r"""

_optional_components = ["safety_checker", "feature_extractor"]

def __init__(

self,

vae: AutoencoderKL,

text_encoder: CLIPTextModel,

tokenizer: CLIPTokenizer,

unet: UNet2DConditionModel,

scheduler: Union[

DDIMScheduler,

PNDMScheduler,

LMSDiscreteScheduler,

EulerDiscreteScheduler,

EulerAncestralDiscreteScheduler,

DPMSolverMultistepScheduler,

],

safety_checker: StableDiffusionSafetyChecker,

feature_extractor: CLIPImageProcessor,

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f"The configuration file of this scheduler: {scheduler} is outdated.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 and requires_safety_checker:

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 ."

)

if safety_checker is not None and feature_extractor is None:

raise ValueError(

"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"

" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."

)

is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(

version.parse(unet.config._diffusers_version).base_version

) < version.parse("0.9.0.dev0")

is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64

if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:

deprecation_message = (

"The configuration file of the unet has set the default `sample_size` to smaller than"

" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"

" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"

" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"

" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"

" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"

" 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 `unet/config.json` file"

)

deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)

new_config = dict(unet.config)

new_config["sample_size"] = 64

unet._internal_dict = FrozenDict(new_config)

self.register_modules(

vae=vae,

text_encoder=text_encoder,

tokenizer=tokenizer,

unet=unet,

scheduler=scheduler,

safety_checker=safety_checker,

feature_extractor=feature_extractor,

)

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"""

self.vae.enable_slicing()

def disable_vae_slicing(self):

r"""

self.vae.disable_slicing()

def enable_sequential_cpu_offload(self, gpu_id=0):

r"""

if is_accelerate_available():

from accelerate import cpu_offload

else:

raise ImportError("Please install accelerate via `pip install accelerate`")

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

for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:

if cpu_offloaded_model is not None:

cpu_offload(cpu_offloaded_model, device)

if self.safety_checker is not None:

# TODO(Patrick) - there is currently a bug with cpu offload of nn.Parameter in accelerate

# fix by only offloading self.safety_checker for now

cpu_offload(self.safety_checker.vision_model, device)

@property

def _execution_device(self):

r"""

if self.device != torch.device("meta") or 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, negative_prompt):

r"""

batch_size = len(prompt) if isinstance(prompt, list) else 1

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

text_embeddings = self.text_encoder(

text_input_ids.to(device),

attention_mask=attention_mask,

)

text_embeddings = text_embeddings[0]

# duplicate text embeddings for each generation per prompt, using mps friendly method

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)

# 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 = text_input_ids.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

uncond_embeddings = self.text_encoder(

uncond_input.input_ids.to(device),

attention_mask=attention_mask,

)

uncond_embeddings = uncond_embeddings[0]

# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

seq_len = uncond_embeddings.shape[1]

uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)

uncond_embeddings = uncond_embeddings.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

text_embeddings = torch.cat([uncond_embeddings, text_embeddings])

return text_embeddings

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 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 check_inputs(self, prompt, height, width, 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 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 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 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

@torch.no_grad()

def __call__(

self,

prompt: Union[str, List[str]],

height: Optional[int] = None,

width: Optional[int] = None,

num_inference_steps: int = 50,

guidance_scale: float = 7.5,

negative_prompt: Optional[Union[str, List[str]]] = None,

num_images_per_prompt: Optional[int] = 1,

eta: float = 0.0,

generator: Optional[torch.Generator] = None,

latents: Optional[torch.FloatTensor] = None,

output_type: Optional[str] = "pil",

return_dict: bool = True,

callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,

callback_steps: int = 1,

weights: Optional[str] = "",

):

r"""

# 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

# 1. Check inputs. Raise error if not correct

self.check_inputs(prompt, height, width, 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

if "|" in prompt:

prompt = [x.strip() for x in prompt.split("|")]

print(f"composing {prompt}...")

if not weights:

# specify weights for prompts (excluding the unconditional score)

print("using equal positive weights (conjunction) for all prompts...")

weights = torch.tensor([guidance_scale] * len(prompt), device=self.device).reshape(-1, 1, 1, 1)

else:

# set prompt weight for each

num_prompts = len(prompt) if isinstance(prompt, list) else 1

weights = [float(w.strip()) for w in weights.split("|")]

# guidance scale as the default

if len(weights) < num_prompts:

weights.append(guidance_scale)

else:

weights = weights[:num_prompts]

assert len(weights) == len(prompt), "weights specified are not equal to the number of prompts"

weights = torch.tensor(weights, device=self.device).reshape(-1, 1, 1, 1)

else:

weights = guidance_scale

# 3. Encode input prompt

text_embeddings = self._encode_prompt(

prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt

)

# 4. Prepare timesteps

self.scheduler.set_timesteps(num_inference_steps, device=device)

timesteps = self.scheduler.timesteps

# 5. Prepare latent variables

num_channels_latents = self.unet.config.in_channels

latents = self.prepare_latents(

batch_size * num_images_per_prompt,

num_channels_latents,

height,

width,

text_embeddings.dtype,

device,

generator,

latents,

)

# composable diffusion

if isinstance(prompt, list) and batch_size == 1:

# remove extra unconditional embedding

# N = one unconditional embed + conditional embeds

text_embeddings = text_embeddings[len(prompt) - 1 :]

# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline

extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

# 7. Denoising loop

num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order

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)

# predict the noise residual

noise_pred = []

for j in range(text_embeddings.shape[0]):

noise_pred.append(

self.unet(latent_model_input[:1], t, encoder_hidden_states=text_embeddings[j : j + 1]).sample

)

noise_pred = torch.cat(noise_pred, dim=0)

# perform guidance

if do_classifier_free_guidance:

noise_pred_uncond, noise_pred_text = noise_pred[:1], noise_pred[1:]

noise_pred = noise_pred_uncond + (weights * (noise_pred_text - noise_pred_uncond)).sum(

dim=0, keepdims=True

)

# compute the previous noisy sample x_t -> x_t-1

latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_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 callback is not None and i % callback_steps == 0:

step_idx = i // getattr(self.scheduler, "order", 1)

callback(step_idx, t, latents)

# 8. Post-processing

image = self.decode_latents(latents)

# 9. Run safety checker

image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)

# 10. Convert to PIL

if output_type == "pil":

image = self.numpy_to_pil(image)

if not return_dict:

return (image, has_nsfw_concept)