text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
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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,
d... | 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_di... | 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,
... | 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_... | 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()]
... | 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: Opti... | 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... | 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... | 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_embed... | 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)
... | 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.Tens... | 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)
... | 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_... | 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]
... | 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)
interpola... | 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
a... | 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].
- "d... | 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"
... | 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 gre... | 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_callba... | 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 ... | 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"],
... | 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... | 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
modelin... | 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:
... | 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... | 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))... | 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 input... | 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: UNet2D... | 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"
... | 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'` i... | 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[torc... | 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,
... | 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.... | 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 (`b... | 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... | 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... | 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 = sel... | 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_... | 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, devic... | 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 no... | 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(
... | 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... | 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):
... | 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... | 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 Value... | 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:
... | 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: `pr... | 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,
... | 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:
m... | 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_schedule... | 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... | 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 boun... | 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 impro... | 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.
... | 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 th... | 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
[`s... | 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 image... | 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,
)
... | 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=negati... | 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,
gene... | 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_... | 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] = ... | 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... | 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.v... | 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)
... | 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
... | 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.... | 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,
enco... | 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.sch... | 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_d... | 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 me... | 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))... | 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 th... | 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: C... | 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"
... | 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'` i... | 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... | 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.... | 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 (`b... | 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... | 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... | 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 = sel... | 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_... | 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, devic... | 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 no... | 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(
... | 44 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py |
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