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# 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,
int(height) // self.vae_scale_factor,
int(width) // self.vae_scale_factor,
)
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."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device) | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.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_inference_steps: int = 50,
guidance_scale: float = 7.5,
sag_scale: float = 0.75,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None, | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
callback_steps: Optional[int] = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
clip_skip: Optional[int] = None,
):
r"""
The call function to the pipeline for generation. | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.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 of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
sag_scale (`float`, *optional*, defaults to 0.75):
Chosen between [0, 1.0] for better quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
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*): | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.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 image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.Tensor`, *optional*):
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 to easily tweak text inputs (prompt weighting). If | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
ip_adapter_image: (`PipelineImageInput`, *optional*):
Optional image input to work with IP Adapters.
ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
Pre-generated image embeddings for IP-Adapter. If not provided, embeddings are computed from the
`ip_adapter_image` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
callback_steps (`int`, *optional*, defaults to 1):
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
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
Examples: | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
) | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.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
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# and `sag_scale` is` `s` of equation (16)
# of the self-attention guidance paper: https://arxiv.org/pdf/2210.00939.pdf
# `sag_scale = 0` means no self-attention guidance
do_self_attention_guidance = sag_scale > 0.0 | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
ip_adapter_image_embeds = self.prepare_ip_adapter_image_embeds(
ip_adapter_image,
ip_adapter_image_embeds,
device,
batch_size * num_images_per_prompt,
do_classifier_free_guidance,
)
if do_classifier_free_guidance:
image_embeds = []
negative_image_embeds = []
for tmp_image_embeds in ip_adapter_image_embeds:
single_negative_image_embeds, single_image_embeds = tmp_image_embeds.chunk(2)
image_embeds.append(single_image_embeds)
negative_image_embeds.append(single_negative_image_embeds)
else:
image_embeds = ip_adapter_image_embeds | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
# 3. Encode input prompt
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
clip_skip=clip_skip,
)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
if do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
if timesteps.dtype not in [torch.int16, torch.int32, torch.int64]:
raise ValueError(
f"{self.__class__.__name__} does not support using a scheduler of type {self.scheduler.__class__.__name__}. Please make sure to use one of 'DDIMScheduler, PNDMScheduler, DDPMScheduler, DEISMultistepScheduler, UniPCMultistepScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler'."
)
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
# 6.1 Add image embeds for IP-Adapter
added_cond_kwargs = (
{"image_embeds": image_embeds}
if ip_adapter_image is not None or ip_adapter_image_embeds is not None
else None
)
if do_classifier_free_guidance:
added_uncond_kwargs = (
{"image_embeds": negative_image_embeds}
if ip_adapter_image is not None or ip_adapter_image_embeds is not None
else None
)
# 7. Denoising loop
original_attn_proc = self.unet.attn_processors
store_processor = CrossAttnStoreProcessor()
self.unet.mid_block.attentions[0].transformer_blocks[0].attn1.processor = store_processor
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
map_size = None
def get_map_size(module, input, output):
nonlocal map_size
map_size = output[0].shape[-2:] | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
with self.unet.mid_block.attentions[0].register_forward_hook(get_map_size):
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
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
).sample | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.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) | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
# perform self-attention guidance with the stored self-attention map
if do_self_attention_guidance:
# classifier-free guidance produces two chunks of attention map
# and we only use unconditional one according to equation (25)
# in https://arxiv.org/pdf/2210.00939.pdf
if do_classifier_free_guidance:
# DDIM-like prediction of x0
pred_x0 = self.pred_x0(latents, noise_pred_uncond, t)
# get the stored attention maps
uncond_attn, cond_attn = store_processor.attention_probs.chunk(2)
# self-attention-based degrading of latents
degraded_latents = self.sag_masking(
pred_x0, uncond_attn, map_size, t, self.pred_epsilon(latents, noise_pred_uncond, t)
) | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
uncond_emb, _ = prompt_embeds.chunk(2)
# forward and give guidance
degraded_pred = self.unet(
degraded_latents,
t,
encoder_hidden_states=uncond_emb,
added_cond_kwargs=added_uncond_kwargs,
).sample
noise_pred += sag_scale * (noise_pred_uncond - degraded_pred)
else:
# DDIM-like prediction of x0
pred_x0 = self.pred_x0(latents, noise_pred, t)
# get the stored attention maps
cond_attn = store_processor.attention_probs
# self-attention-based degrading of latents
degraded_latents = self.sag_masking( | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
pred_x0, cond_attn, map_size, t, self.pred_epsilon(latents, noise_pred, t)
)
# forward and give guidance
degraded_pred = self.unet(
degraded_latents,
t,
encoder_hidden_states=prompt_embeds,
added_cond_kwargs=added_cond_kwargs,
).sample
noise_pred += sag_scale * (noise_pred - degraded_pred) | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.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):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
if XLA_AVAILABLE:
xm.mark_step()
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
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 StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
def sag_masking(self, original_latents, attn_map, map_size, t, eps):
# Same masking process as in SAG paper: https://arxiv.org/pdf/2210.00939.pdf
bh, hw1, hw2 = attn_map.shape
b, latent_channel, latent_h, latent_w = original_latents.shape
h = self.unet.config.attention_head_dim
if isinstance(h, list):
h = h[-1] | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
# Produce attention mask
attn_map = attn_map.reshape(b, h, hw1, hw2)
attn_mask = attn_map.mean(1, keepdim=False).sum(1, keepdim=False) > 1.0
attn_mask = (
attn_mask.reshape(b, map_size[0], map_size[1])
.unsqueeze(1)
.repeat(1, latent_channel, 1, 1)
.type(attn_map.dtype)
)
attn_mask = F.interpolate(attn_mask, (latent_h, latent_w))
# Blur according to the self-attention mask
degraded_latents = gaussian_blur_2d(original_latents, kernel_size=9, sigma=1.0)
degraded_latents = degraded_latents * attn_mask + original_latents * (1 - attn_mask)
# Noise it again to match the noise level
degraded_latents = self.scheduler.add_noise(degraded_latents, noise=eps, timesteps=t[None])
return degraded_latents | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
# Modified from diffusers.schedulers.scheduling_ddim.DDIMScheduler.step
# Note: there are some schedulers that clip or do not return x_0 (PNDMScheduler, DDIMScheduler, etc.)
def pred_x0(self, sample, model_output, timestep):
alpha_prod_t = self.scheduler.alphas_cumprod[timestep].to(sample.device) | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
beta_prod_t = 1 - alpha_prod_t
if self.scheduler.config.prediction_type == "epsilon":
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
elif self.scheduler.config.prediction_type == "sample":
pred_original_sample = model_output
elif self.scheduler.config.prediction_type == "v_prediction":
pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
# predict V
model_output = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
f"prediction_type given as {self.scheduler.config.prediction_type} must be one of `epsilon`, `sample`,"
" or `v_prediction`"
)
return pred_original_sample
def pred_epsilon(self, sample, model_output, timestep):
alpha_prod_t = self.scheduler.alphas_cumprod[timestep] | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
beta_prod_t = 1 - alpha_prod_t
if self.scheduler.config.prediction_type == "epsilon":
pred_eps = model_output
elif self.scheduler.config.prediction_type == "sample":
pred_eps = (sample - (alpha_prod_t**0.5) * model_output) / (beta_prod_t**0.5)
elif self.scheduler.config.prediction_type == "v_prediction":
pred_eps = (beta_prod_t**0.5) * sample + (alpha_prod_t**0.5) * model_output
else:
raise ValueError(
f"prediction_type given as {self.scheduler.config.prediction_type} must be one of `epsilon`, `sample`,"
" or `v_prediction`"
)
return pred_eps | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
class MusicLDMPipeline(DiffusionPipeline, StableDiffusionMixin):
r"""
Pipeline for text-to-audio generation using MusicLDM.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.). | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.ClapModel`]):
Frozen text-audio embedding model (`ClapTextModel`), specifically the
[laion/clap-htsat-unfused](https://huggingface.co/laion/clap-htsat-unfused) variant.
tokenizer ([`PreTrainedTokenizer`]):
A [`~transformers.RobertaTokenizer`] to tokenize text.
feature_extractor ([`~transformers.ClapFeatureExtractor`]):
Feature extractor to compute mel-spectrograms from audio waveforms.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded audio latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
vocoder ([`~transformers.SpeechT5HifiGan`]):
Vocoder of class `SpeechT5HifiGan`.
""" | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
def __init__(
self,
vae: AutoencoderKL,
text_encoder: Union[ClapTextModelWithProjection, ClapModel],
tokenizer: Union[RobertaTokenizer, RobertaTokenizerFast],
feature_extractor: Optional[ClapFeatureExtractor],
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
vocoder: SpeechT5HifiGan,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
feature_extractor=feature_extractor,
unet=unet,
scheduler=scheduler,
vocoder=vocoder,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8 | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
def _encode_prompt(
self,
prompt,
device,
num_waveforms_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
):
r"""
Encodes the prompt into text encoder hidden states. | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device (`torch.device`):
torch device
num_waveforms_per_prompt (`int`):
number of waveforms that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the audio generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.Tensor`, *optional*):
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. | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.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.
"""
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] | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
if prompt_embeds is None:
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
attention_mask = text_inputs.attention_mask
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.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[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLAP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
prompt_embeds = self.text_encoder.get_text_features(
text_input_ids.to(device),
attention_mask=attention_mask.to(device),
)
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.text_model.dtype, device=device) | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.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_waveforms_per_prompt)
prompt_embeds = prompt_embeds.view(bs_embed * num_waveforms_per_prompt, seq_len) | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.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 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 isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
) | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
else:
uncond_tokens = negative_prompt | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
uncond_input_ids = uncond_input.input_ids.to(device)
attention_mask = uncond_input.attention_mask.to(device)
negative_prompt_embeds = self.text_encoder.get_text_features(
uncond_input_ids,
attention_mask=attention_mask,
)
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=self.text_encoder.text_model.dtype, device=device) | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_waveforms_per_prompt)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_waveforms_per_prompt, seq_len)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.audioldm.pipeline_audioldm.AudioLDMPipeline.mel_spectrogram_to_waveform
def mel_spectrogram_to_waveform(self, mel_spectrogram):
if mel_spectrogram.dim() == 4:
mel_spectrogram = mel_spectrogram.squeeze(1) | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
waveform = self.vocoder(mel_spectrogram)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
waveform = waveform.cpu().float()
return waveform | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
# Copied from diffusers.pipelines.audioldm2.pipeline_audioldm2.AudioLDM2Pipeline.score_waveforms
def score_waveforms(self, text, audio, num_waveforms_per_prompt, device, dtype):
if not is_librosa_available():
logger.info(
"Automatic scoring of the generated audio waveforms against the input prompt text requires the "
"`librosa` package to resample the generated waveforms. Returning the audios in the order they were "
"generated. To enable automatic scoring, install `librosa` with: `pip install librosa`."
)
return audio
inputs = self.tokenizer(text, return_tensors="pt", padding=True)
resampled_audio = librosa.resample(
audio.numpy(), orig_sr=self.vocoder.config.sampling_rate, target_sr=self.feature_extractor.sampling_rate
)
inputs["input_features"] = self.feature_extractor( | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
list(resampled_audio), return_tensors="pt", sampling_rate=self.feature_extractor.sampling_rate
).input_features.type(dtype)
inputs = inputs.to(device) | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
# compute the audio-text similarity score using the CLAP model
logits_per_text = self.text_encoder(**inputs).logits_per_text
# sort by the highest matching generations per prompt
indices = torch.argsort(logits_per_text, dim=1, descending=True)[:, :num_waveforms_per_prompt]
audio = torch.index_select(audio, 0, indices.reshape(-1).cpu())
return audio
# 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 with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1] | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.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).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
# Copied from diffusers.pipelines.audioldm.pipeline_audioldm.AudioLDMPipeline.check_inputs
def check_inputs(
self,
prompt,
audio_length_in_s,
vocoder_upsample_factor,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
min_audio_length_in_s = vocoder_upsample_factor * self.vae_scale_factor
if audio_length_in_s < min_audio_length_in_s:
raise ValueError(
f"`audio_length_in_s` has to be a positive value greater than or equal to {min_audio_length_in_s}, but "
f"is {audio_length_in_s}."
) | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
if self.vocoder.config.model_in_dim % self.vae_scale_factor != 0:
raise ValueError(
f"The number of frequency bins in the vocoder's log-mel spectrogram has to be divisible by the "
f"VAE scale factor, but got {self.vocoder.config.model_in_dim} bins and a scale factor of "
f"{self.vae_scale_factor}."
)
if (callback_steps is None) or (
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)}."
) | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.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:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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."
) | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.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: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
) | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
# Copied from diffusers.pipelines.audioldm.pipeline_audioldm.AudioLDMPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
int(height) // self.vae_scale_factor,
int(self.vocoder.config.model_in_dim) // self.vae_scale_factor,
)
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."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device) | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}") | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
model_sequence = [
self.text_encoder.text_model,
self.text_encoder.text_projection,
self.unet,
self.vae,
self.vocoder,
self.text_encoder,
]
hook = None
for cpu_offloaded_model in model_sequence:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
audio_length_in_s: Optional[float] = None,
num_inference_steps: int = 200,
guidance_scale: float = 2.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_waveforms_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
callback_steps: Optional[int] = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
output_type: Optional[str] = "np",
):
r"""
The call function to the pipeline for generation. | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide audio generation. If not defined, you need to pass `prompt_embeds`.
audio_length_in_s (`int`, *optional*, defaults to 10.24):
The length of the generated audio sample in seconds.
num_inference_steps (`int`, *optional*, defaults to 200):
The number of denoising steps. More denoising steps usually lead to a higher quality audio at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 2.0):
A higher guidance scale value encourages the model to generate audio that is closely linked to the text
`prompt` at the expense of lower sound quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*): | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
The prompt or prompts to guide what to not include in audio generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_waveforms_per_prompt (`int`, *optional*, defaults to 1):
The number of waveforms to generate per prompt. If `num_waveforms_per_prompt > 1`, the text encoding
model is a joint text-audio model ([`~transformers.ClapModel`]), and the tokenizer is a
`[~transformers.ClapProcessor]`, then automatic scoring will be performed between the generated outputs
and the input text. This scoring ranks the generated waveforms based on their cosine similarity to text
input in the joint text-audio embedding space.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
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 sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.Tensor`, *optional*):
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*): | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
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.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.AudioPipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
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` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*): | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
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).
output_type (`str`, *optional*, defaults to `"np"`):
The output format of the generated audio. Choose between `"np"` to return a NumPy `np.ndarray` or
`"pt"` to return a PyTorch `torch.Tensor` object. Set to `"latent"` to return the latent diffusion
model (LDM) output. | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
Examples:
Returns:
[`~pipelines.AudioPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.AudioPipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated audio.
"""
# 0. Convert audio input length from seconds to spectrogram height
vocoder_upsample_factor = np.prod(self.vocoder.config.upsample_rates) / self.vocoder.config.sampling_rate
if audio_length_in_s is None:
audio_length_in_s = self.unet.config.sample_size * self.vae_scale_factor * vocoder_upsample_factor
height = int(audio_length_in_s / vocoder_upsample_factor) | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
original_waveform_length = int(audio_length_in_s * self.vocoder.config.sampling_rate)
if height % self.vae_scale_factor != 0:
height = int(np.ceil(height / self.vae_scale_factor)) * self.vae_scale_factor
logger.info(
f"Audio length in seconds {audio_length_in_s} is increased to {height * vocoder_upsample_factor} "
f"so that it can be handled by the model. It will be cut to {audio_length_in_s} after the "
f"denoising process."
)
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
audio_length_in_s,
vocoder_upsample_factor,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
) | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.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
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt,
device,
num_waveforms_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
) | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.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_waveforms_per_prompt,
num_channels_latents,
height,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.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
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=None,
class_labels=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0] | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.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
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):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
if XLA_AVAILABLE:
xm.mark_step()
self.maybe_free_model_hooks() | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py |
# 8. Post-processing
if not output_type == "latent":
latents = 1 / self.vae.config.scaling_factor * latents
mel_spectrogram = self.vae.decode(latents).sample
else:
return AudioPipelineOutput(audios=latents)
audio = self.mel_spectrogram_to_waveform(mel_spectrogram)
audio = audio[:, :original_waveform_length]
# 9. Automatic scoring
if num_waveforms_per_prompt > 1 and prompt is not None:
audio = self.score_waveforms(
text=prompt,
audio=audio,
num_waveforms_per_prompt=num_waveforms_per_prompt,
device=device,
dtype=prompt_embeds.dtype,
)
if output_type == "np":
audio = audio.numpy()
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=audio) | 132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.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 equal to
2, due to classifier-free guidance.
"""
def __init__(self, batch_size=2):
self.batch_size = batch_size
def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None):
batch_size, sequence_length, _ = hidden_states.shape
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
query = attn.to_q(hidden_states) | 133 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.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)
value = attn.to_v(encoder_hidden_states)
# Cross Frame Attention
if not is_cross_attention:
video_length = key.size()[0] // self.batch_size
first_frame_index = [0] * video_length
# 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)
value = value[:, first_frame_index] | 133 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.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_mask)
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
return hidden_states | 133 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.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, batch_size should be equal to
2, due to classifier-free guidance.
"""
def __init__(self, batch_size=2):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
self.batch_size = batch_size
def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None):
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
inner_dim = hidden_states.shape[-1] | 134 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.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(batch_size, attn.heads, -1, attention_mask.shape[-1])
query = attn.to_q(hidden_states)
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)
value = attn.to_v(encoder_hidden_states)
# Cross Frame Attention
if not is_cross_attention:
video_length = max(1, key.size()[0] // self.batch_size)
first_frame_index = [0] * video_length | 134 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.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)
value = value[:, first_frame_index]
# rearrange back to original shape
key = rearrange_4(key)
value = rearrange_4(value)
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | 134 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.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_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
return hidden_states | 134 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
class TextToVideoSDXLPipelineOutput(BaseOutput):
"""
Output class for zero-shot text-to-video pipeline.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
"""
images: Union[List[PIL.Image.Image], np.ndarray] | 135 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
class TextToVideoZeroSDXLPipeline(
DiffusionPipeline,
StableDiffusionMixin,
StableDiffusionXLLoraLoaderMixin,
TextualInversionLoaderMixin,
):
r"""
Pipeline for zero-shot text-to-video generation using Stable Diffusion XL.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.). | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.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. Stable Diffusion XL uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
text_encoder_2 ([` CLIPTextModelWithProjection`]):
Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
specifically the
[laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`CLIPTokenizer`):
Second Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
""" | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
_optional_components = [
"tokenizer",
"tokenizer_2",
"text_encoder",
"text_encoder_2",
"image_encoder",
"feature_extractor",
] | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
text_encoder_2: CLIPTextModelWithProjection,
tokenizer: CLIPTokenizer,
tokenizer_2: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
image_encoder: CLIPVisionModelWithProjection = None,
feature_extractor: CLIPImageProcessor = None,
force_zeros_for_empty_prompt: bool = True,
add_watermarker: Optional[bool] = None,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
tokenizer=tokenizer,
tokenizer_2=tokenizer_2,
unet=unet,
scheduler=scheduler,
image_encoder=image_encoder,
feature_extractor=feature_extractor,
)
self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt) | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
self.default_sample_size = (
self.unet.config.sample_size
if hasattr(self, "unet") and self.unet is not None and hasattr(self.unet.config, "sample_size")
else 128
)
add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
if add_watermarker:
self.watermark = StableDiffusionXLWatermarker()
else:
self.watermark = None
# 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 with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1] | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.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).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.upcast_vae
def upcast_vae(self):
dtype = self.vae.dtype
self.vae.to(dtype=torch.float32)
use_torch_2_0_or_xformers = isinstance(
self.vae.decoder.mid_block.attentions[0].processor,
(
AttnProcessor2_0,
XFormersAttnProcessor,
FusedAttnProcessor2_0,
),
)
# if xformers or torch_2_0 is used attention block does not need
# to be in float32 which can save lots of memory
if use_torch_2_0_or_xformers:
self.vae.post_quant_conv.to(dtype)
self.vae.decoder.conv_in.to(dtype)
self.vae.decoder.mid_block.to(dtype) | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline._get_add_time_ids
def _get_add_time_ids(
self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None
):
add_time_ids = list(original_size + crops_coords_top_left + target_size)
passed_add_embed_dim = (
self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
)
expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
if expected_add_embed_dim != passed_add_embed_dim:
raise ValueError(
f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
) | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
return add_time_ids
# 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,
int(height) // self.vae_scale_factor,
int(width) // self.vae_scale_factor,
)
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."
) | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def check_inputs(
self,
prompt,
prompt_2,
height,
width,
callback_steps,
negative_prompt=None,
negative_prompt_2=None,
prompt_embeds=None,
negative_prompt_embeds=None,
pooled_prompt_embeds=None,
negative_pooled_prompt_embeds=None,
callback_on_step_end_tensor_inputs=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.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_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 callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
) | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.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_2 is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}") | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.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."
)
elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
) | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.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: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
if prompt_embeds is not None and pooled_prompt_embeds is None:
raise ValueError(
"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
) | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
raise ValueError(
"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
) | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
def encode_prompt(
self,
prompt: str,
prompt_2: Optional[str] = None,
device: Optional[torch.device] = None,
num_images_per_prompt: int = 1,
do_classifier_free_guidance: bool = True,
negative_prompt: Optional[str] = None,
negative_prompt_2: Optional[str] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
pooled_prompt_embeds: Optional[torch.Tensor] = None,
negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
lora_scale: Optional[float] = None,
clip_skip: Optional[int] = None,
):
r"""
Encodes the prompt into text encoder hidden states. | 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*):
prompt to be encoded
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
used in both text-encoders
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
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. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
negative_prompt_2 (`str` or `List[str]`, *optional*): | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
prompt_embeds (`torch.Tensor`, *optional*):
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 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.
pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
If not provided, pooled text embeddings will be generated from `prompt` input argument.
negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
input argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
"""
device = device or self._execution_device | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if self.text_encoder is not None:
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 self.text_encoder_2 is not None:
if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
else:
scale_lora_layers(self.text_encoder_2, lora_scale)
prompt = [prompt] if isinstance(prompt, str) else prompt
if prompt is not None:
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0] | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
# Define tokenizers and text encoders
tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
text_encoders = (
[self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
)
if prompt_embeds is None:
prompt_2 = prompt_2 or prompt
prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
# textual inversion: process multi-vector tokens if necessary
prompt_embeds_list = []
prompts = [prompt, prompt_2]
for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, tokenizer) | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {tokenizer.model_max_length} tokens: {removed_text}"
) | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
# We are only ALWAYS interested in the pooled output of the final text encoder
if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
pooled_prompt_embeds = prompt_embeds[0]
if clip_skip is None:
prompt_embeds = prompt_embeds.hidden_states[-2]
else:
# "2" because SDXL always indexes from the penultimate layer.
prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
prompt_embeds_list.append(prompt_embeds)
prompt_embeds = torch.concat(prompt_embeds_list, dim=-1) | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
# get unconditional embeddings for classifier free guidance
zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
negative_prompt_embeds = torch.zeros_like(prompt_embeds)
negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
elif do_classifier_free_guidance and negative_prompt_embeds is None:
negative_prompt = negative_prompt or ""
negative_prompt_2 = negative_prompt_2 or negative_prompt
# normalize str to list
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
negative_prompt_2 = (
batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
) | 136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py |
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