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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)).
tokenizer (`CLIPTokenizer`):
A [`~transformers.CLIPTokenizer`] to tokenize text.
unet ([`UNet2DConditionModel`]):
A [`UNet2DConditionModel`] used to create a UNetMotionModel to denoise the encoded video latents.
motion_adapter ([`MotionAdapter`]):
A [`MotionAdapter`] to be used in combination with `unet` to denoise the encoded video latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
""" | 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: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: Union[UNet2DConditionModel, UNetMotionModel],
motion_adapter: MotionAdapter,
controlnet: SparseControlNetModel,
scheduler: KarrasDiffusionSchedulers,
feature_extractor: CLIPImageProcessor = None,
image_encoder: CLIPVisionModelWithProjection = None,
):
super().__init__()
if isinstance(unet, UNet2DConditionModel):
unet = UNetMotionModel.from_unet2d(unet, motion_adapter) | 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_encoder=image_encoder,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
self.video_processor = VideoProcessor(do_resize=False, vae_scale_factor=self.vae_scale_factor)
self.control_image_processor = VaeImageProcessor(
vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
) | 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_prompt=None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
lora_scale: Optional[float] = None,
clip_skip: Optional[int] = None,
):
r"""
Encodes the prompt into text encoder hidden states. | 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 (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument. | 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_scale (`float`, *optional*):
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
"""
# 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, StableDiffusionLoraLoaderMixin):
self._lora_scale = lora_scale | 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
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: process multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer) | 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 = self.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 = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
) | 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_mask, output_hidden_states=True
)
# Access the `hidden_states` first, that contains a tuple of
# all the hidden states from the encoder layers. Then index into
# the tuple to access the hidden states from the desired layer.
prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
# We also need to apply the final LayerNorm here to not mess with the
# representations. The `last_hidden_states` that we typically use for
# obtaining the final prompt representations passes through the LayerNorm
# layer. | 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, device=device)
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_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) | 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 not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" | 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(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0] | 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_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
if self.text_encoder is not None:
if isinstance(self, StableDiffusionLoraLoaderMixin) and USE_PEFT_BACKEND:
# Retrieve the original scale by scaling back the LoRA layers
unscale_lora_layers(self.text_encoder, lora_scale)
return prompt_embeds, negative_prompt_embeds | 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):
image = self.feature_extractor(image, return_tensors="pt").pixel_values | 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_image_enc_hidden_states = self.image_encoder(
torch.zeros_like(image), output_hidden_states=True
).hidden_states[-2]
uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
num_images_per_prompt, dim=0
)
return image_enc_hidden_states, uncond_image_enc_hidden_states
else:
image_embeds = self.image_encoder(image).image_embeds
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds | 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 = []
if do_classifier_free_guidance:
negative_image_embeds = []
if ip_adapter_image_embeds is None:
if not isinstance(ip_adapter_image, list):
ip_adapter_image = [ip_adapter_image]
if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
raise ValueError(
f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
) | 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.encode_image(
single_ip_adapter_image, device, 1, output_hidden_state
)
image_embeds.append(single_image_embeds[None, :])
if do_classifier_free_guidance:
negative_image_embeds.append(single_negative_image_embeds[None, :])
else:
for single_image_embeds in ip_adapter_image_embeds:
if do_classifier_free_guidance:
single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)
negative_image_embeds.append(single_negative_image_embeds)
image_embeds.append(single_image_embeds) | 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]] * num_images_per_prompt, dim=0)
single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds], dim=0)
single_image_embeds = single_image_embeds.to(device=device)
ip_adapter_image_embeds.append(single_image_embeds)
return ip_adapter_image_embeds
# Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents
def decode_latents(self, latents):
latents = 1 / self.vae.config.scaling_factor * latents | 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, 1, 3, 4)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
video = video.float()
return video
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1] | 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).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
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=None,
callback_on_step_end_tensor_inputs=None,
image=None,
controlnet_conditioning_scale: float = 1.0,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") | 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 callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
) | 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:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
) | 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: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
raise ValueError(
"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
) | 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 in [3, 4]:
raise ValueError(
f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D"
)
is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
self.controlnet, torch._dynamo.eval_frame.OptimizedModule
) | 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(image_, prompt, prompt_embeds)
else:
self.check_image(image, prompt, prompt_embeds)
else:
assert False
# Check `controlnet_conditioning_scale`
if (
isinstance(self.controlnet, SparseControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, SparseControlNetModel)
):
if not isinstance(controlnet_conditioning_scale, float):
raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
else:
assert False | 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(image, np.ndarray)
image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)
image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)
image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray) | 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, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}"
)
if image_is_pil:
image_batch_size = 1
else:
image_batch_size = len(image)
if prompt is not None and isinstance(prompt, str):
prompt_batch_size = 1
elif prompt is not None and isinstance(prompt, list):
prompt_batch_size = len(prompt)
elif prompt_embeds is not None:
prompt_batch_size = prompt_embeds.shape[0] | 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,
num_channels_latents,
num_frames,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device) | 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)
controlnet_images = image.unsqueeze(0).to(device, dtype)
batch_size, num_frames, channels, height, width = controlnet_images.shape
# TODO: remove below line
assert controlnet_images.min() >= 0 and controlnet_images.max() <= 1 | 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.config.scaling_factor
conditioning_frames = conditioning_frames.reshape(
batch_size, num_frames, 4, height // self.vae_scale_factor, width // self.vae_scale_factor
)
else:
conditioning_frames = controlnet_images
conditioning_frames = conditioning_frames.permute(0, 2, 1, 3, 4) # [b, c, f, h, w]
return conditioning_frames | 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(controlnet_frame_indices)
batch_size, channels, _, height, width = conditioning_frames.shape
controlnet_cond = torch.zeros((batch_size, channels, num_frames, height, width), dtype=dtype, device=device)
controlnet_cond_mask = torch.zeros((batch_size, 1, num_frames, height, width), dtype=dtype, device=device)
controlnet_cond[:, :, controlnet_frame_indices] = conditioning_frames[:, :, : len(controlnet_frame_indices)]
controlnet_cond_mask[:, :, controlnet_frame_indices] = 1
return controlnet_cond, controlnet_cond_mask
@property
def guidance_scale(self):
return self._guidance_scale | 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 do_classifier_free_guidance(self):
return self._guidance_scale > 1
@property
def cross_attention_kwargs(self):
return self._cross_attention_kwargs
@property
def num_timesteps(self):
return self._num_timesteps | 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_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_videos_per_prompt: int = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
conditioning_frames: Optional[List[PipelineImageInput]] = None,
output_type: str = "pil",
return_dict: bool = True, | 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], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
):
r"""
The call function to the pipeline for generation. | 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 of the generated video.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated video.
num_frames (`int`, *optional*, defaults to 16):
The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds
amounts to 2 seconds of video.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality videos at the
expense of slower inference. | 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` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*): | 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
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`. Latents should be of shape
`(batch_size, num_channel, num_frames, height, width)`.
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*): | 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 work with IP Adapters.
ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
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.
conditioning_frames (`List[PipelineImageInput]`, *optional*): | 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 `True`):
Whether or not to return a [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] instead
of a plain tuple.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added | 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
provided to guide the model to generate similar structure outputs, where the `unet` can
"fill-in-the-gaps" for interpolation videos, or a single frame could be provided for general expected
structure. Must have the same length as `conditioning_frames`.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
callback_on_step_end (`Callable`, *optional*): | 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 specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class. | 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 is a list with the generated frames.
"""
controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
num_videos_per_prompt = 1 | 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_image=ip_adapter_image,
ip_adapter_image_embeds=ip_adapter_image_embeds,
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
image=conditioning_frames,
controlnet_conditioning_scale=controlnet_conditioning_scale,
)
self._guidance_scale = guidance_scale
self._clip_skip = clip_skip
self._cross_attention_kwargs = cross_attention_kwargs | 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
global_pool_conditions = (
controlnet.config.global_pool_conditions
if isinstance(controlnet, SparseControlNetModel)
else controlnet.nets[0].config.global_pool_conditions
)
guess_mode = guess_mode or global_pool_conditions | 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_prompt,
self.do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
clip_skip=self.clip_skip,
)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
prompt_embeds = prompt_embeds.repeat_interleave(repeats=num_frames, dim=0) | 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_per_prompt,
self.do_classifier_free_guidance,
)
# 5. Prepare controlnet conditioning
conditioning_frames = self.prepare_image(conditioning_frames, width, height, device, controlnet.dtype)
controlnet_cond, controlnet_cond_mask = self.prepare_sparse_control_conditioning(
conditioning_frames, num_frames, controlnet_frame_indices, device, controlnet.dtype
)
# 6. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps | 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,
device,
generator,
latents,
)
# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 9. 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
) | 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.dtype, generator
)
self._num_timesteps = len(timesteps)
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
# 10. 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] * 2) if self.do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) | 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)
controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
else:
control_model_input = latent_model_input
controlnet_prompt_embeds = prompt_embeds | 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_cond_mask,
conditioning_scale=controlnet_conditioning_scale,
guess_mode=guess_mode,
return_dict=False,
) | 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=added_cond_kwargs,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
).sample
# perform guidance
if self.do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample | 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)
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
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if XLA_AVAILABLE:
xm.mark_step() | 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_model_hooks()
if not return_dict:
return (video,)
return AnimateDiffPipelineOutput(frames=video) | 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 inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
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.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters | 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)).
tokenizer (`CLIPTokenizer`):
A [`~transformers.CLIPTokenizer`] to tokenize text.
unet ([`UNet2DConditionModel`]):
A [`UNet2DConditionModel`] used to create a UNetMotionModel to denoise the encoded video latents.
motion_adapter ([`MotionAdapter`]):
A [`MotionAdapter`] to be used in combination with `unet` to denoise the encoded video latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
""" | 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: CLIPTokenizer,
unet: Union[UNet2DConditionModel, UNetMotionModel],
motion_adapter: MotionAdapter,
controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],
scheduler: KarrasDiffusionSchedulers,
feature_extractor: Optional[CLIPImageProcessor] = None,
image_encoder: Optional[CLIPVisionModelWithProjection] = None,
):
super().__init__()
if isinstance(unet, UNet2DConditionModel):
unet = UNetMotionModel.from_unet2d(unet, motion_adapter)
if isinstance(controlnet, (list, tuple)):
controlnet = MultiControlNetModel(controlnet) | 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_encoder=image_encoder,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor)
self.control_video_processor = VideoProcessor(
vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
) | 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_prompt=None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
lora_scale: Optional[float] = None,
clip_skip: Optional[int] = None,
):
r"""
Encodes the prompt into text encoder hidden states. | 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 (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument. | 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_scale (`float`, *optional*):
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
"""
# 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, StableDiffusionLoraLoaderMixin):
self._lora_scale = lora_scale | 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
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: process multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer) | 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 = self.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 = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
) | 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_mask, output_hidden_states=True
)
# Access the `hidden_states` first, that contains a tuple of
# all the hidden states from the encoder layers. Then index into
# the tuple to access the hidden states from the desired layer.
prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
# We also need to apply the final LayerNorm here to not mess with the
# representations. The `last_hidden_states` that we typically use for
# obtaining the final prompt representations passes through the LayerNorm
# layer. | 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, device=device)
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_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) | 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 not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" | 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(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0] | 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_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
if self.text_encoder is not None:
if isinstance(self, StableDiffusionLoraLoaderMixin) and USE_PEFT_BACKEND:
# Retrieve the original scale by scaling back the LoRA layers
unscale_lora_layers(self.text_encoder, lora_scale)
return prompt_embeds, negative_prompt_embeds | 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):
image = self.feature_extractor(image, return_tensors="pt").pixel_values | 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_image_enc_hidden_states = self.image_encoder(
torch.zeros_like(image), output_hidden_states=True
).hidden_states[-2]
uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
num_images_per_prompt, dim=0
)
return image_enc_hidden_states, uncond_image_enc_hidden_states
else:
image_embeds = self.image_encoder(image).image_embeds
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds | 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 = []
if do_classifier_free_guidance:
negative_image_embeds = []
if ip_adapter_image_embeds is None:
if not isinstance(ip_adapter_image, list):
ip_adapter_image = [ip_adapter_image]
if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
raise ValueError(
f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
) | 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.encode_image(
single_ip_adapter_image, device, 1, output_hidden_state
)
image_embeds.append(single_image_embeds[None, :])
if do_classifier_free_guidance:
negative_image_embeds.append(single_negative_image_embeds[None, :])
else:
for single_image_embeds in ip_adapter_image_embeds:
if do_classifier_free_guidance:
single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)
negative_image_embeds.append(single_negative_image_embeds)
image_embeds.append(single_image_embeds) | 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]] * num_images_per_prompt, dim=0)
single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds], dim=0)
single_image_embeds = single_image_embeds.to(device=device)
ip_adapter_image_embeds.append(single_image_embeds)
return ip_adapter_image_embeds
# Copied from diffusers.pipelines.animatediff.pipeline_animatediff.AnimateDiffPipeline.decode_latents
def decode_latents(self, latents, decode_chunk_size: int = 16):
latents = 1 / self.vae.config.scaling_factor * latents | 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]
batch_latents = self.vae.decode(batch_latents).sample
video.append(batch_latents)
video = torch.cat(video)
video = video[None, :].reshape((batch_size, num_frames, -1) + video.shape[2:]).permute(0, 2, 1, 3, 4)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
video = video.float()
return video | 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 with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs | 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,
control_guidance_start=0.0,
control_guidance_end=1.0,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
) | 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:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and not isinstance(prompt, (str, list, dict)):
raise ValueError(f"`prompt` has to be of type `str`, `list` or `dict` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
) | 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: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# `prompt` needs more sophisticated handling when there are multiple
# conditionings.
if isinstance(self.controlnet, MultiControlNetModel):
if isinstance(prompt, list):
logger.warning(
f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}"
" prompts. The conditionings will be fixed across the prompts."
) | 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._orig_mod, ControlNetModel)
):
if not isinstance(video, list):
raise TypeError(f"For single controlnet, `image` must be of type `list` but got {type(video)}")
if len(video) != num_frames:
raise ValueError(f"Excepted image to have length {num_frames} but got {len(video)=}")
elif (
isinstance(self.controlnet, MultiControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
):
if not isinstance(video, list) or not isinstance(video[0], list): | 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):
raise ValueError("All conditioning frame batches for multicontrolnet must be same size")
else:
assert False | 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("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
elif (
isinstance(self.controlnet, MultiControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
):
if isinstance(controlnet_conditioning_scale, list):
if any(isinstance(i, list) for i in controlnet_conditioning_scale):
raise ValueError("A single batch of multiple conditionings are supported at the moment.")
elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
self.controlnet.nets
): | 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):
raise ValueError(
f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list."
)
if isinstance(self.controlnet, MultiControlNetModel):
if len(control_guidance_start) != len(self.controlnet.nets):
raise ValueError(
f"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}."
) | 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 ValueError(f"control guidance start: {start} can't be smaller than 0.")
if end > 1.0:
raise ValueError(f"control guidance end: {end} can't be larger than 1.0.") | 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 Equation (7) of [FreeNoise](https://arxiv.org/abs/2310.15169)
if self.free_noise_enabled:
latents = self._prepare_latents_free_noise(
batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents
)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
) | 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)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents | 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, width=width).to(
dtype=torch.float32
)
video = video.permute(0, 2, 1, 3, 4).flatten(0, 1)
video_batch_size = video.shape[0]
if video_batch_size == 1:
repeat_by = batch_size
else:
# image batch size is the same as prompt batch size
repeat_by = num_videos_per_prompt
video = video.repeat_interleave(repeat_by, dim=0)
video = video.to(device=device, dtype=dtype)
if do_classifier_free_guidance and not guess_mode:
video = torch.cat([video] * 2)
return video
@property
def guidance_scale(self):
return self._guidance_scale | 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 do_classifier_free_guidance(self):
return self._guidance_scale > 1
@property
def cross_attention_kwargs(self):
return self._cross_attention_kwargs
@property
def num_timesteps(self):
return self._num_timesteps
@property
def interrupt(self):
return self._interrupt | 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[Union[str, List[str]]] = None,
num_videos_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
ip_adapter_image_embeds: Optional[PipelineImageInput] = None,
conditioning_frames: Optional[List[PipelineImageInput]] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True, | 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[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,
):
r"""
The call function to the pipeline for generation. | 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 of the generated video.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated video.
num_frames (`int`, *optional*, defaults to 16):
The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds
amounts to 2 seconds of video.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality videos at the
expense of slower inference. | 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` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*): | 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
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`. Latents should be of shape
`(batch_size, num_channel, num_frames, height, width)`.
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*): | 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 work with IP Adapters.
ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
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.
conditioning_frames (`List[PipelineImageInput]`, *optional*): | 151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py |
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