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# NOTE: when a LCM is distilled from an LDM via latent consistency distillation (Algorithm 1) with guided
# distillation, the forward pass of the LCM learns to approximate sampling from the LDM using CFG with the
# unconditional prompt "" (the empty string). Due to this, LCMs currently do not support ne... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
# 5. Prepare timesteps
timesteps, num_inference_steps = retrieve_timesteps(
self.scheduler,
num_inference_steps,
device,
timesteps,
original_inference_steps=original_inference_steps,
strength=strength,
)
# 6. Prepare latent... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
# 6. Get Guidance Scale Embedding
# NOTE: We use the Imagen CFG formulation that StableDiffusionPipeline uses rather than the original LCM paper
# CFG formulation, so we need to subtract 1 from the input guidance_scale.
# LCM CFG formulation: cfg_noise = noise_cond + cfg_scale * (noise_cond - n... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
# 8. LCM Multistep Sampling Loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
self._num_timesteps = len(timesteps)
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
latents = latents.... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
# compute the previous noisy sample x_t -> x_t-1
latents, denoised = self.scheduler.step(model_pred, t, latents, **extra_step_kwargs, return_dict=False)
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
# 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.sch... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
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
... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
class LatentConsistencyModelPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
IPAdapterMixin,
StableDiffusionLoraLoaderMixin,
FromSingleFileMixin,
):
r"""
Pipeline for text-to-image generation using a latent consistency model.
This model inherits from [... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionLoraLoaderMixi... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.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 ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14))... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `saf... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
_exclude_from_cpu_offload = ["safety_checker"]
_callback_tensor_inputs = ["latents", "denoised", "prompt_embeds", "w_embedding"]
def __init__(
self,
vae: Aut... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.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"
... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.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'` i... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
def encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
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 (`b... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
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... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.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)
if prompt is not None and isinstance(prompt, str):
batch_size = 1... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
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 = sel... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.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 | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
if clip_skip is None:
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
prompt_embeds = prompt_embeds[0]
else:
prompt_embeds = self.text_encoder(
text_input_ids.to(device), attention_mask=attention_... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
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 = prompt_embeds.dtype
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, devic... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is no... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
# textual inversion: process multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
if do_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)
negative... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
image = image.to(device=device, dtype=dtype)
if output_hidden_states:
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
uncond_imag... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
def prepare_ip_adapter_image_embeds(
self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
):
image_embeds = ... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
for single_ip_adapter_image, image_proj_layer in zip(
ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
):
output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
single_image_embeds, single_negative_image_embeds = self.encod... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
ip_adapter_image_embeds = []
for i, single_image_embeds in enumerate(image_embeds):
single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
single_negative_image_embeds = torch.cat([negative_image_embeds[i]] * ... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.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):
... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def get_guidance_scale_embedding(
self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
) -> torch.Tensor:
... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
Returns:
`torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
"""
assert len(w.shape) == 1
w = w * 1000.0
half_dim = embedding_dim // 2
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=dty... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
# 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 w... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
# Currently StableDiffusionPipeline.check_inputs with negative prompt stuff removed
def check_inputs(
self,
prompt: Union[str, List[str]],
height: int,
width: int,
callback_steps: int,
prompt_embeds: Optional[torch.Tensor] = None,
ip_adapter_image=None,
... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.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 cal... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
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:
... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
if ip_adapter_image_embeds is not None:
if not isinstance(ip_adapter_image_embeds, list):
raise ValueError(
f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}"
)
elif ip_adapter_image_embeds[0].ndim not ... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 4,
original_inference_steps: int = None,
timeste... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
**kwargs,
):
r"""
The call function to the pipeline for generation. | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
we will draw `num_inference_steps` evenly spaced timesteps from as our final timestep schedule,
following the Skipping-Step method in the paper (see Section 4.3). If not set this will default to the
scheduler's `original_inference_steps` attribute.
timesteps (`List[int]`, *op... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
guidance scales are decreased by 1 (so in the paper formulation CFG is enabled when `guidance_scale >
0`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
generator (`torch.Generator` or `List[torch.Generator]... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
ip_adapter_image: (`PipelineImageInput`, *optional*):
Optional image input to work with IP Adapters.
... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passe... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
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... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with ... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
if callback is not None:
deprecate(
"callback",
"1.0.0",
"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
)
if callback_steps is not None:
deprecate(
"ca... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
height,
width,
callback_steps,
prompt_embeds,
ip_adapter_image,
ip_adapter_image_embeds,
callback_on_step_end_tensor_inputs,
)
... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
image_embeds = self.prepare_ip_adapter_image_embeds(
ip_adapter_image,
ip_adapter_image_embeds,
device,
batch_size * num_images_per_prompt,
self.do_classifi... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
# NOTE: when a LCM is distilled from an LDM via latent consistency distillation (Algorithm 1) with guided
# distillation, the forward pass of the LCM learns to approximate sampling from the LDM using CFG with the
# unconditional prompt "" (the empty string). Due to this, LCMs currently do not support ne... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
# 5. Prepare latent variable
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
gener... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, None)
# 7.1 Add image embeds for IP-Adapter
added_cond_kwargs = (
{"image_embeds": image_embeds}
if ip_adapter_image... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
# model prediction (v-prediction, eps, x)
model_pred = self.unet(
latents,
t,
timestep_cond=w_embedding,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=self.cross_attention_kwargs,
... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
w_embedding = callback_outputs.pop("w_embedding", w_embedding)
denoised = callback_outputs.pop("denoised", denoised)
# call... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
denoised = denoised.to(prompt_embeds.dtype)
if not output_type == "latent":
image = self.vae.decode(denoised / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = den... | 115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py |
class MarigoldImageProcessor(ConfigMixin):
config_name = CONFIG_NAME
@register_to_config
def __init__(
self,
vae_scale_factor: int = 8,
do_normalize: bool = True,
do_range_check: bool = True,
):
super().__init__() | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
@staticmethod
def expand_tensor_or_array(images: Union[torch.Tensor, np.ndarray]) -> Union[torch.Tensor, np.ndarray]:
"""
Expand a tensor or array to a specified number of images.
"""
if isinstance(images, np.ndarray):
if images.ndim == 2: # [H,W] -> [1,H,W,1]
... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
@staticmethod
def pt_to_numpy(images: torch.Tensor) -> np.ndarray:
"""
Convert a PyTorch tensor to a NumPy image.
"""
images = images.cpu().permute(0, 2, 3, 1).float().numpy()
return images
@staticmethod
def numpy_to_pt(images: np.ndarray) -> torch.Tensor:
""... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
@staticmethod
def resize_antialias(
image: torch.Tensor, size: Tuple[int, int], mode: str, is_aa: Optional[bool] = None
) -> torch.Tensor:
if not torch.is_tensor(image):
raise ValueError(f"Invalid input type={type(image)}.")
if not torch.is_floating_point(image):
... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
@staticmethod
def resize_to_max_edge(image: torch.Tensor, max_edge_sz: int, mode: str) -> torch.Tensor:
if not torch.is_tensor(image):
raise ValueError(f"Invalid input type={type(image)}.")
if not torch.is_floating_point(image):
raise ValueError(f"Invalid input dtype={image.d... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
@staticmethod
def pad_image(image: torch.Tensor, align: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
if not torch.is_tensor(image):
raise ValueError(f"Invalid input type={type(image)}.")
if not torch.is_floating_point(image):
raise ValueError(f"Invalid input dtype={image.dty... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
@staticmethod
def unpad_image(image: torch.Tensor, padding: Tuple[int, int]) -> torch.Tensor:
if not torch.is_tensor(image):
raise ValueError(f"Invalid input type={type(image)}.")
if not torch.is_floating_point(image):
raise ValueError(f"Invalid input dtype={image.dtype}.")
... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
image_dtype_max = None
if isinstance(image, (np.ndarray, torch.Tensor)):
image = MarigoldImageProcessor.expand_tensor_or_array(image)
if image.ndim != 4:
raise ValueError("Input image is not 2-, 3-, or 4-dimensional.")
if isinstance(image, np.ndarray):
... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
image = image.astype(np.float32) # because torch does not have unsigned dtypes beyond torch.uint8
image = MarigoldImageProcessor.numpy_to_pt(image) | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
if torch.is_tensor(image) and not torch.is_floating_point(image) and image_dtype_max is None:
if image.dtype != torch.uint8:
raise ValueError(f"Image dtype={image.dtype} is not supported.")
image_dtype_max = 255
if not torch.is_tensor(image):
raise ValueError... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
@staticmethod
def check_image_values_range(image: torch.Tensor) -> None:
if not torch.is_tensor(image):
raise ValueError(f"Invalid input type={type(image)}.")
if not torch.is_floating_point(image):
raise ValueError(f"Invalid input dtype={image.dtype}.")
if image.min()... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
def preprocess(
self,
image: PipelineImageInput,
processing_resolution: Optional[int] = None,
resample_method_input: str = "bilinear",
device: torch.device = torch.device("cpu"),
dtype: torch.dtype = torch.float32,
):
if isinstance(image, list):
im... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
image = self.load_image_canonical(image, device, dtype) # [N,3,H,W] | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
original_resolution = image.shape[2:]
if self.config.do_range_check:
self.check_image_values_range(image)
if self.config.do_normalize:
image = image * 2.0 - 1.0
if processing_resolution is not None and processing_resolution > 0:
image = self.resize_to_max_e... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
@staticmethod
def colormap(
image: Union[np.ndarray, torch.Tensor],
cmap: str = "Spectral",
bytes: bool = False,
_force_method: Optional[str] = None,
) -> Union[np.ndarray, torch.Tensor]:
"""
Converts a monochrome image into an RGB image by applying the specified ... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
Args:
image: 2D tensor of values between 0 and 1, either as np.ndarray or torch.Tensor.
cmap: Colormap name.
bytes: Whether to return the output as uint8 or floating point image.
_force_method:
Can be used to specify whether to use the native implementatio... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
supported_cmaps = {
"binary": [
(1.0, 1.0, 1.0),
(0.0, 0.0, 0.0),
],
"Spectral": [ # Taken from matplotlib/_cm.py
(0.61960784313725492, 0.003921568627450980, 0.25882352941176473), # 0.0 -> [0]
(0.83529411764705885, 0.2... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
(0.36862745098039218, 0.30980392156862746, 0.63529411764705879), # 1.0 -> [K-1]
],
} | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
def method_matplotlib(image, cmap, bytes=False):
if is_matplotlib_available():
import matplotlib
else:
return None
arg_is_pt, device = torch.is_tensor(image), None
if arg_is_pt:
image, device = image.cpu().numpy(), image.de... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
def method_custom(image, cmap, bytes=False):
arg_is_np = isinstance(image, np.ndarray)
if arg_is_np:
image = torch.tensor(image)
if image.dtype == torch.uint8:
image = image.float() / 255
else:
image = image.float()
... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
d = (pos - left.float()).unsqueeze(-1)
left_colors = cmap[left]
right_colors = cmap[right]
out = (1 - d) * left_colors + d * right_colors
if bytes:
out = (out * 255).to(torch.uint8)
if arg_is_np:
out = out.numpy()
... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
@staticmethod
def visualize_depth(
depth: Union[
PIL.Image.Image,
np.ndarray,
torch.Tensor,
List[PIL.Image.Image],
List[np.ndarray],
List[torch.Tensor],
],
val_min: float = 0.0,
val_max: float = 1.0,
colo... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
Args:
depth (`Union[PIL.Image.Image, np.ndarray, torch.Tensor, List[PIL.Image.Image], List[np.ndarray],
List[torch.Tensor]]`): Depth maps.
val_min (`float`, *optional*, defaults to `0.0`): Minimum value of the visualized depth range.
val_max (`float`, *optional*, defa... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
def visualize_depth_one(img, idx=None):
prefix = "Depth" + (f"[{idx}]" if idx else "")
if isinstance(img, PIL.Image.Image):
if img.mode != "I;16":
raise ValueError(f"{prefix}: invalid PIL mode={img.mode}.")
img = np.array(img).astype(np.float32... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
img = MarigoldImageProcessor.colormap(img, cmap=color_map, bytes=True) # [H,W,3]
img = PIL.Image.fromarray(img.cpu().numpy())
return img | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
if depth is None or isinstance(depth, list) and any(o is None for o in depth):
raise ValueError("Input depth is `None`")
if isinstance(depth, (np.ndarray, torch.Tensor)):
depth = MarigoldImageProcessor.expand_tensor_or_array(depth)
if isinstance(depth, np.ndarray):
... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
@staticmethod
def export_depth_to_16bit_png(
depth: Union[np.ndarray, torch.Tensor, List[np.ndarray], List[torch.Tensor]],
val_min: float = 0.0,
val_max: float = 1.0,
) -> Union[PIL.Image.Image, List[PIL.Image.Image]]:
def export_depth_to_16bit_png_one(img, idx=None):
... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
img = PIL.Image.fromarray(img, mode="I;16")
return img | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
if depth is None or isinstance(depth, list) and any(o is None for o in depth):
raise ValueError("Input depth is `None`")
if isinstance(depth, (np.ndarray, torch.Tensor)):
depth = MarigoldImageProcessor.expand_tensor_or_array(depth)
if isinstance(depth, np.ndarray):
... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
@staticmethod
def visualize_normals(
normals: Union[
np.ndarray,
torch.Tensor,
List[np.ndarray],
List[torch.Tensor],
],
flip_x: bool = False,
flip_y: bool = False,
flip_z: bool = False,
) -> Union[PIL.Image.Image, List[PIL.I... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
Args:
normals (`Union[np.ndarray, torch.Tensor, List[np.ndarray], List[torch.Tensor]]`):
Surface normals.
flip_x (`bool`, *optional*, defaults to `False`): Flips the X axis of the normals frame of reference.
Default direction is right.
flip_y (`b... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
Returns: `PIL.Image.Image` or `List[PIL.Image.Image]` with surface normals visualization.
"""
flip_vec = None
if any((flip_x, flip_y, flip_z)):
flip_vec = torch.tensor(
[
(-1) ** flip_x,
(-1) ** flip_y,
(-1) ... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
if normals is None or isinstance(normals, list) and any(o is None for o in normals):
raise ValueError("Input normals is `None`")
if isinstance(normals, (np.ndarray, torch.Tensor)):
normals = MarigoldImageProcessor.expand_tensor_or_array(normals)
if isinstance(normals, np.ndar... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
@staticmethod
def visualize_uncertainty(
uncertainty: Union[
np.ndarray,
torch.Tensor,
List[np.ndarray],
List[torch.Tensor],
],
saturation_percentile=95,
) -> Union[PIL.Image.Image, List[PIL.Image.Image]]:
"""
Visualizes den... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
def visualize_uncertainty_one(img, idx=None):
prefix = "Uncertainty" + (f"[{idx}]" if idx else "")
if img.min() < 0:
raise ValueError(f"{prefix}: unexected data range, min={img.min()}.")
img = img.squeeze(0).cpu().numpy()
saturation_value = np.percentile(i... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
if uncertainty is None or isinstance(uncertainty, list) and any(o is None for o in uncertainty):
raise ValueError("Input uncertainty is `None`")
if isinstance(uncertainty, (np.ndarray, torch.Tensor)):
uncertainty = MarigoldImageProcessor.expand_tensor_or_array(uncertainty)
if... | 116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py |
class MarigoldDepthOutput(BaseOutput):
"""
Output class for Marigold monocular depth prediction pipeline.
Args:
prediction (`np.ndarray`, `torch.Tensor`):
Predicted depth maps with values in the range [0, 1]. The shape is always $numimages \times 1 \times height
\times width... | 117 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/pipeline_marigold_depth.py |
prediction: Union[np.ndarray, torch.Tensor]
uncertainty: Union[None, np.ndarray, torch.Tensor]
latent: Union[None, torch.Tensor] | 117 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/pipeline_marigold_depth.py |
class MarigoldDepthPipeline(DiffusionPipeline):
"""
Pipeline for monocular depth estimation using the Marigold method: https://marigoldmonodepth.github.io.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipel... | 118 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/pipeline_marigold_depth.py |
Args:
unet (`UNet2DConditionModel`):
Conditional U-Net to denoise the depth latent, conditioned on image latent.
vae (`AutoencoderKL`):
Variational Auto-Encoder (VAE) Model to encode and decode images and predictions to and from latent
representations.
schedul... | 118 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/pipeline_marigold_depth.py |
"affine-invariant". NB: overriding this value is not supported.
shift_invariant (`bool`, *optional*):
A model property specifying whether the predicted depth maps are shift-invariant. This value must be set in
the model config. When used together with the `scale_invariant=True` flag, the... | 118 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/marigold/pipeline_marigold_depth.py |
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