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# 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 = ... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.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... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.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]] * ... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.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,... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step)... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.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... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt_2 is not None and prompt_embeds is not ... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}") | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `pr... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
raise ValueError(
"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.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,
... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def _get_add_time_ids(
self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None
):
add_time_id... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
return add_time_ids
def upcast_vae(self):
dtype = self.vae.dtype
self.vae.to(dtype=torch.float32)
use_torch_2_0_or_xformers = isinstance(
self.vae.decoder.mid_block.attentions[0].processor,
(
... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
# Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
def get_guidance_scale_embedding(
self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
) -> torch.Tensor:
""... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
Returns:
`torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
"""
assert len(w.shape) == 1
w = w * 1000.0
half_dim = embedding_dim // 2
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=dty... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
@property
def do_classifier_free_guidance(self):
return self._guidance_scale... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
num_frames: int = 16,
height: Optional[int] = None,
width: Optional[int] = None,
num... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
pooled_prompt_embeds: Optional[torch.Tensor] = None,
negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
output_type: Optional[str] = "pil",
return_dict:... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
Function invoked when calling the pipeline for generation. | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the video generation. If not defined, one has to pass `prompt_embeds`.
instead.
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to the `to... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
[stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
and checkpoints that are not specifically fine-tuned on low resolutions.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
Th... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
passed will be used. Must be in descending order.
sigmas (`List[float]`, ... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
"Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#re... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
The prompt or prompts not to guide the video generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
negative_prompt_2 (`str` or `List[str]`, *optional*):
... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
One or a list of [torch generator(s)](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
... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
Pre-generated image embeddings for IP-Adapter. If not provided, embeddings are computed from the
`ip_adapter_image` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated video. Choose between
[PIL](https://pil... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
guidance_rescale (`float`, *optional*, defaults to 0.0):
Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
[Common Diffusion Noise ... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
`crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
`crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
To negatively condition the generation process based on a specific image resolution. Part of SDXL's
micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](ht... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
To negatively condition the generation process based on a target image resolution. It should be as same
as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
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... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.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... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
prompt_2,
height,
width,
negative_prompt,
negative_prompt_2,
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
# 3. Encode input prompt
lora_scale = (
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
)
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
# 4. Prepare timesteps
timesteps, num_inference_steps = retrieve_timesteps(
self.scheduler, num_inference_steps, device, timesteps, sigmas
)
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
# 7. Prepare added time ids & embeddings
add_text_embeds = pooled_prompt_embeds
if self.text_encoder_2 is None:
text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
else:
text_encoder_projection_dim = self.text_encoder_2.config.projection_dim | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
add_time_ids = self._get_add_time_ids(
original_size,
crops_coords_top_left,
target_size,
dtype=prompt_embeds.dtype,
text_encoder_projection_dim=text_encoder_projection_dim,
)
if negative_original_size is not None and negative_target_size is no... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
prompt_e... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
# 7.1 Apply denoising_end
if (
self.denoising_end is not None
and isinstance(self.denoising_end, float)
and self.denoising_end > 0
and self.denoising_end < 1
):
discrete_timestep_cutoff = int(
round(
self.sch... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
# 8. Optionally get Guidance Scale Embedding
timestep_cond = None
if self.unet.config.time_cond_proj_dim is not None:
guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_videos_per_prompt)
timestep_cond = self.get_guidance_scale_embedding(
... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
# 9. Denoising loop
with self.progress_bar(total=self._num_timesteps) as progress_bar:
for i, t in enumerate(timesteps):
if self.interrupt:
continue
# expand the latents if we are doing classifier free guidance
... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
timestep_cond=timestep_cond,
cross_attention_kwargs=self.cross_attention_kwargs,
added_cond_... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
noise_pred = rescale_noise_cfg(
noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale
... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
add_text_embeds = callback_outpu... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
# make sure the VAE is in float32 mode, as it overflows in float16
needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
if needs_upcasting:
self.upcast_vae()
latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
# 10... | 147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py |
class AnimateDiffPipelineOutput(BaseOutput):
r"""
Output class for AnimateDiff pipelines.
Args:
frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
de... | 148 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_output.py |
class AnimateDiffPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
IPAdapterMixin,
StableDiffusionLoraLoaderMixin,
FreeInitMixin,
AnimateDiffFreeNoiseMixin,
):
r"""
Pipeline for text-to-video generation.
This model inherits from [`DiffusionPipeline`... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
toke... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
_optional_components = ["feature_extractor", "image_encoder", "motion_adapter"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLI... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
motion_adapter=motion_adapter,
scheduler=scheduler,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
)
... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt
def encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`b... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
# dynamically adjust the LoRA scale
if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
else:
scale_lora_layers(self.text_encoder, lora_scale)
if prompt is not None and isinstance(prompt, str):
batch_size = 1... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = sel... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
if clip_skip is None:
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
prompt_embeds = prompt_embeds[0]
else:
prompt_embeds = self.text_encoder(
text_input_ids.to(device), attention_mask=attention_... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
if self.text_encoder is not None:
prompt_embeds_dtype = self.text_encoder.dtype
elif self.unet is not None:
prompt_embeds_dtype = self.unet.dtype
else:
prompt_embeds_dtype = prompt_embeds.dtype
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, devic... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is no... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
# textual inversion: process multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
negative... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
image = image.to(device=device, dtype=dtype)
if output_hidden_states:
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
uncond_imag... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
def prepare_ip_adapter_image_embeds(
self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
):
image_embeds = ... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
for single_ip_adapter_image, image_proj_layer in zip(
ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
):
output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
single_image_embeds, single_negative_image_embeds = self.encod... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
ip_adapter_image_embeds = []
for i, single_image_embeds in enumerate(image_embeds):
single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
single_negative_image_embeds = torch.cat([negative_image_embeds[i]] * ... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
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((ba... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used w... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
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,
... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if callback_on_step_end_tensor_... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `pr... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
if ip_adapter_image_embeds is not None:
if not isinstance(ip_adapter_image_embeds, list):
raise ValueError(
f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}"
)
elif ip_adapter_image_embeds[0].ndim not ... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
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."
... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.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... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Optional[Union[str, List[str]]] = None,
num_frames: Optional[int] = 16,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
gu... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
clip_skip: Optional[int] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
decode_chunk_size: int = 16,
**kwargs,
):
r"""
The call function to the pipeline for generation. | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` ... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video
gener... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
ip_adapter_image: (`PipelineImageInput`, *optional*):
Optional image input to... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
The output format of the generated video. Choose between `torch.Tensor`, `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] instead
of a plain tuple.
... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as sp... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
Examples:
Returns:
[`~pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] is
returned, otherwise a `tuple` is returned where the first element i... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
# 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
# 1. Check inputs. Raise error if not correct
self.check_inputs(
... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
# 2. Define call parameters
if prompt is not None and isinstance(prompt, (str, dict)):
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_dev... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
# 3. Encode input prompt
text_encoder_lora_scale = (
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
)
if self.free_noise_enabled:
prompt_embeds, negative_prompt_embeds = self._encode_prompt_free_noise(
p... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
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,
) | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_videos_per_prompt,
num_channels_latents,
num_frames,
height,
width,
prompt_embeds.dtype,
... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.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.... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
# 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)
# predict the noise resid... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, l... | 149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py |
class AnimateDiffSparseControlNetPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
IPAdapterMixin,
StableDiffusionLoraLoaderMixin,
FreeInitMixin,
):
r"""
Pipeline for controlled text-to-video generation using the method described in [SparseCtrl: Adding Spars... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionLoraLoaderMixi... | 150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py |
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