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for location in from_where:
for bs_item in attention_maps[f"{location}_{'cross' if is_cross else 'self'}"]:
for batch, item in enumerate(bs_item):
if item.shape[1] == num_pixels:
cross_maps = item.reshape(len(prompts), -1, *resolution, item.shape[-1])[select]
out[batch].append(cross_maps)
out = torch.stack([torch.cat(x, dim=0) for x in out])
# average over heads
out = out.sum(1) / out.shape[1]
return out | 105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
def __init__(self, average: bool, batch_size=1, max_resolution=16, max_size: int = None):
self.step_store = self.get_empty_store()
self.attention_store = []
self.cur_step = 0
self.average = average
self.batch_size = batch_size
if max_size is None:
self.max_size = max_resolution**2
elif max_size is not None and max_resolution is None:
self.max_size = max_size
else:
raise ValueError("Only allowed to set one of max_resolution or max_size") | 105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
class LeditsGaussianSmoothing:
def __init__(self, device):
kernel_size = [3, 3]
sigma = [0.5, 0.5]
# The gaussian kernel is the product of the gaussian function of each dimension.
kernel = 1
meshgrids = torch.meshgrid([torch.arange(size, dtype=torch.float32) for size in kernel_size], indexing="ij")
for size, std, mgrid in zip(kernel_size, sigma, meshgrids):
mean = (size - 1) / 2
kernel *= 1 / (std * math.sqrt(2 * math.pi)) * torch.exp(-(((mgrid - mean) / (2 * std)) ** 2))
# Make sure sum of values in gaussian kernel equals 1.
kernel = kernel / torch.sum(kernel)
# Reshape to depthwise convolutional weight
kernel = kernel.view(1, 1, *kernel.size())
kernel = kernel.repeat(1, *[1] * (kernel.dim() - 1))
self.weight = kernel.to(device) | 106 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
def __call__(self, input):
"""
Arguments:
Apply gaussian filter to input.
input (torch.Tensor): Input to apply gaussian filter on.
Returns:
filtered (torch.Tensor): Filtered output.
"""
return F.conv2d(input, weight=self.weight.to(input.dtype)) | 106 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
class LEDITSCrossAttnProcessor:
def __init__(self, attention_store, place_in_unet, pnp, editing_prompts):
self.attnstore = attention_store
self.place_in_unet = place_in_unet
self.editing_prompts = editing_prompts
self.pnp = pnp
def __call__(
self,
attn: Attention,
hidden_states,
encoder_hidden_states,
attention_mask=None,
temb=None,
):
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) | 107 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
query = attn.head_to_batch_dim(query)
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value)
attention_probs = attn.get_attention_scores(query, key, attention_mask)
self.attnstore(
attention_probs,
is_cross=True,
place_in_unet=self.place_in_unet,
editing_prompts=self.editing_prompts,
PnP=self.pnp,
)
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states | 107 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
class LEditsPPPipelineStableDiffusion(
DiffusionPipeline, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin
):
"""
Pipeline for textual image editing using LEDits++ with Stable Diffusion.
This model inherits from [`DiffusionPipeline`] and builds on the [`StableDiffusionPipeline`]. Check the superclass
documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular
device, etc.). | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer ([`~transformers.CLIPTokenizer`]):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`DPMSolverMultistepScheduler`] or [`DDIMScheduler`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
[`DPMSolverMultistepScheduler`] or [`DDIMScheduler`]. If any other scheduler is passed it will
automatically be set to [`DPMSolverMultistepScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
""" | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
model_cpu_offload_seq = "text_encoder->unet->vae"
_exclude_from_cpu_offload = ["safety_checker"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
_optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, DPMSolverMultistepScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__() | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if not isinstance(scheduler, DDIMScheduler) and not isinstance(scheduler, DPMSolverMultistepScheduler):
scheduler = DPMSolverMultistepScheduler.from_config(
scheduler.config, algorithm_type="sde-dpmsolver++", solver_order=2
)
logger.warning(
"This pipeline only supports DDIMScheduler and DPMSolverMultistepScheduler. "
"The scheduler has been changed to DPMSolverMultistepScheduler."
) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if scheduler is not None and getattr(scheduler.config, "steps_offset", 1) != 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) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if scheduler is not None and getattr(scheduler.config, "clip_sample", False) 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) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
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 ."
) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
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."
) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
is_unet_version_less_0_9_0 = (
unet is not None
and 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 = (
unet is not None and 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" | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
" \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) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
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) if getattr(self, "vae", None) else 8
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
self.inversion_steps = None | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
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 | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, eta, generator=None):
# 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 | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
def check_inputs(
self,
negative_prompt=None,
editing_prompt_embeddings=None,
negative_prompt_embeds=None,
callback_on_step_end_tensor_inputs=None,
):
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
)
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if editing_prompt_embeddings is not None and negative_prompt_embeds is not None:
if editing_prompt_embeddings.shape != negative_prompt_embeds.shape:
raise ValueError(
"`editing_prompt_embeddings` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `editing_prompt_embeddings` {editing_prompt_embeddings.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, latents):
# shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
# if latents.shape != shape:
# raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
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 prepare_unet(self, attention_store, PnP: bool = False):
attn_procs = {}
for name in self.unet.attn_processors.keys():
if name.startswith("mid_block"):
place_in_unet = "mid"
elif name.startswith("up_blocks"):
place_in_unet = "up"
elif name.startswith("down_blocks"):
place_in_unet = "down"
else:
continue | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if "attn2" in name and place_in_unet != "mid":
attn_procs[name] = LEDITSCrossAttnProcessor(
attention_store=attention_store,
place_in_unet=place_in_unet,
pnp=PnP,
editing_prompts=self.enabled_editing_prompts,
)
else:
attn_procs[name] = AttnProcessor()
self.unet.set_attn_processor(attn_procs)
def encode_prompt(
self,
device,
num_images_per_prompt,
enable_edit_guidance,
negative_prompt=None,
editing_prompt=None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
editing_prompt_embeds: Optional[torch.Tensor] = None,
lora_scale: Optional[float] = None,
clip_skip: Optional[int] = None,
):
r"""
Encodes the prompt into text encoder hidden states. | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
Args:
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
enable_edit_guidance (`bool`):
whether to perform any editing or reconstruct the input image instead
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
editing_prompt (`str` or `List[str]`, *optional*):
Editing prompt(s) to be encoded. If not defined, one has to pass `editing_prompt_embeds` instead.
editing_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *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.
lora_scale (`float`, *optional*):
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if lora_scale is not None and isinstance(self, StableDiffusionLoraLoaderMixin):
self._lora_scale = lora_scale | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# dynamically adjust the LoRA scale
if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
else:
scale_lora_layers(self.text_encoder, lora_scale)
batch_size = self.batch_size
num_edit_tokens = None | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
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 exoected"
f"{batch_size} based on the input images. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if self.text_encoder is not None:
prompt_embeds_dtype = self.text_encoder.dtype
elif self.unet is not None:
prompt_embeds_dtype = self.unet.dtype
else:
prompt_embeds_dtype = negative_prompt_embeds.dtype | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
if enable_edit_guidance:
if editing_prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
# if isinstance(self, TextualInversionLoaderMixin):
# prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
if isinstance(editing_prompt, str):
editing_prompt = [editing_prompt]
max_length = negative_prompt_embeds.shape[1]
text_inputs = self.tokenizer(
[x for item in editing_prompt for x in repeat(item, batch_size)],
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
return_length=True,
) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
num_edit_tokens = text_inputs.length - 2 # not counting startoftext and endoftext
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(
[x for item in editing_prompt for x in repeat(item, batch_size)],
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}"
) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
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 | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if clip_skip is None:
editing_prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
editing_prompt_embeds = editing_prompt_embeds[0]
else:
editing_prompt_embeds = self.text_encoder(
text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
)
# Access the `hidden_states` first, that contains a tuple of
# all the hidden states from the encoder layers. Then index into
# the tuple to access the hidden states from the desired layer.
editing_prompt_embeds = editing_prompt_embeds[-1][-(clip_skip + 1)]
# We also need to apply the final LayerNorm here to not mess with the
# representations. The `last_hidden_states` that we typically use for | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# obtaining the final prompt representations passes through the LayerNorm
# layer.
editing_prompt_embeds = self.text_encoder.text_model.final_layer_norm(editing_prompt_embeds) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
editing_prompt_embeds = editing_prompt_embeds.to(dtype=negative_prompt_embeds.dtype, device=device)
bs_embed_edit, seq_len, _ = editing_prompt_embeds.shape
editing_prompt_embeds = editing_prompt_embeds.to(dtype=negative_prompt_embeds.dtype, device=device)
editing_prompt_embeds = editing_prompt_embeds.repeat(1, num_images_per_prompt, 1)
editing_prompt_embeds = editing_prompt_embeds.view(bs_embed_edit * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
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)
if isinstance(self, StableDiffusionLoraLoaderMixin) and USE_PEFT_BACKEND:
# Retrieve the original scale by scaling back the LoRA layers
unscale_lora_layers(self.text_encoder, lora_scale)
return editing_prompt_embeds, negative_prompt_embeds, num_edit_tokens
@property
def guidance_rescale(self):
return self._guidance_rescale
@property
def clip_skip(self):
return self._clip_skip
@property
def cross_attention_kwargs(self):
return self._cross_attention_kwargs | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
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 enabled, 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 for saving a large amount of memory and to allow
processing larger images.
"""
self.vae.enable_tiling() | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
def disable_vae_tiling(self):
r"""
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_tiling() | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
negative_prompt: Optional[Union[str, List[str]]] = None,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
editing_prompt: Optional[Union[str, List[str]]] = None,
editing_prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
reverse_editing_direction: Optional[Union[bool, List[bool]]] = False,
edit_guidance_scale: Optional[Union[float, List[float]]] = 5,
edit_warmup_steps: Optional[Union[int, List[int]]] = 0,
edit_cooldown_steps: Optional[Union[int, List[int]]] = None,
edit_threshold: Optional[Union[float, List[float]]] = 0.9,
user_mask: Optional[torch.Tensor] = None,
sem_guidance: Optional[List[torch.Tensor]] = None,
use_cross_attn_mask: bool = False, | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
use_intersect_mask: bool = True,
attn_store_steps: Optional[List[int]] = [],
store_averaged_over_steps: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
guidance_rescale: float = 0.0,
clip_skip: Optional[int] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
**kwargs,
):
r"""
The call function to the pipeline for editing. The
[`~pipelines.ledits_pp.LEditsPPPipelineStableDiffusion.invert`] method has to be called beforehand. Edits will
always be performed for the last inverted image(s). | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
Args:
negative_prompt (`str` or `List[str]`, *optional*):
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`).
generator (`torch.Generator`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ledits_pp.LEditsPPDiffusionPipelineOutput`] instead of a plain
tuple.
editing_prompt (`str` or `List[str]`, *optional*): | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
The prompt or prompts to guide the image generation. The image is reconstructed by setting
`editing_prompt = None`. Guidance direction of prompt should be specified via
`reverse_editing_direction`.
editing_prompt_embeds (`torch.Tensor>`, *optional*):
Pre-computed embeddings to use for guiding the image generation. Guidance direction of embedding should
be specified via `reverse_editing_direction`.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
reverse_editing_direction (`bool` or `List[bool]`, *optional*, defaults to `False`):
Whether the corresponding prompt in `editing_prompt` should be increased or decreased. | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
edit_guidance_scale (`float` or `List[float]`, *optional*, defaults to 5):
Guidance scale for guiding the image generation. If provided as list values should correspond to
`editing_prompt`. `edit_guidance_scale` is defined as `s_e` of equation 12 of [LEDITS++
Paper](https://arxiv.org/abs/2301.12247).
edit_warmup_steps (`float` or `List[float]`, *optional*, defaults to 10):
Number of diffusion steps (for each prompt) for which guidance will not be applied.
edit_cooldown_steps (`float` or `List[float]`, *optional*, defaults to `None`):
Number of diffusion steps (for each prompt) after which guidance will no longer be applied.
edit_threshold (`float` or `List[float]`, *optional*, defaults to 0.9):
Masking threshold of guidance. Threshold should be proportional to the image region that is modified. | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
'edit_threshold' is defined as 'λ' of equation 12 of [LEDITS++
Paper](https://arxiv.org/abs/2301.12247).
user_mask (`torch.Tensor`, *optional*):
User-provided mask for even better control over the editing process. This is helpful when LEDITS++'s
implicit masks do not meet user preferences.
sem_guidance (`List[torch.Tensor]`, *optional*):
List of pre-generated guidance vectors to be applied at generation. Length of the list has to
correspond to `num_inference_steps`.
use_cross_attn_mask (`bool`, defaults to `False`):
Whether cross-attention masks are used. Cross-attention masks are always used when use_intersect_mask
is set to true. Cross-attention masks are defined as 'M^1' of equation 12 of [LEDITS++
paper](https://arxiv.org/pdf/2311.16711.pdf).
use_intersect_mask (`bool`, defaults to `True`): | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
Whether the masking term is calculated as intersection of cross-attention masks and masks derived from
the noise estimate. Cross-attention mask are defined as 'M^1' and masks derived from the noise estimate
are defined as 'M^2' of equation 12 of [LEDITS++ paper](https://arxiv.org/pdf/2311.16711.pdf).
attn_store_steps (`List[int]`, *optional*):
Steps for which the attention maps are stored in the AttentionStore. Just for visualization purposes.
store_averaged_over_steps (`bool`, defaults to `True`):
Whether the attention maps for the 'attn_store_steps' are stored averaged over the diffusion steps. If
False, attention maps for each step are stores separately. Just for visualization purposes.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
guidance_rescale (`float`, *optional*, defaults to 0.0):
Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are
Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when
using zero terminal SNR.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class. | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
Examples:
Returns:
[`~pipelines.ledits_pp.LEditsPPDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.ledits_pp.LEditsPPDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When
returning a tuple, the first element is a list with the generated images, and the second element is a list
of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw)
content, according to the `safety_checker`.
"""
if self.inversion_steps is None:
raise ValueError(
"You need to invert an input image first before calling the pipeline. The `invert` method has to be called beforehand. Edits will always be performed for the last inverted image(s)."
)
eta = self.eta
num_images_per_prompt = 1
latents = self.init_latents
zs = self.zs
self.scheduler.set_timesteps(len(self.scheduler.timesteps)) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if use_intersect_mask:
use_cross_attn_mask = True
if use_cross_attn_mask:
self.smoothing = LeditsGaussianSmoothing(self.device)
if user_mask is not None:
user_mask = user_mask.to(self.device)
org_prompt = ""
# 1. Check inputs. Raise error if not correct
self.check_inputs(
negative_prompt,
editing_prompt_embeds,
negative_prompt_embeds,
callback_on_step_end_tensor_inputs,
)
self._guidance_rescale = guidance_rescale
self._clip_skip = clip_skip
self._cross_attention_kwargs = cross_attention_kwargs
# 2. Define call parameters
batch_size = self.batch_size | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if editing_prompt:
enable_edit_guidance = True
if isinstance(editing_prompt, str):
editing_prompt = [editing_prompt]
self.enabled_editing_prompts = len(editing_prompt)
elif editing_prompt_embeds is not None:
enable_edit_guidance = True
self.enabled_editing_prompts = editing_prompt_embeds.shape[0]
else:
self.enabled_editing_prompts = 0
enable_edit_guidance = False
# 3. Encode input prompt
lora_scale = (
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
edit_concepts, uncond_embeddings, num_edit_tokens = self.encode_prompt(
editing_prompt=editing_prompt,
device=self.device,
num_images_per_prompt=num_images_per_prompt,
enable_edit_guidance=enable_edit_guidance,
negative_prompt=negative_prompt,
editing_prompt_embeds=editing_prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=lora_scale,
clip_skip=self.clip_skip,
)
# 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
if enable_edit_guidance:
text_embeddings = torch.cat([uncond_embeddings, edit_concepts])
self.text_cross_attention_maps = [editing_prompt] if isinstance(editing_prompt, str) else editing_prompt
else:
text_embeddings = torch.cat([uncond_embeddings]) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# 4. Prepare timesteps
# self.scheduler.set_timesteps(num_inference_steps, device=self.device)
timesteps = self.inversion_steps
t_to_idx = {int(v): k for k, v in enumerate(timesteps[-zs.shape[0] :])}
if use_cross_attn_mask:
self.attention_store = LeditsAttentionStore(
average=store_averaged_over_steps,
batch_size=batch_size,
max_size=(latents.shape[-2] / 4.0) * (latents.shape[-1] / 4.0),
max_resolution=None,
)
self.prepare_unet(self.attention_store, PnP=False)
resolution = latents.shape[-2:]
att_res = (int(resolution[0] / 4), int(resolution[1] / 4)) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# 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,
None,
None,
text_embeddings.dtype,
self.device,
latents,
)
# 6. Prepare extra step kwargs.
extra_step_kwargs = self.prepare_extra_step_kwargs(eta)
self.sem_guidance = None
self.activation_mask = None
# 7. Denoising loop
num_warmup_steps = 0
with self.progress_bar(total=len(timesteps)) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
if enable_edit_guidance:
latent_model_input = torch.cat([latents] * (1 + self.enabled_editing_prompts))
else:
latent_model_input = latents | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
text_embed_input = text_embeddings
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embed_input).sample
noise_pred_out = noise_pred.chunk(1 + self.enabled_editing_prompts) # [b,4, 64, 64]
noise_pred_uncond = noise_pred_out[0]
noise_pred_edit_concepts = noise_pred_out[1:]
noise_guidance_edit = torch.zeros(
noise_pred_uncond.shape,
device=self.device,
dtype=noise_pred_uncond.dtype,
)
if sem_guidance is not None and len(sem_guidance) > i:
noise_guidance_edit += sem_guidance[i].to(self.device) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
elif enable_edit_guidance:
if self.activation_mask is None:
self.activation_mask = torch.zeros(
(len(timesteps), len(noise_pred_edit_concepts), *noise_pred_edit_concepts[0].shape)
)
if self.sem_guidance is None:
self.sem_guidance = torch.zeros((len(timesteps), *noise_pred_uncond.shape))
for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts):
if isinstance(edit_warmup_steps, list):
edit_warmup_steps_c = edit_warmup_steps[c]
else:
edit_warmup_steps_c = edit_warmup_steps
if i < edit_warmup_steps_c:
continue | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if isinstance(edit_guidance_scale, list):
edit_guidance_scale_c = edit_guidance_scale[c]
else:
edit_guidance_scale_c = edit_guidance_scale
if isinstance(edit_threshold, list):
edit_threshold_c = edit_threshold[c]
else:
edit_threshold_c = edit_threshold
if isinstance(reverse_editing_direction, list):
reverse_editing_direction_c = reverse_editing_direction[c]
else:
reverse_editing_direction_c = reverse_editing_direction | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if isinstance(edit_cooldown_steps, list):
edit_cooldown_steps_c = edit_cooldown_steps[c]
elif edit_cooldown_steps is None:
edit_cooldown_steps_c = i + 1
else:
edit_cooldown_steps_c = edit_cooldown_steps
if i >= edit_cooldown_steps_c:
continue
noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred_uncond
if reverse_editing_direction_c:
noise_guidance_edit_tmp = noise_guidance_edit_tmp * -1
noise_guidance_edit_tmp = noise_guidance_edit_tmp * edit_guidance_scale_c
if user_mask is not None:
noise_guidance_edit_tmp = noise_guidance_edit_tmp * user_mask | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if use_cross_attn_mask:
out = self.attention_store.aggregate_attention(
attention_maps=self.attention_store.step_store,
prompts=self.text_cross_attention_maps,
res=att_res,
from_where=["up", "down"],
is_cross=True,
select=self.text_cross_attention_maps.index(editing_prompt[c]),
)
attn_map = out[:, :, :, 1 : 1 + num_edit_tokens[c]] # 0 -> startoftext
# average over all tokens
if attn_map.shape[3] != num_edit_tokens[c]:
raise ValueError(
f"Incorrect shape of attention_map. Expected size {num_edit_tokens[c]}, but found {attn_map.shape[3]}!"
) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
attn_map = torch.sum(attn_map, dim=3)
# gaussian_smoothing
attn_map = F.pad(attn_map.unsqueeze(1), (1, 1, 1, 1), mode="reflect")
attn_map = self.smoothing(attn_map).squeeze(1)
# torch.quantile function expects float32
if attn_map.dtype == torch.float32:
tmp = torch.quantile(attn_map.flatten(start_dim=1), edit_threshold_c, dim=1)
else:
tmp = torch.quantile(
attn_map.flatten(start_dim=1).to(torch.float32), edit_threshold_c, dim=1
).to(attn_map.dtype)
attn_mask = torch.where(
attn_map >= tmp.unsqueeze(1).unsqueeze(1).repeat(1, *att_res), 1.0, 0.0
) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# resolution must match latent space dimension
attn_mask = F.interpolate(
attn_mask.unsqueeze(1),
noise_guidance_edit_tmp.shape[-2:], # 64,64
).repeat(1, 4, 1, 1)
self.activation_mask[i, c] = attn_mask.detach().cpu()
if not use_intersect_mask:
noise_guidance_edit_tmp = noise_guidance_edit_tmp * attn_mask | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if use_intersect_mask:
if t <= 800:
noise_guidance_edit_tmp_quantile = torch.abs(noise_guidance_edit_tmp)
noise_guidance_edit_tmp_quantile = torch.sum(
noise_guidance_edit_tmp_quantile, dim=1, keepdim=True
)
noise_guidance_edit_tmp_quantile = noise_guidance_edit_tmp_quantile.repeat(
1, self.unet.config.in_channels, 1, 1
) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# torch.quantile function expects float32
if noise_guidance_edit_tmp_quantile.dtype == torch.float32:
tmp = torch.quantile(
noise_guidance_edit_tmp_quantile.flatten(start_dim=2),
edit_threshold_c,
dim=2,
keepdim=False,
)
else:
tmp = torch.quantile(
noise_guidance_edit_tmp_quantile.flatten(start_dim=2).to(torch.float32),
edit_threshold_c,
dim=2,
keepdim=False,
).to(noise_guidance_edit_tmp_quantile.dtype) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
intersect_mask = (
torch.where(
noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None],
torch.ones_like(noise_guidance_edit_tmp),
torch.zeros_like(noise_guidance_edit_tmp),
)
* attn_mask
)
self.activation_mask[i, c] = intersect_mask.detach().cpu()
noise_guidance_edit_tmp = noise_guidance_edit_tmp * intersect_mask
else:
# print(f"only attention mask for step {i}")
noise_guidance_edit_tmp = noise_guidance_edit_tmp * attn_mask | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
elif not use_cross_attn_mask:
# calculate quantile
noise_guidance_edit_tmp_quantile = torch.abs(noise_guidance_edit_tmp)
noise_guidance_edit_tmp_quantile = torch.sum(
noise_guidance_edit_tmp_quantile, dim=1, keepdim=True
)
noise_guidance_edit_tmp_quantile = noise_guidance_edit_tmp_quantile.repeat(1, 4, 1, 1) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# torch.quantile function expects float32
if noise_guidance_edit_tmp_quantile.dtype == torch.float32:
tmp = torch.quantile(
noise_guidance_edit_tmp_quantile.flatten(start_dim=2),
edit_threshold_c,
dim=2,
keepdim=False,
)
else:
tmp = torch.quantile(
noise_guidance_edit_tmp_quantile.flatten(start_dim=2).to(torch.float32),
edit_threshold_c,
dim=2,
keepdim=False,
).to(noise_guidance_edit_tmp_quantile.dtype) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
self.activation_mask[i, c] = (
torch.where(
noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None],
torch.ones_like(noise_guidance_edit_tmp),
torch.zeros_like(noise_guidance_edit_tmp),
)
.detach()
.cpu()
)
noise_guidance_edit_tmp = torch.where(
noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None],
noise_guidance_edit_tmp,
torch.zeros_like(noise_guidance_edit_tmp),
)
noise_guidance_edit += noise_guidance_edit_tmp
self.sem_guidance[i] = noise_guidance_edit.detach().cpu() | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
noise_pred = noise_pred_uncond + noise_guidance_edit
if enable_edit_guidance and self.guidance_rescale > 0.0:
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
noise_pred = rescale_noise_cfg(
noise_pred,
noise_pred_edit_concepts.mean(dim=0, keepdim=False),
guidance_rescale=self.guidance_rescale,
)
idx = t_to_idx[int(t)]
latents = self.scheduler.step(
noise_pred, t, latents, variance_noise=zs[idx], **extra_step_kwargs
).prev_sample
# step callback
if use_cross_attn_mask:
store_step = i in attn_store_steps
self.attention_store.between_steps(store_step) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = callback_outputs.pop("latents", latents)
# prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
# 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 XLA_AVAILABLE:
xm.mark_step() | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# 8. Post-processing
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
0
]
image, has_nsfw_concept = self.run_safety_checker(image, self.device, text_embeddings.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image, has_nsfw_concept)
return LEditsPPDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
@torch.no_grad()
def invert(
self,
image: PipelineImageInput,
source_prompt: str = "",
source_guidance_scale: float = 3.5,
num_inversion_steps: int = 30,
skip: float = 0.15,
generator: Optional[torch.Generator] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
clip_skip: Optional[int] = None,
height: Optional[int] = None,
width: Optional[int] = None,
resize_mode: Optional[str] = "default",
crops_coords: Optional[Tuple[int, int, int, int]] = None,
):
r"""
The function to the pipeline for image inversion as described by the [LEDITS++
Paper](https://arxiv.org/abs/2301.12247). If the scheduler is set to [`~schedulers.DDIMScheduler`] the
inversion proposed by [edit-friendly DPDM](https://arxiv.org/abs/2304.06140) will be performed instead. | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
Args:
image (`PipelineImageInput`):
Input for the image(s) that are to be edited. Multiple input images have to default to the same aspect
ratio.
source_prompt (`str`, defaults to `""`):
Prompt describing the input image that will be used for guidance during inversion. Guidance is disabled
if the `source_prompt` is `""`.
source_guidance_scale (`float`, defaults to `3.5`):
Strength of guidance during inversion.
num_inversion_steps (`int`, defaults to `30`):
Number of total performed inversion steps after discarding the initial `skip` steps.
skip (`float`, defaults to `0.15`):
Portion of initial steps that will be ignored for inversion and subsequent generation. Lower values
will lead to stronger changes to the input image. `skip` has to be between `0` and `1`.
generator (`torch.Generator`, *optional*): | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make inversion
deterministic.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
height (`int`, *optional*, defaults to `None`):
The height in preprocessed image. If `None`, will use the `get_default_height_width()` to get default
height.
width (`int`, *optional*`, defaults to `None`): | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
The width in preprocessed. If `None`, will use get_default_height_width()` to get the default width.
resize_mode (`str`, *optional*, defaults to `default`):
The resize mode, can be one of `default` or `fill`. If `default`, will resize the image to fit within
the specified width and height, and it may not maintaining the original aspect ratio. If `fill`, will
resize the image to fit within the specified width and height, maintaining the aspect ratio, and then
center the image within the dimensions, filling empty with data from image. If `crop`, will resize the
image to fit within the specified width and height, maintaining the aspect ratio, and then center the
image within the dimensions, cropping the excess. Note that resize_mode `fill` and `crop` are only
supported for PIL image input. | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
crops_coords (`List[Tuple[int, int, int, int]]`, *optional*, defaults to `None`):
The crop coordinates for each image in the batch. If `None`, will not crop the image. | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
Returns:
[`~pipelines.ledits_pp.LEditsPPInversionPipelineOutput`]: Output will contain the resized input image(s)
and respective VAE reconstruction(s).
"""
if height is not None and height % 32 != 0 or width is not None and width % 32 != 0:
raise ValueError("height and width must be a factor of 32.")
# Reset attn processor, we do not want to store attn maps during inversion
self.unet.set_attn_processor(AttnProcessor())
self.eta = 1.0
self.scheduler.config.timestep_spacing = "leading"
self.scheduler.set_timesteps(int(num_inversion_steps * (1 + skip)))
self.inversion_steps = self.scheduler.timesteps[-num_inversion_steps:]
timesteps = self.inversion_steps | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# 1. encode image
x0, resized = self.encode_image(
image,
dtype=self.text_encoder.dtype,
height=height,
width=width,
resize_mode=resize_mode,
crops_coords=crops_coords,
)
self.batch_size = x0.shape[0]
# autoencoder reconstruction
image_rec = self.vae.decode(x0 / self.vae.config.scaling_factor, return_dict=False, generator=generator)[0]
image_rec = self.image_processor.postprocess(image_rec, output_type="pil")
# 2. get embeddings
do_classifier_free_guidance = source_guidance_scale > 1.0
lora_scale = cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
uncond_embedding, text_embeddings, _ = self.encode_prompt(
num_images_per_prompt=1,
device=self.device,
negative_prompt=None,
enable_edit_guidance=do_classifier_free_guidance,
editing_prompt=source_prompt,
lora_scale=lora_scale,
clip_skip=clip_skip,
)
# 3. find zs and xts
variance_noise_shape = (num_inversion_steps, *x0.shape)
# intermediate latents
t_to_idx = {int(v): k for k, v in enumerate(timesteps)}
xts = torch.zeros(size=variance_noise_shape, device=self.device, dtype=uncond_embedding.dtype)
for t in reversed(timesteps):
idx = num_inversion_steps - t_to_idx[int(t)] - 1
noise = randn_tensor(shape=x0.shape, generator=generator, device=self.device, dtype=x0.dtype)
xts[idx] = self.scheduler.add_noise(x0, noise, torch.Tensor([t]))
xts = torch.cat([x0.unsqueeze(0), xts], dim=0) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
self.scheduler.set_timesteps(len(self.scheduler.timesteps))
# noise maps
zs = torch.zeros(size=variance_noise_shape, device=self.device, dtype=uncond_embedding.dtype)
with self.progress_bar(total=len(timesteps)) as progress_bar:
for t in timesteps:
idx = num_inversion_steps - t_to_idx[int(t)] - 1
# 1. predict noise residual
xt = xts[idx + 1]
noise_pred = self.unet(xt, timestep=t, encoder_hidden_states=uncond_embedding).sample
if not source_prompt == "":
noise_pred_cond = self.unet(xt, timestep=t, encoder_hidden_states=text_embeddings).sample
noise_pred = noise_pred + source_guidance_scale * (noise_pred_cond - noise_pred)
xtm1 = xts[idx]
z, xtm1_corrected = compute_noise(self.scheduler, xtm1, xt, t, noise_pred, self.eta)
zs[idx] = z | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# correction to avoid error accumulation
xts[idx] = xtm1_corrected
progress_bar.update()
if XLA_AVAILABLE:
xm.mark_step()
self.init_latents = xts[-1].expand(self.batch_size, -1, -1, -1)
zs = zs.flip(0)
self.zs = zs
return LEditsPPInversionPipelineOutput(images=resized, vae_reconstruction_images=image_rec) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
@torch.no_grad()
def encode_image(self, image, dtype=None, height=None, width=None, resize_mode="default", crops_coords=None):
image = self.image_processor.preprocess(
image=image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords
)
height, width = image.shape[-2:]
if height % 32 != 0 or width % 32 != 0:
raise ValueError(
"Image height and width must be a factor of 32. "
"Consider down-sampling the input using the `height` and `width` parameters"
)
resized = self.image_processor.postprocess(image=image, output_type="pil") | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if max(image.shape[-2:]) > self.vae.config["sample_size"] * 1.5:
logger.warning(
"Your input images far exceed the default resolution of the underlying diffusion model. "
"The output images may contain severe artifacts! "
"Consider down-sampling the input using the `height` and `width` parameters"
)
image = image.to(dtype)
x0 = self.vae.encode(image.to(self.device)).latent_dist.mode()
x0 = x0.to(dtype)
x0 = self.vae.config.scaling_factor * x0
return x0, resized | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
class CogVideoXPipelineOutput(BaseOutput):
r"""
Output class for CogVideo pipelines.
Args:
frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape
`(batch_size, num_frames, channels, height, width)`.
"""
frames: torch.Tensor | 109 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_output.py |
class CogVideoXPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
r"""
Pipeline for text-to-video generation using CogVideoX.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
text_encoder ([`T5EncoderModel`]):
Frozen text-encoder. CogVideoX uses
[T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel); specifically the
[t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
tokenizer (`T5Tokenizer`):
Tokenizer of class
[T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
transformer ([`CogVideoXTransformer3DModel`]):
A text conditioned `CogVideoXTransformer3DModel` to denoise the encoded video latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `transformer` to denoise the encoded video latents.
""" | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
_optional_components = []
model_cpu_offload_seq = "text_encoder->transformer->vae"
_callback_tensor_inputs = [
"latents",
"prompt_embeds",
"negative_prompt_embeds",
]
def __init__(
self,
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
vae: AutoencoderKLCogVideoX,
transformer: CogVideoXTransformer3DModel,
scheduler: Union[CogVideoXDDIMScheduler, CogVideoXDPMScheduler],
):
super().__init__() | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
self.register_modules(
tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler
)
self.vae_scale_factor_spatial = (
2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
)
self.vae_scale_factor_temporal = (
self.vae.config.temporal_compression_ratio if getattr(self, "vae", None) else 4
)
self.vae_scaling_factor_image = self.vae.config.scaling_factor if getattr(self, "vae", None) else 0.7
self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial) | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
def _get_t5_prompt_embeds(
self,
prompt: Union[str, List[str]] = None,
num_videos_per_prompt: int = 1,
max_sequence_length: int = 226,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
):
device = device or self._execution_device
dtype = dtype or self.text_encoder.dtype
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
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[:, max_sequence_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because `max_sequence_length` is set to "
f" {max_sequence_length} tokens: {removed_text}"
)
prompt_embeds = self.text_encoder(text_input_ids.to(device))[0]
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
# duplicate text embeddings for each generation per prompt, using mps friendly method
_, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
return prompt_embeds | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
def encode_prompt(
self,
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
do_classifier_free_guidance: bool = True,
num_videos_per_prompt: int = 1,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
max_sequence_length: int = 226,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
):
r"""
Encodes the prompt into text encoder hidden states. | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
Whether to use classifier free guidance or not.
num_videos_per_prompt (`int`, *optional*, defaults to 1):
Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *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.
device: (`torch.device`, *optional*):
torch device
dtype: (`torch.dtype`, *optional*):
torch dtype
"""
device = device or self._execution_device | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
prompt = [prompt] if isinstance(prompt, str) else prompt
if prompt is not None:
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
prompt_embeds = self._get_t5_prompt_embeds(
prompt=prompt,
num_videos_per_prompt=num_videos_per_prompt,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
)
if do_classifier_free_guidance and negative_prompt_embeds is None:
negative_prompt = negative_prompt or ""
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
if prompt is not None and 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 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`."
)
negative_prompt_embeds = self._get_t5_prompt_embeds(
prompt=negative_prompt,
num_videos_per_prompt=num_videos_per_prompt,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
)
return prompt_embeds, negative_prompt_embeds | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
def prepare_latents(
self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
):
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."
)
shape = (
batch_size,
(num_frames - 1) // self.vae_scale_factor_temporal + 1,
num_channels_latents,
height // self.vae_scale_factor_spatial,
width // self.vae_scale_factor_spatial,
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device) | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def decode_latents(self, latents: torch.Tensor) -> torch.Tensor:
latents = latents.permute(0, 2, 1, 3, 4) # [batch_size, num_channels, num_frames, height, width]
latents = 1 / self.vae_scaling_factor_image * latents
frames = self.vae.decode(latents).sample
return frames
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
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] | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
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
# Copied from diffusers.pipelines.latte.pipeline_latte.LattePipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
negative_prompt,
callback_on_step_end_tensor_inputs,
prompt_embeds=None,
negative_prompt_embeds=None,
):
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}.") | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
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