text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.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 ... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.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... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
def _prepare_rotary_positional_embeddings(
self,
height: int,
width: int,
num_frames: int,
device: torch.device,
) -> Tuple[torch.Tensor, torch.Tensor]:
grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
grid_width = w... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
if p_t is None:
# CogVideoX 1.0
grid_crops_coords = get_resize_crop_region_for_grid(
(grid_height, grid_width), base_size_width, base_size_height
)
freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
embed_dim=self.transformer.config.attention_... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.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... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Optional[Union[str, List[str]]] = None,
negative_prompt: Optional[Union[str, List[str]]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_frames: Opti... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
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]:
"""
Function invoked when calling the pipeline ... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide t... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
num_frames (`int`, defaults to `48`):
Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will
contain 1 extra frame because CogVideoX is conditioned with (num_seconds * fps + 1) frames where
num_seconds is 6 and fps is 8. Ho... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
passed will be used. Must be in descending order.
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
... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
latents (`torch.FloatTensor`, *optional*):
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... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
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... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.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... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
Examples:
Returns:
[`~pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput`] or `tuple`:
[`~pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput`] if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is a list with the ge... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
height,
width,
negative_prompt,
callback_on_step_end_tensor_inputs,
prompt_embeds,
negative_prompt_embeds,
)
self._guidance_scale = guidanc... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.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... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.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.... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
latent_channels = self.transformer.config.in_channels
latents = self.prepare_latents(
batch_size * num_videos_per_prompt,
latent_channels,
num_frames,
height,
width,
prompt_embeds.dtype,
device,
generator,
... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
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):
if self.interrupt:
continue
latent_model_input = torch.cat([latents] * 2) if do... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.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... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.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... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.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,... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.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:
... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
class CogVideoXFunControlPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
r"""
Pipeline for controlled text-to-video generation using CogVideoX Fun.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipeli... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.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... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
_optional_components = []
model_cpu_offload_seq = "text_encoder->vae->transformer->vae"
_callback_tensor_inputs = [
"latents",
"prompt_embeds",
"negative_prompt_embeds",
]
def __init__(
self,
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.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_scal... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
# Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline._get_t5_prompt_embeds
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,
... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.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... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.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 ... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.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... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.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... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.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,
... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.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... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
# Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.prepare_latents
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:
... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
# Adapted from https://github.com/aigc-apps/CogVideoX-Fun/blob/2a93e5c14e02b2b5921d533fd59fc8c0ed69fb24/cogvideox/pipeline/pipeline_cogvideox_control.py#L... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
if masked_image is not None:
mask_pixel_values = []
for i in range(masked_image.size(0)):
mask_pixel_value = masked_image[i].unsqueeze(0)
mask_pixel_value = self.vae.encode(mask_pixel_value)[0]
mask_pixel_value = mask_pixel_value.mode()
... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
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... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.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,
... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.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... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.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 ... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.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... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
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 QKV projections. Doing nothing.")
else:
self.transformer.unfuse_qkv_projections()
... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
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_resize_crop_region_for_grid(
(grid_height, grid_width), base_size_width, base_size_he... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.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... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Optional[Union[str, List[str]]] = None,
negative_prompt: Optional[Union[str, List[str]]] = None,
control_video: Optional[List[Image.Image]] = None,
height: Optional[int] = None,
... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.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]:
"""
... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide t... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.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... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually ... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
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`.
control_video_latents (`torch.Tensor`, *optional*):
Pre-generated control latents, sampled from ... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/sta... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
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,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as sp... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
Examples:
Returns:
[`~pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput`] or `tuple`:
[`~pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput`] if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is a list with the ge... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
height,
width,
negative_prompt,
callback_on_step_end_tensor_inputs,
prompt_embeds,
negative_prompt_embeds,
control_video,
control_v... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.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... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.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.... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
latent_channels = self.transformer.config.in_channels // 2
latents = self.prepare_latents(
batch_size * num_videos_per_prompt,
latent_channels,
num_frames,
height,
width,
prompt_embeds.dtype,
device,
generator,
... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
# 7. Create rotary embeds if required
image_rotary_emb = (
self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device)
if self.transformer.config.use_rotary_positional_embeddings
else None
)
# 8. Denoising loop
num_warmup_steps ... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
latent_control_input = (
torch.cat([control_video_latents] * 2) if do_classifier_free_guidance else control_video_latents
)
latent_model_input = torch.cat([latent_model_input, latent_control_input], dim=2)
# broadcast to batch dimension in a way that'... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
# perform guidance
if use_dynamic_cfg:
self._guidance_scale = 1 + guidance_scale * (
(1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2
)
if do_classifier_free_guidance:
... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.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,... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.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:
... | 111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py |
class CogVideoXImageToVideoPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
r"""
Pipeline for image-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... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.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... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.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: ... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.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, "v... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
# Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline._get_t5_prompt_embeds
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,
... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.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... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.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 ... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.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... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.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... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.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,
... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.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... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
def prepare_latents(
self,
image: torch.Tensor,
batch_size: int = 1,
num_channels_latents: int = 16,
num_frames: int = 13,
height: int = 60,
width: int = 90,
dtype: Optional[torch.dtype] = None,
device: Optional[torch.device] = None,
genera... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
num_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
shape = (
batch_size,
num_frames,
num_channels_latents,
height // self.vae_scale_factor_spatial,
width // self.vae_scale_factor_spatial,
)
# For CogVideoX1.5, the late... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
if not self.vae.config.invert_scale_latents:
image_latents = self.vae_scaling_factor_image * image_latents
else:
# This is awkward but required because the CogVideoX team forgot to multiply the
# scaling factor during training :)
image_latents = 1 / self.vae_scali... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
# Select the first frame along the second dimension
if self.transformer.config.patch_size_t is not None:
first_frame = image_latents[:, : image_latents.size(1) % self.transformer.config.patch_size_t, ...]
image_latents = torch.cat([first_frame, image_latents], dim=1)
if latents ... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
frames = self.vae.decode(latents).sample
return frames
# Copied from diffusers.pipelines.animatediff.pipeline_animatediff_video2video.AnimateDiffVideoToVideoPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, timesteps, strength, device):
# get the original timestep using init_t... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.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... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
def check_inputs(
self,
image,
prompt,
height,
width,
negative_prompt,
callback_on_step_end_tensor_inputs,
latents=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if (
not isinstance(image, torch.Tensor)
... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.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... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.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 ... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.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... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.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... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.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... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.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... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
image: PipelineImageInput,
prompt: Optional[Union[str, List[str]]] = None,
negative_prompt: Optional[Union[str, List[str]]] = None,
height: Optional[int] = None,
width: Optional[in... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
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]:
"""
Function invoked when calling the pipeline ... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
Args:
image (`PipelineImageInput`):
The input image to condition the generation on. Must be an image, a list of images or a `torch.Tensor`.
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to p... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.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_frames (`int`, defaults to `48`):
Number of frames to generate. Mu... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
passed will be used. Must be in descending order.
guidance_scale (`float`... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generat... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
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.
output_type (`str`, *optional*, defaults to `"pil"`):
The... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the inference. The function is called
... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
max_sequence_length (`int`, defaults to `226`):
Maximum sequence length in encoded prompt. Must be consistent with
`self.transformer.config.max_text_seq_length` otherwise may lead to poor results. | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.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... | 112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py |
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