text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@staticmethod
def _compute_max_attention_per_index(
attention_maps: torch.Tensor,
indices: List[int],
) -> List[torch.Tensor]:
... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
# Extract the maximum values
max_indices_list = []
for i in indices:
image = attention_for_text[:, :, i]
smoothing = GaussianSmoothing().to(attention_maps.device)
input = F.pad(image.unsqueeze(0).unsqueeze(0), (1, 1, 1, 1), mode="reflect")
image = smoothin... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
@staticmethod
def _compute_loss(max_attention_per_index: List[torch.Tensor]) -> torch.Tensor:
"""Computes the attend-and-excite loss using the maximum attention value for each token."""
losses = [max(0, 1.0 - curr_max) for curr_max in max_attention_per_index]
loss = max(losses)
retur... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
def _perform_iterative_refinement_step(
self,
latents: torch.Tensor,
indices: List[int],
loss: torch.Tensor,
threshold: float,
text_embeddings: torch.Tensor,
step_size: float,
t: int,
max_refinement_steps: int = 20,
):
"""
Perfo... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
# Get max activation value for each subject token
max_attention_per_index = self._aggregate_and_get_max_attention_per_token(
indices=indices,
)
loss = self._compute_loss(max_attention_per_index)
if loss != 0:
latents = self._update_latent... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
# Get max activation value for each subject token
max_attention_per_index = self._aggregate_and_get_max_attention_per_token(
indices=indices,
)
loss = self._compute_loss(max_attention_per_index)
logger.info(f"\t Finished with loss of: {loss}")
return loss, latents, ma... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
self.unet.set_attn_processor(attn_procs)
self.attention_store.num_att_layers = cross_att_count
def get_indices(self, prompt: str) -> Dict[str, int]:
"""Utility function to list the indices of the tokens you wish to alte"""
ids = self.tokenizer(prompt).input_ids
indices = {i: tok for... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
token_indices: Union[List[int], List[List[int]]],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
g... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
thresholds: dict = {0: 0.05, 10: 0.5, 20: 0.8},
scale_factor: int = 20,
attn_res: Optional[Tuple[int]] = (16, 16),
clip_skip: Optional[int] = None,
):
r"""
The call function to the pipeline for generation. | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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`.
token_indices (`List[int]`):
The token indices to alter with attend-and-excite.
height (`int`, *optional... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to g... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
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
gener... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
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 `... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
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).
max_iter_to_alter (`int`, *optional*, defaults to `25`):
Numbe... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
attn_res (`tuple`, *optional*, default computed from width and height):
The 2D resolution of the semantic attention map.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the ... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with ... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
token_indices,
height,
width,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
)
# 2. Define call param... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
text_embeddings = (
prompt_embeds[batch_size * num_images_per_prompt :] if do_classifier_free_guidance else prompt_embeds
)
if isinstance(token_indices[0], int):
token_indices = [token_indices]
indices = []
for ind in token_indices:
indices = indice... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
# 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):
# Attend and excite process
with torch.enable_grad():
... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
self.unet(
latent,
t,
encoder_hidden_states=text_embedding,
cross_attention_kwargs=cross_attention_kwargs,
).sample
self.unet.zero_grad()
... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
# If this is an iterative refinement step, verify we have reached the desired threshold for all
if i in thresholds.keys() and loss > 1.0 - thresholds[i]:
loss, latent, max_attention_per_index = self._perform_iterative_refinement_step(
l... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
# Perform gradient update
if i < max_iter_to_alter:
if loss != 0:
latent = self._update_latent(
latents=latent,
loss=loss,
step_... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
# 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
... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
self.maybe_free_model_hooks()
# make sure to set the original attention processors back
self.unet.set_attn_processor(original_attn_proc)
if not return_dict:
return (image,... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
class GaussianSmoothing(torch.nn.Module):
"""
Arguments:
Apply gaussian smoothing on a 1d, 2d or 3d tensor. Filtering is performed seperately for each channel in the input
using a depthwise convolution.
channels (int, sequence): Number of channels of the input tensors. Output will
ha... | 71 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
# The gaussian kernel is the product of the
# gaussian function of each dimension.
kernel = 1
meshgrids = torch.meshgrid([torch.arange(size, dtype=torch.float32) for size in kernel_size])
for size, std, mgrid in zip(kernel_size, sigma, meshgrids):
mean = (size - 1) / 2
... | 71 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
if dim == 1:
self.conv = F.conv1d
elif dim == 2:
self.conv = F.conv2d
elif dim == 3:
self.conv = F.conv3d
else:
raise RuntimeError("Only 1, 2 and 3 dimensions are supported. Received {}.".format(dim))
def forward(self, input):
"""
... | 71 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
class KandinskyImg2ImgPipeline(DiffusionPipeline):
"""
Pipeline for image-to-image generation using Kandinsky
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 o... | 72 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py |
def __init__(
self,
text_encoder: MultilingualCLIP,
movq: VQModel,
tokenizer: XLMRobertaTokenizer,
unet: UNet2DConditionModel,
scheduler: DDIMScheduler,
):
super().__init__()
self.register_modules(
text_encoder=text_encoder,
to... | 72 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py |
def prepare_latents(self, latents, latent_timestep, shape, dtype, device, generator, scheduler):
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected laten... | 72 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py |
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
):
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.tokenizer(
... | 72 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py |
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 ... | 72 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py |
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `... | 72 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py |
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=77,
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
uncond_t... | 72 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py |
seq_len = uncond_text_encoder_hidden_states.shape[1]
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt, seq_len, ... | 72 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py |
return prompt_embeds, text_encoder_hidden_states, text_mask
# add_noise method to overwrite the one in schedule because it use a different beta schedule for adding noise vs sampling
def add_noise(
self,
original_samples: torch.Tensor,
noise: torch.Tensor,
timesteps: torch.IntTe... | 72 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py |
sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
noisy_samp... | 72 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]],
image_embeds: torch.Tensor,
negative_image_embeds: torch.Tensor,
... | 72 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
image (`torch.Tensor`, `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
... | 72 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py |
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a hig... | 72 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py |
guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance ... | 72 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py |
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
(`np.array`) or `"pt"` (`torch.Tensor`).
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
... | 72 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py |
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
# 1. Define call parameters
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`p... | 72 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py |
if do_classifier_free_guidance:
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(
... | 72 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py |
latents = self.movq.encode(image)["latents"]
latents = latents.repeat_interleave(num_images_per_prompt, dim=0)
# 4. set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps_tensor, num_inference_steps = self.get_timesteps(num_inference_steps, strength, d... | 72 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py |
# 5. Create initial latent
latents = self.prepare_latents(
latents,
latent_timestep,
(batch_size, num_channels_latents, height, width),
text_encoder_hidden_states.dtype,
device,
generator,
self.scheduler,
)
# 6.... | 72 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py |
if do_classifier_free_guidance:
noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
_, variance_pred_text = variance_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * ... | 72 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py |
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()
# 7. post-processing
image = self.movq.decode(latents, force_not_quantize... | 72 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py |
class KandinskyPriorPipelineOutput(BaseOutput):
"""
Output class for KandinskyPriorPipeline.
Args:
image_embeds (`torch.Tensor`)
clip image embeddings for text prompt
negative_image_embeds (`List[PIL.Image.Image]` or `np.ndarray`)
clip image embeddings for unconditio... | 73 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
class KandinskyPriorPipeline(DiffusionPipeline):
"""
Pipeline for generating image prior for Kandinsky
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 par... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
_exclude_from_cpu_offload = ["prior"]
model_cpu_offload_seq = "text_encoder->prior"
def __init__(
self,
prior: PriorTransformer,
image_encoder: CLIPVisionModelWithProjection,
text_encoder: CLIPTextModelWithProjection,
tokenizer: CLIPTokenizer,
scheduler: UnCLIPSc... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_INTERPOLATE_DOC_STRING)
def interpolate(
self,
images_and_prompts: List[Union[str, PIL.Image.Image, torch.Tensor]],
weights: List[float],
num_images_per_prompt: int = 1,
num_inference_steps: int = 25,
generator: ... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
Args:
images_and_prompts (`List[Union[str, PIL.Image.Image, torch.Tensor]]`):
list of prompts and images to guide the image generation.
weights: (`List[float]`):
list of weights for each condition in `images_and_prompts`
num_images_per_prompt (`int`, *... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually ... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
Examples:
Returns:
[`KandinskyPriorPipelineOutput`] or `tuple`
"""
device = device or self.device
if len(images_and_prompts) != len(weights):
raise ValueError(
f"`images_and_prompts` contains {len(images_and_prompts)} items and `weights` contain... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
elif isinstance(cond, (PIL.Image.Image, torch.Tensor)):
if isinstance(cond, PIL.Image.Image):
cond = (
self.image_processor(cond, return_tensors="pt")
.pixel_values[0]
.unsqueeze(0)
.to(dt... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
out_zero = self(
negative_prompt,
num_inference_steps=num_inference_steps,
num_images_per_prompt=num_images_per_prompt,
generator=generator,
latents=latents,
negative_prompt=negative_prior_prompt,
guidance_scale=guidance_scale,
... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
# Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
if ... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
):
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.tokenizer(
... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
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 ... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0) | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
uncond_text_mask = uncond_input.attention_mask.bool().to(device)... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
seq_len = uncond_text_encoder_hidden_states.shape[1]
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt, seq_len, ... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
return prompt_embeds, text_encoder_hidden_states, text_mask
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: int = 1,
num... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
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 will ge generated by samp... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
The output format of the generate image. Choose between: `"np"` (`np.array`) or `"pt"`
(`torch.Tensor`).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
Examples:
Returns:
[`KandinskyPriorPipelineOutput`] or `tuple`
"""
if isinstance(prompt, str):
prompt = [prompt]
elif not isinstance(prompt, list):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if isin... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
do_classifier_free_guidance = guidance_scale > 1.0
prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(
prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
# prior
self.scheduler.set_timesteps(num_inference_steps, devi... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
predicted_image_embedding = self.prior(
latent_model_input,
timestep=t,
proj_embedding=prompt_embeds,
encoder_hidden_states=text_encoder_hidden_states,
attention_mask=text_mask,
).predicted_image_embedding
if do_cla... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
latents = self.scheduler.step(
predicted_image_embedding,
timestep=t,
sample=latents,
generator=generator,
prev_timestep=prev_timestep,
).prev_sample
if XLA_AVAILABLE:
xm.mark_step()
latents... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
if output_type not in ["pt", "np"]:
raise ValueError(f"Only the output types `pt` and `np` are supported not output_type={output_type}")
if output_type == "np":
image_embeddings = image_embeddings.cpu().numpy()
zero_embeds = zero_embeds.cpu().numpy()
if not return_d... | 74 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py |
class KandinskyInpaintPipeline(DiffusionPipeline):
"""
Pipeline for text-guided image inpainting using Kandinsky2.1
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, run... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
def __init__(
self,
text_encoder: MultilingualCLIP,
movq: VQModel,
tokenizer: XLMRobertaTokenizer,
unet: UNet2DConditionModel,
scheduler: DDIMScheduler,
):
super().__init__()
self.register_modules(
text_encoder=text_encoder,
mo... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
# Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
if ... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
):
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.tokenizer(
... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
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 ... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=77,
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
uncond_t... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
seq_len = uncond_text_encoder_hidden_states.shape[1]
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt, seq_len, ... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
return prompt_embeds, text_encoder_hidden_states, text_mask | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
image: Union[torch.Tensor, PIL.Image.Image],
mask_image: Union[torch.Tensor, PIL.Image.Image, np.ndarray],
image_embeds: torch.Tensor,
negative_image... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
image (`torch.Tensor`, `PIL.Image.Image` or `np.ndarray`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
pro... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
will be converted to a single channel (luminance) before use. If it is a nummpy array, the expected
shape is `(H, W)`.
image_embeds (`torch.Tensor` or `List[torch.Tensor]`):
The clip image embeddings for text prompt, that will be used to condition the image generation.
... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
num_inference_steps (`int`, *optional*, defaults to 100):
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 4.0):
Guidance scale as de... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
One or a list of [torch generator(s)](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
... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
return_dict (`boo... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
Examples: | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
if not self._warn_has_been_called and version.parse(version.parse(__version__).base_version) < version.parse(
"0.23.0.dev0"
):
logger.warning(
"Please note that the expected format of `... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
)
self._warn_has_been_called = True | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
# Define call parameters
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
device = self._execution_device
... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
if do_classifier_free_guidance:
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(
... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
mask_image = mask_image.repeat_interleave(num_images_per_prompt, dim=0)
masked_image = masked_image.repeat_interleave(num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
mask_image = mask_image.repeat(2, 1, 1, 1)
masked_image = masked_image.repeat(2, 1, 1, 1)
s... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
# Check that sizes of mask, masked image and latents match with expected
num_channels_mask = mask_image.shape[1]
num_channels_masked_image = masked_image.shape[1]
if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
raise ValueError... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# 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 = torch.cat([latent_model_input, masked_image, mask_i... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
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