text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
Args:
features (`torch.Tensor` of shape `(B, L, D)`):
Text embedding features to generate captions from.
eos_token_id (`int`):
The token ID of the EOS token for the text decoder model.
device:
Device to perform text generation on.
... | 121 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_text_decoder.py |
features = torch.split(features, 1, dim=0)
generated_tokens = []
generated_seq_lengths = []
for feature in features:
feature = self.decode_prefix(feature.to(device)) # back to the clip feature
# Only support beam search for now
output_tokens, seq_lengths = se... | 121 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_text_decoder.py |
@torch.no_grad()
def generate_beam(
self,
input_ids=None,
input_embeds=None,
device=None,
beam_size: int = 5,
entry_length: int = 67,
temperature: float = 1.0,
eos_token_id: Optional[int] = None,
):
"""
Generates text using the give... | 121 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_text_decoder.py |
Args:
eos_token_id (`int`, *optional*):
The token ID of the EOS token for the text decoder model.
input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`, *optional*):
Tokenizer indices of input sequence tokens in the vocabulary. One of `input_ids`... | 121 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_text_decoder.py |
temperature (`float`, *optional*, defaults to 1.0):
The temperature to use when performing the softmax over logits from the decoding model. | 121 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_text_decoder.py |
Returns:
`Tuple(torch.Tensor, torch.Tensor)`: A tuple of tensors where the first element is a tensor of generated
token sequences sorted by score in descending order, and the second element is the sequence lengths
corresponding to those sequences.
"""
# Generates text... | 121 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_text_decoder.py |
for i in range(entry_length):
outputs = self.transformer(inputs_embeds=generated)
logits = outputs.logits
logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
logits = logits.softmax(-1).log() | 121 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_text_decoder.py |
if scores is None:
scores, next_tokens = logits.topk(beam_size, -1)
generated = generated.expand(beam_size, *generated.shape[1:])
next_tokens, scores = next_tokens.permute(1, 0), scores.squeeze(0)
if tokens is None:
tokens = next_tokens... | 121 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_text_decoder.py |
next_tokens = next_tokens % scores_sum.shape[1]
next_tokens = next_tokens.unsqueeze(1)
tokens = tokens[next_tokens_source]
tokens = torch.cat((tokens, next_tokens), dim=1)
generated = generated[next_tokens_source]
scores = scores_sum_averag... | 121 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_text_decoder.py |
next_token_embed = self.transformer.transformer.wte(next_tokens.squeeze()).view(generated.shape[0], 1, -1)
generated = torch.cat((generated, next_token_embed), dim=1)
is_stopped = is_stopped + next_tokens.eq(stop_token_index).squeeze()
if is_stopped.all():
break
... | 121 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_text_decoder.py |
class ImageTextPipelineOutput(BaseOutput):
"""
Output class for joint image-text pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
... | 122 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
class UniDiffuserPipeline(DiffusionPipeline):
r"""
Pipeline for a bimodal image-text model which supports unconditional text and image generation, text-conditioned
image generation, image-conditioned text generation, and joint image-text generation.
This model inherits from [`DiffusionPipeline`]. Check... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. This
is part of the UniDiffuser image representation along with the CLIP vision encoding.
text_encoder ([`CLIPTextModel`]):
Frozen text-... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
Frozen text decoder. This is a GPT-style model which is used to generate text from the UniDiffuser
embedding.
text_tokenizer ([`GPT2Tokenizer`]):
A [`~transformers.GPT2Tokenizer`] to decode text for text generation; used along with the `text_decoder`.
unet ([`UniDiffuserModel`]):... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# TODO: support for moving submodules for components with enable_model_cpu_offload
model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae->text_decoder"
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
image_encoder: CLIPVisionModelWithProjection,
... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
image_encoder=image_encoder,
clip_image_processor=clip_image_processor,
clip_tokenizer=clip_tokenizer,
text_decoder=text_decoder,
text_tokenizer=text_tokenizer,
unet... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
self.text_intermediate_dim = self.text_encoder_hidden_size
if self.text_decoder.prefix_hidden_dim is not None:
self.text_intermediate_dim = self.text_decoder.prefix_hidden_dim
self.mode = None
# TODO: handle safety checking?
self.safety_checker = None
# Copied from dif... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# 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
def _infer_mode(self, prompt, prompt_embeds, imag... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
prompt_latents_available = prompt_latents is not None
vae_latents_available = vae_latents is not None
clip_latents_available = clip_latents is not None
full_latents_available = latents is not None
image_latents_available = vae_latents_available and clip_latents_available
all_indv... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
if self.mode is not None:
# Preferentially use the mode set by the user
mode = self.mode
elif prompt_available:
mode = "text2img"
elif image_available:
mode = "img2text"
else:
# Neither prompt nor image supplied, infer based on availabi... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
if self.mode is None and not input_available:
if vae_latents_available != clip_latents_available:
# Exactly one of vae_latents and clip_latents is supplied
logger.warning(
f"You have supplied exactly one of `vae_latents` and `clip_latents`, whereas either ... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.enable_vae_slicing
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.enable_vae_tiling
def enable_vae_tiling(self):
r"""
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
compute decoding and encoding in several steps. This is useful f... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
def set_image_mode(self):
r"""Manually set the generation mode to unconditional ("marginal") image generation."""
self.mode = "img"
def set_text_to_image_mode(self):
r"""Manually set the generation mode to text-conditioned image generation."""
self.mode = "text2img"
def set_ima... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
def _infer_batch_size(
self,
mode,
prompt,
prompt_embeds,
image,
num_images_per_prompt,
num_prompts_per_image,
latents,
prompt_latents,
vae_latents,
clip_latents,
):
r"""Infers the batch size and multiplier depending on ... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
if mode in ["text2img"]:
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:
# Either prompt or prompt_embeds must be present for text2im... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
batch_size = clip_latents.shape[0]
else:
batch_size = 1
multiplier = num_images_per_prompt
elif mode in ["text"]:
if prompt_latents is not None:
batch_size = prompt_latents.shape[0]
else:
batch_size = 1
m... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
if num_images_per_prompt == num_prompts_per_image:
multiplier = num_images_per_prompt
else:
multiplier = min(num_images_per_prompt, num_prompts_per_image)
logger.warning(
f"You are using mode `{mode}` and `num_images_per_prompt`: {num_image... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.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... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.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,
... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with self.tokenizer->self.clip_tokenizer
def encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=Non... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.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... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.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... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.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... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
text_inputs = self.clip_tokenizer(
prompt,
padding="max_length",
max_length=self.clip_tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.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 | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.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_... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.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... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.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... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# textual inversion: process multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.clip_tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.clip_tokenizer(
... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.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... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_instruct_pix2pix.StableDiffusionInstructPix2PixPipeline.prepare_image_latents
# Add num_prompts_per_image argument, sample from autoencoder moment distribution
def encode_image_vae_latents(
self,
image,
batch_... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
batch_size = batch_size * num_prompts_per_image
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 t... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
# expand image_latents for batch_size
deprecation_message = (
f"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial"
" images (`image`)... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
raise ValueError(
f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
)
else:
image_latents = torch.cat([image_latents], dim=0) | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
if do_classifier_free_guidance:
uncond_image_latents = torch.zeros_like(image_latents)
image_latents = torch.cat([image_latents, image_latents, uncond_image_latents], dim=0)
return image_latents
def encode_image_clip_latents(
self,
image,
batch_size,
... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
batch_size = batch_size * num_prompts_per_image
if isinstance(generator, list):
image_latents = [
self.image_encoder(**preprocessed_image[i : i + 1]).image_embeds for i in range(batch_size)
]
image_latents = torch.cat(image_latents, dim=0)
else:
... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
# expand image_latents for batch_size
deprecation_message = (
f"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial"
" images (`image`)... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
raise ValueError(
f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
)
else:
image_latents = torch.cat([image_latents], dim=0) | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
# latents is assumed to have shace (B, L, D)
latents = latents.repeat(num_images_per_prompt, 1, 1)
latents = latents.to(device=device, dtype=dtype)
# ... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
# Rename prepare_latents -> prepare_image_vae_latents and add num_prompts_per_image argument.
def prepare_image_vae_latents(
self,
batch_size,
num_prompts_per_image,
... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
# latents is assumed to have shape (B, C, H, W)
latents = latents.repeat(num_prompts_per_image, 1, 1, 1)
latents = latents.to(device=device, dtype=dtype)
... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
def prepare_image_clip_latents(
self, batch_size, num_prompts_per_image, clip_img_dim, dtype, device, generator, latents=None
):
# Prepare latents for the CLIP embedded image.
shape = (batch_size * num_prompts_per_image, 1, clip_img_dim)
if isinstance(generator, list) and len(generat... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def decode_text_latents(self, text_latents, device):
output_token_list, seq_lengths = self.text_decoder.generate_captions(
text_latent... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
def _split(self, x, height, width):
r"""
Splits a flattened embedding x of shape (B, C * H * W + clip_img_dim) into two tensors of shape (B, C, H, W)
and (B, 1, clip_img_dim)
"""
batch_size = x.shape[0]
latent_height = height // self.vae_scale_factor
latent_width ... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
def _combine(self, img_vae, img_clip):
r"""
Combines a latent iamge img_vae of shape (B, C, H, W) and a CLIP-embedded image img_clip of shape (B, 1,
clip_img_dim) into a single tensor of shape (B, C * H * W + clip_img_dim).
"""
img_vae = torch.reshape(img_vae, (img_vae.shape[0], ... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
def _split_joint(self, x, height, width):
r"""
Splits a flattened embedding x of shape (B, C * H * W + clip_img_dim + text_seq_len * text_dim] into (img_vae,
img_clip, text) where img_vae is of shape (B, C, H, W), img_clip is of shape (B, 1, clip_img_dim), and text is
of shape (B, text_s... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
img_vae = torch.reshape(img_vae, (batch_size, self.num_channels_latents, latent_height, latent_width))
img_clip = torch.reshape(img_clip, (batch_size, 1, self.image_encoder_projection_dim))
text = torch.reshape(text, (batch_size, self.text_encoder_seq_len, self.text_intermediate_dim))
return img... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
def _get_noise_pred(
self,
mode,
latents,
t,
prompt_embeds,
img_vae,
img_clip,
max_timestep,
data_type,
guidance_scale,
generator,
device,
height,
width,
):
r"""
Gets the noise prediction ... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# Classifier-free guidance
img_vae_T = randn_tensor(img_vae.shape, generator=generator, device=device, dtype=img_vae.dtype)
img_clip_T = randn_tensor(img_clip.shape, generator=generator, device=device, dtype=img_clip.dtype)
text_T = randn_tensor(prompt_embeds.shape, generator=generat... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
return guidance_scale * x_out + (1.0 - guidance_scale) * x_out_uncond
elif mode == "text2img":
# Text-conditioned image generation
img_vae_latents, img_clip_latents = self._split(latents, height, width)
img_vae_out, img_clip_out, text_out = self.unet(
img_vae... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
img_vae_out_uncond, img_clip_out_uncond, text_out_uncond = self.unet(
img_vae_latents,
img_clip_latents,
text_T,
timestep_img=t,
timestep_text=max_timestep,
data_type=data_type,
)
img_out_uncond = se... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# Classifier-free guidance
img_vae_T = randn_tensor(img_vae.shape, generator=generator, device=device, dtype=img_vae.dtype)
img_clip_T = randn_tensor(img_clip.shape, generator=generator, device=device, dtype=img_clip.dtype)
img_vae_out_uncond, img_clip_out_uncond, text_out_uncond = ... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
return text_out
elif mode == "img":
# Unconditional ("marginal") image generation (no CFG)
img_vae_latents, img_clip_latents = self._split(latents, height, width)
img_vae_out, img_clip_out, text_out = self.unet(
img_vae_latents,
img_clip_laten... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
def check_latents_shape(self, latents_name, latents, expected_shape):
latents_shape = latents.shape
expected_num_dims = len(expected_shape) + 1 # expected dimensions plus the batch dimension
expected_shape_str = ", ".join(str(dim) for dim in expected_shape)
if len(latents_shape) != expe... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
def check_inputs(
self,
mode,
prompt,
image,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
latents=None,
prompt_latents=None,
vae_latents=None,
clip_lat... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
if mode == "text2img":
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."
)
... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
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."
... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# Check provided latents
latent_height = height // self.vae_scale_factor
latent_width = width // self.vae_scale_factor
full_latents_available = latents is not None
prompt_latents_available = prompt_latents is not None
vae_latents_available = vae_latents is not None
clip_l... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
if full_latents_available:
individual_latents_available = (
prompt_latents is not None or vae_latents is not None or clip_latents is not None
)
if individual_latents_available:
logger.warning(
"You have supplied both `latents` and a... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# Check individual latent shapes, if present
if prompt_latents_available:
prompt_latents_expected_shape = (self.text_encoder_seq_len, self.text_encoder_hidden_size)
self.check_latents_shape("prompt_latents", prompt_latents, prompt_latents_expected_shape)
if vae_latents_available... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
if mode in ["text2img", "img"] and vae_latents_available and clip_latents_available:
if vae_latents.shape[0] != clip_latents.shape[0]:
raise ValueError(
f"Both `vae_latents` and `clip_latents` are supplied, but their batch dimensions are not equal:"
f"... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
@torch.no_grad()
def __call__(
self,
prompt: Optional[Union[str, List[str]]] = None,
image: Optional[Union[torch.Tensor, PIL.Image.Image]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
data_type: Optional[int] = 1,
num_inference_steps: int... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
callback_steps: int = 1,
):
r"""
The call function to the pipeline for generation. | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.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`.
Required for text-conditioned image generation (`text2img`) mode.
image (`torch.Tensor` or `PIL.Image.Image`, *optio... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
embedding; this is added for compatibility with the
[UniDiffuser-v1](https://huggingface.co/thu-ml/unidiffuser-v1) checkpoint.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image a... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
text-conditioned image generation (`text2img`) mode.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt. Used in `text2img` (text-conditioned image generation) and
`img` mode. If the mode is joint and both `num_images_per_pro... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https:/... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
prompt_latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for text
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by samp... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
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 twea... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImageTextPipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
Returns:
[`~pipelines.unidiffuser.ImageTextPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.unidiffuser.ImageTextPipelineOutput`] is returned, otherwise a
`tuple` is returned where the first element is a list with the generated images and the second eleme... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# 1. Check inputs
# Recalculate mode for each call to the pipeline.
mode = self._infer_mode(prompt, prompt_embeds, image, latents, prompt_latents, vae_latents, clip_latents)
self.check_inputs(
mode,
prompt,
image,
height,
width,
... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# 2. Define call parameters
batch_size, multiplier = self._infer_batch_size(
mode,
prompt,
prompt_embeds,
image,
num_images_per_prompt,
num_prompts_per_image,
latents,
prompt_latents,
vae_latents,
... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# 3. Encode input prompt, if available; otherwise prepare text latents
if latents is not None:
# Overwrite individual latents
vae_latents, clip_latents, prompt_latents = self._split_joint(latents, height, width)
if mode in ["text2img"]:
# 3.1. Encode input prompt, if... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# if do_classifier_free_guidance:
# prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
else:
# 3.2. Prepare text latent variables, if input not available
prompt_embeds = self.prepare_text_latents(
batch_size=batch_size,
num_... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# 4. Encode image, if available; otherwise prepare image latents
if mode in ["img2text"]:
# 4.1. Encode images, if available
assert image is not None, "`img2text` requires a conditioning image"
# Encode image using VAE
image_vae = self.image_processor.preprocess(i... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# Encode image using CLIP
image_clip_latents = self.encode_image_clip_latents(
image=image,
batch_size=batch_size,
num_prompts_per_image=multiplier,
dtype=prompt_embeds.dtype,
device=device,
generator=generator,
... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
generator=generator,
latents=vae_latents,
) | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# Prepare image CLIP latents
image_clip_latents = self.prepare_image_clip_latents(
batch_size=batch_size,
num_prompts_per_image=multiplier,
clip_img_dim=self.image_encoder_projection_dim,
dtype=prompt_embeds.dtype,
device=device... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# 6. Prepare latent variables
if mode == "joint":
latents = self._combine_joint(image_vae_latents, image_clip_latents, prompt_embeds)
elif mode in ["text2img", "img"]:
latents = self._combine(image_vae_latents, image_clip_latents)
elif mode in ["img2text", "text"]:
... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# 8. 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):
# predict the noise residual
# Also applies classifier-free ... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.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... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
text = self.decode_text_latents(text_latents, device)
elif mode in ["text2img", "img"]:
image_vae_latents, image_clip_latents = self._split(latents, height, width)
if not output_type == "latent":
# Map latent VAE image back to pixel space
image = self.vae... | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, text)
return ImageTextPipelineOutput(images=image, text=text) | 123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py |
class PatchEmbed(nn.Module):
"""2D Image to Patch Embedding"""
def __init__(
self,
height=224,
width=224,
patch_size=16,
in_channels=3,
embed_dim=768,
layer_norm=False,
flatten=True,
bias=True,
use_pos_embed=True,
):
su... | 124 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
self.use_pos_embed = use_pos_embed
if self.use_pos_embed:
pos_embed = get_2d_sincos_pos_embed(embed_dim, int(num_patches**0.5), output_type="pt")
self.register_buffer("pos_embed", pos_embed.float().unsqueeze(0), persistent=False)
def forward(self, latent):
latent = self.proj... | 124 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
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