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[Community Pipeline] fix lpw_stable_diffusion (#1570)

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* fix lpw_stable_diffusion

* rollback preprocess_mask resample
This commit is contained in:
SkyTNT
2022-12-07 18:20:01 +08:00
committed by GitHub
parent 896c98a2ae
commit 4eb9ad0d1c
2 changed files with 551 additions and 525 deletions
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564
examples/community/lpw_stable_diffusion.py
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@@ -6,38 +6,13 @@ import numpy as np
import torch
import PIL
from diffusers.configuration_utils import FrozenDict
from diffusers import SchedulerMixin, StableDiffusionPipeline
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging
# TODO: remove and import from diffusers.utils when the new version of diffusers is released
from packaging import version
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
from diffusers.utils import PIL_INTERPOLATION, deprecate, logging
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
PIL_INTERPOLATION = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling.BILINEAR,
"bicubic": PIL.Image.Resampling.BICUBIC,
"lanczos": PIL.Image.Resampling.LANCZOS,
"nearest": PIL.Image.Resampling.NEAREST,
}
else:
PIL_INTERPOLATION = {
"linear": PIL.Image.LINEAR,
"bilinear": PIL.Image.BILINEAR,
"bicubic": PIL.Image.BICUBIC,
"lanczos": PIL.Image.LANCZOS,
"nearest": PIL.Image.NEAREST,
}
# ------------------------------------------------------------------------------
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
re_attention = re.compile(
@@ -146,7 +121,7 @@ def parse_prompt_attention(text):
return res
def get_prompts_with_weights(pipe: DiffusionPipeline, prompt: List[str], max_length: int):
def get_prompts_with_weights(pipe: StableDiffusionPipeline, prompt: List[str], max_length: int):
r"""
Tokenize a list of prompts and return its tokens with weights of each token.
@@ -207,7 +182,7 @@ def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, no_boseos_midd
def get_unweighted_text_embeddings(
pipe: DiffusionPipeline,
pipe: StableDiffusionPipeline,
text_input: torch.Tensor,
chunk_length: int,
no_boseos_middle: Optional[bool] = True,
@@ -247,10 +222,10 @@ def get_unweighted_text_embeddings(
def get_weighted_text_embeddings(
pipe: DiffusionPipeline,
pipe: StableDiffusionPipeline,
prompt: Union[str, List[str]],
uncond_prompt: Optional[Union[str, List[str]]] = None,
max_embeddings_multiples: Optional[int] = 1,
max_embeddings_multiples: Optional[int] = 3,
no_boseos_middle: Optional[bool] = False,
skip_parsing: Optional[bool] = False,
skip_weighting: Optional[bool] = False,
@@ -264,14 +239,14 @@ def get_weighted_text_embeddings(
Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.
Args:
pipe (`DiffusionPipeline`):
pipe (`StableDiffusionPipeline`):
Pipe to provide access to the tokenizer and the text encoder.
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
uncond_prompt (`str` or `List[str]`):
The unconditional prompt or prompts for guide the image generation. If unconditional prompt
is provided, the embeddings of prompt and uncond_prompt are concatenated.
max_embeddings_multiples (`int`, *optional*, defaults to `1`):
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
no_boseos_middle (`bool`, *optional*, defaults to `False`):
If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and
@@ -387,11 +362,11 @@ def preprocess_image(image):
return 2.0 * image - 1.0
def preprocess_mask(mask):
def preprocess_mask(mask, scale_factor=8):
mask = mask.convert("L")
w, h = mask.size
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
mask = mask.resize((w // 8, h // 8), resample=PIL_INTERPOLATION["nearest"])
mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"])
mask = np.array(mask).astype(np.float32) / 255.0
mask = np.tile(mask, (4, 1, 1))
mask = mask[None].transpose(0, 1, 2, 3) # what does this step do?
@@ -400,7 +375,7 @@ def preprocess_mask(mask):
return mask
class StableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
r"""
Pipeline for text-to-image generation using Stable Diffusion without tokens length limit, and support parsing
weighting in prompt.
@@ -435,50 +410,12 @@ class StableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
scheduler: SchedulerMixin,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
requires_safety_checker: bool = True,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
self.register_modules(
super().__init__(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
@@ -486,51 +423,171 @@ class StableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
requires_safety_checker=requires_safety_checker,
)
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
max_embeddings_multiples,
):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Encodes the prompt into text encoder hidden states.
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
`attention_head_dim` must be a multiple of `slice_size`.
prompt (`str` or `list(int)`):
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]`):
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`).
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(slice_size)
batch_size = len(prompt) if isinstance(prompt, list) else 1
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
back to computing attention in one step.
"""
# set slice_size = `None` to disable `attention slicing`
self.enable_attention_slicing(None)
if negative_prompt is None:
negative_prompt = [""] * batch_size
elif isinstance(negative_prompt, str):
negative_prompt = [negative_prompt] * batch_size
if 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`."
)
def enable_sequential_cpu_offload(self):
r"""
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
"""
if is_accelerate_available():
from accelerate import cpu_offload
text_embeddings, uncond_embeddings = get_weighted_text_embeddings(
pipe=self,
prompt=prompt,
uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
max_embeddings_multiples=max_embeddings_multiples,
)
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
if do_classifier_free_guidance:
bs_embed, seq_len, _ = uncond_embeddings.shape
uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
return text_embeddings
def check_inputs(self, prompt, height, width, strength, callback_steps):
if 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 strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
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)}."
)
def get_timesteps(self, num_inference_steps, strength, device, is_text2img):
if is_text2img:
return self.scheduler.timesteps.to(device), num_inference_steps
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
# get the original timestep using init_timestep
offset = self.scheduler.config.get("steps_offset", 0)
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
device = self.device
t_start = max(num_inference_steps - init_timestep + offset, 0)
timesteps = self.scheduler.timesteps[t_start:].to(device)
return timesteps, num_inference_steps - t_start
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(cpu_offloaded_model, device)
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
else:
has_nsfw_concept = None
return image, has_nsfw_concept
def decode_latents(self, latents):
latents = 1 / 0.18215 * 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 bfloa16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
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
def prepare_latents(self, image, timestep, batch_size, height, width, dtype, device, generator, latents=None):
if image is None:
shape = (
batch_size,
self.unet.in_channels,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
if latents is None:
if device.type == "mps":
# randn does not work reproducibly on mps
latents = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device)
else:
latents = torch.randn(shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents, None, None
else:
init_latent_dist = self.vae.encode(image).latent_dist
init_latents = init_latent_dist.sample(generator=generator)
init_latents = 0.18215 * init_latents
init_latents = torch.cat([init_latents] * batch_size, dim=0)
init_latents_orig = init_latents
shape = init_latents.shape
# add noise to latents using the timesteps
if device.type == "mps":
noise = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device)
else:
noise = torch.randn(shape, generator=generator, device=device, dtype=dtype)
latents = self.scheduler.add_noise(init_latents, noise, timestep)
return latents, init_latents_orig, noise
@torch.no_grad()
def __call__(
@@ -634,221 +691,111 @@ class StableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
init_image = deprecate("init_image", "0.12.0", message, take_from=kwargs)
image = init_image or image
if isinstance(prompt, str):
batch_size = 1
prompt = [prompt]
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
# 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
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
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)}."
)
# get prompt text embeddings
# 1. Check inputs. Raise error if not correct
self.check_inputs(prompt, height, width, strength, callback_steps)
# 2. Define call parameters
batch_size = 1 if isinstance(prompt, str) else len(prompt)
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
# get unconditional embeddings for classifier free guidance
if negative_prompt is None:
negative_prompt = [""] * batch_size
elif isinstance(negative_prompt, str):
negative_prompt = [negative_prompt] * batch_size
if 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`."
)
text_embeddings, uncond_embeddings = get_weighted_text_embeddings(
pipe=self,
prompt=prompt,
uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
max_embeddings_multiples=max_embeddings_multiples,
**kwargs,
# 3. Encode input prompt
text_embeddings = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
max_embeddings_multiples,
)
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
dtype = text_embeddings.dtype
if do_classifier_free_guidance:
bs_embed, seq_len, _ = uncond_embeddings.shape
uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
latents_dtype = text_embeddings.dtype
init_latents_orig = None
mask = None
noise = None
if image is None:
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
latents_shape = (
batch_size * num_images_per_prompt,
self.unet.in_channels,
height // 8,
width // 8,
)
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
latents = torch.randn(
latents_shape,
generator=generator,
device="cpu",
dtype=latents_dtype,
).to(self.device)
else:
latents = torch.randn(
latents_shape,
generator=generator,
device=self.device,
dtype=latents_dtype,
)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
latents = latents.to(self.device)
timesteps = self.scheduler.timesteps.to(self.device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
# 4. Preprocess image and mask
if isinstance(image, PIL.Image.Image):
image = preprocess_image(image)
if image is not None:
image = image.to(device=self.device, dtype=dtype)
if isinstance(mask_image, PIL.Image.Image):
mask_image = preprocess_mask(mask_image, self.vae_scale_factor)
if mask_image is not None:
mask = mask_image.to(device=self.device, dtype=dtype)
mask = torch.cat([mask] * batch_size * num_images_per_prompt)
else:
if isinstance(image, PIL.Image.Image):
image = preprocess_image(image)
# encode the init image into latents and scale the latents
image = image.to(device=self.device, dtype=latents_dtype)
init_latent_dist = self.vae.encode(image).latent_dist
init_latents = init_latent_dist.sample(generator=generator)
init_latents = 0.18215 * init_latents
init_latents = torch.cat([init_latents] * batch_size * num_images_per_prompt, dim=0)
init_latents_orig = init_latents
mask = None
# preprocess mask
if mask_image is not None:
if isinstance(mask_image, PIL.Image.Image):
mask_image = preprocess_mask(mask_image)
mask_image = mask_image.to(device=self.device, dtype=latents_dtype)
mask = torch.cat([mask_image] * batch_size * num_images_per_prompt)
# 5. set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device, image is None)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
# check sizes
if not mask.shape == init_latents.shape:
raise ValueError("The mask and image should be the same size!")
# 6. Prepare latent variables
latents, init_latents_orig, noise = self.prepare_latents(
image,
latent_timestep,
batch_size * num_images_per_prompt,
height,
width,
dtype,
device,
generator,
latents,
)
# get the original timestep using init_timestep
offset = self.scheduler.config.get("steps_offset", 0)
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
# 7. 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)
timesteps = self.scheduler.timesteps[-init_timestep]
timesteps = torch.tensor([timesteps] * batch_size * num_images_per_prompt, device=self.device)
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
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)
# add noise to latents using the timesteps
if self.device.type == "mps":
# randn does not exist on mps
noise = torch.randn(
init_latents.shape,
generator=generator,
device="cpu",
dtype=latents_dtype,
).to(self.device)
else:
noise = torch.randn(
init_latents.shape,
generator=generator,
device=self.device,
dtype=latents_dtype,
)
latents = self.scheduler.add_noise(init_latents, noise, timesteps)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
t_start = max(num_inference_steps - init_timestep + offset, 0)
timesteps = self.scheduler.timesteps[t_start:].to(self.device)
# 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)
# 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
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
for i, t in enumerate(self.progress_bar(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 mask is not None:
# masking
init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))
latents = (init_latents_proper * mask) + (latents * (1 - mask))
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
# 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 i % callback_steps == 0:
if callback is not None:
callback(i, t, latents)
if is_cancelled_callback is not None and is_cancelled_callback():
return None
# 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)
# 9. Post-processing
image = self.decode_latents(latents)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
if mask is not None:
# masking
init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))
latents = (init_latents_proper * mask) + (latents * (1 - mask))
# call the callback, if provided
if i % callback_steps == 0:
if callback is not None:
callback(i, t, latents)
if is_cancelled_callback is not None and is_cancelled_callback():
return None
latents = 1 / 0.18215 * 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 bfloa16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
self.device
)
image, has_nsfw_concept = self.safety_checker(
images=image,
clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype),
)
else:
has_nsfw_concept = None
# 10. Run safety checker
image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)
# 11. Convert to PIL
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return image, has_nsfw_concept
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
@@ -868,6 +815,7 @@ class StableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
is_cancelled_callback: Optional[Callable[[], bool]] = None,
callback_steps: Optional[int] = 1,
**kwargs,
):
@@ -915,6 +863,9 @@ class StableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
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)`.
is_cancelled_callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. If the function returns
`True`, the inference will be cancelled.
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.
@@ -940,6 +891,7 @@ class StableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
output_type=output_type,
return_dict=return_dict,
callback=callback,
is_cancelled_callback=is_cancelled_callback,
callback_steps=callback_steps,
**kwargs,
)
@@ -959,6 +911,7 @@ class StableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
is_cancelled_callback: Optional[Callable[[], bool]] = None,
callback_steps: Optional[int] = 1,
**kwargs,
):
@@ -1007,6 +960,9 @@ class StableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
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)`.
is_cancelled_callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. If the function returns
`True`, the inference will be cancelled.
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.
@@ -1031,6 +987,7 @@ class StableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
output_type=output_type,
return_dict=return_dict,
callback=callback,
is_cancelled_callback=is_cancelled_callback,
callback_steps=callback_steps,
**kwargs,
)
@@ -1051,6 +1008,7 @@ class StableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
is_cancelled_callback: Optional[Callable[[], bool]] = None,
callback_steps: Optional[int] = 1,
**kwargs,
):
@@ -1103,6 +1061,9 @@ class StableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
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)`.
is_cancelled_callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. If the function returns
`True`, the inference will be cancelled.
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.
@@ -1128,6 +1089,7 @@ class StableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
output_type=output_type,
return_dict=return_dict,
callback=callback,
is_cancelled_callback=is_cancelled_callback,
callback_steps=callback_steps,
**kwargs,
)

512
examples/community/lpw_stable_diffusion_onnx.py
View File

@@ -6,35 +6,13 @@ import numpy as np
import torch
import PIL
from diffusers.onnx_utils import OnnxRuntimeModel
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers import OnnxStableDiffusionPipeline, SchedulerMixin
from diffusers.onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, logging
# TODO: remove and import from diffusers.utils when the new version of diffusers is released
from packaging import version
from diffusers.utils import PIL_INTERPOLATION, deprecate, logging
from transformers import CLIPFeatureExtractor, CLIPTokenizer
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
PIL_INTERPOLATION = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling.BILINEAR,
"bicubic": PIL.Image.Resampling.BICUBIC,
"lanczos": PIL.Image.Resampling.LANCZOS,
"nearest": PIL.Image.Resampling.NEAREST,
}
else:
PIL_INTERPOLATION = {
"linear": PIL.Image.LINEAR,
"bilinear": PIL.Image.BILINEAR,
"bicubic": PIL.Image.BICUBIC,
"lanczos": PIL.Image.LANCZOS,
"nearest": PIL.Image.NEAREST,
}
# ------------------------------------------------------------------------------
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
re_attention = re.compile(
@@ -262,7 +240,7 @@ def get_weighted_text_embeddings(
Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.
Args:
pipe (`DiffusionPipeline`):
pipe (`OnnxStableDiffusionPipeline`):
Pipe to provide access to the tokenizer and the text encoder.
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
@@ -392,11 +370,11 @@ def preprocess_image(image):
return 2.0 * image - 1.0
def preprocess_mask(mask):
def preprocess_mask(mask, scale_factor=8):
mask = mask.convert("L")
w, h = mask.size
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
mask = mask.resize((w // 8, h // 8), resample=PIL_INTERPOLATION["nearest"])
mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"])
mask = np.array(mask).astype(np.float32) / 255.0
mask = np.tile(mask, (4, 1, 1))
mask = mask[None].transpose(0, 1, 2, 3) # what does this step do?
@@ -404,7 +382,7 @@ def preprocess_mask(mask):
return mask
class OnnxStableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
class OnnxStableDiffusionLongPromptWeightingPipeline(OnnxStableDiffusionPipeline):
r"""
Pipeline for text-to-image generation using Stable Diffusion without tokens length limit, and support parsing
weighting in prompt.
@@ -420,12 +398,12 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
text_encoder: OnnxRuntimeModel,
tokenizer: CLIPTokenizer,
unet: OnnxRuntimeModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
scheduler: SchedulerMixin,
safety_checker: OnnxRuntimeModel,
feature_extractor: CLIPFeatureExtractor,
requires_safety_checker: bool = True,
):
super().__init__()
self.register_modules(
super().__init__(
vae_encoder=vae_encoder,
vae_decoder=vae_decoder,
text_encoder=text_encoder,
@@ -434,7 +412,170 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
requires_safety_checker=requires_safety_checker,
)
self.unet_in_channels = 4
self.vae_scale_factor = 8
def _encode_prompt(
self,
prompt,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
max_embeddings_multiples,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `list(int)`):
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 (`str` or `List[str]`):
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`).
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
"""
batch_size = len(prompt) if isinstance(prompt, list) else 1
if negative_prompt is None:
negative_prompt = [""] * batch_size
elif isinstance(negative_prompt, str):
negative_prompt = [negative_prompt] * batch_size
if 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`."
)
text_embeddings, uncond_embeddings = get_weighted_text_embeddings(
pipe=self,
prompt=prompt,
uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
max_embeddings_multiples=max_embeddings_multiples,
)
text_embeddings = text_embeddings.repeat(num_images_per_prompt, 0)
if do_classifier_free_guidance:
uncond_embeddings = uncond_embeddings.repeat(num_images_per_prompt, 0)
text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
return text_embeddings
def check_inputs(self, prompt, height, width, strength, callback_steps):
if 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 strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
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)}."
)
def get_timesteps(self, num_inference_steps, strength, is_text2img):
if is_text2img:
return self.scheduler.timesteps, num_inference_steps
else:
# get the original timestep using init_timestep
offset = self.scheduler.config.get("steps_offset", 0)
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
t_start = max(num_inference_steps - init_timestep + offset, 0)
timesteps = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def run_safety_checker(self, image):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(
self.numpy_to_pil(image), return_tensors="np"
).pixel_values.astype(image.dtype)
# There will throw an error if use safety_checker directly and batchsize>1
images, has_nsfw_concept = [], []
for i in range(image.shape[0]):
image_i, has_nsfw_concept_i = self.safety_checker(
clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
)
images.append(image_i)
has_nsfw_concept.append(has_nsfw_concept_i[0])
image = np.concatenate(images)
else:
has_nsfw_concept = None
return image, has_nsfw_concept
def decode_latents(self, latents):
latents = 1 / 0.18215 * latents
# image = self.vae_decoder(latent_sample=latents)[0]
# 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])]
)
image = np.clip(image / 2 + 0.5, 0, 1)
image = image.transpose((0, 2, 3, 1))
return image
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
def prepare_latents(self, image, timestep, batch_size, height, width, dtype, generator, latents=None):
if image is None:
shape = (
batch_size,
self.unet_in_channels,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
if latents is None:
latents = torch.randn(shape, generator=generator, device="cpu").numpy().astype(dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
# scale the initial noise by the standard deviation required by the scheduler
latents = (torch.from_numpy(latents) * self.scheduler.init_noise_sigma).numpy()
return latents, None, None
else:
init_latents = self.vae_encoder(sample=image)[0]
init_latents = 0.18215 * init_latents
init_latents = np.concatenate([init_latents] * batch_size, axis=0)
init_latents_orig = init_latents
shape = init_latents.shape
# add noise to latents using the timesteps
noise = torch.randn(shape, generator=generator, device="cpu").numpy().astype(dtype)
latents = self.scheduler.add_noise(
torch.from_numpy(init_latents), torch.from_numpy(noise), timestep
).numpy()
return latents, init_latents_orig, noise
@torch.no_grad()
def __call__(
@@ -450,7 +591,7 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
strength: float = 0.8,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[np.random.RandomState] = None,
generator: Optional[torch.Generator] = None,
latents: Optional[np.ndarray] = None,
max_embeddings_multiples: Optional[int] = 3,
output_type: Optional[str] = "pil",
@@ -501,8 +642,9 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
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 (`np.random.RandomState`, *optional*):
A np.random.RandomState to make generation deterministic.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`np.ndarray`, *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
@@ -537,204 +679,123 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
init_image = deprecate("init_image", "0.12.0", message, take_from=kwargs)
image = init_image or image
if isinstance(prompt, str):
batch_size = 1
prompt = [prompt]
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
# 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
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
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)}."
)
# get prompt text embeddings
# 1. Check inputs. Raise error if not correct
self.check_inputs(prompt, height, width, strength, callback_steps)
# 2. Define call parameters
batch_size = 1 if isinstance(prompt, str) else len(prompt)
# 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
# get unconditional embeddings for classifier free guidance
if negative_prompt is None:
negative_prompt = [""] * batch_size
elif isinstance(negative_prompt, str):
negative_prompt = [negative_prompt] * batch_size
if 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`."
)
if generator is None:
generator = np.random
# 3. Encode input prompt
text_embeddings = self._encode_prompt(
prompt,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
max_embeddings_multiples,
)
dtype = text_embeddings.dtype
text_embeddings, uncond_embeddings = get_weighted_text_embeddings(
pipe=self,
prompt=prompt,
uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
max_embeddings_multiples=max_embeddings_multiples,
**kwargs,
# 4. Preprocess image and mask
if isinstance(image, PIL.Image.Image):
image = preprocess_image(image)
if image is not None:
image = image.astype(dtype)
if isinstance(mask_image, PIL.Image.Image):
mask_image = preprocess_mask(mask_image, self.vae_scale_factor)
if mask_image is not None:
mask = mask_image.astype(dtype)
mask = np.concatenate([mask] * batch_size * num_images_per_prompt)
else:
mask = None
# 5. set timesteps
self.scheduler.set_timesteps(num_inference_steps)
timestep_dtype = next(
(input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
)
timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, image is None)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
# 6. Prepare latent variables
latents, init_latents_orig, noise = self.prepare_latents(
image,
latent_timestep,
batch_size * num_images_per_prompt,
height,
width,
dtype,
generator,
latents,
)
text_embeddings = text_embeddings.repeat(num_images_per_prompt, 0)
if do_classifier_free_guidance:
uncond_embeddings = uncond_embeddings.repeat(num_images_per_prompt, 0)
text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
# 7. 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)
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
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 = 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.numpy()
latents_dtype = text_embeddings.dtype
init_latents_orig = None
mask = None
noise = None
if image is None:
latents_shape = (
batch_size * num_images_per_prompt,
4,
height // 8,
width // 8,
)
if latents is None:
latents = generator.randn(*latents_shape).astype(latents_dtype)
elif latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
timesteps = self.scheduler.timesteps.to(self.device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
else:
if isinstance(image, PIL.Image.Image):
image = preprocess_image(image)
# encode the init image into latents and scale the latents
image = image.astype(latents_dtype)
init_latents = self.vae_encoder(sample=image)[0]
init_latents = 0.18215 * init_latents
init_latents = np.concatenate([init_latents] * batch_size * num_images_per_prompt)
init_latents_orig = init_latents
# preprocess mask
if mask_image is not None:
if isinstance(mask_image, PIL.Image.Image):
mask_image = preprocess_mask(mask_image)
mask_image = mask_image.astype(latents_dtype)
mask = np.concatenate([mask_image] * batch_size * num_images_per_prompt)
# check sizes
if not mask.shape == init_latents.shape:
print(mask.shape, init_latents.shape)
raise ValueError("The mask and image should be the same size!")
# get the original timestep using init_timestep
offset = self.scheduler.config.get("steps_offset", 0)
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
timesteps = self.scheduler.timesteps[-init_timestep]
timesteps = torch.tensor([timesteps] * batch_size * num_images_per_prompt)
# add noise to latents using the timesteps
noise = generator.randn(*init_latents.shape).astype(latents_dtype)
latents = self.scheduler.add_noise(
torch.from_numpy(init_latents), torch.from_numpy(noise), timesteps
).numpy()
t_start = max(num_inference_steps - init_timestep + offset, 0)
timesteps = self.scheduler.timesteps[t_start:]
# 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
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(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
sample=latent_model_input,
timestep=np.array([t]),
encoder_hidden_states=text_embeddings,
)
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)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample.numpy()
if mask is not None:
# masking
init_latents_proper = self.scheduler.add_noise(
torch.from_numpy(init_latents_orig),
torch.from_numpy(noise),
torch.tensor([t]),
).numpy()
latents = (init_latents_proper * mask) + (latents * (1 - mask))
# call the callback, if provided
if i % callback_steps == 0:
if callback is not None:
callback(i, t, latents)
if is_cancelled_callback is not None and is_cancelled_callback():
return None
latents = 1 / 0.18215 * latents
# image = self.vae_decoder(latent_sample=latents)[0]
# it seems likes there is a problem for using half-precision vae decoder if batchsize>1
image = []
for i in range(latents.shape[0]):
image.append(self.vae_decoder(latent_sample=latents[i : i + 1])[0])
image = np.concatenate(image)
image = np.clip(image / 2 + 0.5, 0, 1)
image = image.transpose((0, 2, 3, 1))
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(
self.numpy_to_pil(image), return_tensors="np"
).pixel_values.astype(image.dtype)
# There will throw an error if use safety_checker directly and batchsize>1
images, has_nsfw_concept = [], []
for i in range(image.shape[0]):
image_i, has_nsfw_concept_i = self.safety_checker(
clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
# predict the noise residual
noise_pred = self.unet(
sample=latent_model_input,
timestep=np.array([t], dtype=timestep_dtype),
encoder_hidden_states=text_embeddings,
)
images.append(image_i)
has_nsfw_concept.append(has_nsfw_concept_i[0])
image = np.concatenate(images)
else:
has_nsfw_concept = None
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)
# 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()
if mask is not None:
# masking
init_latents_proper = self.scheduler.add_noise(
torch.from_numpy(init_latents_orig),
torch.from_numpy(noise),
t,
).numpy()
latents = (init_latents_proper * mask) + (latents * (1 - mask))
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if i % callback_steps == 0:
if callback is not None:
callback(i, t, latents)
if is_cancelled_callback is not None and is_cancelled_callback():
return None
# 9. Post-processing
image = self.decode_latents(latents)
# 10. Run safety checker
image, has_nsfw_concept = self.run_safety_checker(image)
# 11. Convert to PIL
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return image, has_nsfw_concept
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
@@ -748,7 +809,7 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
guidance_scale: float = 7.5,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[np.random.RandomState] = None,
generator: Optional[torch.Generator] = None,
latents: Optional[np.ndarray] = None,
max_embeddings_multiples: Optional[int] = 3,
output_type: Optional[str] = "pil",
@@ -783,8 +844,9 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
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 (`np.random.RandomState`, *optional*):
A np.random.RandomState to make generation deterministic.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`np.ndarray`, *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
@@ -839,7 +901,7 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
guidance_scale: Optional[float] = 7.5,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.0,
generator: Optional[np.random.RandomState] = None,
generator: Optional[torch.Generator] = None,
max_embeddings_multiples: Optional[int] = 3,
output_type: Optional[str] = "pil",
return_dict: bool = True,
@@ -878,8 +940,9 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
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 (`np.random.RandomState`, *optional*):
A np.random.RandomState to make generation deterministic.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
output_type (`str`, *optional*, defaults to `"pil"`):
@@ -930,7 +993,7 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
guidance_scale: Optional[float] = 7.5,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.0,
generator: Optional[np.random.RandomState] = None,
generator: Optional[torch.Generator] = None,
max_embeddings_multiples: Optional[int] = 3,
output_type: Optional[str] = "pil",
return_dict: bool = True,
@@ -973,8 +1036,9 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
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 (`np.random.RandomState`, *optional*):
A np.random.RandomState to make generation deterministic.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
output_type (`str`, *optional*, defaults to `"pil"`):