text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
<Tip warning={true}>
This argument exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a
future release.
</Tip>
Examples:
Returns:
[`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or... | 95 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_flax_controlnet.py |
if isinstance(guidance_scale, float):
# Convert to a tensor so each device gets a copy. Follow the prompt_ids for
# shape information, as they may be sharded (when `jit` is `True`), or not.
guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0])
if len(prompt_i... | 95 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_flax_controlnet.py |
if jit:
images = _p_generate(
self,
prompt_ids,
image,
params,
prng_seed,
num_inference_steps,
guidance_scale,
latents,
neg_prompt_ids,
controlnet_c... | 95 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_flax_controlnet.py |
images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3)
images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit)
images = np.array(images)
# block images
if any(has_nsfw_co... | 95 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_flax_controlnet.py |
class LTXPipelineOutput(BaseOutput):
r"""
Output class for LTX pipelines.
Args:
frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
denoised PIL image seq... | 96 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_output.py |
class LTXPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraLoaderMixin):
r"""
Pipeline for text-to-video generation.
Reference: https://github.com/Lightricks/LTX-Video | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
Args:
transformer ([`LTXVideoTransformer3DModel`]):
Conditional Transformer architecture to denoise the encoded video latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
v... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
[T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
""" | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
model_cpu_offload_seq = "text_encoder->transformer->vae"
_optional_components = []
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
def __init__(
self,
scheduler: FlowMatchEulerDiscreteScheduler,
vae: AutoencoderKLLTXVideo,
text_encoder: T5Enc... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
self.vae_spatial_compression_ratio = (
self.vae.spatial_compression_ratio if getattr(self, "vae", None) is not None else 32
)
self.vae_temporal_compression_ratio = (
self.vae.temporal_compression_ratio if getattr(self, "vae", None) is not None else 8
)
self.transf... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
def _get_t5_prompt_embeds(
self,
prompt: Union[str, List[str]] = None,
num_videos_per_prompt: int = 1,
max_sequence_length: int = 128,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
):
device = device or self._execution_device
... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.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... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
prompt_attention_mask = prompt_attention_mask.view(batch_size, -1)
prompt_attention_mask = prompt_attention_mask.repeat(num_videos_per_prompt, 1)
return prompt_embeds, prompt_attention_mask
# Copied from diffusers.pipelines.mochi.pipeline_mochi.MochiPipeline.encode_prompt with 256->128
def enc... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.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... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.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... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.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, prompt_attention_mask = self._get_t5_prompt_embeds(
pro... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.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... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask
def check_inputs(
self,
prompt,
height,
width,
callback_on_step_end_tensor_inputs=None,
prompt_embeds=None,
negative_prompt_embeds=None,
prompt_attenti... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.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:
... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
if negative_prompt_embeds is not None and negative_prompt_attention_mask is None:
raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.") | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.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... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
@staticmethod
def _pack_latents(latents: torch.Tensor, patch_size: int = 1, patch_size_t: int = 1) -> torch.Tensor:
# Unpacked latents of shape are [B, C, F, H, W] are patched into tokens of shape [B, C, F // p_t, p_t, H // p, p, W // p, p].
# The patch dimensions are then permuted and collapsed int... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
patch_size,
)
latents = latents.permute(0, 2, 4, 6, 1, 3, 5, 7).flatten(4, 7).flatten(1, 3)
return latents | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
@staticmethod
def _unpack_latents(
latents: torch.Tensor, num_frames: int, height: int, width: int, patch_size: int = 1, patch_size_t: int = 1
) -> torch.Tensor:
# Packed latents of shape [B, S, D] (S is the effective video sequence length, D is the effective feature dimensions)
# are un... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
@staticmethod
def _normalize_latents(
latents: torch.Tensor, latents_mean: torch.Tensor, latents_std: torch.Tensor, scaling_factor: float = 1.0
) -> torch.Tensor:
# Normalize latents across the channel dimension [B, C, F, H, W]
latents_mean = latents_mean.view(1, -1, 1, 1, 1).to(latents.... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
@staticmethod
def _denormalize_latents(
latents: torch.Tensor, latents_mean: torch.Tensor, latents_std: torch.Tensor, scaling_factor: float = 1.0
) -> torch.Tensor:
# Denormalize latents across the channel dimension [B, C, F, H, W]
latents_mean = latents_mean.view(1, -1, 1, 1, 1).to(late... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
def prepare_latents(
self,
batch_size: int = 1,
num_channels_latents: int = 128,
height: int = 512,
width: int = 704,
num_frames: int = 161,
dtype: Optional[torch.dtype] = None,
device: Optional[torch.device] = None,
generator: Optional[torch.Gener... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
negative_prompt: Optional[Union[str, List[str]]] = None,
height: int = 512,
width: int = 704,
num_frames: int = 161,
frame_rate: int =... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
output_type: Optional[str] = "pil",
return_dict: bool = True,
attention_kwargs: Optional[Dict[str, Any]] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
max_sequence_length: int = 128,
... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.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.
height (`int`, defaults to `512`):
The height in pixels of the generated image. This is ... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.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`... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.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.Tensor`, *optional*):
Pre-generated no... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. For PixArt-Sigma this negative prompt should be "". If not
provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.
negative_prompt_attention_mas... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ltx.LTXPipelineOutput`] instead of a plain tuple.
attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined ... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
Examples:
Returns:
[`~pipelines.ltx.LTXPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ltx.LTXPipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images.
"""
if isin... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
self._guidance_scale = guidance_scale
self._attention_kwargs = attention_kwargs
self._interrupt = False
# 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):
bat... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
# 3. Prepare text embeddings
(
prompt_embeds,
prompt_attention_mask,
negative_prompt_embeds,
negative_prompt_attention_mask,
) = self.encode_prompt(
prompt=prompt,
negative_prompt=negative_prompt,
do_classifier_free_guid... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
# 4. Prepare latent variables
num_channels_latents = self.transformer.config.in_channels
latents = self.prepare_latents(
batch_size * num_videos_per_prompt,
num_channels_latents,
height,
width,
num_frames,
torch.float32,
... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
# 5. Prepare timesteps
latent_num_frames = (num_frames - 1) // self.vae_temporal_compression_ratio + 1
latent_height = height // self.vae_spatial_compression_ratio
latent_width = width // self.vae_spatial_compression_ratio
video_sequence_length = latent_num_frames * latent_height * laten... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
self._num_timesteps = len(timesteps) | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
# 6. Prepare micro-conditions
latent_frame_rate = frame_rate / self.vae_temporal_compression_ratio
rope_interpolation_scale = (
1 / latent_frame_rate,
self.vae_spatial_compression_ratio,
self.vae_spatial_compression_ratio,
)
# 7. Denoising loop
... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
noise_pred = self.transformer(
hidden_states=latent_model_input,
encoder_hidden_states=prompt_embeds,
timestep=timestep,
encoder_attention_mask=prompt_attention_mask,
num_frames=latent_num_frames,
hei... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
if output_type == "latent":
video = latents
else:
latents = self._unpack_latents(
latents,
latent_num_frames,
latent_height,
latent_width,
self.transformer_spatial_patch_size,
self.transformer... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
if not self.vae.config.timestep_conditioning:
timestep = None
else:
noise = randn_tensor(latents.shape, generator=generator, device=device, dtype=latents.dtype)
if not isinstance(decode_timestep, list):
decode_timestep = [decode_timestep] *... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
video = self.vae.decode(latents, timestep, return_dict=False)[0]
video = self.video_processor.postprocess_video(video, output_type=output_type)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (video,)
return LTXPipelineOutput(frame... | 97 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py |
class LTXImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraLoaderMixin):
r"""
Pipeline for image-to-video generation.
Reference: https://github.com/Lightricks/LTX-Video | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
Args:
transformer ([`LTXVideoTransformer3DModel`]):
Conditional Transformer architecture to denoise the encoded video latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
v... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
[T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
""" | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
model_cpu_offload_seq = "text_encoder->transformer->vae"
_optional_components = []
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
def __init__(
self,
scheduler: FlowMatchEulerDiscreteScheduler,
vae: AutoencoderKLLTXVideo,
text_encoder: T5Enc... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
self.vae_spatial_compression_ratio = (
self.vae.spatial_compression_ratio if getattr(self, "vae", None) is not None else 32
)
self.vae_temporal_compression_ratio = (
self.vae.temporal_compression_ratio if getattr(self, "vae", None) is not None else 8
)
self.transf... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
def _get_t5_prompt_embeds(
self,
prompt: Union[str, List[str]] = None,
num_videos_per_prompt: int = 1,
max_sequence_length: int = 128,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
):
device = device or self._execution_device
... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_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... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
prompt_attention_mask = prompt_attention_mask.view(batch_size, -1)
prompt_attention_mask = prompt_attention_mask.repeat(num_videos_per_prompt, 1)
return prompt_embeds, prompt_attention_mask
# Copied from diffusers.pipelines.mochi.pipeline_mochi.MochiPipeline.encode_prompt with 256->128
def enc... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_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... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_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... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_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, prompt_attention_mask = self._get_t5_prompt_embeds(
pro... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_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... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask
# Copied from diffusers.pipelines.ltx.pipeline_ltx.LTXPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
callback_on_step_end_tensor_inputs=None,
... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_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... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.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:
... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
if negative_prompt_embeds is not None and negative_prompt_attention_mask is None:
raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.") | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_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... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
@staticmethod
# Copied from diffusers.pipelines.ltx.pipeline_ltx.LTXPipeline._pack_latents
def _pack_latents(latents: torch.Tensor, patch_size: int = 1, patch_size_t: int = 1) -> torch.Tensor:
# Unpacked latents of shape are [B, C, F, H, W] are patched into tokens of shape [B, C, F // p_t, p_t, H // p, ... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
post_patch_height,
patch_size,
post_patch_width,
patch_size,
)
latents = latents.permute(0, 2, 4, 6, 1, 3, 5, 7).flatten(4, 7).flatten(1, 3)
return latents | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
@staticmethod
# Copied from diffusers.pipelines.ltx.pipeline_ltx.LTXPipeline._unpack_latents
def _unpack_latents(
latents: torch.Tensor, num_frames: int, height: int, width: int, patch_size: int = 1, patch_size_t: int = 1
) -> torch.Tensor:
# Packed latents of shape [B, S, D] (S is the effec... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
@staticmethod
# Copied from diffusers.pipelines.ltx.pipeline_ltx.LTXPipeline._normalize_latents
def _normalize_latents(
latents: torch.Tensor, latents_mean: torch.Tensor, latents_std: torch.Tensor, scaling_factor: float = 1.0
) -> torch.Tensor:
# Normalize latents across the channel dimensio... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
@staticmethod
# Copied from diffusers.pipelines.ltx.pipeline_ltx.LTXPipeline._denormalize_latents
def _denormalize_latents(
latents: torch.Tensor, latents_mean: torch.Tensor, latents_std: torch.Tensor, scaling_factor: float = 1.0
) -> torch.Tensor:
# Denormalize latents across the channel di... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
def prepare_latents(
self,
image: Optional[torch.Tensor] = None,
batch_size: int = 1,
num_channels_latents: int = 128,
height: int = 512,
width: int = 704,
num_frames: int = 161,
dtype: Optional[torch.dtype] = None,
device: Optional[torch.device] =... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
if latents is not None:
conditioning_mask = latents.new_zeros(shape)
conditioning_mask[:, :, 0] = 1.0
conditioning_mask = self._pack_latents(
conditioning_mask, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
)
return ... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
init_latents = [
retrieve_latents(self.vae.encode(image[i].unsqueeze(0).unsqueeze(2)), generator[i])
for i in range(batch_size)
]
else:
init_latents = [
retrieve_latents(self.vae.encode(img.unsqueeze(0).unsqueeze(2)), generator) for img in ... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
conditioning_mask = self._pack_latents(
conditioning_mask, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
).squeeze(-1)
latents = self._pack_latents(
latents, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
)
r... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
image: PipelineImageInput = None,
prompt: Union[str, List[str]] = None,
negative_prompt: Optional[Union[str, List[str]]] = None,
height: int = 512,
width: int = 704,
num_fr... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
decode_noise_scale: Optional[Union[float, List[float]]] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
attention_kwargs: Optional[Dict[str, Any]] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_in... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_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... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
expense of slower inference.
timesteps (`List[int]`, *optional*):
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
... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
The number of videos to generate per prompt.
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 (`tor... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
Pre-generated attention mask for text embeddings.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. For PixArt-Sigma this negative prompt should be "". If not
provided, negative_prompt_embeds will be generated from `negative_pro... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
[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.ltx.LTXPipelineOutput`] instead of a plain tuple.
attention_kwargs (`dict`, *optional*):
... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specifie... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
Examples:
Returns:
[`~pipelines.ltx.LTXPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ltx.LTXPipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images.
"""
if isin... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
self._guidance_scale = guidance_scale
self._attention_kwargs = attention_kwargs
self._interrupt = False
# 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):
bat... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
# 3. Prepare text embeddings
(
prompt_embeds,
prompt_attention_mask,
negative_prompt_embeds,
negative_prompt_attention_mask,
) = self.encode_prompt(
prompt=prompt,
negative_prompt=negative_prompt,
do_classifier_free_guid... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
# 4. Prepare latent variables
if latents is None:
image = self.video_processor.preprocess(image, height=height, width=width)
image = image.to(device=device, dtype=prompt_embeds.dtype)
num_channels_latents = self.transformer.config.in_channels
latents, conditioning_mask =... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
# 5. Prepare timesteps
latent_num_frames = (num_frames - 1) // self.vae_temporal_compression_ratio + 1
latent_height = height // self.vae_spatial_compression_ratio
latent_width = width // self.vae_spatial_compression_ratio
video_sequence_length = latent_num_frames * latent_height * laten... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
self._num_timesteps = len(timesteps) | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
# 6. Prepare micro-conditions
latent_frame_rate = frame_rate / self.vae_temporal_compression_ratio
rope_interpolation_scale = (
1 / latent_frame_rate,
self.vae_spatial_compression_ratio,
self.vae_spatial_compression_ratio,
)
# 7. Denoising loop
... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
noise_pred = self.transformer(
hidden_states=latent_model_input,
encoder_hidden_states=prompt_embeds,
timestep=timestep,
encoder_attention_mask=prompt_attention_mask,
num_frames=latent_num_frames,
hei... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
# compute the previous noisy sample x_t -> x_t-1
noise_pred = self._unpack_latents(
noise_pred,
latent_num_frames,
latent_height,
latent_width,
self.transformer_spatial_patch_size,
sel... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
latents = torch.cat([latents[:, :, :1], pred_latents], dim=2)
latents = self._pack_latents(
latents, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
)
if callback_on_step_end is not None:
callback_kwargs =... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
if output_type == "latent":
video = latents
else:
latents = self._unpack_latents(
latents,
latent_num_frames,
latent_height,
latent_width,
self.transformer_spatial_patch_size,
self.transformer... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
if not self.vae.config.timestep_conditioning:
timestep = None
else:
noise = torch.randn(latents.shape, generator=generator, device=device, dtype=latents.dtype)
if not isinstance(decode_timestep, list):
decode_timestep = [decode_timestep] * ... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
video = self.vae.decode(latents, timestep, return_dict=False)[0]
video = self.video_processor.postprocess_video(video, output_type=output_type)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (video,)
return LTXPipelineOutput(frame... | 98 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py |
class LeditsAttentionStore:
@staticmethod
def get_empty_store():
return {"down_cross": [], "mid_cross": [], "up_cross": [], "down_self": [], "mid_self": [], "up_self": []}
def __call__(self, attn, is_cross: bool, place_in_unet: str, editing_prompts, PnP=False):
# attn.shape = batch_size * h... | 99 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
def between_steps(self, store_step=True):
if store_step:
if self.average:
if len(self.attention_store) == 0:
self.attention_store = self.step_store
else:
for key in self.attention_store:
for i in range(le... | 99 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
def get_attention(self, step: int):
if self.average:
attention = {
key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store
}
else:
assert step is not None
attention = self.attention_store[step]
... | 99 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
for location in from_where:
for bs_item in attention_maps[f"{location}_{'cross' if is_cross else 'self'}"]:
for batch, item in enumerate(bs_item):
if item.shape[1] == num_pixels:
cross_maps = item.reshape(len(prompts), -1, *resolution, item.shape[-... | 99 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
def __init__(self, average: bool, batch_size=1, max_resolution=16, max_size: int = None):
self.step_store = self.get_empty_store()
self.attention_store = []
self.cur_step = 0
self.average = average
self.batch_size = batch_size
if max_size is None:
self.max_siz... | 99 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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