kxhit
update
5f093a6
# Copyright 2023 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 List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNet2DConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from .text_encoder import MultilingualCLIP
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
>>> import torch
>>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
>>> pipe_prior.to("cuda")
>>> prompt = "red cat, 4k photo"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> negative_image_emb = out.negative_image_embeds
>>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
>>> pipe.to("cuda")
>>> image = pipe(
... prompt,
... image_embeds=image_emb,
... negative_image_embeds=negative_image_emb,
... height=768,
... width=768,
... num_inference_steps=100,
... ).images
>>> image[0].save("cat.png")
```
"""
def get_new_h_w(h, w, scale_factor=8):
new_h = h // scale_factor**2
if h % scale_factor**2 != 0:
new_h += 1
new_w = w // scale_factor**2
if w % scale_factor**2 != 0:
new_w += 1
return new_h * scale_factor, new_w * scale_factor
class KandinskyPipeline(DiffusionPipeline):
"""
Pipeline for text-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:
text_encoder ([`MultilingualCLIP`]):
Frozen text-encoder.
tokenizer ([`XLMRobertaTokenizer`]):
Tokenizer of class
scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):
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.
"""
def __init__(
self,
text_encoder: MultilingualCLIP,
tokenizer: XLMRobertaTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, DDPMScheduler],
movq: VQModel,
):
super().__init__()
self.register_modules(
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
movq=movq,
)
self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
# 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",
truncation=True,
max_length=77,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
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.to(device)
text_mask = text_inputs.attention_mask.to(device)
prompt_embeds, text_encoder_hidden_states = self.text_encoder(
input_ids=text_input_ids, attention_mask=text_mask
)
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)
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=77,
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
uncond_text_input_ids = uncond_input.input_ids.to(device)
uncond_text_mask = uncond_input.attention_mask.to(device)
negative_prompt_embeds, uncond_text_encoder_hidden_states = self.text_encoder(
input_ids=uncond_text_input_ids, attention_mask=uncond_text_mask
)
# 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)
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])
return prompt_embeds, text_encoder_hidden_states, text_mask
def enable_sequential_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's
models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only
when their specific submodule has its `forward` method called.
"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
device = torch.device(f"cuda:{gpu_id}")
models = [
self.unet,
self.text_encoder,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(cpu_offloaded_model, device)
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def _execution_device(self):
r"""
Returns the device on which the pipeline's models will be executed. After calling
`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
hooks.
"""
if not hasattr(self.unet, "_hf_hook"):
return self.device
for module in self.unet.modules():
if (
hasattr(module, "_hf_hook")
and hasattr(module._hf_hook, "execution_device")
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device)
return self.device
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
image_embeds: Union[torch.FloatTensor, List[torch.FloatTensor]],
negative_image_embeds: Union[torch.FloatTensor, List[torch.FloatTensor]],
negative_prompt: Optional[Union[str, List[str]]] = None,
height: int = 512,
width: int = 512,
num_inference_steps: int = 100,
guidance_scale: float = 4.0,
num_images_per_prompt: int = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
):
"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
The clip image embeddings for text prompt, that will be used to condition the image generation.
negative_image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
The clip image embeddings for negative text prompt, will be used to condition 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`).
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):
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.
latents (`torch.FloatTensor`, *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.
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
device = self._execution_device
batch_size = batch_size * num_images_per_prompt
do_classifier_free_guidance = guidance_scale > 1.0
prompt_embeds, text_encoder_hidden_states, _ = self._encode_prompt(
prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
if isinstance(image_embeds, list):
image_embeds = torch.cat(image_embeds, dim=0)
if isinstance(negative_image_embeds, list):
negative_image_embeds = torch.cat(negative_image_embeds, dim=0)
if do_classifier_free_guidance:
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(
dtype=prompt_embeds.dtype, device=device
)
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps_tensor = self.scheduler.timesteps
num_channels_latents = self.unet.config.in_channels
height, width = get_new_h_w(height, width, self.movq_scale_factor)
# create initial latent
latents = self.prepare_latents(
(batch_size, num_channels_latents, height, width),
text_encoder_hidden_states.dtype,
device,
generator,
latents,
self.scheduler,
)
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
added_cond_kwargs = {"text_embeds": prompt_embeds, "image_embeds": image_embeds}
noise_pred = self.unet(
sample=latent_model_input,
timestep=t,
encoder_hidden_states=text_encoder_hidden_states,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
if do_classifier_free_guidance:
noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
_, variance_pred_text = variance_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1)
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,
).prev_sample
# post-processing
image = self.movq.decode(latents, force_not_quantize=True)["sample"]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}")
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)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)