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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,
... | 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: floa... | 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... | 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 pr... | 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
gener... | 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-gen... | 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_k... | 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 image... | 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
... | 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_cl... | 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=negati... | 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... | 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 = (
{... | 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_... | 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)
#... | 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,
... | 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,
... | 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.ord... | 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_hoo... | 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)
... | 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
... | 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.p... | 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 particu... | 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-unfu... | 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: KarrasDiffusion... | 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 ... | 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_guidanc... | 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.
"""
... | 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... | 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]
)
logg... | 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_pro... | 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)... | 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.inpu... | 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 uncond... | 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 agains... | 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.... | 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)... | 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... | 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 "
... | 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:
... | 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: `pr... | 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_s... | 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... | 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,
... | 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, L... | 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.
... | 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 wavefo... | 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.
... | 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 retur... | 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 outpu... | 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 ... | 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 increa... | 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
... | 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_p... | 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
... | 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
... | 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_spectro... | 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... | 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)
... | 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_m... | 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, batc... | 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(b... | 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)
... | 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_state... | 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... | 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 ... | 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/t... | 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).
... | 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:... | 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... | 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)... | 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].p... | 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_coor... | 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=... | 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
... | 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... | 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 ... | 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."
)
... | 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: `pr... | 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... | 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_c... | 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
... | 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 tex... | 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... | 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... | 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_embe... | 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
... | 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]
... | 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_lik... | 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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