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motion_module.transformer_blocks[i] = FreeNoiseTransformerBlock(
dim=basic_transfomer_block.dim,
num_attention_heads=basic_transfomer_block.num_attention_heads,
attention_head_dim=basic_transfomer_block.attention_head_dim,
dropout=basic_transfomer_block.dropout,
cross_attention_dim=basic_transfomer_block.cross_attention_dim,
activation_fn=basic_transfomer_block.activation_fn,
attention_bias=basic_transfomer_block.attention_bias,
only_cross_attention=basic_transfomer_block.only_cross_attention,
double_self_attention=basic_transfomer_block.double_self_attention,
positional_embeddings=basic_transfomer_block.positional_embeddings,
num_positional_embeddings=basic_transfomer_block.num_positional_embeddings, | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
context_length=self._free_noise_context_length,
context_stride=self._free_noise_context_stride,
weighting_scheme=self._free_noise_weighting_scheme,
).to(device=self.device, dtype=self.dtype) | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
motion_module.transformer_blocks[i].load_state_dict(
basic_transfomer_block.state_dict(), strict=True
)
motion_module.transformer_blocks[i].set_chunk_feed_forward(
basic_transfomer_block._chunk_size, basic_transfomer_block._chunk_dim
)
def _disable_free_noise_in_block(self, block: Union[CrossAttnDownBlockMotion, DownBlockMotion, UpBlockMotion]):
r"""Helper function to disable FreeNoise in transformer blocks."""
for motion_module in block.motion_modules:
num_transformer_blocks = len(motion_module.transformer_blocks)
for i in range(num_transformer_blocks):
if isinstance(motion_module.transformer_blocks[i], FreeNoiseTransformerBlock):
free_noise_transfomer_block = motion_module.transformer_blocks[i] | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
motion_module.transformer_blocks[i] = BasicTransformerBlock(
dim=free_noise_transfomer_block.dim,
num_attention_heads=free_noise_transfomer_block.num_attention_heads,
attention_head_dim=free_noise_transfomer_block.attention_head_dim,
dropout=free_noise_transfomer_block.dropout,
cross_attention_dim=free_noise_transfomer_block.cross_attention_dim,
activation_fn=free_noise_transfomer_block.activation_fn,
attention_bias=free_noise_transfomer_block.attention_bias,
only_cross_attention=free_noise_transfomer_block.only_cross_attention,
double_self_attention=free_noise_transfomer_block.double_self_attention,
positional_embeddings=free_noise_transfomer_block.positional_embeddings, | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
num_positional_embeddings=free_noise_transfomer_block.num_positional_embeddings,
).to(device=self.device, dtype=self.dtype) | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
motion_module.transformer_blocks[i].load_state_dict(
free_noise_transfomer_block.state_dict(), strict=True
)
motion_module.transformer_blocks[i].set_chunk_feed_forward(
free_noise_transfomer_block._chunk_size, free_noise_transfomer_block._chunk_dim
)
def _check_inputs_free_noise(
self,
prompt,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
num_frames,
) -> None:
if not isinstance(prompt, (str, dict)):
raise ValueError(f"Expected `prompt` to have type `str` or `dict` but found {type(prompt)=}")
if negative_prompt is not None:
if not isinstance(negative_prompt, (str, dict)):
raise ValueError(
f"Expected `negative_prompt` to have type `str` or `dict` but found {type(negative_prompt)=}"
) | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
if prompt_embeds is not None or negative_prompt_embeds is not None:
raise ValueError("`prompt_embeds` and `negative_prompt_embeds` is not supported in FreeNoise yet.")
frame_indices = [isinstance(x, int) for x in prompt.keys()]
frame_prompts = [isinstance(x, str) for x in prompt.values()]
min_frame = min(list(prompt.keys()))
max_frame = max(list(prompt.keys()))
if not all(frame_indices):
raise ValueError("Expected integer keys in `prompt` dict for FreeNoise.")
if not all(frame_prompts):
raise ValueError("Expected str values in `prompt` dict for FreeNoise.")
if min_frame != 0:
raise ValueError("The minimum frame index in `prompt` dict must be 0 as a starting prompt is necessary.")
if max_frame >= num_frames:
raise ValueError(
f"The maximum frame index in `prompt` dict must be lesser than {num_frames=} and follow 0-based indexing."
) | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
def _encode_prompt_free_noise(
self,
prompt: Union[str, Dict[int, str]],
num_frames: int,
device: torch.device,
num_videos_per_prompt: int,
do_classifier_free_guidance: bool,
negative_prompt: Optional[Union[str, Dict[int, str]]] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
lora_scale: Optional[float] = None,
clip_skip: Optional[int] = None,
) -> torch.Tensor:
if negative_prompt is None:
negative_prompt = ""
# Ensure that we have a dictionary of prompts
if isinstance(prompt, str):
prompt = {0: prompt}
if isinstance(negative_prompt, str):
negative_prompt = {0: negative_prompt}
self._check_inputs_free_noise(prompt, negative_prompt, prompt_embeds, negative_prompt_embeds, num_frames) | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
# Sort the prompts based on frame indices
prompt = dict(sorted(prompt.items()))
negative_prompt = dict(sorted(negative_prompt.items()))
# Ensure that we have a prompt for the last frame index
prompt[num_frames - 1] = prompt[list(prompt.keys())[-1]]
negative_prompt[num_frames - 1] = negative_prompt[list(negative_prompt.keys())[-1]]
frame_indices = list(prompt.keys())
frame_prompts = list(prompt.values())
frame_negative_indices = list(negative_prompt.keys())
frame_negative_prompts = list(negative_prompt.values()) | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
# Generate and interpolate positive prompts
prompt_embeds, _ = self.encode_prompt(
prompt=frame_prompts,
device=device,
num_images_per_prompt=num_videos_per_prompt,
do_classifier_free_guidance=False,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
lora_scale=lora_scale,
clip_skip=clip_skip,
)
shape = (num_frames, *prompt_embeds.shape[1:])
prompt_interpolation_embeds = prompt_embeds.new_zeros(shape)
for i in range(len(frame_indices) - 1):
start_frame = frame_indices[i]
end_frame = frame_indices[i + 1]
start_tensor = prompt_embeds[i].unsqueeze(0)
end_tensor = prompt_embeds[i + 1].unsqueeze(0) | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
prompt_interpolation_embeds[start_frame : end_frame + 1] = self._free_noise_prompt_interpolation_callback(
start_frame, end_frame, start_tensor, end_tensor
)
# Generate and interpolate negative prompts
negative_prompt_embeds = None
negative_prompt_interpolation_embeds = None
if do_classifier_free_guidance:
_, negative_prompt_embeds = self.encode_prompt(
prompt=[""] * len(frame_negative_prompts),
device=device,
num_images_per_prompt=num_videos_per_prompt,
do_classifier_free_guidance=True,
negative_prompt=frame_negative_prompts,
prompt_embeds=None,
negative_prompt_embeds=None,
lora_scale=lora_scale,
clip_skip=clip_skip,
)
negative_prompt_interpolation_embeds = negative_prompt_embeds.new_zeros(shape) | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
for i in range(len(frame_negative_indices) - 1):
start_frame = frame_negative_indices[i]
end_frame = frame_negative_indices[i + 1]
start_tensor = negative_prompt_embeds[i].unsqueeze(0)
end_tensor = negative_prompt_embeds[i + 1].unsqueeze(0)
negative_prompt_interpolation_embeds[
start_frame : end_frame + 1
] = self._free_noise_prompt_interpolation_callback(start_frame, end_frame, start_tensor, end_tensor)
prompt_embeds = prompt_interpolation_embeds
negative_prompt_embeds = negative_prompt_interpolation_embeds
if do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds, negative_prompt_embeds | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
def _prepare_latents_free_noise(
self,
batch_size: int,
num_channels_latents: int,
num_frames: int,
height: int,
width: int,
dtype: torch.dtype,
device: torch.device,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.Tensor] = None,
):
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."
)
context_num_frames = (
self._free_noise_context_length if self._free_noise_context_length == "repeat_context" else num_frames
) | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
shape = (
batch_size,
num_channels_latents,
context_num_frames,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
if self._free_noise_noise_type == "random":
return latents
else:
if latents.size(2) == num_frames:
return latents
elif latents.size(2) != self._free_noise_context_length:
raise ValueError(
f"You have passed `latents` as a parameter to FreeNoise. The expected number of frames is either {num_frames} or {self._free_noise_context_length}, but found {latents.size(2)}"
)
latents = latents.to(device) | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
if self._free_noise_noise_type == "shuffle_context":
for i in range(self._free_noise_context_length, num_frames, self._free_noise_context_stride):
# ensure window is within bounds
window_start = max(0, i - self._free_noise_context_length)
window_end = min(num_frames, window_start + self._free_noise_context_stride)
window_length = window_end - window_start
if window_length == 0:
break
indices = torch.LongTensor(list(range(window_start, window_end)))
shuffled_indices = indices[torch.randperm(window_length, generator=generator)] | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
current_start = i
current_end = min(num_frames, current_start + window_length)
if current_end == current_start + window_length:
# batch of frames perfectly fits the window
latents[:, :, current_start:current_end] = latents[:, :, shuffled_indices]
else:
# handle the case where the last batch of frames does not fit perfectly with the window
prefix_length = current_end - current_start
shuffled_indices = shuffled_indices[:prefix_length]
latents[:, :, current_start:current_end] = latents[:, :, shuffled_indices]
elif self._free_noise_noise_type == "repeat_context":
num_repeats = (num_frames + self._free_noise_context_length - 1) // self._free_noise_context_length
latents = torch.cat([latents] * num_repeats, dim=2)
latents = latents[:, :, :num_frames]
return latents | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
def _lerp(
self, start_index: int, end_index: int, start_tensor: torch.Tensor, end_tensor: torch.Tensor
) -> torch.Tensor:
num_indices = end_index - start_index + 1
interpolated_tensors = []
for i in range(num_indices):
alpha = i / (num_indices - 1)
interpolated_tensor = (1 - alpha) * start_tensor + alpha * end_tensor
interpolated_tensors.append(interpolated_tensor)
interpolated_tensors = torch.cat(interpolated_tensors)
return interpolated_tensors
def enable_free_noise(
self,
context_length: Optional[int] = 16,
context_stride: int = 4,
weighting_scheme: str = "pyramid",
noise_type: str = "shuffle_context",
prompt_interpolation_callback: Optional[
Callable[[DiffusionPipeline, int, int, torch.Tensor, torch.Tensor], torch.Tensor]
] = None,
) -> None:
r"""
Enable long video generation using FreeNoise. | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
Args:
context_length (`int`, defaults to `16`, *optional*):
The number of video frames to process at once. It's recommended to set this to the maximum frames the
Motion Adapter was trained with (usually 16/24/32). If `None`, the default value from the motion
adapter config is used.
context_stride (`int`, *optional*):
Long videos are generated by processing many frames. FreeNoise processes these frames in sliding
windows of size `context_length`. Context stride allows you to specify how many frames to skip between
each window. For example, a context length of 16 and context stride of 4 would process 24 frames as:
[0, 15], [4, 19], [8, 23] (0-based indexing)
weighting_scheme (`str`, defaults to `pyramid`):
Weighting scheme for averaging latents after accumulation in FreeNoise blocks. The following weighting | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
schemes are supported currently:
- "flat"
Performs weighting averaging with a flat weight pattern: [1, 1, 1, 1, 1].
- "pyramid"
Performs weighted averaging with a pyramid like weight pattern: [1, 2, 3, 2, 1].
- "delayed_reverse_sawtooth"
Performs weighted averaging with low weights for earlier frames and high-to-low weights for
later frames: [0.01, 0.01, 3, 2, 1].
noise_type (`str`, defaults to "shuffle_context"):
Must be one of ["shuffle_context", "repeat_context", "random"].
- "shuffle_context"
Shuffles a fixed batch of `context_length` latents to create a final latent of size
`num_frames`. This is usually the best setting for most generation scenarious. However, there | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
might be visible repetition noticeable in the kinds of motion/animation generated.
- "repeated_context"
Repeats a fixed batch of `context_length` latents to create a final latent of size
`num_frames`.
- "random"
The final latents are random without any repetition.
""" | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
allowed_weighting_scheme = ["flat", "pyramid", "delayed_reverse_sawtooth"]
allowed_noise_type = ["shuffle_context", "repeat_context", "random"]
if context_length > self.motion_adapter.config.motion_max_seq_length:
logger.warning(
f"You have set {context_length=} which is greater than {self.motion_adapter.config.motion_max_seq_length=}. This can lead to bad generation results."
)
if weighting_scheme not in allowed_weighting_scheme:
raise ValueError(
f"The parameter `weighting_scheme` must be one of {allowed_weighting_scheme}, but got {weighting_scheme=}"
)
if noise_type not in allowed_noise_type:
raise ValueError(f"The parameter `noise_type` must be one of {allowed_noise_type}, but got {noise_type=}") | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
self._free_noise_context_length = context_length or self.motion_adapter.config.motion_max_seq_length
self._free_noise_context_stride = context_stride
self._free_noise_weighting_scheme = weighting_scheme
self._free_noise_noise_type = noise_type
self._free_noise_prompt_interpolation_callback = prompt_interpolation_callback or self._lerp
if hasattr(self.unet.mid_block, "motion_modules"):
blocks = [*self.unet.down_blocks, self.unet.mid_block, *self.unet.up_blocks]
else:
blocks = [*self.unet.down_blocks, *self.unet.up_blocks]
for block in blocks:
self._enable_free_noise_in_block(block)
def disable_free_noise(self) -> None:
r"""Disable the FreeNoise sampling mechanism."""
self._free_noise_context_length = None | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
if hasattr(self.unet.mid_block, "motion_modules"):
blocks = [*self.unet.down_blocks, self.unet.mid_block, *self.unet.up_blocks]
else:
blocks = [*self.unet.down_blocks, *self.unet.up_blocks]
blocks = [*self.unet.down_blocks, self.unet.mid_block, *self.unet.up_blocks]
for block in blocks:
self._disable_free_noise_in_block(block)
def _enable_split_inference_motion_modules_(
self, motion_modules: List[AnimateDiffTransformer3D], spatial_split_size: int
) -> None:
for motion_module in motion_modules:
motion_module.proj_in = SplitInferenceModule(motion_module.proj_in, spatial_split_size, 0, ["input"]) | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
for i in range(len(motion_module.transformer_blocks)):
motion_module.transformer_blocks[i] = SplitInferenceModule(
motion_module.transformer_blocks[i],
spatial_split_size,
0,
["hidden_states", "encoder_hidden_states"],
)
motion_module.proj_out = SplitInferenceModule(motion_module.proj_out, spatial_split_size, 0, ["input"])
def _enable_split_inference_attentions_(
self, attentions: List[Transformer2DModel], temporal_split_size: int
) -> None:
for i in range(len(attentions)):
attentions[i] = SplitInferenceModule(
attentions[i], temporal_split_size, 0, ["hidden_states", "encoder_hidden_states"]
) | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
def _enable_split_inference_resnets_(self, resnets: List[ResnetBlock2D], temporal_split_size: int) -> None:
for i in range(len(resnets)):
resnets[i] = SplitInferenceModule(resnets[i], temporal_split_size, 0, ["input_tensor", "temb"])
def _enable_split_inference_samplers_(
self, samplers: Union[List[Downsample2D], List[Upsample2D]], temporal_split_size: int
) -> None:
for i in range(len(samplers)):
samplers[i] = SplitInferenceModule(samplers[i], temporal_split_size, 0, ["hidden_states"])
def enable_free_noise_split_inference(self, spatial_split_size: int = 256, temporal_split_size: int = 16) -> None:
r"""
Enable FreeNoise memory optimizations by utilizing
[`~diffusers.pipelines.free_noise_utils.SplitInferenceModule`] across different intermediate modeling blocks. | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
Args:
spatial_split_size (`int`, defaults to `256`):
The split size across spatial dimensions for internal blocks. This is used in facilitating split
inference across the effective batch dimension (`[B x H x W, F, C]`) of intermediate tensors in motion
modeling blocks.
temporal_split_size (`int`, defaults to `16`):
The split size across temporal dimensions for internal blocks. This is used in facilitating split
inference across the effective batch dimension (`[B x F, H x W, C]`) of intermediate tensors in spatial
attention, resnets, downsampling and upsampling blocks.
"""
# TODO(aryan): Discuss on what's the best way to provide more control to users
blocks = [*self.unet.down_blocks, self.unet.mid_block, *self.unet.up_blocks]
for block in blocks:
if getattr(block, "motion_modules", None) is not None: | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
self._enable_split_inference_motion_modules_(block.motion_modules, spatial_split_size)
if getattr(block, "attentions", None) is not None:
self._enable_split_inference_attentions_(block.attentions, temporal_split_size)
if getattr(block, "resnets", None) is not None:
self._enable_split_inference_resnets_(block.resnets, temporal_split_size)
if getattr(block, "downsamplers", None) is not None:
self._enable_split_inference_samplers_(block.downsamplers, temporal_split_size)
if getattr(block, "upsamplers", None) is not None:
self._enable_split_inference_samplers_(block.upsamplers, temporal_split_size) | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
@property
def free_noise_enabled(self):
return hasattr(self, "_free_noise_context_length") and self._free_noise_context_length is not None | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
class StableDiffusionGLIGENPipeline(DiffusionPipeline, StableDiffusionMixin):
r"""
Pipeline for text-to-image generation using Stable Diffusion with Grounded-Language-to-Image Generation (GLIGEN).
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.). | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image 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`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful. | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
more details about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
""" | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
_optional_components = ["safety_checker", "feature_extractor"]
model_cpu_offload_seq = "text_encoder->unet->vae"
_exclude_from_cpu_offload = ["safety_checker"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__() | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
) | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)
self.register_to_config(requires_safety_checker=requires_safety_checker) | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
lora_scale: Optional[float] = None,
**kwargs,
):
deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False) | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
prompt_embeds_tuple = self.encode_prompt(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=lora_scale,
**kwargs,
)
# concatenate for backwards comp
prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])
return prompt_embeds | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
def encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
lora_scale: Optional[float] = None,
clip_skip: Optional[int] = None,
):
r"""
Encodes the prompt into text encoder hidden states. | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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. | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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 | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
# dynamically adjust the LoRA scale
if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
else:
scale_lora_layers(self.text_encoder, lora_scale)
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: process multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer) | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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}"
) | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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 | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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. | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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) | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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" | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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] | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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 | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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):
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 | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
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 | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
gligen_phrases,
gligen_boxes,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=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}.")
if (callback_steps is None) or (
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)}."
) | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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."
) | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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 len(gligen_phrases) != len(gligen_boxes):
raise ValueError(
"length of `gligen_phrases` and `gligen_boxes` has to be same, but"
f" got: `gligen_phrases` {len(gligen_phrases)} != `gligen_boxes` {len(gligen_boxes)}"
) | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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) | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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:
module.enabled = enabled
def draw_inpaint_mask_from_boxes(self, boxes, size):
inpaint_mask = torch.ones(size[0], size[1])
for box in boxes:
x0, x1 = box[0] * size[0], box[2] * size[0]
y0, y1 = box[1] * size[1], box[3] * size[1]
inpaint_mask[int(y0) : int(y1), int(x0) : int(x1)] = 0
return inpaint_mask
def crop(self, im, new_width, new_height):
width, height = im.size
left = (width - new_width) / 2
top = (height - new_height) / 2
right = (width + new_width) / 2
bottom = (height + new_height) / 2
return im.crop((left, top, right, bottom)) | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
def target_size_center_crop(self, im, new_hw):
width, height = im.size
if width != height:
im = self.crop(im, min(height, width), min(height, width))
return im.resize((new_hw, new_hw), PIL.Image.LANCZOS) | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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_scheduled_sampling_beta: float = 0.3,
gligen_phrases: List[str] = None,
gligen_boxes: List[List[float]] = None,
gligen_inpaint_image: Optional[PIL.Image.Image] = None,
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,
output_type: Optional[str] = "pil",
return_dict: bool = True, | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
clip_skip: Optional[int] = None,
):
r"""
The call function to the pipeline for generation. | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
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.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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 bounding box.
gligen_boxes (`List[List[float]]`):
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].
gligen_inpaint_image (`PIL.Image.Image`, *optional*):
The input image, if provided, is inpainted with objects described by the `gligen_boxes` and
`gligen_phrases`. Otherwise, it is treated as a generation task on a blank input image. | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
gligen_scheduled_sampling_beta (`float`, defaults to 0.3):
Scheduled Sampling factor from [GLIGEN: Open-Set Grounded Text-to-Image
Generation](https://arxiv.org/pdf/2301.07093.pdf). Scheduled Sampling factor is only varied for
scheduled sampling during inference for improved quality and controllability.
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.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
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*):
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*): | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
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.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
callback_steps (`int`, *optional*, defaults to 1): | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
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).
guidance_rescale (`float`, *optional*, defaults to 0.0):
Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are
Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when
using zero terminal SNR.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
the output of the pre-final layer will be used for computing the prompt embeddings.
Examples: | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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.
"""
# 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 | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
height,
width,
callback_steps,
gligen_phrases,
gligen_boxes,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# 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 | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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=negative_prompt_embeds,
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])
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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,
generator,
latents,
) | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
# 5.1 Prepare GLIGEN variables
max_objs = 30
if len(gligen_boxes) > max_objs:
warnings.warn(
f"More that {max_objs} objects found. Only first {max_objs} objects will be processed.",
FutureWarning,
)
gligen_phrases = gligen_phrases[:max_objs]
gligen_boxes = gligen_boxes[:max_objs]
# prepare batched input to the GLIGENTextBoundingboxProjection (boxes, phrases, mask)
# Get tokens for phrases from pre-trained CLIPTokenizer
tokenizer_inputs = self.tokenizer(gligen_phrases, padding=True, return_tensors="pt").to(device)
# For the token, we use the same pre-trained text encoder
# to obtain its text feature
_text_embeddings = self.text_encoder(**tokenizer_inputs).pooler_output
n_objs = len(gligen_boxes)
# For each entity, described in phrases, is denoted with a bounding box,
# we represent the location information as (xmin,ymin,xmax,ymax) | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
boxes = torch.zeros(max_objs, 4, device=device, dtype=self.text_encoder.dtype)
boxes[:n_objs] = torch.tensor(gligen_boxes)
text_embeddings = torch.zeros(
max_objs, self.unet.config.cross_attention_dim, device=device, dtype=self.text_encoder.dtype
)
text_embeddings[:n_objs] = _text_embeddings
# Generate a mask for each object that is entity described by phrases
masks = torch.zeros(max_objs, device=device, dtype=self.text_encoder.dtype)
masks[:n_objs] = 1 | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
repeat_batch = batch_size * num_images_per_prompt
boxes = boxes.unsqueeze(0).expand(repeat_batch, -1, -1).clone()
text_embeddings = text_embeddings.unsqueeze(0).expand(repeat_batch, -1, -1).clone()
masks = masks.unsqueeze(0).expand(repeat_batch, -1).clone()
if do_classifier_free_guidance:
repeat_batch = repeat_batch * 2
boxes = torch.cat([boxes] * 2)
text_embeddings = torch.cat([text_embeddings] * 2)
masks = torch.cat([masks] * 2)
masks[: repeat_batch // 2] = 0
if cross_attention_kwargs is None:
cross_attention_kwargs = {}
cross_attention_kwargs["gligen"] = {"boxes": boxes, "positive_embeddings": text_embeddings, "masks": masks} | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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.vae.sample_size):
gligen_inpaint_image = self.target_size_center_crop(gligen_inpaint_image, self.vae.sample_size)
# Convert a single image into a batch of images with a batch size of 1
# The resulting shape becomes (1, C, H, W), where C is the number of channels,
# and H and W are the height and width of the image.
# scales the pixel values to a range [-1, 1]
gligen_inpaint_image = self.image_processor.preprocess(gligen_inpaint_image)
gligen_inpaint_image = gligen_inpaint_image.to(dtype=self.vae.dtype, device=self.vae.device)
# Run AutoEncoder to get corresponding latents | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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)
# 1, everywhere else
gligen_inpaint_mask = self.draw_inpaint_mask_from_boxes(gligen_boxes, gligen_inpaint_latent.shape[2:])
gligen_inpaint_mask = gligen_inpaint_mask.to(
dtype=gligen_inpaint_latent.dtype, device=gligen_inpaint_latent.device
)
gligen_inpaint_mask = gligen_inpaint_mask[None, None]
gligen_inpaint_mask_addition = torch.cat(
(gligen_inpaint_latent * gligen_inpaint_mask, gligen_inpaint_mask), dim=1
)
# Convert a single mask into a batch of masks with a batch size of 1 | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
gligen_inpaint_mask_addition = gligen_inpaint_mask_addition.expand(repeat_batch, -1, -1, -1).clone() | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
num_grounding_steps = 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 = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# Scheduled sampling
if i == num_grounding_steps:
self.enable_fuser(False)
if latents.shape[1] != 4:
latents = torch.randn_like(latents[:, :4]) | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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.shape[0], -1, -1, -1)
.clone()
)
latents = gligen_inpaint_latent_with_noise * gligen_inpaint_mask + latents * (
1 - gligen_inpaint_mask
)
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
).sample
# perform guidance
if 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 | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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 not output_type == "latent":
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
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] | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.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_detected=has_nsfw_concept) | 43 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py |
class StableDiffusionGLIGENTextImagePipeline(DiffusionPipeline, StableDiffusionMixin):
r"""
Pipeline for text-to-image generation using Stable Diffusion with Grounded-Language-to-Image Generation (GLIGEN).
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.). | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
processor ([`~transformers.CLIPProcessor`]):
A `CLIPProcessor` to procces reference image.
image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):
Frozen image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
image_project ([`CLIPImageProjection`]):
A `CLIPImageProjection` to project image embedding into phrases embedding space.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents. | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
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`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
more details about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
""" | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor"]
_exclude_from_cpu_offload = ["safety_checker"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
processor: CLIPProcessor,
image_encoder: CLIPVisionModelWithProjection,
image_project: CLIPImageProjection,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__() | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
) | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
) | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
image_encoder=image_encoder,
processor=processor,
image_project=image_project,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)
self.register_to_config(requires_safety_checker=requires_safety_checker) | 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.encode_prompt
def encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
lora_scale: Optional[float] = None,
clip_skip: Optional[int] = None,
):
r"""
Encodes the prompt into text encoder hidden states. | 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*):
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. | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.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 | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
# dynamically adjust the LoRA scale
if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
else:
scale_lora_layers(self.text_encoder, lora_scale)
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: process multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer) | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.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}"
) | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.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 | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.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. | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.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) | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.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" | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.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] | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
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