text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
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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... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
# 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):
... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.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... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
gligen_images,
gligen_phrases,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
callback_on_step_end_tensor_inputs=None,
):
if height %... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.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_... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.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:
... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.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... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.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,
... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def enable_fuser(self, enabled=True):
for module in self.unet.modules():
if type(module) is GatedSelfAttentionDense:
m... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
def crop(self, im, new_width, new_height):
"""
Crop the input image to the specified dimensions.
"""
width, height = im.size
left = (width - new_width) / 2
top = (height - new_height) / 2
right = (width + new_width) / 2
bottom = (height + new_height) / 2
... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
def complete_mask(self, has_mask, max_objs, device):
"""
Based on the input mask corresponding value `0 or 1` for each phrases and image, mask the features
corresponding to phrases and images.
"""
mask = torch.ones(1, max_objs).type(self.text_encoder.dtype).to(device)
if ... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
def get_clip_feature(self, input, normalize_constant, device, is_image=False):
"""
Get image and phrases embedding by using CLIP pretrain model. The image embedding is transformed into the
phrases embedding space through a projection.
"""
if is_image:
if input is None... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
outputs = self.image_encoder(**inputs)
feature = outputs.image_embeds
feature = self.image_project(feature).squeeze(0)
feature = (feature / feature.norm()) * normalize_constant
feature = feature.unsqueeze(0)
else:
if input is None:
retu... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
def get_cross_attention_kwargs_with_grounded(
self,
hidden_size,
gligen_phrases,
gligen_images,
gligen_boxes,
input_phrases_mask,
input_images_mask,
repeat_batch,
normalize_constant,
max_objs,
device,
):
"""
Prep... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
boxes = torch.zeros(max_objs, 4, device=device, dtype=self.text_encoder.dtype)
masks = torch.zeros(max_objs, device=device, dtype=self.text_encoder.dtype)
phrases_masks = torch.zeros(max_objs, device=device, dtype=self.text_encoder.dtype)
image_masks = torch.zeros(max_objs, device=device, dtype=... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
for idx, (box, text_feature, image_feature) in enumerate(zip(gligen_boxes, text_features, image_features)):
boxes[idx] = torch.tensor(box)
masks[idx] = 1
if text_feature is not None:
phrases_embeddings[idx] = text_feature
phrases_masks[idx] = 1
... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
input_phrases_mask = self.complete_mask(input_phrases_mask, max_objs, device)
phrases_masks = phrases_masks.unsqueeze(0).repeat(repeat_batch, 1) * input_phrases_mask
input_images_mask = self.complete_mask(input_images_mask, max_objs, device)
image_masks = image_masks.unsqueeze(0).repeat(repeat_b... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
def get_cross_attention_kwargs_without_grounded(self, hidden_size, repeat_batch, max_objs, device):
"""
Prepare the cross-attention kwargs without information about the grounded input (boxes, mask, image embedding,
phrases embedding) (All are zero tensor).
"""
boxes = torch.zeros... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
out = {
"boxes": boxes.unsqueeze(0).repeat(repeat_batch, 1, 1),
"masks": masks.unsqueeze(0).repeat(repeat_batch, 1),
"phrases_masks": phrases_masks.unsqueeze(0).repeat(repeat_batch, 1),
"image_masks": image_masks.unsqueeze(0).repeat(repeat_batch, 1),
"phrases_... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
gligen_schedule... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
negative_prompt_embeds: Optional[torch.Tensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
glig... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
gligen_phrases (`List[str]`):
The phrases to guide what to include in each of the regions defined by the corresponding
`gligen_boxes`. There should only be one phrase per boun... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
The bounding boxes that identify rectangular regions of the image that are going to be filled with the
content described by the corresponding `gligen_phrases`. Each rectangular box is defined as a
`List[float]` of 4 elements `[xmin, ymin, xmax, ymax]` where each value is between [0,1].
... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
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`,... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
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`.
prom... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
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.
callback (`... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
gligen_normalize_constant (`float`, *optional*, defaults to 28.7):
The normalize value of the image embedding.
clip_skip (`int`, *optional*):
Number... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
Examples:
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 ... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
height,
width,
callback_steps,
gligen_images,
gligen_phrases,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
)
... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
# 3. Encode input prompt
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=negati... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
# 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,
gene... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
if cross_attention_kwargs is None:
cross_attention_kwargs = {}
hidden_size = prompt_embeds.shape[2]
cross_attention_kwargs["gligen"] = self.get_cross_attention_kwargs_with_grounded(
hidden_size=hidden_size,
gligen_phrases=gligen_phrases,
gligen_images=gl... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
# Prepare latent variables for GLIGEN inpainting
if gligen_inpaint_image is not None:
# if the given input image is not of the same size as expected by VAE
# center crop and resize the input image to expected shape
if gligen_inpaint_image.size != (self.vae.sample_size, self.v... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
gligen_inpaint_latent = self.vae.encode(gligen_inpaint_image).latent_dist.sample()
gligen_inpaint_latent = self.vae.config.scaling_factor * gligen_inpaint_latent
# Generate an inpainting mask
# pixel value = 0, where the object is present (defined by bounding boxes above)
... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
gligen_inpaint_mask_addition = gligen_inpaint_mask_addition.expand(repeat_batch, -1, -1, -1).clone() | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
int(gligen_scheduled_sampling_beta * len(timesteps))
self.enable_fuser(True)
# 6. 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. Denoising loop
num_warmup_steps = le... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
if gligen_inpaint_image is not None:
gligen_inpaint_latent_with_noise = (
self.scheduler.add_noise(
gligen_inpaint_latent, torch.randn_like(gligen_inpaint_latent), torch.tensor([t])
)
.expand(latents.... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
if gligen_inpaint_image is not None:
latent_model_input = torch.cat((latent_model_input, gligen_inpaint_mask_addition), dim=1)
# predict the noise residual with grounded information
noise_pred_with_grounding = self.unet(
latent_model_input,
... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
# perform guidance
if do_classifier_free_guidance:
# Using noise_pred_text from noise residual with grounded information and noise_pred_uncond from noise residual without grounded information
_, noise_pred_text = noise_pred_with_grounding.chunk(2)
... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.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... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_d... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
class StableVideoDiffusionPipelineOutput(BaseOutput):
r"""
Output class for Stable Video Diffusion pipeline.
Args:
frames (`[List[List[PIL.Image.Image]]`, `np.ndarray`, `torch.Tensor`]):
List of denoised PIL images of length `batch_size` or numpy array or torch tensor of shape `(batch_s... | 45 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
class StableVideoDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline to generate video from an input image using Stable Video Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
Args:
vae ([`AutoencoderKLTemporalDecoder`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):
Frozen CLIP image-encoder
([laion/CLIP-ViT-H-14-laion2B-... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
def __init__(
self,
vae: AutoencoderKLTemporalDecoder,
image_encoder: CLIPVisionModelWithProjection,
unet: UNetSpatioTemporalConditionModel,
scheduler: EulerDiscreteScheduler,
feature_extractor: CLIPImageProcessor,
):
super().__init__()
self.register_... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
if not isinstance(image, torch.Tensor):
image = self.video_processor.pil_to_numpy(image)
image = self.video_processor.numpy_to_pt(image)
# We normalize the image before resizing to match with the original implementation.
# Then we unnormalize it after resizing.
... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
# duplicate image embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = image_embeddings.shape
image_embeddings = image_embeddings.repeat(1, num_videos_per_prompt, 1)
image_embeddings = image_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)
... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
def _encode_vae_image(
self,
image: torch.Tensor,
device: Union[str, torch.device],
num_videos_per_prompt: int,
do_classifier_free_guidance: bool,
):
image = image.to(device=device)
image_latents = self.vae.encode(image).latent_dist.mode()
# duplicate... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
def _get_add_time_ids(
self,
fps: int,
motion_bucket_id: int,
noise_aug_strength: float,
dtype: torch.dtype,
batch_size: int,
num_videos_per_prompt: int,
do_classifier_free_guidance: bool,
):
add_time_ids = [fps, motion_bucket_id, noise_aug_str... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
add_time_ids = add_time_ids.repeat(batch_size * num_videos_per_prompt, 1)
if do_classifier_free_guidance:
add_time_ids = torch.cat([add_time_ids, add_time_ids])
return add_time_ids
def decode_latents(self, latents: torch... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
# decode decode_chunk_size frames at a time to avoid OOM
frames = []
for i in range(0, latents.shape[0], decode_chunk_size):
num_frames_in = latents[i : i + decode_chunk_size].shape[0]
decode_kwargs = {}
if accepts_num_frames:
# we only pass num_frames... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
def check_inputs(self, image, height, width):
if (
not isinstance(image, torch.Tensor)
and not isinstance(image, PIL.Image.Image)
and not isinstance(image, list)
):
raise ValueError(
"`image` has to be of type `torch.Tensor` or `PIL.Image.I... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
def prepare_latents(
self,
batch_size: int,
num_frames: int,
num_channels_latents: int,
height: int,
width: int,
dtype: torch.dtype,
device: Union[str, torch.device],
generator: torch.Generator,
latents: Optional[torch.Tensor] = None,
)... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor],
height: int = 576,
width: int = 1024,
num_frames: Optional[int] = None,
num_inference_steps: int = 25,
s... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
The call function to the pipeline for generation. | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
Args:
image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.Tensor`):
Image(s) to guide image generation. If you provide a tensor, the expected value range is between `[0,
1]`.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
expense of slower inference. This parameter is modulated by `strength`.
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 w... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
motion_bucket_id (`int`, *optional*, defaults to 127):
Used for conditioning the amount of motion for the generation. The higher the number the more motion
will be in the video.
noise_aug_strength (`float`, *optional*, defaults to 0.02):
The amount of noise ad... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
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 samp... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
`callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`.
`callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
Examples:
Returns:
[`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] is
returned, otherwise a `tuple` of (`List[List[PIL.Image.Image]]` or `np.... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
# 2. Define call parameters
if isinstance(image, PIL.Image.Image):
batch_size = 1
elif isinstance(image, list):
batch_size = len(image)
else:
batch_size = image.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog ... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
# 4. Encode input image using VAE
image = self.video_processor.preprocess(image, height=height, width=width).to(device)
noise = randn_tensor(image.shape, generator=generator, device=device, dtype=image.dtype)
image = image + noise_aug_strength * noise
needs_upcasting = self.vae.dtype ==... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
# Repeat the image latents for each frame so we can concatenate them with the noise
# image_latents [batch, channels, height, width] ->[batch, num_frames, channels, height, width]
image_latents = image_latents.unsqueeze(1).repeat(1, num_frames, 1, 1, 1)
# 5. Get Added Time IDs
added_tim... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
# 7. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_videos_per_prompt,
num_frames,
num_channels_latents,
height,
width,
image_embeddings.dtype,
... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
# 9. Denoising loop
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):
# expand the latents if we are ... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=image_embeddings,
added_time_ids=added_time_ids,
return_dict=False,
)[0]
... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = ... | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
self.maybe_free_model_hooks()
if not return_dict:
return frames
return StableVideoDiffusionPipelineOutput(frames=frames) | 46 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py |
class PIAPipelineOutput(BaseOutput):
r"""
Output class for PIAPipeline.
Args:
frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
Nested list of length `batch_size` with denoised PIL image sequences of length `num_frames`, NumPy array of
shape `(batch_size... | 47 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
class PIAPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
IPAdapterMixin,
StableDiffusionLoraLoaderMixin,
FromSingleFileMixin,
FreeInitMixin,
):
r"""
Pipeline for text-to-video generation.
This model inherits from [`DiffusionPipeline`]. Check the s... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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,
)
... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt
def encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
# dynamically adjust the LoRA scale
if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
else:
scale_lora_layers(self.text_encoder, lora_scale)
if prompt is not None and isinstance(prompt, str):
batch_size = 1... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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 | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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_... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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(
... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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):
... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
# Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents
def decode_latents(self, latents):
latents = 1 / self.vae.config.scaling_factor * latents
batch_size, channels, num_frames, height, width = latents.shape
latents = lat... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
def check_inputs(
self,
prompt,
height,
width,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
ip_adapter_image=None,
ip_adapter_image_embeds=None,
callback_on_step_end_tensor_inputs=None,
):
if height % 8... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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:
... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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 ... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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 = ... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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]] * ... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
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