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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
d... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
Args:
latents (torch.Tensor): The input latents to decode.
padding (int, optional): The number of latents to add on each side for padding. Defaults to 8.
Returns:
torch.Tensor: The decoded image with padding removed.
Notes:
- The padding is added to remo... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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_... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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:
... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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 ... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
# Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embeddi... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
Args:
w (`torch.Tensor`):
Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
embedding_dim (`int`, *optional*, defaults to 512):
Dimension of the embeddings to generate.
dtype (`torch.dtype`, *optiona... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
half_dim = embedding_dim // 2
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
emb = w.to(dtype)[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero ... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
Args:
panorama_height (int): The height of the panorama.
panorama_width (int): The width of the panorama.
window_size (int, optional): The size of the window. Defaults to 64.
stride (int, optional): The stride value. Defaults to 8.
circular_padding (bool, opti... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
"""
panorama_height /= 8
panorama_width /= 8
num_blocks_height = (panorama_height - window_size) // stride + 1 if panorama_height > window_size else 1
if circular_padding:
num_blocks_width = panorama_width // stride if panorama_width > window_size else 1
else:
... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
@property
def cross_attention_kwargs(self):
return self._cross_attention_kwargs
@property
def clip_skip(self):
return self._clip_skip
@property
def do_classifier_free_guidance(self):
return False
@property
def num_timesteps(self):
return self._num_timesteps... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = 512,
width: Optional[int] = 2048,
num_inference_steps: int = 50,
timesteps: List[int] = None,
guidance_scale: ... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
guidance_rescale: float = 0.0,
circular_padding: 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] = ["late... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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 512):
The height in pixels of the generated image.
width (`int`, ... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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`.
view_batch_size (`int`,... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
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]`... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
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*):
Pre-generated negative text embeddings. Can be used... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
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.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose ... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
A rescaling factor for the guidance embeddings. A value of 0.0 means no rescaling is applied.
circular_padding (`bool`, *optional*, defaults to `False`):
If set to `True`, circular padding is applied to ensure there are no stitching artifacts. Circular
padding allows the mode... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
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... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated image... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
if callback is not None:
deprecate(
"callback",
"1.0.0",
"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
)
if callback_steps is not None:
deprecate(
"... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
height,
width,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
ip_adapter_image,
ip_adapter_image_embeds,
... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
device = self._execution_device
# 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.
do_classifier_free_guidance = guidan... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
device,
num_images_per_prompt,
... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
# 4. Prepare timesteps
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_pr... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
# 7. 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)
# 7.1 Add image embeds for IP-Adapter
added_cond_kwargs = (
{"image_embeds": image_embeds}
if ip_adapter_image ... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
# 8. Denoising loop
# Each denoising step also includes refinement of the latents with respect to the
# views.
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
self._num_timesteps = len(timesteps)
with self.progress_bar(total=num_inference_steps) as ... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
# generate views
# Here, we iterate through different spatial crops of the latents and denoise them. These
# denoised (latent) crops are then averaged to produce the final latent
# for the current timestep via MultiDiffusion. Please see Sec. 4.1 in the
# M... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
latents[:, :, h_start:h_end, w_start:],
latents[:, :, h_start:h_end, : w_end - latents.shape[3]],
),
axis=-1,
)
else:
... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
# rematch block's scheduler status
self.scheduler.__dict__.update(views_scheduler_status[j])
# expand the latents if we are doing classifier free guidance
latent_model_input = (
latents_for_view.repeat_interleave(2, dim=0)
... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds_input,
timestep_cond=timestep_cond,
cross_attention_kwargs=cross_att... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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
... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
# extract value from batch
for latents_view_denoised, (h_start, h_end, w_start, w_end) in zip(
latents_denoised_batch.chunk(vb_size), batch_view
):
if circular_padding and w_end > latents.shape[3]:
# Case... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
value[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised
count[:, :, h_start:h_end, w_start:w_end] += 1 | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
# take the MultiDiffusion step. Eq. 5 in MultiDiffusion paper: https://arxiv.org/abs/2302.08113
latents = torch.where(count > 0, value / count, value)
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_i... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.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:
step_idx = i // getattr(self.sch... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
self.maybe_free_model_hoo... | 144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py |
class AnimateDiffVideoToVideoPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
IPAdapterMixin,
StableDiffusionLoraLoaderMixin,
FreeInitMixin,
AnimateDiffFreeNoiseMixin,
):
r"""
Pipeline for video-to-video generation.
This model inherits from [`Diffu... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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,
)
... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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, dict)):
batch_... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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 | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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_... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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(
... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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):
... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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 = ... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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]] * ... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
def encode_video(self, video, generator, decode_chunk_size: int = 16) -> torch.Tensor:
latents = []
for i in range(0, len(video), decode_chunk_size):
batch_video = video[i : i + decode_chunk_size]
batch_video = retrieve_latents(self.vae.encode(batch_video), generator=generator)
... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
def check_inputs(
self,
prompt,
strength,
height,
width,
video=None,
latents=None,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
ip_adapter_image=None,
ip_adapter_image_embeds=None,
callback_... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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:
... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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 ... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
def prepare_latents(
self,
video: Optional[torch.Tensor] = None,
height: int = 64,
width: int = 64,
num_channels_latents: int = 4,
batch_size: int = 1,
timestep: Optional[int] = None,
dtype: Optional[torch.dtype] = None,
device: Optional[torch.devi... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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."
... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
# restore vae to original dtype
if self.vae.config.force_upcast:
self.vae.to(dtype)
init_latents = init_latents.to(dtype)
init_latents = self.vae.config.scaling_factor * init_latents | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
# expand init_latents for batch_size
error_message = (
f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
" images (`i... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
noise = randn_tensor(init_latents.shape, generator=generator, device=device, dtype=dtype)
latents = self.scheduler.add_noise(init_latents, noise, timestep).permute(0, 2, 1, 3, 4)
else:
if shape != latents.shape:
# [B, C, F, H, W]
raise ValueError(f"`latent... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
@torch.no_grad()
def __call__(
self,
video: List[List[PipelineImageInput]] = None,
prompt: Optional[Union[str, List[str]]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
enforce_inference_steps: bool = False,
... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
output_type: Optional[str] = "pil",
return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
clip_skip: Optional[int] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["laten... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
Args:
video (`List[PipelineImageInput]`):
The input video to condition the generation on. Must be a list of images/frames of the video.
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `pr... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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]`, ... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
`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_e... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
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 b... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
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-a... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
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).
clip_skip (`int`,... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
`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 v... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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 ... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt=prompt,
strength=strength,
height=height,
width=width,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
# 3. Prepare timesteps
if not enforce_inference_steps:
timesteps, num_inference_steps = retrieve_timesteps(
self.scheduler, num_inference_steps, device, timesteps, sigmas
)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, timesteps, str... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
# 4. Prepare latent variables
if latents is None:
video = self.video_processor.preprocess_video(video, height=height, width=width)
# Move the number of frames before the number of channels.
video = video.permute(0, 2, 1, 3, 4)
video = video.to(device=device, dtype... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
# 5. Encode input prompt
text_encoder_lora_scale = (
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
)
num_frames = latents.shape[2]
if self.free_noise_enabled:
prompt_embeds, negative_prompt_embeds = self._encod... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
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,
... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
# 8. 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
)
num_free_init_iters = self._free_init_num_iters if self.free_init_enabled else 1
... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.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
... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
# 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 s... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
# call the callback, if provided
... | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
return AnimateDiffPipelineOutput(frames=video) | 145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py |
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