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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... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
motion_adapter=motion_adapter,
controlnet=controlnet,
scheduler=scheduler,
feature_extractor=feature_extractor,
image_enc... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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 | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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_... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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(
... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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):
... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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 = ... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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]] * ... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
batch_size, channels, num_frames, height, width = latents.shape
latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
image = self.vae.decode(latents).sample
video = image[None, :].reshape((batch_size, num_frames, -1) + image.shape[2:]).permute(0, 2,... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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)... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in cal... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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:
... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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 ... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
# check `image`
if (
isinstance(self.controlnet, SparseControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, SparseControlNetModel)
):
if isinstance(image, list):
for image_ in image:
self.check_image... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
# Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image
def check_image(self, image, prompt, prompt_embeds):
image_is_pil = isinstance(image, PIL.Image.Image)
image_is_tensor = isinstance(image, torch.Tensor)
image_is_np = isinstance(ima... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
if (
not image_is_pil
and not image_is_tensor
and not image_is_np
and not image_is_pil_list
and not image_is_tensor_list
and not image_is_np_list
):
raise TypeError(
f"image must be passed and be one of PIL image... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
if image_batch_size != 1 and image_batch_size != prompt_batch_size:
raise ValueError(
f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
) | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_synth.TextToVideoSDPipeline.prepare_latents
def prepare_latents(
self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
):
shape = (
batch_size,
... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def prepare_image(self, image, width, height, device, dtype):
image = self.control_image_processor.preprocess(image, height=height, width=width)
... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
if self.controlnet.use_simplified_condition_embedding:
controlnet_images = controlnet_images.reshape(batch_size * num_frames, channels, height, width)
controlnet_images = 2 * controlnet_images - 1
conditioning_frames = retrieve_latents(self.vae.encode(controlnet_images)) * self.vae.c... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
def prepare_sparse_control_conditioning(
self,
conditioning_frames: torch.Tensor,
num_frames: int,
controlnet_frame_indices: int,
device: torch.device,
dtype: torch.dtype,
) -> Tuple[torch.Tensor, torch.Tensor]:
assert conditioning_frames.shape[2] >= len(contr... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
@property
def clip_skip(self):
return self._clip_skip
# 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... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Optional[Union[str, List[str]]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_frames: int = 16,
num_inference_steps: int = 50,
guidance_sca... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
controlnet_frame_indices: List[int] = [0],
guess_mode: bool = False,
clip_skip: Optional[int] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict],... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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` ... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
The SparseControlNet input to provide guidance to the `unet` for generation.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated video. Choose between `torch.Tensor`, `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `Tru... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set
the corresponding scale as a list.
controlnet_frame_indices (`List[int]`):
The indices where the conditioning frames must be applied for generation. Multiple frames can be
... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt=prompt,
height=height,
width=width,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
ip_adapter_... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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
... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
# 4. Prepare IP-Adapter embeddings
if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
image_embeds = self.prepare_ip_adapter_image_embeds(
ip_adapter_image,
ip_adapter_image_embeds,
device,
batch_size * num_videos_p... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
# 7. 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,
... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
num_free_init_iters = self._free_init_num_iters if self.free_init_enabled else 1
for free_init_iter in range(num_free_init_iters):
if self.free_init_enabled:
latents, timesteps = self._apply_free_init(
latents, free_init_iter, num_inference_steps, device, latents.... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
if guess_mode and self.do_classifier_free_guidance:
# Infer SparseControlNetModel only for the conditional batch.
control_model_input = latents
control_model_input = self.scheduler.scale_model_input(control_model_input, t)
c... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
down_block_res_samples, mid_block_res_sample = self.controlnet(
control_model_input,
t,
encoder_hidden_states=controlnet_prompt_embeds,
controlnet_cond=controlnet_cond,
conditioning_mask=controlnet_co... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
# 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=ad... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.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)
... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
# 11. Post processing
if output_type == "latent":
video = latents
else:
video_tensor = self.decode_latents(latents)
video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)
# 12. Offload all models
self.maybe_free_mo... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
class AnimateDiffControlNetPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
IPAdapterMixin,
StableDiffusionLoraLoaderMixin,
FreeInitMixin,
AnimateDiffFreeNoiseMixin,
):
r"""
Pipeline for text-to-video generation with ControlNet guidance.
This model... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py |
model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["feature_extractor", "image_encoder"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CL... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
motion_adapter=motion_adapter,
controlnet=controlnet,
scheduler=scheduler,
feature_extractor=feature_extractor,
image_enc... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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 | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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_... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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(
... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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):
... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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 = ... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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]] * ... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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]
... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py |
def check_inputs(
self,
prompt,
height,
width,
num_frames,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
callback_on_step_end_tensor_inputs=None,
video=None,
controlnet_conditioning_scale=1.0,
contro... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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:
... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py |
# Check `image`
is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
self.controlnet, torch._dynamo.eval_frame.OptimizedModule
)
if (
isinstance(self.controlnet, ControlNetModel)
or is_compiled
and isinstance(self.controlnet._or... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py |
raise TypeError(f"For multiple controlnets: `image` must be type list of lists but got {type(video)=}")
if len(video[0]) != num_frames:
raise ValueError(f"Expected length of image sublist as {num_frames} but got {len(video[0])=}")
if any(len(img) != len(video[0]) for img in video... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py |
# Check `controlnet_conditioning_scale`
if (
isinstance(self.controlnet, ControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, ControlNetModel)
):
if not isinstance(controlnet_conditioning_scale, float):
raise TypeError(... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py |
raise ValueError(
"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
" the same length as the number of controlnets"
)
else:
assert False | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py |
if not isinstance(control_guidance_start, (tuple, list)):
control_guidance_start = [control_guidance_start]
if not isinstance(control_guidance_end, (tuple, list)):
control_guidance_end = [control_guidance_end]
if len(control_guidance_start) != len(control_guidance_end):
... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py |
for start, end in zip(control_guidance_start, control_guidance_end):
if start >= end:
raise ValueError(
f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}."
)
if start < 0.0:
raise ValueEr... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py |
def prepare_video(
self,
video,
width,
height,
batch_size,
num_videos_per_prompt,
device,
dtype,
do_classifier_free_guidance=False,
guess_mode=False,
):
video = self.control_video_processor.preprocess_video(video, height=height,... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py |
@property
def clip_skip(self):
return self._clip_skip
# 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... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py |
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
num_frames: Optional[int] = 16,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py |
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
guess_mode: bool = False,
control_guidance_start: Union[float, List[float]] = 0.0,
control_guidance_end: Union[float, List[float]] = 1.0,
clip_skip: Optional[i... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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` ... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.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 ... | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py |
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