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import inspect
import os
import time
from typing import Any, Callable, Dict, List, Optional, Union, Tuple
import gc
import torch
import numpy as np
from glob import glob
from diffusers import StableDiffusionXLInpaintPipeline, UNet2DConditionModel
from diffusers.loaders import TextualInversionLoaderMixin
from diffusers.image_processor import VaeImageProcessor, PipelineImageInput
from diffusers.models import AutoencoderKL
from diffusers.schedulers import (DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
KarrasDiffusionSchedulers)
from diffusers.models.embeddings import TimestepEmbedding, Timesteps
from diffusers.utils.torch_utils import randn_tensor
from diffusers.utils import logging
from PIL import Image
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPTextModelWithProjection
from .lyrasd_vae_model import LyraSdVaeModel
from .module.lyrasd_ip_adapter import LyraIPAdapter
from .lora_util import add_text_lora_layer, add_xltext_lora_layer, add_lora_to_opt_model, load_state_dict
from safetensors.torch import load_file
from .lyrasdxl_pipeline_base import LyraSDXLPipelineBase
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 retrieve_latents(
encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
):
if sample_mode == "sample":
return encoder_output.sample(generator)
elif sample_mode == "argmax":
return encoder_output.mode()
else:
return encoder_output
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:
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
class LyraSdXLTxt2ImgInpaintPipeline(LyraSDXLPipelineBase, StableDiffusionXLInpaintPipeline):
device = torch.device("cpu")
dtype = torch.float32
def __init__(self, device=torch.device("cuda"), dtype=torch.float16, vae_scale_factor=8, vae_scaling_factor=0.13025, num_channels_unet=9, num_channels_latents=4, requires_aesthetics_score: bool = False,
force_zeros_for_empty_prompt: bool = True) -> None:
self.register_to_config(
force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
self.register_to_config(
requires_aesthetics_score=requires_aesthetics_score)
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_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 _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
dtype = image.dtype
# if self.vae.config.force_upcast:
# image = image.float()
# self.vae.to(dtype=torch.float32)
if isinstance(generator, list):
image_latents = [
retrieve_latents(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(self.vae.encode(image), generator=generator)
image_latents = image_latents.to(dtype)
image_latents = self.vae.scaling_factor * image_latents
return image_latents
def _get_add_time_ids(
self,
original_size,
crops_coords_top_left,
target_size,
aesthetic_score,
negative_aesthetic_score,
negative_original_size,
negative_crops_coords_top_left,
negative_target_size,
dtype,
text_encoder_projection_dim=None,
):
if self.config.requires_aesthetics_score:
add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,))
add_neg_time_ids = list(
negative_original_size + negative_crops_coords_top_left + (negative_aesthetic_score,)
)
else:
add_time_ids = list(original_size + crops_coords_top_left + target_size)
add_neg_time_ids = list(negative_original_size + crops_coords_top_left + negative_target_size)
passed_add_embed_dim = (
self.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
)
expected_add_embed_dim = self.add_embedding.linear_1.in_features
if (
expected_add_embed_dim > passed_add_embed_dim
and (expected_add_embed_dim - passed_add_embed_dim) == self.addition_time_embed_dim
):
raise ValueError(
f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model."
)
elif (
expected_add_embed_dim < passed_add_embed_dim
and (passed_add_embed_dim - expected_add_embed_dim) == self.addition_time_embed_dim
):
raise ValueError(
f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model."
)
elif expected_add_embed_dim != passed_add_embed_dim:
raise ValueError(
f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
)
add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype)
return add_time_ids, add_neg_time_ids
def load_ip_adapter(self, dir_ip_adapter, ip_plus, image_encoder_path, num_ip_tokens, ip_projection_dim, dir_face_in=None, num_fp_tokens=1, fp_projection_dim=None, sdxl=True):
self.ip_adapter_helper = LyraIPAdapter(self, sdxl, "cuda", dir_ip_adapter, ip_plus, image_encoder_path,
num_ip_tokens, ip_projection_dim, dir_face_in, num_fp_tokens, fp_projection_dim)
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[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 = 0.9999,
num_inference_steps: int = 50,
timesteps: List[int] = None,
denoising_start: Optional[float] = None,
denoising_end: Optional[float] = None,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
negative_prompt_2: 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,
pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
guidance_rescale: float = 0.0,
original_size: Tuple[int, int] = None,
crops_coords_top_left: Tuple[int, int] = (0, 0),
target_size: Tuple[int, int] = None,
negative_original_size: Optional[Tuple[int, int]] = None,
negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
negative_target_size: Optional[Tuple[int, int]] = None,
aesthetic_score: float = 6.0,
negative_aesthetic_score: float = 2.5,
clip_skip: Optional[int] = None,
extra_tensor_dict: Optional[Dict[str, torch.FloatTensor]] = {},
param_scale_dict: Optional[Dict[str, int]] = {},
**kwargs
):
callback = kwargs.pop("callback", None)
callback_steps = kwargs.pop("callback_steps", None)
# 0. Default height and width to unet
height = height or self.default_sample_size * self.vae_scale_factor
width = width or self.default_sample_size * self.vae_scale_factor
original_size = original_size or (height, width)
target_size = target_size or (height, width)
self._guidance_scale = guidance_scale
self._guidance_rescale = guidance_rescale
self._clip_skip = clip_skip
self._cross_attention_kwargs = cross_attention_kwargs
self._denoising_end = denoising_end
self._denoising_start = denoising_start
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
prompt_2,
height,
width,
strength,
callback_steps,
negative_prompt,
negative_prompt_2,
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
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,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = self.encode_prompt(
prompt=prompt,
prompt_2=prompt_2,
device=device,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
negative_prompt=negative_prompt,
negative_prompt_2=negative_prompt_2,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
lora_scale=text_encoder_lora_scale,
clip_skip=clip_skip
)
def denoising_value_valid(dnv):
return isinstance(self.denoising_end, float) and 0 < dnv < 1
# 4. Prepare timesteps
timesteps, num_inference_steps = retrieve_timesteps(
self.scheduler, num_inference_steps, device, timesteps)
timesteps, num_inference_steps = self.get_timesteps(
num_inference_steps,
strength,
device,
denoising_start=self.denoising_start if denoising_value_valid else None,
)
latent_timestep = timesteps[:1].repeat(
batch_size * num_images_per_prompt)
is_strength_max = strength == 1.0
# 5. Prepare latent variables
init_image = self.image_processor.preprocess(
image, height=height, width=width)
init_image = init_image.to(dtype=torch.float32)
mask = self.mask_processor.preprocess(
mask_image, height=height, width=width)
if masked_image_latents is not None:
masked_image = masked_image_latents
elif init_image.shape[1] == 4:
# if images are in latent space, we can't mask it
masked_image = None
else:
masked_image = init_image * (mask < 0.5)
add_noise = True if self.denoising_start is None else False
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,
add_noise=add_noise,
return_noise=True,
return_image_latents=return_image_latents,
)
if return_image_latents:
latents, noise, image_latents = latents_outputs
else:
latents, noise = latents_outputs
mask, masked_image_latents = self.prepare_mask_latents(
mask,
masked_image,
batch_size * num_images_per_prompt,
height,
width,
prompt_embeds.dtype,
device,
generator,
do_classifier_free_guidance,
)
# 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. Prepare added time ids & embeddings
add_text_embeds = pooled_prompt_embeds
if self.text_encoder_2 is None:
text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
else:
text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
if negative_original_size is None:
negative_original_size = original_size
if negative_target_size is None:
negative_target_size = target_size
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,
negative_original_size,
negative_crops_coords_top_left,
negative_target_size,
dtype=prompt_embeds.dtype,
text_encoder_projection_dim=text_encoder_projection_dim,
)
add_time_ids = add_time_ids.repeat(
batch_size * num_images_per_prompt, 1)
if do_classifier_free_guidance:
prompt_embeds = torch.cat(
[negative_prompt_embeds, prompt_embeds], dim=0)
add_text_embeds = torch.cat(
[negative_pooled_prompt_embeds, add_text_embeds], dim=0)
add_neg_time_ids = add_neg_time_ids.repeat(
batch_size * num_images_per_prompt, 1)
add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)
prompt_embeds = prompt_embeds.to(device)
add_text_embeds = add_text_embeds.to(device)
add_time_ids = add_time_ids.to(device)
# 8. Denoising loop
num_warmup_steps = max(
len(timesteps) - num_inference_steps * self.scheduler.order, 0)
# 7.1 Apply denoising_end
if denoising_end is not None and type(denoising_end) == float and denoising_end > 0 and denoising_end < 1:
discrete_timestep_cutoff = int(
round(
self.scheduler.config.num_train_timesteps
- (denoising_end * self.scheduler.config.num_train_timesteps)
)
)
num_inference_steps = len(
list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
timesteps = timesteps[:num_inference_steps]
aug_emb = self._get_aug_emb(
add_time_ids, add_text_embeds, prompt_embeds.dtype)
extra_tensor_dict2 = {}
for name in extra_tensor_dict:
if name in ["fp_hidden_states", "ip_hidden_states"]:
v1, v2 = extra_tensor_dict[name][0], extra_tensor_dict[name][1]
extra_tensor_dict2[name] = torch.cat(
[v1.repeat(num_images_per_prompt, 1, 1), v2.repeat(num_images_per_prompt, 1, 1)])
else:
extra_tensor_dict2[name] = extra_tensor_dict[name]
# np.save("/workspace/prompt_embeds.npy", prompt_embeds.detach().cpu().numpy())
# prompt_embeds = torch.from_numpy(np.load("/workspace/gt_prompt_embeds.npy")).cuda()
self._num_timesteps = len(timesteps)
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)
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()
noise_pred = self.unet.forward(latent_model_input, prompt_embeds, t, aug_emb, None, None,
None, None, None, extra_tensor_dict2, param_scale_dict).permute(0, 3, 1, 2).contiguous()
# perform guidance
if do_classifier_free_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 do_classifier_free_guidance and self.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=self.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]
if self.num_channels_unet == 4:
init_latents_proper = image_latents
if 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
# 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 output_type == "latent":
return latents
image = self.vae.decode(1 / self.vae.scaling_factor * latents)
image = self.image_processor.postprocess(
image, output_type=output_type)
# 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
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