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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):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept | 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"
deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
def decode_latents_with_padding(self, latents: torch.Tensor, padding: int = 8) -> torch.Tensor:
"""
Decode the given latents with padding for circular inference. | 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 remove boundary artifacts and improve the output quality.
- This would slightly increase the memory usage.
- The padding pixels are then removed from the decoded image.
"""
latents = 1 / self.vae.config.scaling_factor * latents
latents_left = latents[..., :padding]
latents_right = latents[..., -padding:]
latents = torch.cat((latents_right, latents, latents_left), axis=-1)
image = self.vae.decode(latents, return_dict=False)[0]
padding_pix = self.vae_scale_factor * padding
image = image[..., padding_pix:-padding_pix]
return 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.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 | 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,
ip_adapter_image=None,
ip_adapter_image_embeds=None,
callback_on_step_end_tensor_inputs=None,
):
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}.") | 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_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]}"
) | 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:
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."
) | 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: `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."
) | 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 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"
) | 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,
int(height) // self.vae_scale_factor,
int(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) | 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_embedding
def get_guidance_scale_embedding(
self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
) -> torch.Tensor:
"""
See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 | 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`, *optional*, defaults to `torch.float32`):
Data type of the generated embeddings.
Returns:
`torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
"""
assert len(w.shape) == 1
w = w * 1000.0 | 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 pad
emb = torch.nn.functional.pad(emb, (0, 1))
assert emb.shape == (w.shape[0], embedding_dim)
return emb
def get_views(
self,
panorama_height: int,
panorama_width: int,
window_size: int = 64,
stride: int = 8,
circular_padding: bool = False,
) -> List[Tuple[int, int, int, int]]:
"""
Generates a list of views based on the given parameters. Here, we define the mappings F_i (see Eq. 7 in the
MultiDiffusion paper https://arxiv.org/abs/2302.08113). If panorama's height/width < window_size, num_blocks of
height/width should return 1. | 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, optional): Whether to apply circular padding. Defaults to False.
Returns:
List[Tuple[int, int, int, int]]: A list of tuples representing the views. Each tuple contains four integers
representing the start and end coordinates of the window in the panorama. | 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:
num_blocks_width = (panorama_width - window_size) // stride + 1 if panorama_width > window_size else 1
total_num_blocks = int(num_blocks_height * num_blocks_width)
views = []
for i in range(total_num_blocks):
h_start = int((i // num_blocks_width) * stride)
h_end = h_start + window_size
w_start = int((i % num_blocks_width) * stride)
w_end = w_start + window_size
views.append((h_start, h_end, w_start, w_end))
return views
@property
def guidance_scale(self):
return self._guidance_scale
@property
def guidance_rescale(self):
return self._guidance_rescale | 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
@property
def interrupt(self):
return self._interrupt | 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: float = 7.5,
view_batch_size: int = 1,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_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[List[torch.Tensor]] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True, | 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] = ["latents"],
**kwargs: Any,
):
r"""
The call function to the pipeline for generation. | 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`, *optional*, defaults to 2048):
The width in pixels of the generated image. The width is kept high because the pipeline is supposed
generate panorama-like images.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
timesteps (`List[int]`, *optional*):
The timesteps at which to generate the images. If not specified, then the default timestep spacing
strategy of the scheduler is used. | 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`, *optional*, defaults to 1):
The batch size to denoise split views. For some GPUs with high performance, higher view batch size can
speedup the generation and increase the VRAM usage.
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`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt. | 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]`, *optional*):
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 image
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`.
prompt_embeds (`torch.Tensor`, *optional*): | 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 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 | 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 between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
guidance_rescale (`float`, *optional*, defaults to 0.0): | 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 model to seamlessly generate a transition from the rightmost part of the image to
the leftmost part, maintaining consistency in a 360-degree sense.
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*):
A function that calls at the end of each denoising steps during the inference. The function is called | 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[str]`, *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.
Examples: | 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 images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
callback = kwargs.pop("callback", None)
callback_steps = kwargs.pop("callback_steps", None) | 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(
"callback_steps",
"1.0.0",
"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
)
# 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 | 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,
callback_on_step_end_tensor_inputs,
)
self._guidance_scale = guidance_scale
self._guidance_rescale = guidance_rescale
self._clip_skip = clip_skip
self._cross_attention_kwargs = cross_attention_kwargs
self._interrupt = False
# 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] | 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 = guidance_scale > 1.0
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_images_per_prompt,
self.do_classifier_free_guidance,
) | 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,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
clip_skip=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 do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) | 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_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Define panorama grid and initialize views for synthesis.
# prepare batch grid
views = self.get_views(height, width, circular_padding=circular_padding)
views_batch = [views[i : i + view_batch_size] for i in range(0, len(views), view_batch_size)]
views_scheduler_status = [copy.deepcopy(self.scheduler.__dict__)] * len(views_batch)
count = torch.zeros_like(latents)
value = torch.zeros_like(latents) | 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 is not None or ip_adapter_image_embeds is not None
else None
)
# 7.2 Optionally get Guidance Scale Embedding
timestep_cond = None
if self.unet.config.time_cond_proj_dim is not None:
guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
timestep_cond = self.get_guidance_scale_embedding(
guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
).to(device=device, dtype=latents.dtype) | 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 progress_bar:
for i, t in enumerate(timesteps):
if self.interrupt:
continue
count.zero_()
value.zero_() | 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
# MultiDiffusion paper for more details: https://arxiv.org/abs/2302.08113
# Batch views denoise
for j, batch_view in enumerate(views_batch):
vb_size = len(batch_view)
# get the latents corresponding to the current view coordinates
if circular_padding:
latents_for_view = []
for h_start, h_end, w_start, w_end in batch_view:
if w_end > latents.shape[3]:
# Add circular horizontal padding
latent_view = torch.cat(
( | 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:
latent_view = latents[:, :, h_start:h_end, w_start:w_end]
latents_for_view.append(latent_view)
latents_for_view = torch.cat(latents_for_view)
else:
latents_for_view = torch.cat(
[
latents[:, :, h_start:h_end, w_start:w_end]
for h_start, h_end, w_start, w_end in batch_view
]
) | 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)
if do_classifier_free_guidance
else latents_for_view
)
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# repeat prompt_embeds for batch
prompt_embeds_input = torch.cat([prompt_embeds] * vb_size) | 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_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred[::2], noise_pred[1::2]
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) | 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
)
# compute the previous noisy sample x_t -> x_t-1
latents_denoised_batch = self.scheduler.step(
noise_pred, t, latents_for_view, **extra_step_kwargs
).prev_sample
# save views scheduler status after sample
views_scheduler_status[j] = copy.deepcopy(self.scheduler.__dict__) | 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 for circular padding
value[:, :, h_start:h_end, w_start:] += latents_view_denoised[
:, :, h_start:h_end, : latents.shape[3] - w_start
]
value[:, :, h_start:h_end, : w_end - latents.shape[3]] += latents_view_denoised[
:, :, h_start:h_end, latents.shape[3] - w_start :
]
count[:, :, h_start:h_end, w_start:] += 1
count[:, :, h_start:h_end, : w_end - latents.shape[3]] += 1
else: | 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_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) | 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.scheduler, "order", 1)
callback(step_idx, t, latents)
if XLA_AVAILABLE:
xm.mark_step()
if output_type != "latent":
if circular_padding:
image = self.decode_latents_with_padding(latents)
else:
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None | 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_hooks()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) | 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 [`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 | 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)).
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`].
""" | 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: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
motion_adapter: MotionAdapter,
scheduler: Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
],
feature_extractor: CLIPImageProcessor = None,
image_encoder: CLIPVisionModelWithProjection = None,
):
super().__init__()
if isinstance(unet, UNet2DConditionModel):
unet = UNetMotionModel.from_unet2d(unet, motion_adapter) | 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,
)
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)
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. | 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 (`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. | 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_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 | 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_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) | 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 = 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}"
) | 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_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. | 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, 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) | 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 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" | 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(
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] | 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_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 | 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):
image = self.feature_extractor(image, return_tensors="pt").pixel_values | 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_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 | 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 = []
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."
) | 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.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) | 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]] * 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 | 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)
latents.append(batch_video)
return torch.cat(latents)
# 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
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) | 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((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 | 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 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 | 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_on_step_end_tensor_inputs=None,
):
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
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}.") | 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 callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
) | 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:
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."
) | 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: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
if video is not None and latents is not None:
raise ValueError("Only one of `video` or `latents` should be provided")
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."
) | 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 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"
)
def get_timesteps(self, num_inference_steps, timesteps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = timesteps[t_start * self.scheduler.order :]
return timesteps, num_inference_steps - t_start | 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.device] = None,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
decode_chunk_size: int = 16,
add_noise: bool = False,
) -> torch.Tensor:
num_frames = video.shape[1] if latents is None else latents.shape[2]
shape = (
batch_size,
num_channels_latents,
num_frames,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
) | 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."
)
if latents is None:
# make sure the VAE is in float32 mode, as it overflows in float16
if self.vae.config.force_upcast:
video = video.float()
self.vae.to(dtype=torch.float32)
if isinstance(generator, list):
init_latents = [
self.encode_video(video[i], generator[i], decode_chunk_size).unsqueeze(0)
for i in range(batch_size)
]
else:
init_latents = [self.encode_video(vid, generator, decode_chunk_size).unsqueeze(0) for vid in video]
init_latents = torch.cat(init_latents, dim=0) | 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 (`image`). Please make sure to update your script to pass as many initial images as text prompts"
)
raise ValueError(error_message)
elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
raise ValueError(
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
)
else:
init_latents = torch.cat([init_latents], dim=0) | 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"`latents` expected to have {shape=}, but found {latents.shape=}")
latents = latents.to(device, dtype=dtype)
if add_noise:
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
latents = self.scheduler.add_noise(latents, noise, timestep)
return latents
@property
def guidance_scale(self):
return self._guidance_scale
@property
def clip_skip(self):
return self._clip_skip | 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 > 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 | 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,
timesteps: Optional[List[int]] = None,
sigmas: Optional[List[float]] = None,
guidance_scale: float = 7.5,
strength: float = 0.8,
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[List[torch.Tensor]] = None, | 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] = ["latents"],
decode_chunk_size: int = 16,
):
r"""
The call function to the pipeline for generation. | 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 `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_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.
timesteps (`List[int]`, *optional*): | 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]`, *optional*):
Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
will be used.
strength (`float`, *optional*, defaults to 0.8):
Higher strength leads to more differences between original video and generated video.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text | 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_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*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.Tensor`, *optional*): | 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 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*):
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. | 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-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.
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 [`AnimateDiffPipelineOutput`] instead of a plain tuple. | 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`, *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*):
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 | 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 variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class.
decode_chunk_size (`int`, defaults to `16`):
The number of frames to decode at a time when calling `decode_latents` method. | 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 a list with the generated frames.
"""
# 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 | 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_embeds,
video=video,
latents=latents,
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,
)
self._guidance_scale = guidance_scale
self._clip_skip = clip_skip
self._cross_attention_kwargs = cross_attention_kwargs
self._interrupt = False | 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_device
dtype = self.dtype | 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, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_videos_per_prompt)
else:
denoising_inference_steps = int(num_inference_steps / strength)
timesteps, denoising_inference_steps = retrieve_timesteps(
self.scheduler, denoising_inference_steps, device, timesteps, sigmas
)
timesteps = timesteps[-num_inference_steps:]
latent_timestep = timesteps[:1].repeat(batch_size * num_videos_per_prompt) | 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=dtype)
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
video=video,
height=height,
width=width,
num_channels_latents=num_channels_latents,
batch_size=batch_size * num_videos_per_prompt,
timestep=latent_timestep,
dtype=dtype,
device=device,
generator=generator,
latents=latents,
decode_chunk_size=decode_chunk_size,
add_noise=enforce_inference_steps,
) | 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._encode_prompt_free_noise(
prompt=prompt,
num_frames=num_frames,
device=device,
num_videos_per_prompt=num_videos_per_prompt,
do_classifier_free_guidance=self.do_classifier_free_guidance,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
clip_skip=self.clip_skip,
)
else:
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
device, | 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, prompt_embeds])
prompt_embeds = prompt_embeds.repeat_interleave(repeats=num_frames, dim=0)
# 6. 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,
)
# 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) | 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
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
)
num_inference_steps = len(timesteps)
# make sure to readjust timesteps based on strength
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, timesteps, strength, device)
self._num_timesteps = len(timesteps)
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order | 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
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 residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=self.cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
).sample | 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 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_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) | 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
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()
# 10. Post-processing
if output_type == "latent":
video = latents
else:
video_tensor = self.decode_latents(latents, decode_chunk_size)
video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)
# 11. Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (video,) | 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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