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import inspect
import os
import sys
import time
from typing import Any, Callable, Dict, List, Optional, Union
import GPUtil
import torch
from diffusers.loaders import TextualInversionLoaderMixin
from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
from diffusers.models.modeling_outputs import AutoencoderKLOutput
from diffusers.utils.torch_utils import logging, randn_tensor
from PIL import Image
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
import gc
import numpy as np
from .lyrasd_vae_model import LyraSdVaeModel
from diffusers.models.embeddings import ImageProjection
from transformers import (
CLIPImageProcessor,
CLIPVisionModelWithProjection,
)
from .lyrasd_pipeline_base import LyraSDXLPipelineBase
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
"""
Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
"""
std_text = noise_pred_text.std(
dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
# rescale the results from guidance (fixes overexposure)
noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
noise_cfg = guidance_rescale * noise_pred_rescaled + \
(1 - guidance_rescale) * noise_cfg
return noise_cfg
def numpy_to_pil(images):
"""
Convert a numpy image or a batch of images to a PIL image.
"""
if images.ndim == 3:
images = images[None, ...]
images = (images * 255).round().astype("uint8")
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
pil_images = [Image.fromarray(image.squeeze(), mode="L")
for image in images]
else:
pil_images = [Image.fromarray(image) for image in images]
return pil_images
def retrieve_timesteps(
scheduler,
num_inference_steps: Optional[int] = None,
device: Optional[Union[str, torch.device]] = None,
timesteps: Optional[List[int]] = None,
**kwargs,
):
"""
Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
Args:
scheduler (`SchedulerMixin`):
The scheduler to get timesteps from.
num_inference_steps (`int`):
The number of diffusion steps used when generating samples with a pre-trained model. If used,
`timesteps` must be `None`.
device (`str` or `torch.device`, *optional*):
The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
timesteps (`List[int]`, *optional*):
Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default
timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
must be `None`.
Returns:
`Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
second element is the number of inference steps.
"""
if timesteps is not None:
print("set(inspect.signature(scheduler.set_timesteps).parameters.keys())", set(
inspect.signature(scheduler.set_timesteps).parameters.keys()))
accepts_timesteps = "timesteps" in set(
inspect.signature(scheduler.set_timesteps).parameters.keys())
if not accepts_timesteps:
raise ValueError(
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
f" timestep schedules. Please check whether you are using the correct scheduler."
)
scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
timesteps = scheduler.timesteps
num_inference_steps = len(timesteps)
else:
scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
timesteps = scheduler.timesteps
return timesteps, num_inference_steps
def retrieve_latents(
encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
):
if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
return encoder_output.latent_dist.sample(generator)
elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
return encoder_output.latent_dist.mode()
elif hasattr(encoder_output, "latents"):
return encoder_output.latents
else:
raise AttributeError(
"Could not access latents of provided encoder_output")
class LyraSdTxt2ImgInpaintPipeline(LyraSDXLPipelineBase):
def __init__(self, device=torch.device("cuda"), dtype=torch.float16, vae_scale_factor=8, vae_scaling_factor=0.18215, num_channels_unet=9, num_channels_latents=4) -> None:
super().__init__(device, dtype, num_channels_unet=num_channels_unet, num_channels_latents=num_channels_latents,
vae_scale_factor=vae_scale_factor, vae_scaling_factor=vae_scaling_factor)
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
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 (`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`).
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.
"""
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]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(
prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.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" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(
dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(
bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
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
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(
uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(
dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(
1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.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
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
def load_ip_adapter(self,
pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
subfolder: str,
weight_name: str,
**kwargs
):
# if hasattr(self, "feature_extractor") and getattr(self, "feature_extractor", None) is None:
self.feature_extractor = CLIPImageProcessor()
# if hasattr(self, "image_encoder") and getattr(self, "image_encoder", None) is None:
self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(
pretrained_model_name_or_path_or_dict,
subfolder=os.path.join(subfolder, "image_encoder"),
).to(self.device, dtype=self.dtype)
# else:
# print("kio: already has image_encoder", hasattr(self, "image_encoder"), getattr(self, "feature_extractor", None) is None)
# kiotodo: init ImageProjection
model_path = os.path.join(
pretrained_model_name_or_path_or_dict, subfolder, weight_name)
state_dict = torch.load(model_path, map_location="cpu")
clip_embeddings_dim = state_dict["image_proj"]["proj.weight"].shape[-1]
cross_attention_dim = state_dict["image_proj"]["proj.weight"].shape[0] // 4
self.encoder_hid_proj = ImageProjection(
cross_attention_dim=cross_attention_dim, image_embed_dim=clip_embeddings_dim, num_image_text_embeds=4
)
image_proj_state_dict = {}
image_proj_state_dict.update(
{
"image_embeds.weight": state_dict["image_proj"]["proj.weight"],
"image_embeds.bias": state_dict["image_proj"]["proj.bias"],
"norm.weight": state_dict["image_proj"]["norm.weight"],
"norm.bias": state_dict["image_proj"]["norm.bias"],
}
)
self.encoder_hid_proj.load_state_dict(image_proj_state_dict)
self.encoder_hid_proj.to(dtype=self.dtype, device=self.device)
dir_ipadapter = os.path.join(
pretrained_model_name_or_path_or_dict, subfolder, '.'.join(weight_name.split(".")[:-1]))
self.unet.load_ip_adapter(dir_ipadapter, "", 1, "fp16")
def encode_image(self, image, device, num_images_per_prompt):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(
image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeds = self.image_encoder(image).image_embeds
image_embeds = image_embeds.repeat_interleave(
num_images_per_prompt, dim=0)
uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds
def decode_latents(self, latents):
latents = 1 / self.vae.scaling_factor * latents
image = self.vae.decode(latents).sample
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
def lyra_decode_latents(self, latents):
# print("lyra_decode_latents")
# np.save("", latents.)
# np.save(f"/workspace/vae_model/latent.npy", latents.detach().cpu().numpy())
latents = 1 / self.vae.scaling_factor * latents
# latents = latents.permute(0, 2, 3, 1).contiguous()
image = self.vae.decode(latents)
image = image.permute(0, 2, 3, 1)
# print(image)
# GPUtil.showUtilization(all=True)
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().float().numpy()
return image
def get_timesteps(self, num_inference_steps, strength, device):
# 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:]
return timesteps, num_inference_steps - t_start
def check_inputs(
self,
prompt,
height,
width,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if height % 64 != 0 or width % 64 != 0: # 初版暂时只支持 64 的倍数的 height 和 width
raise ValueError(
f"`height` and `width` have to be divisible by 64 but are {height} and {width}.")
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 _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
if isinstance(generator, list):
image_latents = [
retrieve_latents(AutoencoderKLOutput(
latent_dist=self.vae.encode(image[i: i + 1])), generator=generator[i])
for i in range(image.shape[0])
]
image_latents = torch.cat(image_latents, dim=0)
else:
image_latents = retrieve_latents(AutoencoderKLOutput(
latent_dist=self.vae.encode(image)), generator=generator)
image_latents = self.vae_scaling_factor * image_latents
return image_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None,
image=None, timestep=None, is_strength_max=True, return_noise=False, return_image_latents=False):
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 (image is None or timestep is None) and not is_strength_max:
raise ValueError(
"Since strength < 1. initial latents are to be initialised as a combination of Image + Noise."
"However, either the image or the noise timestep has not been provided."
)
if return_image_latents or (latents is None and not is_strength_max):
image = image.to(device=device, dtype=dtype)
if image.shape[1] == 4:
image_latents = image
else:
image_latents = self._encode_vae_image(
image=image, generator=generator)
image_latents = image_latents.repeat(
batch_size // image_latents.shape[0], 1, 1, 1)
if latents is None:
noise = randn_tensor(shape, generator=generator,
device=device, dtype=dtype)
# if strength is 1. then initialise the latents to noise, else initial to image + noise
latents = noise if is_strength_max else self.scheduler.add_noise(
image_latents, noise, timestep)
# if pure noise then scale the initial latents by the Scheduler's init sigma
latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents
else:
noise = latents.to(device)
latents = noise * self.scheduler.init_noise_sigma
outputs = (latents,)
if return_noise:
outputs += (noise,)
if return_image_latents:
outputs += (image_latents,)
return outputs
def prepare_mask_latents(
self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
):
# resize the mask to latents shape as we concatenate the mask to the latents
# we do that before converting to dtype to avoid breaking in case we're using cpu_offload
# and half precision
mask = torch.nn.functional.interpolate(
mask, size=(height // self.vae_scale_factor,
width // self.vae_scale_factor)
)
mask = mask.to(device=device, dtype=dtype)
masked_image = masked_image.to(device=device, dtype=dtype)
if masked_image.shape[1] == 4:
masked_image_latents = masked_image
else:
masked_image_latents = self._encode_vae_image(
masked_image, generator=generator)
# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
if mask.shape[0] < batch_size:
if not batch_size % mask.shape[0] == 0:
raise ValueError(
"The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
" of masks that you pass is divisible by the total requested batch size."
)
mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
if masked_image_latents.shape[0] < batch_size:
if not batch_size % masked_image_latents.shape[0] == 0:
raise ValueError(
"The passed images and the required batch size don't match. Images are supposed to be duplicated"
f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
" Make sure the number of images that you pass is divisible by the total requested batch size."
)
masked_image_latents = masked_image_latents.repeat(
batch_size // masked_image_latents.shape[0], 1, 1, 1)
mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
masked_image_latents = (
torch.cat([masked_image_latents] *
2) if do_classifier_free_guidance else masked_image_latents
)
# aligning device to prevent device errors when concating it with the latent model input
masked_image_latents = masked_image_latents.to(
device=device, dtype=dtype)
return mask, masked_image_latents
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
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: PipelineImageInput = None,
mask_image: PipelineImageInput = None,
masked_image_latents: torch.FloatTensor = None,
height: Optional[int] = None,
width: Optional[int] = None,
strength: float = 1.0,
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,
ip_adapter_image: Optional[PipelineImageInput] = None,
param_scale_dict: Optional[dict] = {}
):
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.
"""
# 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
# self.unet_config.sample_size = 64
# height = 512
# width = 512
# 1. Check inputs. Raise error if not correct
# self.check_inputs(
# prompt, height, width, 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.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
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,
)
# 3.5 Encode ipadapter_image
if ip_adapter_image is not None:
image_embeds, negative_image_embeds = self.encode_image(
ip_adapter_image, device, num_images_per_prompt)
if do_classifier_free_guidance:
image_embeds = torch.cat([negative_image_embeds, image_embeds])
image_embeds = self.encoder_hid_proj(image_embeds).to(self.dtype)
# 4. Prepare timesteps
# self.scheduler.set_timesteps(num_inference_steps, device=device)
# timesteps = self.scheduler.timesteps
# 4.5 Prepare mask and image
timesteps = None
timesteps, num_inference_steps = retrieve_timesteps(
self.scheduler, num_inference_steps, device, timesteps)
timesteps, num_inference_steps = self.get_timesteps(
num_inference_steps=num_inference_steps, strength=strength, device=device
)
# check that number of inference steps is not < 1 - as this doesn't make sense
if num_inference_steps < 1:
raise ValueError(
f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline"
f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline."
)
# at which timestep to set the initial noise (n.b. 50% if strength is 0.5)
latent_timestep = timesteps[:1].repeat(
batch_size * num_images_per_prompt)
# create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise
is_strength_max = strength == 1.0
# 5. Preprocess mask and image
init_image = self.image_processor.preprocess(
image, height=height, width=width)
init_image = init_image.to(dtype=torch.float32)
# 5. Prepare latent variables
return_image_latents = self.num_channels_unet == 4
latents_outputs = self.prepare_latents(
batch_size * num_images_per_prompt,
self.num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
image=init_image,
timestep=latent_timestep,
is_strength_max=is_strength_max,
return_noise=True,
return_image_latents=return_image_latents
)
if return_image_latents:
latents, noise, image_latents = latents_outputs
else:
latents, noise = latents_outputs
# 5.5 Prepare mask latent variables
mask_condition = self.mask_processor.preprocess(
mask_image, height=height, width=width)
if masked_image_latents is None:
masked_image = init_image * (mask_condition < 0.5)
else:
masked_image = masked_image_latents
mask, masked_image_latents = self.prepare_mask_latents(
mask_condition,
masked_image,
batch_size * num_images_per_prompt,
height,
width,
prompt_embeds.dtype,
device,
generator,
do_classifier_free_guidance,
)
# Check that sizes of mask, masked image and latents match
if self.num_channels_unet == 9:
# default case for runwayml/stable-diffusion-inpainting
num_channels_mask = mask.shape[1]
num_channels_masked_image = masked_image_latents.shape[1]
if self.num_channels_latents + num_channels_mask + num_channels_masked_image != self.num_channels_unet:
raise ValueError(
f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
f" {self.num_channels_latents} but received `num_channels_latents`: {self.num_channels_latents} +"
f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
f" = {self.num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
" `pipeline.unet` or your `mask_image` or `image` input."
)
elif self.num_channels_unet != 4:
raise ValueError(
f"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}."
)
# 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
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)
if self.num_channels_unet == 9:
latent_model_input = torch.cat(
[latent_model_input, mask, masked_image_latents], dim=1)
latent_model_input = latent_model_input.permute(
0, 2, 3, 1).contiguous()
# latent_model_input = latent_model_input[:,:4,:,:].
# 后边三个 None 是给到controlnet 的参数,暂时给到 None 当 placeholder
# todo: forward ip image_embeds
# break
if ip_adapter_image is not None:
noise_pred = self.unet.forward(
latent_model_input, prompt_embeds, t, None, None, None, None, {"ip_hidden_states": image_embeds}, param_scale_dict)
else:
noise_pred = self.unet.forward(
latent_model_input, prompt_embeds, t)
noise_pred = noise_pred.permute(0, 3, 1, 2).contiguous()
# saver.save_v(f"latent_model_input_{i}", latent_model_input)
# saver.save_v(f"noise_pred_{i}", noise_pred)
# saver.save_v(f"prompt_embeds_{i}", prompt_embeds)
# 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)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(
noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
if self.num_channels_unet == 4:
init_latents_proper = image_latents
if self.do_classifier_free_guidance:
init_mask, _ = mask.chunk(2)
else:
init_mask = mask
if i < len(timesteps) - 1:
noise_timestep = timesteps[i + 1]
init_latents_proper = self.scheduler.add_noise(
init_latents_proper, noise, torch.tensor(
[noise_timestep])
)
latents = (1 - init_mask) * init_latents_proper + \
init_mask * latents
# 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 = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# image = self.decode_latents(latents)
image = self.lyra_decode_latents(latents)
image = numpy_to_pil(image)
return image
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