text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
uncond_tokens: List[str]
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)}."
)
... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
negative_prompt_embeds_list = []
for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
if isinstance(self, TextualInversionLoaderMixin):
negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
max_l... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
# We are only ALWAYS interested in the pooled output of the final text encoder
if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
negative_pooled_prompt_embeds = negative_prompt_embeds[0]
negative_prompt_embeds = negative_prompt_embeds.h... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
if do_clas... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed * num_images_per_prompt, -1
)
if do_classifier_free_guidance:
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed ... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoZeroPipeline.forward_loop
def forward_loop(self, x_t0, t0, t1, generator):
"""
Perform DDPM forward ... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
Returns:
x_t1:
Forward process applied to x_t0 from time t0 to t1.
"""
eps = randn_tensor(x_t0.size(), generator=generator, dtype=x_t0.dtype, device=x_t0.device)
alpha_vec = torch.prod(self.scheduler.alphas[t0:t1])
x_t1 = torch.sqrt(alpha_vec) * x_t0 + torch.s... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
Args:
latents:
Latents at time timesteps[0].
timesteps:
Time steps along which to perform backward process.
prompt_embeds:
Pre-generated text embeddings.
guidance_scale:
A higher guidance scale value encourag... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
Extra_step_kwargs.
cross_attention_kwargs:
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
num_war... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
Returns:
latents: latents of backward process output at time timesteps[-1]
"""
do_classifier_free_guidance = guidance_scale > 1.0
num_steps = (len(timesteps) - num_warmup_steps) // self.scheduler.order
with self.progress_bar(total=num_steps) as progress_bar:
for... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
# predict the noise residual
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
prompt_2: Optional[Union[str, List[str]]] = None,
video_length: Optional[int] = 8,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
denoising_e... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
motion_field_strength_x: float = 12,
motion_field_strength_y: float = 12,
output_type: Optional[str] = "tensor",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[s... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.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.
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to the `to... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
denoising_end (`float`, *optional*):
When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
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 scale is enabled by setting `guidance_scale >
1`. Highe... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
num_videos_per_prompt (`int`, *optional*, defaults to 1):
The number of videos to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can b... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
input argument.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generatio... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.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... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
guidance_rescale (`float`, *optional... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
`original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as
explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
crops_coords_top_left (`Tuple[int]`, *optional*, defaults to ... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in
section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
t0 (`int`, *optional*, defaults to 44):
Timestep t0. Should be in the ran... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
Returns:
[`~pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoSDXLPipelineOutput`] or
`tuple`: [`~pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoSDXLPipelineOutput`]
if `return_dict` is True, otherwise a `tuple`. When returning a tuple... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
if isinstance(prompt, str):
prompt = [prompt]
if isinstance(negative_prompt, str):
negative_prompt = [negative_prompt]
# 0. Default height and width to unet
height = height or self.default_sample_size * self.vae_scale_factor
width = width or self.default_sample_s... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
# 2. Define call parameters
batch_size = (
1 if isinstance(prompt, str) else len(prompt) if isinstance(prompt, list) else prompt_embeds.shape[0]
)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embed... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_videos_per... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
add_time_ids = self._get_add_time_ids(
original_size,
crops_coords_top_left,
target_size,
dtype=prompt_embeds.dtype,
text_encoder_projection_dim=text_encoder_projection_dim,
)
if do_classifier_free_guidance:
prompt_embeds = torch.c... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
# Perform the first backward process up to time T_1
x_1_t1 = self.backward_loop(
timesteps=timesteps[: -t1 - 1],
prompt_embeds=prompt_embeds,
latents=latents,
guidance_scale=guidance_scale,
callback=callback,
callback_steps=callback_steps,
... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
# Perform the second backward process up to time T_0
x_1_t0 = self.backward_loop(
timesteps=timesteps[-t1 - 1 : -t0 - 1],
prompt_embeds=prompt_embeds,
latents=x_1_t1,
guidance_scale=guidance_scale,
callback=callback,
callback_steps=callback... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
# Perform forward process up to time T_1
x_2k_t1 = self.forward_loop(
x_t0=x_2k_t0,
t0=timesteps[-t0 - 1].to(torch.long),
t1=timesteps[-t1 - 1].to(torch.long),
generator=generator,
)
# Perform backward process from time T_1 to 0
latents = ... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
# 7.1 Apply denoising_end
if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:
discrete_timestep_cutoff = int(
round(
self.scheduler.config.num_train_timesteps
- (denoising_end * self.sc... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
if not output_type == "latent":
# make sure the VAE is in float32 mode, as it overflows in float16
needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
if needs_upcasting:
self.upcast_vae()
latents = latents.to(next(iter(... | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
self.maybe_free_model_hooks()
# make sure to set the original attention processors back
self.unet.set_attn_processor(original_attn_proc)
if not return_dict:
return (image,)
return TextToVideoSDXLPipelineOutput(images=image) | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
class CrossFrameAttnProcessor:
"""
Cross frame attention processor. Each frame attends the first frame.
Args:
batch_size: The number that represents actual batch size, other than the frames.
For example, calling unet with a single prompt and num_images_per_prompt=1, batch_size should be... | 137 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
is_cross_attention = encoder_hidden_states is not None
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
... | 137 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
# rearrange back to original shape
key = rearrange_4(key)
value = rearrange_4(value)
query = attn.head_to_batch_dim(query)
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value)
attention_probs = attn.get_attention_scores(query, key, attention_m... | 137 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
class CrossFrameAttnProcessor2_0:
"""
Cross frame attention processor with scaled_dot_product attention of Pytorch 2.0.
Args:
batch_size: The number that represents actual batch size, other than the frames.
For example, calling unet with a single prompt and num_images_per_prompt=1, batc... | 138 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(b... | 138 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
# rearrange keys to have batch and frames in the 1st and 2nd dims respectively
key = rearrange_3(key, video_length)
key = key[:, first_frame_index]
# rearrange values to have batch and frames in the 1st and 2nd dims respectively
value = rearrange_3(value, video_length)
... | 138 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_state... | 138 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
class TextToVideoPipelineOutput(BaseOutput):
r"""
Output class for zero-shot text-to-video pipeline.
Args:
images (`[List[PIL.Image.Image]`, `np.ndarray`]):
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`... | 139 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
class TextToVideoZeroPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
StableDiffusionLoraLoaderMixin,
FromSingleFileMixin,
):
r"""
Pipeline for zero-shot text-to-video generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
toke... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
more details about a model's potential harms.
feature_extractor ([`CLIPImageProcessor`]):
A [`CLIPImageProcessor`] to extract features from generated images; used as inputs to the `sa... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
re... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its result... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
def forward_loop(self, x_t0, t0, t1, generator):
"""
Perform DDPM forward process from time t0 to t1. This is the same as adding noise with corresponding variance.
Args:
x_t0:
Latent code at time t0.
t0:
Timestep at t0.
t1:
... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
def backward_loop(
self,
latents,
timesteps,
prompt_embeds,
guidance_scale,
callback,
callback_steps,
num_warmup_steps,
extra_step_kwargs,
cross_attention_kwargs=None,
):
"""
Perform backward process given list of time s... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
Args:
latents:
Latents at time timesteps[0].
timesteps:
Time steps along which to perform backward process.
prompt_embeds:
Pre-generated text embeddings.
guidance_scale:
A higher guidance scale value encourag... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
Extra_step_kwargs.
cross_attention_kwargs:
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
num_war... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
Returns:
latents:
Latents of backward process output at time timesteps[-1].
"""
do_classifier_free_guidance = guidance_scale > 1.0
num_steps = (len(timesteps) - num_warmup_steps) // self.scheduler.order
with self.progress_bar(total=num_steps) as progress_bar:
... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
# Copied from diffusers.pipelines.stable_diffusion_k_diffusion.pipeline_stable_diffusion_k_diffusion.StableDiffusionKDiffusionPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.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_... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.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:
... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.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... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
video_length: Optional[int] = 8,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.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`.
video_length (`int`, *optional*, defaults to 8):
The number of generated video frames.
height (`int`, *o... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to g... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video
gener... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` func... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
[paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1.
t1 (`int`, *optional*, defaults to 47):
Timestep t0. Should be in the range [t0 + 1, num_inference_steps - 1]. See the
[paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1.
frame_ids (`List[int]`, *optional... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
Returns:
[`~pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoPipelineOutput`]:
The output contains a `ndarray` of the generated video, when `output_type` != `"latent"`, otherwise a
latent code of generated videos and a list of `bool`s indicating whethe... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
if isinstance(prompt, str):
prompt = [prompt]
if isinstance(negative_prompt, str):
negative_prompt = [negative_prompt]
# Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sam... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
# Encode input prompt
prompt_embeds_tuple = self.encode_prompt(
prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt
)
prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])
# Prepare timesteps
self.scheduler.set_ti... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
# Perform the first backward process up to time T_1
x_1_t1 = self.backward_loop(
timesteps=timesteps[: -t1 - 1],
prompt_embeds=prompt_embeds,
latents=latents,
guidance_scale=guidance_scale,
callback=callback,
callback_steps=callback_steps,
... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
# Propagate first frame latents at time T_0 to remaining frames
x_2k_t0 = x_1_t0.repeat(video_length - 1, 1, 1, 1)
# Add motion in latents at time T_0
x_2k_t0 = create_motion_field_and_warp_latents(
motion_field_strength_x=motion_field_strength_x,
motion_field_strength_y... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
self.scheduler = scheduler_copy
x_1k_0 = self.backward_loop(
timesteps=timesteps[-t1 - 1 :],
prompt_embeds=prompt_embeds,
latents=x_1k_t1,
guidance_scale=guidance_scale,
callback=callback,
callback_steps=callback_steps,
extra_st... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
# Offload all models
self.maybe_free_model_hooks()
# make sure to set the original attention processors back
self.unet.set_attn_processor(original_attn_proc)
if not return_dict:
return (image, has_nsfw_concept)
return TextToVideoPipelineOutput(images=image, nsfw_con... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.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):
... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.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... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.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.... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.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... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.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... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.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... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.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... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.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 | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.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_... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.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... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.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... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.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(
... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.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... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
def decode_latents(self, latents):
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
... | 140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py |
class TextToVideoSDPipelineOutput(BaseOutput):
"""
Output class for text-to-video 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
... | 141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_output.py |
class VideoToVideoSDPipeline(
DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin
):
r"""
Pipeline for text-guided video-to-video generation.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
i... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
toke... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet3DConditionModel,
scheduler: KarrasDiffusionSchedulers,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_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[torc... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
prompt_embeds_tuple = self.encode_prompt(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_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.... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_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... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_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... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_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... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
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