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@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,
timesteps: List[int] = None,
sigmas: List[fl... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
guidance_rescale: float = 0.0,
clip_skip: Optional[int] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
pag_scale: float = 3.0,
... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.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... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
passed will be used. Must be in descending order.
sigmas (`List[float]`, *optional*):
Custom sigmas to use for the denoising process with schedulers which support a `sigmas` ar... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
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 paramet... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
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... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
provided, embeddings are computed from the `ip_adapter_image` input argument.
ou... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
guidance_rescale (`float`, *optional*, defaults to 0.0):
Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are
Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when
using zero terminal SNR.
... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callbac... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
Examples:
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 ... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
height,
width,
None,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
ip_adapter_image,
ip_adapter_image_embeds,
... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
# 3. Encode input prompt
lora_scale = (
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
)
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
device,
num_images_per_prompt,
... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
# 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 self.do_perturbed_attention_guidance:
prompt_embeds = self._prepare_perturbed_attention_guidance(
... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
for i, image_embeds in enumerate(ip_adapter_image_embeds):
negative_image_embeds = None
if self.do_classifier_free_guidance:
negative_image_embeds, image_embeds = image_embeds.chunk(2)
if self.do_perturbed_attention_guidance:
image_... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
# 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,
gene... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
# 6.2 Optionally get Guidance Scale Embedding
timestep_cond = None
if self.unet.config.time_cond_proj_dim is not None:
guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
timestep_cond = self.get_guidance_scale_embedding(
... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
if self.do_perturbed_attention_guidance:
original_attn_proc = self.unet.attn_processors
self._set_pag_attn_processor(
pag_applied_layers=self.pag_applied_layers,
... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
timestep_cond=timestep_cond,
cross_attention_kwargs=self.cross_attention_kwargs,
... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
# compute the previous noisy sample x_... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
# call the callback, if provided
... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.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)
# Offload all models
... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
class AnimateDiffPAGPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
IPAdapterMixin,
StableDiffusionLoraLoaderMixin,
FreeInitMixin,
AnimateDiffFreeNoiseMixin,
PAGMixin,
):
r"""
Pipeline for text-to-video generation using
[AnimateDiff](https://hu... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionLoraLoaderMixi... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
toke... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
_optional_components = ["feature_extractor", "image_encoder", "motion_adapter"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLI... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
motion_adapter=motion_adapter,
scheduler=scheduler,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
)
... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt
def encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`b... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
# dynamically adjust the LoRA scale
if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
else:
scale_lora_layers(self.text_encoder, lora_scale)
if prompt is not None and isinstance(prompt, str):
batch_size = 1... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = sel... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
if clip_skip is None:
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
prompt_embeds = prompt_embeds[0]
else:
prompt_embeds = self.text_encoder(
text_input_ids.to(device), attention_mask=attention_... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
if self.text_encoder is not None:
prompt_embeds_dtype = self.text_encoder.dtype
elif self.unet is not None:
prompt_embeds_dtype = self.unet.dtype
else:
prompt_embeds_dtype = prompt_embeds.dtype
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, devic... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is no... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
# textual inversion: process multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
negative... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
image = image.to(device=device, dtype=dtype)
if output_hidden_states:
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
uncond_imag... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
def prepare_ip_adapter_image_embeds(
self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
):
image_embeds = ... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
for single_ip_adapter_image, image_proj_layer in zip(
ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
):
output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
single_image_embeds, single_negative_image_embeds = self.encod... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
ip_adapter_image_embeds = []
for i, single_image_embeds in enumerate(image_embeds):
single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
single_negative_image_embeds = torch.cat([negative_image_embeds[i]] * ... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
batch_size, channels, num_frames, height, width = latents.shape
latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
video = []
for i in range(0, latents.shape[0], decode_chunk_size):
batch_latents = latents[i : i + decode_chunk_size]
... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used w... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
# Copied from diffusers.pipelines.pia.pipeline_pia.PIAPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
ip_adapter_image=None,
ip_adapter_image_embeds=Non... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.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:
... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.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... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
if ip_adapter_image_embeds is not None:
if not isinstance(ip_adapter_image_embeds, list):
raise ValueError(
f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}"
)
elif ip_adapter_image_embeds[0].ndim not ... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
# Copied from diffusers.pipelines.animatediff.pipeline_animatediff.AnimateDiffPipeline.prepare_latents
def prepare_latents(
self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
):
# If FreeNoise is enabled, generate latents as described in Equ... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
shape = (
batch_size,
num_channels_latents,
num_frames,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
e... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
# 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.
@property
def do_classifier_free_guidance(self):
return self._guidance_scale... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Optional[Union[str, List[str]]] = None,
num_frames: Optional[int] = 16,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
gu... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
clip_skip: Optional[int] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
decode_chunk_size: int = 16,
pag_scale: float = 3.0,
pag_adaptive_scale: float = 0.0,
):
r"""
... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.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... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` ... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video
gener... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.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.
ip_adapter_image: (`PipelineImageInput`, *optional*):
Optional image input to... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
The output format of the generated video. Choose between `torch.Tensor`, `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] instead
of a plain tuple.
... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as sp... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
pag_adaptive_scale (`float`, *optional*, defaults to 0.0):
The adaptive scale factor for the perturbed attention guidance. If it is set to 0.0, `pag_scale` is
used. | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
Examples:
Returns:
[`~pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] is
returned, otherwise a `tuple` is returned where the first element i... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
self._guidance_scale = guidance_scale
self._clip_skip = clip_skip
self._cross_attention_kwargs = cross_attention_kwargs
self._pag_scale = pag_scale
self._pag_adaptive_scale = pag_adaptive_scale
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str)... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
# 3. Encode input prompt
text_encoder_lora_scale = (
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
)
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
device,
num_videos_per_pr... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
# 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 self.do_perturbed_attention_guidance:
prompt_embeds = self._prepare_perturbed_attention_guidance(
... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
for i, image_embeds in enumerate(ip_adapter_image_embeds):
negative_image_embeds = None
if self.do_classifier_free_guidance:
negative_image_embeds, image_embeds = image_embeds.chunk(2)
if self.do_perturbed_attention_guidance:
image_... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_videos_per_prompt,
num_channels_latents,
num_frames,
height,
width,
prompt_embeds.dtype,
... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
if self.do_perturbed_attention_guidance:
original_attn_proc = self.unet.attn_processors
self._set_pag_attn_processor(
pag_applied_layers=self.pag_applied_layers,
do_classifier_free_guidance=self.do_classifier_free_guidance,
)
num_free_init_ite... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
# 8. Denoising loop
with self.progress_bar(total=self._num_timesteps) 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] * (... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
# perform guidance
if self.do_perturbed_attention_guidance:
noise_pred = self._apply_perturbed_attention_guidance(
noise_pred, self.do_classifier_free_guidance, self.guidance_scale, t
)
elif self.do_class... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
# 9. Post processing
if output_type == "latent":
video = latents
else:
video_tensor = self.decode_latents(latents, decode_chunk_size)
video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)
# 10. Offload all models
... | 347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py |
class StableDiffusion3PAGImg2ImgPipeline(DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin, PAGMixin):
r"""
[PAG pipeline](https://huggingface.co/docs/diffusers/main/en/using-diffusers/pag) for image-to-image generation
using Stable Diffusion 3. | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
Args:
transformer ([`SD3Transformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
[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.
text_encoder_3 ([`T5EncoderModel`]):... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
Tokenizer of class
[T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
""" | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
model_cpu_offload_seq = "text_encoder->text_encoder_2->text_encoder_3->transformer->vae"
_optional_components = []
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds", "negative_pooled_prompt_embeds"]
def __init__(
self,
transformer: SD3Transformer2DModel,
... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
text_encoder_3=text_encoder_3,
tokenizer=tokenizer,
tokenizer_2=tokenizer_2,
tokenizer_3=tokenizer_3,
transformer=transformer,
... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
self.transformer.config.patch_size if hasattr(self, "transformer") and self.transformer is not None else 2
) | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
self.set_pag_applied_layers(
pag_applied_layers, pag_attn_processors=(PAGCFGJointAttnProcessor2_0(), PAGJointAttnProcessor2_0())
)
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline._get_t5_prompt_embeds
def _get_t5_prompt_embeds(
... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
if self.text_encoder_3 is None:
return torch.zeros(
(
batch_size * num_images_per_prompt,
self.tokenizer_max_length,
self.transformer.config.joint_attention_dim,
),
device=device,
dtyp... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer_3.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated beca... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline._get_clip_prompt_embeds
def _get_clip_prompt_embeds(
self,
prompt: Union[str, List[str]],
num_images_per_prompt: int = 1,
device: Optional[torch.device] = None,
clip_skip... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
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[:,... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
_, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
pooled_prompt_e... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.encode_prompt
def encode_prompt(
self,
prompt: Union[str, List[str]],
prompt_2: Union[str, List[str]],
prompt_3: Union[str, List[str]],
device: Optional[torch.device] = N... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
r""" | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.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 all text-encoders
... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
`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*):
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
negative_prompt_embeds (`torch.FloatTensor`, *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.
... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
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.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers o... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.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, SD3LoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if self.text_encoder is not None and ... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
prompt_embed, pooled_prompt_embed = self._get_clip_prompt_embeds(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
clip_skip=clip_skip,
clip_model_index=0,
)
prompt_2_embed, pooled_prompt_2_... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
clip_prompt_embeds = torch.nn.functional.pad(
clip_prompt_embeds, (0, t5_prompt_embed.shape[-1] - clip_prompt_embeds.shape[-1])
)
prompt_embeds = torch.cat([clip_prompt_embeds, t5_prompt_embed], dim=-2)
pooled_prompt_embeds = torch.cat([pooled_prompt_embed, pooled_pr... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
# 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
)
negative... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
if prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif batch_size != len(negative_pro... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
negative_prompt_embed, negative_pooled_prompt_embed = self._get_clip_prompt_embeds(
negative_prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
clip_skip=None,
clip_model_index=0,
)
negative_prompt_2... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
negative_clip_prompt_embeds = torch.nn.functional.pad(
negative_clip_prompt_embeds,
(0, t5_negative_prompt_embed.shape[-1] - negative_clip_prompt_embeds.shape[-1]),
)
negative_prompt_embeds = torch.cat([negative_clip_prompt_embeds, t5_negative_prompt_embed], dim=... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
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