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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 number of images to generate per prompt.
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 scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
num_inference_steps (`int`, *optional*, defaults to 100): | 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):
The width in pixels of the generated image.
prior_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 scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality. | 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*):
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
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`.
output_type (`str`, *optional*, defaults to `"pil"`): | 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.
prior_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: `prior_callback_on_step_end(self: DiffusionPipeline, step: int, timestep:
int, callback_kwargs: Dict)`.
prior_callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `prior_callback_on_step_end` function. The tensors specified in the
list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in | 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: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
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. | 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",
"1.0.0",
"Passing `prior_callback` as an input argument to `__call__` is deprecated, consider use `prior_callback_on_step_end`",
)
if kwargs.get("prior_callback_steps", None) is not None:
deprecate(
"prior_callback_steps",
"1.0.0",
"Passing `prior_callback_steps` as an input argument to `__call__` is deprecated, consider use `prior_callback_on_step_end`",
)
prior_kwargs["callback_steps"] = kwargs.pop("prior_callback_steps") | 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_guidance_scale,
output_type="pt",
return_dict=False,
callback_on_step_end=prior_callback_on_step_end,
callback_on_step_end_tensor_inputs=prior_callback_on_step_end_tensor_inputs,
**prior_kwargs,
)
image_embeds = prior_outputs[0]
negative_image_embeds = prior_outputs[1]
prompt = [prompt] if not isinstance(prompt, (list, tuple)) else prompt
image = [image] if isinstance(image, PIL.Image.Image) else image
mask_image = [mask_image] if isinstance(mask_image, PIL.Image.Image) else mask_image | 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
):
image = (image_embeds.shape[0] // len(image)) * image
if (
isinstance(mask_image, (list, tuple))
and len(mask_image) < image_embeds.shape[0]
and image_embeds.shape[0] % len(mask_image) == 0
):
mask_image = (image_embeds.shape[0] // len(mask_image)) * mask_image | 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=generator,
guidance_scale=guidance_scale,
output_type=output_type,
return_dict=return_dict,
callback_on_step_end=callback_on_step_end,
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
**kwargs,
)
self.maybe_free_model_hooks()
return outputs | 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 particular device, etc.) | 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-encoder.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
scheduler ([`UnCLIPScheduler`]):
A scheduler to be used in combination with `prior` to generate image embedding.
image_processor ([`CLIPImageProcessor`]):
A image_processor to be used to preprocess image from clip.
"""
model_cpu_offload_seq = "text_encoder->image_encoder->prior"
_exclude_from_cpu_offload = ["prior"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "text_encoder_hidden_states", "text_mask"] | 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__()
self.register_modules(
prior=prior,
text_encoder=text_encoder,
tokenizer=tokenizer,
scheduler=scheduler,
image_encoder=image_encoder,
image_processor=image_processor,
) | 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: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
negative_prior_prompt: Optional[str] = None,
negative_prompt: str = "",
guidance_scale: float = 4.0,
device=None,
):
"""
Function invoked when using the prior pipeline for interpolation. | 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`, *optional*, defaults to 1):
The number of images to generate per prompt.
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*):
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*): | 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`.
negative_prior_prompt (`str`, *optional*):
The prompt not to guide the prior diffusion process. Ignored when not using guidance (i.e., ignored if
`guidance_scale` is less than `1`).
negative_prompt (`str` or `List[str]`, *optional*):
The prompt not to guide the image generation. Ignored when not using guidance (i.e., ignored if
`guidance_scale` is less than `1`).
guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). | 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 at the expense of lower image quality. | 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` contains {len(weights)} items - they should be lists of same length"
)
image_embeddings = []
for cond, weight in zip(images_and_prompts, weights):
if isinstance(cond, str):
image_emb = self(
cond,
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,
).image_embeds.unsqueeze(0) | 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(dtype=self.image_encoder.dtype, device=device)
)
image_emb = self.image_encoder(cond)["image_embeds"].repeat(num_images_per_prompt, 1).unsqueeze(0)
else:
raise ValueError(
f"`images_and_prompts` can only contains elements to be of type `str`, `PIL.Image.Image` or `torch.Tensor` but is {type(cond)}"
)
image_embeddings.append(image_emb * weight)
image_emb = torch.cat(image_embeddings).sum(dim=0) | 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,
)
zero_image_emb = out_zero.negative_image_embeds if negative_prompt == "" else out_zero.image_embeds
return KandinskyPriorPipelineOutput(image_embeds=image_emb, negative_image_embeds=zero_image_emb) | 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 latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
latents = latents * scheduler.init_noise_sigma
return latents | 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(
device=device, dtype=self.image_encoder.dtype
)
zero_image_emb = self.image_encoder(zero_img)["image_embeds"]
zero_image_emb = zero_image_emb.repeat(batch_size, 1)
return zero_image_emb | 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 isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
text_mask = text_inputs.attention_mask.bool().to(device)
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids | 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 because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
text_encoder_output = self.text_encoder(text_input_ids.to(device))
prompt_embeds = text_encoder_output.text_embeds
text_encoder_hidden_states = text_encoder_output.last_hidden_state | 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 `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 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)
negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))
negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds
uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state
# 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.repeat(1, num_images_per_prompt)
negative_prompt_embeds = negative_prompt_embeds.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 |
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, -1
)
uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
text_mask = torch.cat([uncond_text_mask, text_mask]) | 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[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
guidance_scale: float = 4.0,
output_type: Optional[str] = "pt", # pt only
return_dict: bool = True,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
):
"""
Function invoked when calling the pipeline for generation. | 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 `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
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*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic. | 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 sampling using the supplied random `generator`.
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 scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
output_type (`str`, *optional*, defaults to `"pt"`): | 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_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: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list | 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"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
)
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)}") | 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 is defined we double the batch size to
# directly retrieve the negative prompt embedding
if negative_prompt is not None:
prompt = prompt + negative_prompt
negative_prompt = 2 * negative_prompt
device = self._execution_device
batch_size = len(prompt)
batch_size = batch_size * num_images_per_prompt
self._guidance_scale = guidance_scale
prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(
prompt, device, num_images_per_prompt, self.do_classifier_free_guidance, negative_prompt
) | 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,
generator,
latents,
self.scheduler,
)
self._num_timesteps = len(timesteps)
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
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 | 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_image_embedding_uncond
)
if i + 1 == timesteps.shape[0]:
prev_timestep = None
else:
prev_timestep = timesteps[i + 1]
latents = self.scheduler.step(
predicted_image_embedding,
timestep=t,
sample=latents,
generator=generator,
prev_timestep=prev_timestep,
).prev_sample | 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("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
text_encoder_hidden_states = callback_outputs.pop(
"text_encoder_hidden_states", text_encoder_hidden_states
)
text_mask = callback_outputs.pop("text_mask", text_mask)
if XLA_AVAILABLE:
xm.mark_step()
latents = self.prior.post_process_latents(latents)
image_embeddings = latents | 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_model_hooks()
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_dict:
return (image_embeddings, zero_embeds)
return KandinskyPriorPipelineOutput(image_embeds=image_embeddings, negative_image_embeds=zero_embeds) | 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, running on a particular device, etc.)
Args:
scheduler ([`DDIMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ Decoder to generate the image from the latents.
"""
model_cpu_offload_seq = "unet->movq"
_callback_tensor_inputs = ["latents", "image_embeds", "negative_image_embeds"]
def __init__(
self,
unet: UNet2DConditionModel,
scheduler: DDPMScheduler,
movq: VQModel,
):
super().__init__() | 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 get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start | 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)}"
)
image = image.to(device=device, dtype=dtype)
batch_size = batch_size * num_images_per_prompt
if image.shape[1] == 4:
init_latents = image
else:
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."
) | 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.encode(image).latent_dist.sample(generator)
init_latents = self.movq.config.scaling_factor * init_latents
init_latents = torch.cat([init_latents], dim=0)
shape = init_latents.shape
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# get latents
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
latents = init_latents
return latents
@property
def guidance_scale(self):
return self._guidance_scale
@property
def do_classifier_free_guidance(self):
return self._guidance_scale > 1 | 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: Union[torch.Tensor, List[torch.Tensor]],
height: int = 512,
width: int = 512,
num_inference_steps: int = 100,
guidance_scale: float = 4.0,
strength: float = 0.3,
num_images_per_prompt: int = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
**kwargs,
):
"""
Function invoked when calling the pipeline for generation. | 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]`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process. Can also accept image latents as `image`, if passing latents directly, it will not be encoded
again.
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
will be used as a starting point, adding more noise to it the larger the `strength`. The number of
denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will | 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 text prompt, will be used to condition the image generation.
height (`int`, *optional*, defaults to 512):
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 higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 4.0): | 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`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
output_type (`str`, *optional*, defaults to `"pil"`): | 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.
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: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list | 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",
"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
)
if callback_steps is not None:
deprecate(
"callback_steps",
"1.0.0",
"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
) | 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 callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
)
device = self._execution_device
self._guidance_scale = guidance_scale
if isinstance(image_embeds, list):
image_embeds = torch.cat(image_embeds, dim=0)
batch_size = image_embeds.shape[0]
if isinstance(negative_image_embeds, list):
negative_image_embeds = torch.cat(negative_image_embeds, dim=0)
if self.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) | 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 ValueError(
f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support PIL image and pytorch tensor"
)
image = torch.cat([prepare_image(i, width, height) for i in image], dim=0)
image = image.to(dtype=image_embeds.dtype, device=device) | 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 = timesteps[:1].repeat(batch_size * num_images_per_prompt)
height, width = downscale_height_and_width(height, width, self.movq_scale_factor)
latents = self.prepare_latents(
latents, latent_timestep, batch_size, num_images_per_prompt, image_embeds.dtype, device, generator
)
self._num_timesteps = len(timesteps)
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents | 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]
if self.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 + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1) | 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
latents = self.scheduler.step(
noise_pred,
t,
latents,
generator=generator,
)[0]
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) | 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:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
if XLA_AVAILABLE:
xm.mark_step()
if output_type not in ["pt", "np", "pil", "latent"]:
raise ValueError(
f"Only the output types `pt`, `pil` ,`np` and `latent` are supported not output_type={output_type}"
) | 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, 3, 1).float().numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
else:
image = latents
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image) | 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, running on a particular device, etc.)
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-encoder.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
scheduler ([`UnCLIPScheduler`]):
A scheduler to be used in combination with `prior` to generate image embedding.
""" | 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,
scheduler: UnCLIPScheduler,
image_processor: CLIPImageProcessor,
):
super().__init__()
self.register_modules(
prior=prior,
text_encoder=text_encoder,
tokenizer=tokenizer,
scheduler=scheduler,
image_encoder=image_encoder,
image_processor=image_processor,
)
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start:] | 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,
num_inference_steps: int = 25,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
negative_prior_prompt: Optional[str] = None,
negative_prompt: str = "",
guidance_scale: float = 4.0,
device=None,
):
"""
Function invoked when using the prior pipeline for interpolation. | 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`, *optional*, defaults to 1):
The number of images to generate per prompt.
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*):
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*): | 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`.
negative_prior_prompt (`str`, *optional*):
The prompt not to guide the prior diffusion process. Ignored when not using guidance (i.e., ignored if
`guidance_scale` is less than `1`).
negative_prompt (`str` or `List[str]`, *optional*):
The prompt not to guide the image generation. Ignored when not using guidance (i.e., ignored if
`guidance_scale` is less than `1`).
guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). | 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 at the expense of lower image quality. | 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` contains {len(weights)} items - they should be lists of same length"
)
image_embeddings = []
for cond, weight in zip(images_and_prompts, weights):
if isinstance(cond, str):
image_emb = self(
cond,
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,
).image_embeds.unsqueeze(0) | 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 only contains elements to be of type `str`, `PIL.Image.Image` or `torch.Tensor` but is {type(cond)}"
)
image_embeddings.append(image_emb * weight)
image_emb = torch.cat(image_embeddings).sum(dim=0)
return KandinskyPriorPipelineOutput(image_embeds=image_emb, negative_image_embeds=torch.randn_like(image_emb)) | 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.dtype, device=device
)
image_emb = self.image_encoder(image)["image_embeds"] # B, D
image_emb = image_emb.repeat_interleave(num_images_per_prompt, dim=0)
image_emb.to(device=device)
return image_emb
def prepare_latents(self, emb, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
emb = emb.to(device=device, dtype=dtype)
batch_size = batch_size * num_images_per_prompt
init_latents = emb | 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_latents.shape[0] != 0:
raise ValueError(
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
)
else:
init_latents = torch.cat([init_latents], dim=0)
shape = init_latents.shape
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# get latents
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
latents = init_latents
return latents | 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(
device=device, dtype=self.image_encoder.dtype
)
zero_image_emb = self.image_encoder(zero_img)["image_embeds"]
zero_image_emb = zero_image_emb.repeat(batch_size, 1)
return zero_image_emb | 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 isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
text_mask = text_inputs.attention_mask.bool().to(device)
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids | 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 because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
text_encoder_output = self.text_encoder(text_input_ids.to(device))
prompt_embeds = text_encoder_output.text_embeds
text_encoder_hidden_states = text_encoder_output.last_hidden_state | 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 `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 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)
negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))
negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds
uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state
# 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.repeat(1, num_images_per_prompt)
negative_prompt_embeds = negative_prompt_embeds.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 |
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, -1
)
uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
text_mask = torch.cat([uncond_text_mask, text_mask]) | 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: float = 0.3,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: int = 1,
num_inference_steps: int = 25,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
guidance_scale: float = 4.0,
output_type: Optional[str] = "pt", # pt only
return_dict: bool = True,
):
"""
Function invoked when calling the pipeline for generation. | 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 used as a starting point, adding more noise to it the larger the `strength`. The number of
denoising steps depends on the amount of noise initially added.
emb (`torch.Tensor`):
The image embedding.
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 number of images to generate per prompt.
num_inference_steps (`int`, *optional*, defaults to 100): | 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/torch.Generator.html)
to make generation deterministic.
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 scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality. | 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.ImagePipelineOutput`] instead of a plain tuple. | 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 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 is defined we double the batch size to
# directly retrieve the negative prompt embedding
if negative_prompt is not None:
prompt = prompt + negative_prompt
negative_prompt = 2 * negative_prompt
device = self._execution_device
batch_size = len(prompt)
batch_size = batch_size * num_images_per_prompt | 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]
if isinstance(image[0], torch.Tensor):
image = torch.cat(image, dim=0)
if isinstance(image, torch.Tensor) and image.ndim == 2:
# allow user to pass image_embeds directly
image_embeds = image.repeat_interleave(num_images_per_prompt, dim=0)
elif isinstance(image, torch.Tensor) and image.ndim != 4:
raise ValueError(
f" if pass `image` as pytorch tensor, or a list of pytorch tensor, please make sure each tensor has shape [batch_size, channels, height, width], currently {image[0].unsqueeze(0).shape}"
)
else:
image_embeds = self._encode_image(image, device, num_images_per_prompt) | 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(
latents,
latent_timestep,
batch_size // num_images_per_prompt,
num_images_per_prompt,
prompt_embeds.dtype,
device,
generator,
)
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else 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_classifier_free_guidance:
predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)
predicted_image_embedding = predicted_image_embedding_uncond + guidance_scale * (
predicted_image_embedding_text - predicted_image_embedding_uncond
)
if i + 1 == timesteps.shape[0]:
prev_timestep = None
else:
prev_timestep = timesteps[i + 1] | 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 = self.prior.post_process_latents(latents)
image_embeddings = latents
# 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_model_hooks()
if output_type not in ["pt", "np"]:
raise ValueError(f"Only the output types `pt` and `np` are supported not output_type={output_type}") | 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_embeds) | 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) / channel_scale)
self.register_buffer("background", background)
def forward(self, position):
background = self.background[None].to(position.device)
shape = position.shape[:-1]
ones = [1] * (len(shape) - 1)
n_channels = background.shape[-1]
background = torch.broadcast_to(background.view(background.shape[0], *ones, n_channels), [*shape, n_channels])
return background | 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: [batch_size, *shape, n_samples, 1]
Return:
lower: [batch_size, *shape, n_samples, 1] upper: [batch_size, *shape, n_samples, 1] delta: [batch_size,
*shape, n_samples, 1]
where
ts \\in [lower, upper] deltas = upper - lower
"""
mids = (ts[..., 1:, :] + ts[..., :-1, :]) * 0.5
lower = torch.cat([self.t0[..., None, :], mids], dim=-2)
upper = torch.cat([mids, self.t1[..., None, :]], dim=-2)
delta = upper - lower
assert lower.shape == upper.shape == delta.shape == ts.shape
return lower, upper, delta | 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: the left/bottommost corner of the bounding box
bbox_max: the other corner of the bounding box
min_dist: all rays should start at least this distance away from the origin.
"""
super().__init__()
self.min_dist = min_dist
self.min_t_range = min_t_range
self.bbox_min = torch.tensor(bbox_min)
self.bbox_max = torch.tensor(bbox_max)
self.bbox = torch.stack([self.bbox_min, self.bbox_max])
assert self.bbox.shape == (2, 3)
assert min_dist >= 0.0
assert min_t_range > 0.0 | 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 [batch_size, *shape, 1] lower bound of t0 when intersecting this volume.
params: Optional meta parameters in case Volume is parametric
epsilon: to stabilize calculations
Return:
A tuple of (t0, t1, intersected) where each has a shape [batch_size, *shape, 1]. If a ray intersects with
the volume, `o + td` is in the volume for all t in [t0, t1]. If the volume is bounded, t1 is guaranteed to
be on the boundary of the volume.
"""
batch_size, *shape, _ = origin.shape
ones = [1] * len(shape)
bbox = self.bbox.view(1, *ones, 2, 3).to(origin.device) | 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 BB twice.
#
# 1 and 4 are clearly handled from t0 < t1 below.
# Making t0 at least min_dist (>= 0) takes care of 2 and 3.
t0 = ts.min(dim=-2).values.max(dim=-1, keepdim=True).values.clamp(self.min_dist)
t1 = ts.max(dim=-2).values.min(dim=-1, keepdim=True).values
assert t0.shape == t1.shape == (batch_size, *shape, 1)
if t0_lower is not None:
assert t0.shape == t0_lower.shape
t0 = torch.maximum(t0, t0_lower)
intersected = t0 + self.min_t_range < t1
t0 = torch.where(intersected, t0, torch.zeros_like(t0))
t1 = torch.where(intersected, t1, torch.ones_like(t1)) | 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 sampled more densely.
"""
self.depth_mode = depth_mode
assert self.depth_mode in ("linear", "geometric", "harmonic") | 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_samples: number of ts to sample
Return:
sampled ts of shape [batch_size, *shape, n_samples, 1]
"""
ones = [1] * (len(t0.shape) - 1)
ts = torch.linspace(0, 1, n_samples).view(*ones, n_samples).to(t0.dtype).to(t0.device) | 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 scheme for
# spherical scenes, but there could be some weird edge cases when
# the observer crosses from the inner to outer volume.
ts = 1.0 / (1.0 / t0.clamp(epsilon) * (1.0 - ts) + 1.0 / t1.clamp(epsilon) * ts)
mids = 0.5 * (ts[..., 1:] + ts[..., :-1])
upper = torch.cat([mids, t1], dim=-1)
lower = torch.cat([t0, mids], dim=-1)
# yiyi notes: add a random seed here for testing, don't forget to remove
torch.manual_seed(0)
t_rand = torch.rand_like(ts)
ts = lower + (upper - lower) * t_rand
return ts.unsqueeze(-1) | 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,
alpha: float = 1e-5,
):
"""
Args:
volume_range: the range in which a ray intersects the given volume.
ts: earlier samples from the coarse rendering step
weights: discretized version of density * transmittance
blur_pool: if true, use 2-tap max + 2-tap blur filter from mip-NeRF.
alpha: small value to add to weights.
"""
self.volume_range = volume_range
self.ts = ts.clone().detach()
self.weights = weights.clone().detach()
self.blur_pool = blur_pool
self.alpha = alpha | 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:
sampled ts of shape [batch_size, *shape, n_samples, 1]
"""
lower, upper, _ = self.volume_range.partition(self.ts)
batch_size, *shape, n_coarse_samples, _ = self.ts.shape | 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.alpha
pmf = weights / weights.sum(dim=-2, keepdim=True)
inds = sample_pmf(pmf, n_samples)
assert inds.shape == (batch_size, *shape, n_samples, 1)
assert (inds >= 0).all() and (inds < n_coarse_samples).all()
t_rand = torch.rand(inds.shape, device=inds.device)
lower_ = torch.gather(lower, -2, inds)
upper_ = torch.gather(upper, -2, inds)
ts = lower_ + (upper_ - lower_) * t_rand
ts = torch.sort(ts, dim=-2).values
return ts | 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 verts.
vertext_channels (Dict):
vertext coordinates for each color channel
"""
verts: torch.Tensor
faces: torch.Tensor
vertex_channels: Dict[str, torch.Tensor] | 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_buffer("cases", cases)
self.register_buffer("masks", masks)
def forward(self, field: torch.Tensor, min_point: torch.Tensor, size: torch.Tensor):
"""
For a signed distance field, produce a mesh using marching cubes. | 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 the field.
:param size: a tensor of shape [3] containing the per-axis distance from the
(0, 0, 0) field corner and the (-1, -1, -1) field corner.
"""
assert len(field.shape) == 3, "input must be a 3D scalar field"
dev = field.device
cases = self.cases.to(dev)
masks = self.masks.to(dev)
min_point = min_point.to(dev)
size = size.to(dev)
grid_size = field.shape
grid_size_tensor = torch.tensor(grid_size).to(size) | 196 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
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