imagedream / pipeline_mvdream.py

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	import torch
	import torch.nn.functional as F
	import inspect
	import numpy as np
	from typing import Callable, List, Optional, Union
	from transformers import CLIPTextModel, CLIPTokenizer, CLIPVisionModel, CLIPImageProcessor
	from diffusers import AutoencoderKL, DiffusionPipeline
	from diffusers.utils import (
	deprecate,
	is_accelerate_available,
	is_accelerate_version,
	logging,
	)
	from diffusers.configuration_utils import FrozenDict
	from diffusers.schedulers import DDIMScheduler
	from diffusers.utils.torch_utils import randn_tensor

	from mv_unet import MultiViewUNetModel, get_camera

	logger = logging.get_logger(__name__) # pylint: disable=invalid-name


	class MVDreamPipeline(DiffusionPipeline):

	_optional_components = ["feature_extractor", "image_encoder"]

	def __init__(
	self,
	vae: AutoencoderKL,
	unet: MultiViewUNetModel,
	tokenizer: CLIPTokenizer,
	text_encoder: CLIPTextModel,
	scheduler: DDIMScheduler,
	# imagedream variant
	feature_extractor: CLIPImageProcessor,
	image_encoder: CLIPVisionModel,
	requires_safety_checker: bool = False,
	):
	super().__init__()

	if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: # type: ignore
	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	f"The configuration file of this scheduler: {scheduler} is outdated.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 " # type: ignore
	"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: # type: ignore
	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)

	self.register_modules(
	vae=vae,
	unet=unet,
	scheduler=scheduler,
	tokenizer=tokenizer,
	text_encoder=text_encoder,
	feature_extractor=feature_extractor,
	image_encoder=image_encoder,
	)
	self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
	self.register_to_config(requires_safety_checker=requires_safety_checker)

	def enable_vae_slicing(self):
	r"""
	Enable sliced VAE decoding.

	When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
	steps. This is useful to save some memory and allow larger batch sizes.
	"""
	self.vae.enable_slicing()

	def disable_vae_slicing(self):
	r"""
	Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
	computing decoding in one step.
	"""
	self.vae.disable_slicing()

	def enable_vae_tiling(self):
	r"""
	Enable tiled VAE decoding.

	When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in
	several steps. This is useful to save a large amount of memory and to allow the processing of larger images.
	"""
	self.vae.enable_tiling()

	def disable_vae_tiling(self):
	r"""
	Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to
	computing decoding in one step.
	"""
	self.vae.disable_tiling()

	def enable_sequential_cpu_offload(self, gpu_id=0):
	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.
	Note that offloading happens on a submodule basis. Memory savings are higher than with
	`enable_model_cpu_offload`, but performance is lower.
	"""
	if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
	from accelerate import cpu_offload
	else:
	raise ImportError(
	"`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher"
	)

	device = torch.device(f"cuda:{gpu_id}")

	if self.device.type != "cpu":
	self.to("cpu", silence_dtype_warnings=True)
	torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)

	for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
	cpu_offload(cpu_offloaded_model, device)

	def enable_model_cpu_offload(self, gpu_id=0):
	r"""
	Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
	to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
	method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
	`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
	"""
	if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
	from accelerate import cpu_offload_with_hook
	else:
	raise ImportError(
	"`enable_model_offload` requires `accelerate v0.17.0` or higher."
	)

	device = torch.device(f"cuda:{gpu_id}")

	if self.device.type != "cpu":
	self.to("cpu", silence_dtype_warnings=True)
	torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)

	hook = None
	for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
	_, hook = cpu_offload_with_hook(
	cpu_offloaded_model, device, prev_module_hook=hook
	)

	# We'll offload the last model manually.
	self.final_offload_hook = hook

	@property
	def _execution_device(self):
	r"""
	Returns the device on which the pipeline's models will be executed. After calling
	`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
	hooks.
	"""
	if not hasattr(self.unet, "_hf_hook"):
	return self.device
	for module in self.unet.modules():
	if (
	hasattr(module, "_hf_hook")
	and hasattr(module._hf_hook, "execution_device")
	and module._hf_hook.execution_device is not None
	):
	return torch.device(module._hf_hook.execution_device)
	return self.device

	def _encode_prompt(
	self,
	prompt,
	device,
	num_images_per_prompt,
	do_classifier_free_guidance: bool,
	negative_prompt=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. 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:
	raise ValueError(
	f"`prompt` should be either a string or a list of strings, but got {type(prompt)}."
	)

	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:
	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

	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]

	# 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 decode_latents(self, latents):
	latents = 1 / self.vae.config.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 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,
	batch_size,
	num_channels_latents,
	height,
	width,
	dtype,
	device,
	generator,
	latents=None,
	):
	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 latents is None:
	latents = randn_tensor(
	shape, generator=generator, device=device, dtype=dtype
	)
	else:
	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

	def encode_image(self, image, device, num_images_per_prompt):
	dtype = next(self.image_encoder.parameters()).dtype

	if image.dtype == np.float32:
	image = (image * 255).astype(np.uint8)

	image = self.feature_extractor(image, return_tensors="pt").pixel_values
	image = image.to(device=device, dtype=dtype)

	image_embeds = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
	image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)

	return torch.zeros_like(image_embeds), image_embeds

	def encode_image_latents(self, image, device, num_images_per_prompt):

	dtype = next(self.image_encoder.parameters()).dtype

	image = torch.from_numpy(image).unsqueeze(0).permute(0, 3, 1, 2).to(device=device) # [1, 3, H, W]
	image = 2 * image - 1
	image = F.interpolate(image, (256, 256), mode='bilinear', align_corners=False)
	image = image.to(dtype=dtype)

	posterior = self.vae.encode(image).latent_dist
	latents = posterior.sample() * self.vae.config.scaling_factor # [B, C, H, W]
	latents = latents.repeat_interleave(num_images_per_prompt, dim=0)

	return torch.zeros_like(latents), latents

	@torch.no_grad()
	def __call__(
	self,
	prompt: str = "",
	image: Optional[np.ndarray] = None,
	height: int = 256,
	width: int = 256,
	num_inference_steps: int = 50,
	guidance_scale: float = 7.0,
	negative_prompt: str = "",
	num_images_per_prompt: int = 1,
	eta: float = 0.0,
	generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
	output_type: Optional[str] = "numpy", # pil, numpy, latents
	callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
	callback_steps: int = 1,
	num_frames: int = 4,
	device=torch.device("cuda:0"),
	):
	self.unet = self.unet.to(device=device)
	self.vae = self.vae.to(device=device)
	self.text_encoder = self.text_encoder.to(device=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

	# Prepare timesteps
	self.scheduler.set_timesteps(num_inference_steps, device=device)
	timesteps = self.scheduler.timesteps

	# imagedream variant
	if image is not None:
	assert isinstance(image, np.ndarray) and image.dtype == np.float32
	self.image_encoder = self.image_encoder.to(device=device)
	image_embeds_neg, image_embeds_pos = self.encode_image(image, device, num_images_per_prompt)
	image_latents_neg, image_latents_pos = self.encode_image_latents(image, device, num_images_per_prompt)

	_prompt_embeds = self._encode_prompt(
	prompt=prompt,
	device=device,
	num_images_per_prompt=num_images_per_prompt,
	do_classifier_free_guidance=do_classifier_free_guidance,
	negative_prompt=negative_prompt,
	) # type: ignore
	prompt_embeds_neg, prompt_embeds_pos = _prompt_embeds.chunk(2)

	# Prepare latent variables
	actual_num_frames = num_frames if image is None else num_frames + 1
	latents: torch.Tensor = self.prepare_latents(
	actual_num_frames * num_images_per_prompt,
	4,
	height,
	width,
	prompt_embeds_pos.dtype,
	device,
	generator,
	None,
	)

	if image is not None:
	camera = get_camera(num_frames, elevation=5, extra_view=True).to(dtype=latents.dtype, device=device)
	else:
	camera = get_camera(num_frames, elevation=15, extra_view=False).to(dtype=latents.dtype, device=device)
	camera = camera.repeat_interleave(num_images_per_prompt, dim=0)

	# Prepare extra step kwargs.
	extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

	# 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
	multiplier = 2 if do_classifier_free_guidance else 1
	latent_model_input = torch.cat([latents] * multiplier)
	latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

	unet_inputs = {
	'x': latent_model_input,
	'timesteps': torch.tensor([t] * actual_num_frames * multiplier, dtype=latent_model_input.dtype, device=device),
	'context': torch.cat([prompt_embeds_neg] * actual_num_frames + [prompt_embeds_pos] * actual_num_frames),
	'num_frames': actual_num_frames,
	'camera': torch.cat([camera] * multiplier),
	}

	if image is not None:
	unet_inputs['ip'] = torch.cat([image_embeds_neg] * actual_num_frames + [image_embeds_pos] * actual_num_frames)
	unet_inputs['ip_img'] = torch.cat([image_latents_neg] + [image_latents_pos]) # no repeat

	# predict the noise residual
	noise_pred = self.unet.forward(**unet_inputs)

	# 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: torch.Tensor = self.scheduler.step(
	noise_pred, t, latents, **extra_step_kwargs, return_dict=False
	)[0]

	# 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) # type: ignore

	# Post-processing
	if output_type == "latent":
	image = latents
	elif output_type == "pil":
	image = self.decode_latents(latents)
	image = self.numpy_to_pil(image)
	else: # numpy
	image = self.decode_latents(latents)

	# 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