text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
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... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
if clip_skip is None:
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
prompt_embeds = prompt_embeds[0]
else:
prompt_embeds = self.text_encoder(
text_input_ids.to(device), attention_mask=attention_... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
if self.text_encoder is not None:
prompt_embeds_dtype = self.text_encoder.dtype
elif self.unet is not None:
prompt_embeds_dtype = self.unet.dtype
else:
prompt_embeds_dtype = prompt_embeds.dtype
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, devic... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is no... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
# textual inversion: process multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
negative... | 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.text_to_video_synthesis.pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents
def decode_latents(self, latents):
latents = 1 / self.vae.config.scaling_factor * latents
batch_size, channels, num_frames, height, width = latents.shape
latents = lat... | 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.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... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
def check_inputs(
self,
prompt,
strength,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
callback_on_step_end_tensor_inputs=None,
):
if strength < 0 or strength > 1:
raise ValueError(f"The val... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.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... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
... | 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_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), n... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
# change from (b, c, f, h, w) -> (b * f, c, w, h)
bsz, channel, frames, width, height = video.shape
video = video.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height) | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
if video.shape[1] == 4:
init_latents = video
else:
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
raise ValueError(
f"Cannot duplicate `video` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
)
else:
init_latents = torch.cat([init_latents], dim=0)
... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
video: Union[List[np.ndarray], torch.Tensor] = None,
strength: float = 0.6,
num_inference_steps: int = 50,
guidance_scale: float = 15.0,
... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
The call function to the pipeline for generation. | 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*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
video (`List[np.ndarray]` or `torch.Tensor`):
`video` frames or tensor representing a video batch to be used as the ... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
1 essentially ignores `video`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality videos at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https:/... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.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... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
Returns:
[`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] is
returned, otherwise a `tuple` is returned where the first element is a list with the gener... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
device = self._execution_device
# 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 = guidan... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.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 = self.encode_prompt(
prompt,
device,
num_images_per_prompt,
... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
# 5. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
# 6. Prepare latent variables
... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.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)
# reshape latents
bsz, channel, fra... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.sch... | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
if not return_dict:
return (video,)
return TextToVideoSDPipelineOutput(frames=video) | 142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py |
class TextToVideoSDPipeline(
DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin
):
r"""
Pipeline for text-to-video generation.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.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... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py |
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet3DConditionModel,
scheduler: KarrasDiffusionSchedulers,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.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... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.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,
... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.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.... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.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... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.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... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.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... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.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... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.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 | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.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_... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.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... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.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... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.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(
... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.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... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py |
batch_size, channels, num_frames, height, width = latents.shape
latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
image = self.vae.decode(latents).sample
video = image[None, :].reshape((batch_size, num_frames, -1) + image.shape[2:]).permute(0, 2,... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py |
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step)... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.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,
... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.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_... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.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:
... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.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... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py |
def prepare_latents(
self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
):
shape = (
batch_size,
num_channels_latents,
num_frames,
height // self.vae_scale_factor,
width // self.vae_sca... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_frames: int = 16,
num_inference_steps: int = 50,
guidance_scale: float ... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py |
The call function to the pipeline for generation. | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py |
guidance_scale (`float`, *optional*, defaults to 7.5):
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` ... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py |
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents samp... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py |
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
output_type (`str`, *opt... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py |
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`s... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py |
Returns:
[`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] is
returned, otherwise a `tuple` is returned where the first element is a list with the gener... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py |
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.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 = self.encode_prompt(
prompt,
device,
num_images_per_prompt,
... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.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_images_per_... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py |
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.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)
# reshape latents
bsz, channel, fra... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py |
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.sch... | 143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py |
class StableDiffusionPanoramaPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
StableDiffusionLoraLoaderMixin,
IPAdapterMixin,
):
r"""
Pipeline for text-to-image generation using MultiDiffusion.
This model inherits from [`DiffusionPipeline`]. Check the supe... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14))... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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 ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as input... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
_exclude_from_cpu_offload = ["safety_checker"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
def __init__(
self,
vae: Auto... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` i... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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,
... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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.... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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 | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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_... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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(
... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
image = image.to(device=device, dtype=dtype)
if output_hidden_states:
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
uncond_imag... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
def prepare_ip_adapter_image_embeds(
self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
):
image_embeds = ... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
for single_ip_adapter_image, image_proj_layer in zip(
ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
):
output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
single_image_embeds, single_negative_image_embeds = self.encod... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
ip_adapter_image_embeds = []
for i, single_image_embeds in enumerate(image_embeds):
single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
single_negative_image_embeds = torch.cat([negative_image_embeds[i]] * ... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
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