np.array_equal(
text_input_ids, untruncated_ids
):
removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {tokenizer.model_max_length} tokens: {removed_text}"
)
prompt_embeds = text_encoder(
input_ids=text_input_ids.astype(text_encoder.input_dtype.get("input_ids", np.int32))
)
pooled_prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds[-2]
prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)
prompt_embeds_list.append(prompt_embeds)
prompt_embeds = np.concatenate(prompt_embeds_list, axis=-1)
# get unconditional embeddings for classifier free guidance
zero_out_negative_prompt = negative_prompt is None and self.config["force_zeros_for_empty_prompt"]
if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
negative_prompt_embeds = np.zeros_like(prompt_embeds)
negative_pooled_prompt_embeds = np.zeros_like(pooled_prompt_embeds)
elif do_classifier_free_guidance and negative_prompt_embeds is None:
negative_prompt = negative_prompt or ""
if prompt is not None and 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
negative_prompt_embeds_list = []
for tokenizer, text_encoder in zip(tokenizers, text_encoders):
max_length = prompt_embeds.shape[1]
uncond_input = tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="np",
)
negative_prompt_embeds = text_encoder(
input_ids=uncond_input.input_ids.astype(text_encoder.input_dtype.get("input_ids", np.int32))
)
negative_pooled_prompt_embeds = negative_prompt_embeds[0]
negative_prompt_embeds = negative_prompt_embeds[-2]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0)
# 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
negative_prompt_embeds_list.append(negative_prompt_embeds)
negative_prompt_embeds = np.concatenate(negative_prompt_embeds_list, axis=-1)
pooled_prompt_embeds = np.repeat(pooled_prompt_embeds, num_images_per_prompt, axis=0)
negative_pooled_prompt_embeds = np.repeat(negative_pooled_prompt_embeds, num_images_per_prompt, axis=0)
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline.check_inputs
def check_inputs(
self,
prompt: Union[str, List[str]],
strength: float,
callback_steps: int,
negative_prompt: Optional[str] = None,
prompt_embeds: Optional[np.ndarray] = None,
negative_prompt_embeds: Optional[np.ndarray] = None,
):
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
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}."
)
def get_timesteps(self, num_inference_steps, strength):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :].numpy()
return timesteps, num_inference_steps - t_start
# Adapted from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, generator=None):
batch_size = batch_size * num_images_per_prompt
if image.shape[1] == 4:
init_latents = image
else:
init_latents = self.vae_encoder(sample=image)[0] * self.vae_decoder.config.get("scaling_factor", 0.18215)
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
# expand init_latents for batch_size
additional_image_per_prompt = batch_size // init_latents.shape[0]
init_latents = np.concatenate([init_latents] * additional_image_per_prompt, axis=0)
elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
raise ValueError(
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
)
else:
init_latents = np.concatenate([init_latents], axis=0)
# add noise to latents using the timesteps
noise = generator.randn(*init_latents.shape).astype(dtype)
init_latents = self.scheduler.add_noise(
torch.from_numpy(init_latents), torch.from_numpy(noise), torch.from_numpy(timestep)
)
return init_latents.numpy()
def _get_add_time_ids(
self, original_size, crops_coords_top_left, target_size, aesthetic_score, negative_aesthetic_score, dtype
):
if self.config.get("requires_aesthetics_score"):
add_time_ids = (original_size + crops_coords_top_left + (aesthetic_score,),)
add_neg_time_ids = (original_size + crops_coords_top_left + (negative_aesthetic_score,),)
else:
add_time_ids = (original_size + crops_coords_top_left + target_size,)
add_neg_time_ids = (original_size + crops_coords_top_left + target_size,)
add_time_ids = np.array(add_time_ids, dtype=dtype)
add_neg_time_ids = np.array(add_neg_time_ids, dtype=dtype)
return add_time_ids, add_neg_time_ids
# Adapted from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.__call__
def __call__(
self,
prompt: Optional[Union[str, List[str]]] = None,
image: Union[np.ndarray, PIL.Image.Image] = None,
strength: float = 0.3,
num_inference_steps: int = 50,
guidance_scale: float = 5.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: int = 1,
eta: float = 0.0,
generator: Optional[np.random.RandomState] = None,
latents: Optional[np.ndarray] = None,
prompt_embeds: Optional[np.ndarray] = None,
negative_prompt_embeds: Optional[np.ndarray] = None,
pooled_prompt_embeds: Optional[np.ndarray] = None,
negative_pooled_prompt_embeds: Optional[np.ndarray] = None,
output_type: str = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
guidance_rescale: float = 0.0,
original_size: Optional[Tuple[int, int]] = None,
crops_coords_top_left: Tuple[int, int] = (0, 0),
target_size: Optional[Tuple[int, int]] = None,
aesthetic_score: float = 6.0,
negative_aesthetic_score: float = 2.5,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`Optional[Union[str, List[str]]]`, defaults to None):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
image (`Union[np.ndarray, PIL.Image.Image]`):
`Image`, or tensor representing an image batch which will be upscaled.
strength (`float`, defaults to 0.8):
Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
will be used as a starting point, adding more noise to it the larger the `strength`. The number of
denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
num_inference_steps (`int`, 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`, defaults to 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 (`Optional[Union[str, list]]`):
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`, defaults to 1):
The number of images to generate per prompt.
eta (`float`, 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 (`Optional[np.random.RandomState]`, defaults to `None`)::
A np.random.RandomState to make generation deterministic.
latents (`Optional[np.ndarray]`, defaults to `None`):
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 (`Optional[np.ndarray]`, defaults to `None`):
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 (`Optional[np.ndarray]`, defaults to `None`):
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`, 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`, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] instead of a
plain tuple.
callback (Optional[Callable], defaults to `None`):
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`, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
guidance_rescale (`float`, 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.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] 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. Check inputs. Raise error if not correct
self.check_inputs(prompt, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)
# 1. Define call parameters
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if generator is None:
generator = np.random
# 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
# 2. Encode input prompt
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = self._encode_prompt(
prompt,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
)
# 3. Preprocess image
image = self.image_processor.preprocess(image)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
latent_timestep = np.repeat(timesteps[:1], batch_size * num_images_per_prompt, axis=0)
timestep_dtype = self.unet.input_dtype.get("timestep", np.float32)
latents_dtype = prompt_embeds.dtype
image = image.astype(latents_dtype)
# 5. Prepare latent variables
latents = self.prepare_latents(
image, latent_timestep, batch_size, num_images_per_prompt, latents_dtype, generator
)
# 6. Prepare extra step kwargs
extra_step_kwargs = {}
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_eta:
extra_step_kwargs["eta"] = eta
height, width = latents.shape[-2:]
height = height * self.vae_scale_factor
width = width * self.vae_scale_factor
original_size = original_size or (height, width)
target_size = target_size or (height, width)
# 8. Prepare added time ids & embeddings
add_text_embeds = pooled_prompt_embeds
add_time_ids, add_neg_time_ids = self._get_add_time_ids(
original_size,
crops_coords_top_left,
target_size,
aesthetic_score,
negative_aesthetic_score,
dtype=prompt_embeds.dtype,
)
if do_classifier_free_guidance:
prompt_embeds = np.concatenate((negative_prompt_embeds, prompt_embeds), axis=0)
add_text_embeds = np.concatenate((negative_pooled_prompt_embeds, add_text_embeds), axis=0)
add_time_ids = np.concatenate((add_time_ids, add_time_ids), axis=0)
add_time_ids = np.repeat(add_time_ids, batch_size * num_images_per_prompt, axis=0)
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)
latent_model_input = latent_model_input.cpu().numpy()
# predict the noise residual
timestep = np.array([t], dtype=timestep_dtype)
noise_pred = self.unet(
sample=latent_model_input,
timestep=timestep,
encoder_hidden_states=prompt_embeds,
text_embeds=add_text_embeds,
time_ids=add_time_ids,
)
noise_pred = noise_pred[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
if 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
scheduler_output = self.scheduler.step(
torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
)
latents = scheduler_output.prev_sample.numpy()
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if output_type == "latent":
image = latents
else:
latents /= self.vae_decoder.config.get("scaling_factor", 0.18215)
# it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
image = np.concatenate(
[self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
)
# apply watermark if available
if self.watermark is not None:
image = self.watermark.apply_watermark(image)
image = self.image_processor.postprocess(image, output_type=output_type)
if not return_dict:
return (image,)
return StableDiffusionXLPipelineOutput(images=image)