text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
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# 1. Check inputs. Raise error if not correct
self.check_inputs(
image=image,
prompt=prompt,
height=height,
width=width,
negative_prompt=negative_prompt,
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
lat... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode inpu... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
# 4. Prepare timesteps
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
self._num_timesteps = len(timesteps)
# 5. Prepare latents
latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
# For CogVideoX 1.... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
latent_channels = self.transformer.config.in_channels // 2
latents, image_latents = self.prepare_latents(
image,
batch_size * num_videos_per_prompt,
latent_channels,
num_frames,
height,
width,
prompt_embeds.dtype,
de... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
# 8. Denoising loop
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
with self.progress_bar(total=num_inference_steps) as progress_bar:
# for DPM-solver++
old_pred_original_sample = None
for i, t in enumerate(timesteps):
... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
# predict noise model_output
noise_pred = self.transformer(
hidden_states=latent_model_input,
encoder_hidden_states=prompt_embeds,
timestep=timestep,
ofs=ofs_emb,
image_rotary_emb=image_rotary_emb,
... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
# compute the previous noisy sample x_t -> x_t-1
if not isinstance(self.scheduler, CogVideoXDPMScheduler):
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
else:
latents, old_pred_original_sample = se... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
# call the callback, if provided
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,... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
if not output_type == "latent":
# Discard any padding frames that were added for CogVideoX 1.5
latents = latents[:, additional_frames:]
video = self.decode_latents(latents)
video = self.video_processor.postprocess_video(video=video, output_type=output_type)
else:
... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
class CogVideoXVideoToVideoPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
r"""
Pipeline for video-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... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.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#transf... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.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: ... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
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... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
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=... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
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, ... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
# Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.encode_prompt
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 ... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.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. Ign... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.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 wil... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.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,
... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.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_pro... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
def prepare_latents(
self,
video: Optional[torch.Tensor] = None,
batch_size: int = 1,
num_channels_latents: int = 16,
height: int = 60,
width: int = 90,
dtype: Optional[torch.dtype] = None,
device: Optional[torch.device] = None,
generator: Optional... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
shape = (
batch_size,
num_frames,
num_channels_latents,
height // self.vae_scale_factor_spatial,
width // self.vae_scale_factor_spatial,
)
if latents is None:
if isinstance(generator, list):
init_latents = [
... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
# Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.decode_latents
def decode_latents(self, latents: torch.Tensor) -> torc... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = timesteps[t_start * self.scheduler.order :]
return timesteps, num_inference_steps - t_start
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepa... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self,
prompt,
... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.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... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
if prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if negative_prompt is ... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `pr... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
# Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.unfuse_qkv_projections
def unfuse_qkv_projections(self) -> None:
r"""Disable QKV projection fusion if enabled."""
if not self.fusing_transformer:
logger.warning("The Transformer was not initially fused for QK... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
p = self.transformer.config.patch_size
p_t = self.transformer.config.patch_size_t
base_size_width = self.transformer.config.sample_width // p
base_size_height = self.transformer.config.sample_height // p
if p_t is None:
# CogVideoX 1.0
grid_crops_coords = get_re... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
embed_dim=self.transformer.config.attention_head_dim,
crops_coords=None,
grid_size=(grid_height, grid_width),
temporal_size=base_num_frames,
grid_type="slice",
max_size=(base_s... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
video: List[Image.Image] = None,
prompt: Optional[Union[str, List[str]]] = None,
negative_prompt: Optional[Union[str, List[str]]] = None,
height: Optional[int] = None,
width: Optio... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
callback_on_step_end: Optional[
Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
max_sequence_length: int = 226,
) -> Union[CogVideoXPipelineOutput, Tuple]:
"""
... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
Args:
video (`List[PIL.Image.Image]`):
The input video to condition the generation on. Must be a list of images/frames of the video.
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `pr... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
width (`int`, *optional*, defaults to self.transformer.config.sample_height * self.vae_scale_factor_spatial):
The width in pixels of the generated image. This is set to 720 by default for the best results.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
guidance_scale (`float`, *optional*, defaults to 7.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance ... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
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.stable_diffusion_xl.StableDiffusionXLPipelin... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.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... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
Examples:
Returns:
[`~pipelines.cogvideo.pipeline_output.CogVideoXPipelineOutput`] or `tuple`:
[`~pipelines.cogvideo.pipeline_output.CogVideoXPipelineOutput`] if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is a list with the generate... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt=prompt,
height=height,
width=width,
strength=strength,
negative_prompt=negative_prompt,
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode inpu... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
# 4. Prepare timesteps
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, timesteps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_videos_... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
if latents is None:
video = self.video_processor.preprocess_video(video, height=height, width=width)
video = video.to(device=device, dtype=prompt_embeds.dtype)
latent_channels = self.transformer.config.in_channels
latents = self.prepare_latents(
video,
ba... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
# 8. Denoising loop
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
with self.progress_bar(total=num_inference_steps) as progress_bar:
# for DPM-solver++
old_pred_original_sample = None
for i, t in enumerate(timesteps):
... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
# predict noise model_output
noise_pred = self.transformer(
hidden_states=latent_model_input,
encoder_hidden_states=prompt_embeds,
timestep=timestep,
image_rotary_emb=image_rotary_emb,
attention_kwargs=at... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
# compute the previous noisy sample x_t -> x_t-1
if not isinstance(self.scheduler, CogVideoXDPMScheduler):
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
else:
latents, old_pred_original_sample = se... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
# call the callback, if provided
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,... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
if not output_type == "latent":
video = self.decode_latents(latents)
video = self.video_processor.postprocess_video(video=video, output_type=output_type)
else:
video = latents
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
... | 113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py |
class LatentConsistencyModelImg2ImgPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
IPAdapterMixin,
StableDiffusionLoraLoaderMixin,
FromSingleFileMixin,
):
r"""
Pipeline for image-to-image generation using a latent consistency model.
This model inherit... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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))... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
) | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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"
... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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.... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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 | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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_... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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(
... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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):
... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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 = ... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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]] * ... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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):
... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents
def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
else:
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 ... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
init_latents = [
retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
for i in range(batch_size)
]
init_latents = torch.cat(init_latents, dim=0)
else:
init_latents = retrieve_latents(self.vae.encod... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
# expand init_latents for batch_size
deprecation_message = (
f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
" images (`image`). In... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
raise ValueError(
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
)
else:
init_latents = torch.cat([init_latents], dim=0) | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
shape = init_latents.shape
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# get latents
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
latents = init_latents
return latents
# Copied from diffusers.pipelines.latent_consi... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
Args:
w (`torch.Tensor`):
Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
embedding_dim (`int`, *optional*, defaults to 512):
Dimension of the embeddings to generate.
dtype (`torch.dtype`, *optiona... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
half_dim = embedding_dim // 2
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
emb = w.to(dtype)[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero ... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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)... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
if hasattr(self.scheduler, "set_begin_index"):
self.scheduler.set_begin_index(t_start * self.scheduler.order)
return timesteps, num_inference_steps - t_start
... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if callback_on_step_end_tensor... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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:
... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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 ... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: PipelineImageInput = None,
num_inference_steps: int = 4,
strength: float = 0.8,
original_inference_steps: int = None,
timesteps... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
**kwargs,
):
r"""
The call function to the pipeline for generation. | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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]... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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.
... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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 ... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.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... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
... | 114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py |
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