text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
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 `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The num... | 186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
... | 186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
prior_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.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
... | 186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"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.
... | 186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
the `._callback_tensor_inputs` attribute of your pipeline class.
callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_step_end(self: Diff... | 186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
prior_kwargs = {}
if kwargs.get("prior_callback", None) is not None:
prior_kwargs["callback"] = kwargs.pop("prior_callback")
deprecate(
"prior_callback",
... | 186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
prior_outputs = self.prior_pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
num_inference_steps=prior_num_inference_steps,
generator=generator,
latents=latents,
guidance_scale=prior_guid... | 186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
if len(prompt) < image_embeds.shape[0] and image_embeds.shape[0] % len(prompt) == 0:
prompt = (image_embeds.shape[0] // len(prompt)) * prompt
if (
isinstance(image, (list, tuple))
and len(image) < image_embeds.shape[0]
and image_embeds.shape[0] % len(image) == 0
... | 186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
outputs = self.decoder_pipe(
image=image,
mask_image=mask_image,
image_embeds=image_embeds,
negative_image_embeds=negative_image_embeds,
width=width,
height=height,
num_inference_steps=num_inference_steps,
generator=generato... | 186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
class KandinskyV22PriorPipeline(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 ... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior.py |
Args:
prior ([`PriorTransformer`]):
The canonical unCLIP prior to approximate the image embedding from the text embedding.
image_encoder ([`CLIPVisionModelWithProjection`]):
Frozen image-encoder.
text_encoder ([`CLIPTextModelWithProjection`]):
Frozen text-enco... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior.py |
def __init__(
self,
prior: PriorTransformer,
image_encoder: CLIPVisionModelWithProjection,
text_encoder: CLIPTextModelWithProjection,
tokenizer: CLIPTokenizer,
scheduler: UnCLIPScheduler,
image_processor: CLIPImageProcessor,
):
super().__init__()
... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_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: ... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_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`, *... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_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`.
... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_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 ... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_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... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_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... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_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,
... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_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 ... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior.py |
# Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_prior.KandinskyPriorPipeline.get_zero_embed
def get_zero_embed(self, batch_size=1, device=None):
device = device or self.device
zero_img = torch.zeros(1, 3, self.image_encoder.config.image_size, self.image_encoder.config.image_size).to(
... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior.py |
# Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_prior.KandinskyPriorPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
):
batch_size = len(prompt) if is... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_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 ... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_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) | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_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 `... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_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)... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_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, ... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior.py |
return prompt_embeds, text_encoder_hidden_states, text_mask
@property
def do_classifier_free_guidance(self):
return self._guidance_scale > 1
@property
def guidance_scale(self):
return self._guidance_scale
@property
def num_timesteps(self):
return self._num_timesteps | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior.py |
@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_inference_steps: int = 25,
generator: Optional[Union[tor... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_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... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_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... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_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.
callback_on_step_... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior.py |
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class. | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior.py |
Examples:
Returns:
[`KandinskyPriorPipelineOutput`] or `tuple`
"""
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`ca... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior.py |
if isinstance(negative_prompt, str):
negative_prompt = [negative_prompt]
elif not isinstance(negative_prompt, list) and negative_prompt is not None:
raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")
# if the negative prompt i... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior.py |
# prior
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
embedding_dim = self.prior.config.embedding_dim
latents = self.prepare_latents(
(batch_size, embedding_dim),
prompt_embeds.dtype,
device,
... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior.py |
if self.do_classifier_free_guidance:
predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)
predicted_image_embedding = predicted_image_embedding_uncond + self.guidance_scale * (
predicted_image_embedding_text - predicted... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior.py |
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = callback_outputs.pop... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior.py |
# if negative prompt has been defined, we retrieve split the image embedding into two
if negative_prompt is None:
zero_embeds = self.get_zero_embed(latents.shape[0], device=latents.device)
else:
image_embeddings, zero_embeds = image_embeddings.chunk(2)
self.maybe_free_mo... | 187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior.py |
class KandinskyV22Img2ImgPipeline(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, runnin... | 188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py |
self.register_modules(
unet=unet,
scheduler=scheduler,
movq=movq,
)
self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
# Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.KandinskyImg2ImgPipeline.get_timesteps
def ... | 188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py |
def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"... | 188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py |
elif isinstance(generator, list):
init_latents = [
self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
]
init_latents = torch.cat(init_latents, dim=0)
else:
init_latents = self.movq... | 188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py |
@property
def num_timesteps(self):
return self._num_timesteps
@torch.no_grad()
def __call__(
self,
image_embeds: Union[torch.Tensor, List[torch.Tensor]],
image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]],
negative_image_embeds: Un... | 188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py |
Args:
image_embeds (`torch.Tensor` or `List[torch.Tensor]`):
The clip image embeddings for text prompt, that will be used to condition the image generation.
image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]... | 188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py |
be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
negative_image_embeds (`torch.Tensor` or `List[torch.Tensor]`):
The clip image embeddings for negative tex... | 188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py |
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 scale is enabled by setting `guidance_scale >
1`. Highe... | 188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py |
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"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.
... | 188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py |
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class. | 188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py |
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
callback = kwargs.pop("callback", None)
callback_steps = kwargs.pop("callback_steps", None)
if callback is not None:
deprecate(
"callback",
"1.0.0",
... | 188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py |
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in cal... | 188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py |
image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(
dtype=self.unet.dtype, device=device
)
if not isinstance(image, list):
image = [image]
if not all(isinstance(i, (PIL.Image.Image, torch.Tensor)) for i in image):
raise ValueErr... | 188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py |
latents = self.movq.encode(image)["latents"]
latents = latents.repeat_interleave(num_images_per_prompt, dim=0)
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = tim... | 188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py |
added_cond_kwargs = {"image_embeds": image_embeds}
noise_pred = self.unet(
sample=latent_model_input,
timestep=t,
encoder_hidden_states=None,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
... | 188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py |
if not (
hasattr(self.scheduler.config, "variance_type")
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)
# compute the previous noisy sample x_t -> x_t-1
... | 188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py |
latents = callback_outputs.pop("latents", latents)
image_embeds = callback_outputs.pop("image_embeds", image_embeds)
negative_image_embeds = callback_outputs.pop("negative_image_embeds", negative_image_embeds)
if callback is not None and i % callback_steps == 0:
... | 188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py |
if not output_type == "latent":
# post-processing
image = self.movq.decode(latents, force_not_quantize=True)["sample"]
if output_type in ["np", "pil"]:
image = image * 0.5 + 0.5
image = image.clamp(0, 1)
image = image.cpu().permute(0, 2... | 188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py |
class KandinskyV22PriorEmb2EmbPipeline(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, runnin... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.py |
model_cpu_offload_seq = "text_encoder->image_encoder->prior"
_exclude_from_cpu_offload = ["prior"]
def __init__(
self,
prior: PriorTransformer,
image_encoder: CLIPVisionModelWithProjection,
text_encoder: CLIPTextModelWithProjection,
tokenizer: CLIPTokenizer,
sche... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.py |
return timesteps, num_inference_steps - t_start
@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,
... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.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`, *... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.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`.
... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.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 ... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.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... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.py |
elif isinstance(cond, (PIL.Image.Image, torch.Tensor)):
image_emb = self._encode_image(
cond, device=device, num_images_per_prompt=num_images_per_prompt
).unsqueeze(0)
else:
raise ValueError(
f"`images_and_prompts` can ... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.py |
def _encode_image(
self,
image: Union[torch.Tensor, List[PIL.Image.Image]],
device,
num_images_per_prompt,
):
if not isinstance(image, torch.Tensor):
image = self.image_processor(image, return_tensors="pt").pixel_values.to(
dtype=self.image_encoder... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.py |
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
additional_image_per_prompt = batch_size // init_latents.shape[0]
init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
elif batch_size > init_latents.shape[0] and batch_size % init... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.py |
# Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_prior.KandinskyPriorPipeline.get_zero_embed
def get_zero_embed(self, batch_size=1, device=None):
device = device or self.device
zero_img = torch.zeros(1, 3, self.image_encoder.config.image_size, self.image_encoder.config.image_size).to(
... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.py |
# Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_prior.KandinskyPriorPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
):
batch_size = len(prompt) if is... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.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 ... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.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) | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.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 `... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.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)... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.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, ... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.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]],
image: Union[torch.Tensor, List[torch.Tensor], PIL.Image.Image, List[PIL.Image.Image]],
strength: fl... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to transform the reference `emb`. Must be between 0 and 1. `image`
will be u... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.py |
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.py |
output_type (`str`, *optional*, defaults to `"pt"`):
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.ImagePip... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.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... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.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
)
if not isinstance(image, List):
image = [image]
i... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.py |
# prior
self.scheduler.set_timesteps(num_inference_steps, device=device)
latents = image_embeds
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size)
latents = self.prepare_latents(
... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.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... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.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... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.py |
if output_type == "np":
image_embeddings = image_embeddings.cpu().numpy()
zero_embeds = zero_embeds.cpu().numpy()
if not return_dict:
return (image_embeddings, zero_embeds)
return KandinskyPriorPipelineOutput(image_embeds=image_embeddings, negative_image_embeds=zero... | 189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.py |
class VoidNeRFModel(nn.Module):
"""
Implements the default empty space model where all queries are rendered as background.
"""
def __init__(self, background, channel_scale=255.0):
super().__init__()
background = nn.Parameter(torch.from_numpy(np.array(background)).to(dtype=torch.float32)... | 190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
class VolumeRange:
t0: torch.Tensor
t1: torch.Tensor
intersected: torch.Tensor
def __post_init__(self):
assert self.t0.shape == self.t1.shape == self.intersected.shape
def partition(self, ts):
"""
Partitions t0 and t1 into n_samples intervals.
Args:
ts:... | 191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
class BoundingBoxVolume(nn.Module):
"""
Axis-aligned bounding box defined by the two opposite corners.
"""
def __init__(
self,
*,
bbox_min,
bbox_max,
min_dist: float = 0.0,
min_t_range: float = 1e-3,
):
"""
Args:
bbox_min: ... | 192 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
def intersect(
self,
origin: torch.Tensor,
direction: torch.Tensor,
t0_lower: Optional[torch.Tensor] = None,
epsilon=1e-6,
):
"""
Args:
origin: [batch_size, *shape, 3]
direction: [batch_size, *shape, 3]
t0_lower: Optional [b... | 192 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
def _safe_divide(a, b, epsilon=1e-6):
return a / torch.where(b < 0, b - epsilon, b + epsilon)
ts = _safe_divide(bbox - origin[..., None, :], direction[..., None, :], epsilon=epsilon) | 192 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
# Cases to think about:
#
# 1. t1 <= t0: the ray does not pass through the AABB.
# 2. t0 < t1 <= 0: the ray intersects but the BB is behind the origin.
# 3. t0 <= 0 <= t1: the ray starts from inside the BB
# 4. 0 <= t0 < t1: the ray is not inside and intersects with the B... | 192 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
return VolumeRange(t0=t0, t1=t1, intersected=intersected) | 192 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
class StratifiedRaySampler(nn.Module):
"""
Instead of fixed intervals, a sample is drawn uniformly at random from each interval.
"""
def __init__(self, depth_mode: str = "linear"):
"""
:param depth_mode: linear samples ts linearly in depth. harmonic ensures
closer points are... | 193 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
def sample(
self,
t0: torch.Tensor,
t1: torch.Tensor,
n_samples: int,
epsilon: float = 1e-3,
) -> torch.Tensor:
"""
Args:
t0: start time has shape [batch_size, *shape, 1]
t1: finish time has shape [batch_size, *shape, 1]
n_s... | 193 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
if self.depth_mode == "linear":
ts = t0 * (1.0 - ts) + t1 * ts
elif self.depth_mode == "geometric":
ts = (t0.clamp(epsilon).log() * (1.0 - ts) + t1.clamp(epsilon).log() * ts).exp()
elif self.depth_mode == "harmonic":
# The original NeRF recommends this interpolation s... | 193 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
class ImportanceRaySampler(nn.Module):
"""
Given the initial estimate of densities, this samples more from regions/bins expected to have objects.
"""
def __init__(
self,
volume_range: VolumeRange,
ts: torch.Tensor,
weights: torch.Tensor,
blur_pool: bool = False,
... | 194 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
@torch.no_grad()
def sample(self, t0: torch.Tensor, t1: torch.Tensor, n_samples: int) -> torch.Tensor:
"""
Args:
t0: start time has shape [batch_size, *shape, 1]
t1: finish time has shape [batch_size, *shape, 1]
n_samples: number of ts to sample
Return:
... | 194 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
weights = self.weights
if self.blur_pool:
padded = torch.cat([weights[..., :1, :], weights, weights[..., -1:, :]], dim=-2)
maxes = torch.maximum(padded[..., :-1, :], padded[..., 1:, :])
weights = 0.5 * (maxes[..., :-1, :] + maxes[..., 1:, :])
weights = weights + self.... | 194 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
class MeshDecoderOutput(BaseOutput):
"""
A 3D triangle mesh with optional data at the vertices and faces.
Args:
verts (`torch.Tensor` of shape `(N, 3)`):
array of vertext coordinates
faces (`torch.Tensor` of shape `(N, 3)`):
array of triangles, pointing to indices in... | 195 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
class MeshDecoder(nn.Module):
"""
Construct meshes from Signed distance functions (SDFs) using marching cubes method
"""
def __init__(self):
super().__init__()
cases = torch.zeros(256, 5, 3, dtype=torch.long)
masks = torch.zeros(256, 5, dtype=torch.bool)
self.register_b... | 196 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
:param field: a 3D tensor of field values, where negative values correspond
to the outside of the shape. The dimensions correspond to the x, y, and z directions, respectively.
:param min_point: a tensor of shape [3] containing the point corresponding
to (0, 0, 0) in t... | 196 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
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