449 lines
20 KiB
Python
449 lines
20 KiB
Python
# Copyright 2025 The HuggingFace Team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from typing import Callable, Dict, List, Optional, Union
|
|
|
|
import numpy as np
|
|
import torch
|
|
from transformers import CLIPTextModel, CLIPTokenizer
|
|
|
|
from ...schedulers import DDPMWuerstchenScheduler
|
|
from ...utils import deprecate, is_torch_xla_available, logging, replace_example_docstring
|
|
from ...utils.torch_utils import randn_tensor
|
|
from ..pipeline_utils import DeprecatedPipelineMixin, DiffusionPipeline, ImagePipelineOutput
|
|
from .modeling_paella_vq_model import PaellaVQModel
|
|
from .modeling_wuerstchen_diffnext import WuerstchenDiffNeXt
|
|
|
|
|
|
if is_torch_xla_available():
|
|
import torch_xla.core.xla_model as xm
|
|
|
|
XLA_AVAILABLE = True
|
|
else:
|
|
XLA_AVAILABLE = False
|
|
|
|
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
|
|
|
|
|
EXAMPLE_DOC_STRING = """
|
|
Examples:
|
|
```py
|
|
>>> import torch
|
|
>>> from diffusers import WuerstchenPriorPipeline, WuerstchenDecoderPipeline
|
|
|
|
>>> prior_pipe = WuerstchenPriorPipeline.from_pretrained(
|
|
... "warp-ai/wuerstchen-prior", torch_dtype=torch.float16
|
|
... ).to("cuda")
|
|
>>> gen_pipe = WuerstchenDecoderPipeline.from_pretrain("warp-ai/wuerstchen", torch_dtype=torch.float16).to(
|
|
... "cuda"
|
|
... )
|
|
|
|
>>> prompt = "an image of a shiba inu, donning a spacesuit and helmet"
|
|
>>> prior_output = pipe(prompt)
|
|
>>> images = gen_pipe(prior_output.image_embeddings, prompt=prompt)
|
|
```
|
|
"""
|
|
|
|
|
|
class WuerstchenDecoderPipeline(DeprecatedPipelineMixin, DiffusionPipeline):
|
|
"""
|
|
Pipeline for generating images from the Wuerstchen model.
|
|
|
|
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:
|
|
tokenizer (`CLIPTokenizer`):
|
|
The CLIP tokenizer.
|
|
text_encoder (`CLIPTextModel`):
|
|
The CLIP text encoder.
|
|
decoder ([`WuerstchenDiffNeXt`]):
|
|
The WuerstchenDiffNeXt unet decoder.
|
|
vqgan ([`PaellaVQModel`]):
|
|
The VQGAN model.
|
|
scheduler ([`DDPMWuerstchenScheduler`]):
|
|
A scheduler to be used in combination with `prior` to generate image embedding.
|
|
latent_dim_scale (float, `optional`, defaults to 10.67):
|
|
Multiplier to determine the VQ latent space size from the image embeddings. If the image embeddings are
|
|
height=24 and width=24, the VQ latent shape needs to be height=int(24*10.67)=256 and
|
|
width=int(24*10.67)=256 in order to match the training conditions.
|
|
"""
|
|
|
|
model_cpu_offload_seq = "text_encoder->decoder->vqgan"
|
|
_callback_tensor_inputs = [
|
|
"latents",
|
|
"text_encoder_hidden_states",
|
|
"negative_prompt_embeds",
|
|
"image_embeddings",
|
|
]
|
|
|
|
def __init__(
|
|
self,
|
|
tokenizer: CLIPTokenizer,
|
|
text_encoder: CLIPTextModel,
|
|
decoder: WuerstchenDiffNeXt,
|
|
scheduler: DDPMWuerstchenScheduler,
|
|
vqgan: PaellaVQModel,
|
|
latent_dim_scale: float = 10.67,
|
|
) -> None:
|
|
super().__init__()
|
|
self.register_modules(
|
|
tokenizer=tokenizer,
|
|
text_encoder=text_encoder,
|
|
decoder=decoder,
|
|
scheduler=scheduler,
|
|
vqgan=vqgan,
|
|
)
|
|
self.register_to_config(latent_dim_scale=latent_dim_scale)
|
|
|
|
# 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
|
|
|
|
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
|
|
attention_mask = text_inputs.attention_mask
|
|
|
|
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
|
|
|
|
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]
|
|
attention_mask = attention_mask[:, : self.tokenizer.model_max_length]
|
|
|
|
text_encoder_output = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask.to(device))
|
|
text_encoder_hidden_states = text_encoder_output.last_hidden_state
|
|
text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
|
|
|
|
uncond_text_encoder_hidden_states = None
|
|
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
|
|
|
|
uncond_input = self.tokenizer(
|
|
uncond_tokens,
|
|
padding="max_length",
|
|
max_length=self.tokenizer.model_max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
negative_prompt_embeds_text_encoder_output = self.text_encoder(
|
|
uncond_input.input_ids.to(device), attention_mask=uncond_input.attention_mask.to(device)
|
|
)
|
|
|
|
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 = 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
|
|
)
|
|
# 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
|
|
return text_encoder_hidden_states, uncond_text_encoder_hidden_states
|
|
|
|
@property
|
|
def guidance_scale(self):
|
|
return self._guidance_scale
|
|
|
|
@property
|
|
def do_classifier_free_guidance(self):
|
|
return self._guidance_scale > 1
|
|
|
|
@property
|
|
def num_timesteps(self):
|
|
return self._num_timesteps
|
|
|
|
@torch.no_grad()
|
|
@replace_example_docstring(EXAMPLE_DOC_STRING)
|
|
def __call__(
|
|
self,
|
|
image_embeddings: Union[torch.Tensor, List[torch.Tensor]],
|
|
prompt: Union[str, List[str]] = None,
|
|
num_inference_steps: int = 12,
|
|
timesteps: Optional[List[float]] = None,
|
|
guidance_scale: float = 0.0,
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
|
num_images_per_prompt: int = 1,
|
|
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
|
latents: Optional[torch.Tensor] = 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.
|
|
|
|
Args:
|
|
image_embedding (`torch.Tensor` or `List[torch.Tensor]`):
|
|
Image Embeddings either extracted from an image or generated by a Prior Model.
|
|
prompt (`str` or `List[str]`):
|
|
The prompt or prompts to guide the image generation.
|
|
num_inference_steps (`int`, *optional*, defaults to 12):
|
|
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
|
expense of slower inference.
|
|
timesteps (`List[int]`, *optional*):
|
|
Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
|
|
timesteps are used. Must be in descending order.
|
|
guidance_scale (`float`, *optional*, defaults to 0.0):
|
|
Guidance scale as defined in [Classifier-Free Diffusion
|
|
Guidance](https://huggingface.co/papers/2207.12598). `decoder_guidance_scale` is defined as `w` of
|
|
equation 2. of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by
|
|
setting `decoder_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.
|
|
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 `decoder_guidance_scale` is less than `1`).
|
|
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.
|
|
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"`):
|
|
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
|
|
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.
|
|
|
|
Examples:
|
|
|
|
Returns:
|
|
[`~pipelines.ImagePipelineOutput`] or `tuple` [`~pipelines.ImagePipelineOutput`] if `return_dict` is True,
|
|
otherwise a `tuple`. When returning a tuple, the first element is a list with the generated image
|
|
embeddings.
|
|
"""
|
|
|
|
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`",
|
|
)
|
|
|
|
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]}"
|
|
)
|
|
|
|
# 0. Define commonly used variables
|
|
device = self._execution_device
|
|
dtype = self.decoder.dtype
|
|
self._guidance_scale = guidance_scale
|
|
|
|
# 1. Check inputs. Raise error if not correct
|
|
if not isinstance(prompt, list):
|
|
if isinstance(prompt, str):
|
|
prompt = [prompt]
|
|
else:
|
|
raise TypeError(f"'prompt' must be of type 'list' or 'str', but got {type(prompt)}.")
|
|
|
|
if self.do_classifier_free_guidance:
|
|
if negative_prompt is not None and not isinstance(negative_prompt, list):
|
|
if isinstance(negative_prompt, str):
|
|
negative_prompt = [negative_prompt]
|
|
else:
|
|
raise TypeError(
|
|
f"'negative_prompt' must be of type 'list' or 'str', but got {type(negative_prompt)}."
|
|
)
|
|
|
|
if isinstance(image_embeddings, list):
|
|
image_embeddings = torch.cat(image_embeddings, dim=0)
|
|
if isinstance(image_embeddings, np.ndarray):
|
|
image_embeddings = torch.Tensor(image_embeddings, device=device).to(dtype=dtype)
|
|
if not isinstance(image_embeddings, torch.Tensor):
|
|
raise TypeError(
|
|
f"'image_embeddings' must be of type 'torch.Tensor' or 'np.array', but got {type(image_embeddings)}."
|
|
)
|
|
|
|
if not isinstance(num_inference_steps, int):
|
|
raise TypeError(
|
|
f"'num_inference_steps' must be of type 'int', but got {type(num_inference_steps)}\
|
|
In Case you want to provide explicit timesteps, please use the 'timesteps' argument."
|
|
)
|
|
|
|
# 2. Encode caption
|
|
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
|
|
prompt,
|
|
device,
|
|
image_embeddings.size(0) * num_images_per_prompt,
|
|
self.do_classifier_free_guidance,
|
|
negative_prompt,
|
|
)
|
|
text_encoder_hidden_states = (
|
|
torch.cat([prompt_embeds, negative_prompt_embeds]) if negative_prompt_embeds is not None else prompt_embeds
|
|
)
|
|
effnet = (
|
|
torch.cat([image_embeddings, torch.zeros_like(image_embeddings)])
|
|
if self.do_classifier_free_guidance
|
|
else image_embeddings
|
|
)
|
|
|
|
# 3. Determine latent shape of latents
|
|
latent_height = int(image_embeddings.size(2) * self.config.latent_dim_scale)
|
|
latent_width = int(image_embeddings.size(3) * self.config.latent_dim_scale)
|
|
latent_features_shape = (image_embeddings.size(0) * num_images_per_prompt, 4, latent_height, latent_width)
|
|
|
|
# 4. Prepare and set timesteps
|
|
if timesteps is not None:
|
|
self.scheduler.set_timesteps(timesteps=timesteps, device=device)
|
|
timesteps = self.scheduler.timesteps
|
|
num_inference_steps = len(timesteps)
|
|
else:
|
|
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
|
timesteps = self.scheduler.timesteps
|
|
|
|
# 5. Prepare latents
|
|
latents = self.prepare_latents(latent_features_shape, dtype, device, generator, latents, self.scheduler)
|
|
|
|
# 6. Run denoising loop
|
|
self._num_timesteps = len(timesteps[:-1])
|
|
for i, t in enumerate(self.progress_bar(timesteps[:-1])):
|
|
ratio = t.expand(latents.size(0)).to(dtype)
|
|
# 7. Denoise latents
|
|
predicted_latents = self.decoder(
|
|
torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents,
|
|
r=torch.cat([ratio] * 2) if self.do_classifier_free_guidance else ratio,
|
|
effnet=effnet,
|
|
clip=text_encoder_hidden_states,
|
|
)
|
|
|
|
# 8. Check for classifier free guidance and apply it
|
|
if self.do_classifier_free_guidance:
|
|
predicted_latents_text, predicted_latents_uncond = predicted_latents.chunk(2)
|
|
predicted_latents = torch.lerp(predicted_latents_uncond, predicted_latents_text, self.guidance_scale)
|
|
|
|
# 9. Renoise latents to next timestep
|
|
latents = self.scheduler.step(
|
|
model_output=predicted_latents,
|
|
timestep=ratio,
|
|
sample=latents,
|
|
generator=generator,
|
|
).prev_sample
|
|
|
|
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)
|
|
image_embeddings = callback_outputs.pop("image_embeddings", image_embeddings)
|
|
text_encoder_hidden_states = callback_outputs.pop(
|
|
"text_encoder_hidden_states", text_encoder_hidden_states
|
|
)
|
|
|
|
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`, `np`, `pil` and `latent` are supported not output_type={output_type}"
|
|
)
|
|
|
|
if not output_type == "latent":
|
|
# 10. Scale and decode the image latents with vq-vae
|
|
latents = self.vqgan.config.scale_factor * latents
|
|
images = self.vqgan.decode(latents).sample.clamp(0, 1)
|
|
if output_type == "np":
|
|
images = images.permute(0, 2, 3, 1).cpu().float().numpy()
|
|
elif output_type == "pil":
|
|
images = images.permute(0, 2, 3, 1).cpu().float().numpy()
|
|
images = self.numpy_to_pil(images)
|
|
else:
|
|
images = latents
|
|
|
|
# Offload all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return images
|
|
return ImagePipelineOutput(images)
|