Naoki Ainoya
14e3a28c12
The 'CLIPFeatureExtractor' class name has been renamed to 'CLIPImageProcessor' in order to comply with future deprecation. This commit includes the necessary changes to the affected files.
476 lines
22 KiB
Python
Executable File
476 lines
22 KiB
Python
Executable File
# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import importlib
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import warnings
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from typing import Callable, List, Optional, Union
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import torch
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from k_diffusion.external import CompVisDenoiser, CompVisVDenoiser
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from diffusers import DiffusionPipeline, LMSDiscreteScheduler
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from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
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from diffusers.utils import is_accelerate_available, logging
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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class ModelWrapper:
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def __init__(self, model, alphas_cumprod):
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self.model = model
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self.alphas_cumprod = alphas_cumprod
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def apply_model(self, *args, **kwargs):
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if len(args) == 3:
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encoder_hidden_states = args[-1]
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args = args[:2]
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if kwargs.get("cond", None) is not None:
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encoder_hidden_states = kwargs.pop("cond")
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return self.model(*args, encoder_hidden_states=encoder_hidden_states, **kwargs).sample
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class StableDiffusionPipeline(DiffusionPipeline):
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r"""
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Pipeline for text-to-image generation using Stable Diffusion.
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
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library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
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Args:
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vae ([`AutoencoderKL`]):
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Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
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text_encoder ([`CLIPTextModel`]):
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Frozen text-encoder. Stable Diffusion uses the text portion of
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[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
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the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
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tokenizer (`CLIPTokenizer`):
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Tokenizer of class
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[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
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unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
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scheduler ([`SchedulerMixin`]):
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A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
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[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
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safety_checker ([`StableDiffusionSafetyChecker`]):
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Classification module that estimates whether generated images could be considered offensive or harmful.
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Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
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feature_extractor ([`CLIPImageProcessor`]):
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Model that extracts features from generated images to be used as inputs for the `safety_checker`.
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"""
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_optional_components = ["safety_checker", "feature_extractor"]
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def __init__(
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self,
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vae,
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text_encoder,
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tokenizer,
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unet,
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scheduler,
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safety_checker,
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feature_extractor,
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):
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super().__init__()
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if safety_checker is None:
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logger.warning(
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f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
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" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
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" results in services or applications open to the public. Both the diffusers team and Hugging Face"
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" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
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" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
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" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
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)
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# get correct sigmas from LMS
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scheduler = LMSDiscreteScheduler.from_config(scheduler.config)
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self.register_modules(
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vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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unet=unet,
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scheduler=scheduler,
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safety_checker=safety_checker,
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feature_extractor=feature_extractor,
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)
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model = ModelWrapper(unet, scheduler.alphas_cumprod)
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if scheduler.prediction_type == "v_prediction":
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self.k_diffusion_model = CompVisVDenoiser(model)
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else:
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self.k_diffusion_model = CompVisDenoiser(model)
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def set_sampler(self, scheduler_type: str):
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warnings.warn("The `set_sampler` method is deprecated, please use `set_scheduler` instead.")
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return self.set_scheduler(scheduler_type)
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def set_scheduler(self, scheduler_type: str):
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library = importlib.import_module("k_diffusion")
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sampling = getattr(library, "sampling")
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self.sampler = getattr(sampling, scheduler_type)
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def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
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r"""
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Enable sliced attention computation.
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When this option is enabled, the attention module will split the input tensor in slices, to compute attention
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in several steps. This is useful to save some memory in exchange for a small speed decrease.
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Args:
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slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
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When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
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a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
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`attention_head_dim` must be a multiple of `slice_size`.
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"""
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if slice_size == "auto":
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# half the attention head size is usually a good trade-off between
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# speed and memory
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slice_size = self.unet.config.attention_head_dim // 2
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self.unet.set_attention_slice(slice_size)
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def disable_attention_slicing(self):
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r"""
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Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
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back to computing attention in one step.
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"""
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# set slice_size = `None` to disable `attention slicing`
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self.enable_attention_slicing(None)
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def enable_sequential_cpu_offload(self, gpu_id=0):
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r"""
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Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
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text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
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`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
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"""
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if is_accelerate_available():
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from accelerate import cpu_offload
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else:
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raise ImportError("Please install accelerate via `pip install accelerate`")
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device = torch.device(f"cuda:{gpu_id}")
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for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
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if cpu_offloaded_model is not None:
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cpu_offload(cpu_offloaded_model, device)
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@property
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def _execution_device(self):
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r"""
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Returns the device on which the pipeline's models will be executed. After calling
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`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
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hooks.
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"""
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if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
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return self.device
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for module in self.unet.modules():
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if (
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hasattr(module, "_hf_hook")
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and hasattr(module._hf_hook, "execution_device")
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and module._hf_hook.execution_device is not None
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):
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return torch.device(module._hf_hook.execution_device)
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return self.device
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def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
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r"""
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Encodes the prompt into text encoder hidden states.
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Args:
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prompt (`str` or `list(int)`):
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prompt to be encoded
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device: (`torch.device`):
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torch device
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num_images_per_prompt (`int`):
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number of images that should be generated per prompt
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do_classifier_free_guidance (`bool`):
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whether to use classifier free guidance or not
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negative_prompt (`str` or `List[str]`):
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The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
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if `guidance_scale` is less than `1`).
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"""
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batch_size = len(prompt) if isinstance(prompt, list) else 1
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text_inputs = self.tokenizer(
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prompt,
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padding="max_length",
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max_length=self.tokenizer.model_max_length,
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truncation=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
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if not torch.equal(text_input_ids, untruncated_ids):
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removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
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logger.warning(
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"The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.tokenizer.model_max_length} tokens: {removed_text}"
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)
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if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
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attention_mask = text_inputs.attention_mask.to(device)
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else:
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attention_mask = None
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text_embeddings = self.text_encoder(
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text_input_ids.to(device),
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attention_mask=attention_mask,
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)
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text_embeddings = text_embeddings[0]
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# duplicate text embeddings for each generation per prompt, using mps friendly method
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bs_embed, seq_len, _ = text_embeddings.shape
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text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
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text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
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# get unconditional embeddings for classifier free guidance
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if do_classifier_free_guidance:
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uncond_tokens: List[str]
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if negative_prompt is None:
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uncond_tokens = [""] * batch_size
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elif type(prompt) is not type(negative_prompt):
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raise TypeError(
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f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
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f" {type(prompt)}."
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)
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elif isinstance(negative_prompt, str):
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uncond_tokens = [negative_prompt]
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elif batch_size != len(negative_prompt):
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raise ValueError(
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f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
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f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
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" the batch size of `prompt`."
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)
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else:
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uncond_tokens = negative_prompt
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max_length = text_input_ids.shape[-1]
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uncond_input = self.tokenizer(
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uncond_tokens,
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padding="max_length",
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max_length=max_length,
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truncation=True,
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return_tensors="pt",
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)
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if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
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attention_mask = uncond_input.attention_mask.to(device)
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else:
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attention_mask = None
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uncond_embeddings = self.text_encoder(
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uncond_input.input_ids.to(device),
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attention_mask=attention_mask,
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)
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uncond_embeddings = uncond_embeddings[0]
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# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
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seq_len = uncond_embeddings.shape[1]
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uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
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uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
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# For classifier free guidance, we need to do two forward passes.
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# Here we concatenate the unconditional and text embeddings into a single batch
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# to avoid doing two forward passes
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text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
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return text_embeddings
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def run_safety_checker(self, image, device, dtype):
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if self.safety_checker is not None:
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safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
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image, has_nsfw_concept = self.safety_checker(
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images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
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)
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else:
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has_nsfw_concept = None
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return image, has_nsfw_concept
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def decode_latents(self, latents):
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latents = 1 / 0.18215 * latents
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image = self.vae.decode(latents).sample
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image = (image / 2 + 0.5).clamp(0, 1)
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# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
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image = image.cpu().permute(0, 2, 3, 1).float().numpy()
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return image
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def check_inputs(self, prompt, height, width, callback_steps):
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if not isinstance(prompt, str) and not isinstance(prompt, list):
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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if height % 8 != 0 or width % 8 != 0:
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raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
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if (callback_steps is None) or (
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callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
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):
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raise ValueError(
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f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
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f" {type(callback_steps)}."
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)
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def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
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shape = (batch_size, num_channels_latents, height // 8, width // 8)
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if latents is None:
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if device.type == "mps":
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# randn does not work reproducibly on mps
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latents = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device)
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else:
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latents = torch.randn(shape, generator=generator, device=device, dtype=dtype)
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else:
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if latents.shape != shape:
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raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
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latents = latents.to(device)
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# scale the initial noise by the standard deviation required by the scheduler
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return latents
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@torch.no_grad()
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def __call__(
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self,
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prompt: Union[str, List[str]],
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height: int = 512,
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width: int = 512,
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num_inference_steps: int = 50,
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guidance_scale: float = 7.5,
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negative_prompt: Optional[Union[str, List[str]]] = None,
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num_images_per_prompt: Optional[int] = 1,
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eta: float = 0.0,
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generator: Optional[torch.Generator] = None,
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latents: Optional[torch.FloatTensor] = None,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
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callback_steps: int = 1,
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**kwargs,
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):
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r"""
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Function invoked when calling the pipeline for generation.
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Args:
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prompt (`str` or `List[str]`):
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The prompt or prompts to guide the image generation.
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height (`int`, *optional*, defaults to 512):
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The height in pixels of the generated image.
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width (`int`, *optional*, defaults to 512):
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The width in pixels of the generated image.
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num_inference_steps (`int`, *optional*, defaults to 50):
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference.
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guidance_scale (`float`, *optional*, defaults to 7.5):
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Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
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`guidance_scale` is defined as `w` of equation 2. of [Imagen
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Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
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1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
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usually at the expense of lower image quality.
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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
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if `guidance_scale` is less than `1`).
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num_images_per_prompt (`int`, *optional*, defaults to 1):
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The number of images to generate per prompt.
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eta (`float`, *optional*, defaults to 0.0):
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Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
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[`schedulers.DDIMScheduler`], will be ignored for others.
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generator (`torch.Generator`, *optional*):
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A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
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deterministic.
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latents (`torch.FloatTensor`, *optional*):
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Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
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generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
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tensor will ge generated by sampling using the supplied random `generator`.
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output_type (`str`, *optional*, defaults to `"pil"`):
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The output format of the generate image. Choose between
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[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
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plain tuple.
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callback (`Callable`, *optional*):
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A function that will be called every `callback_steps` steps during inference. The function will be
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called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
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callback_steps (`int`, *optional*, defaults to 1):
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The frequency at which the `callback` function will be called. If not specified, the callback will be
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called at every step.
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Returns:
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[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
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[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
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When returning a tuple, the first element is a list with the generated images, and the second element is a
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list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
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(nsfw) content, according to the `safety_checker`.
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"""
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# 1. Check inputs. Raise error if not correct
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self.check_inputs(prompt, height, width, callback_steps)
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# 2. Define call parameters
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batch_size = 1 if isinstance(prompt, str) else len(prompt)
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device = self._execution_device
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# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
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# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
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# corresponds to doing no classifier free guidance.
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do_classifier_free_guidance = True
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if guidance_scale <= 1.0:
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raise ValueError("has to use guidance_scale")
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# 3. Encode input prompt
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text_embeddings = self._encode_prompt(
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prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
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|
)
|
|
|
|
# 4. Prepare timesteps
|
|
self.scheduler.set_timesteps(num_inference_steps, device=text_embeddings.device)
|
|
sigmas = self.scheduler.sigmas
|
|
sigmas = sigmas.to(text_embeddings.dtype)
|
|
|
|
# 5. Prepare latent variables
|
|
num_channels_latents = self.unet.in_channels
|
|
latents = self.prepare_latents(
|
|
batch_size * num_images_per_prompt,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
text_embeddings.dtype,
|
|
device,
|
|
generator,
|
|
latents,
|
|
)
|
|
latents = latents * sigmas[0]
|
|
self.k_diffusion_model.sigmas = self.k_diffusion_model.sigmas.to(latents.device)
|
|
self.k_diffusion_model.log_sigmas = self.k_diffusion_model.log_sigmas.to(latents.device)
|
|
|
|
def model_fn(x, t):
|
|
latent_model_input = torch.cat([x] * 2)
|
|
|
|
noise_pred = self.k_diffusion_model(latent_model_input, t, cond=text_embeddings)
|
|
|
|
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
|
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
|
return noise_pred
|
|
|
|
latents = self.sampler(model_fn, latents, sigmas)
|
|
|
|
# 8. Post-processing
|
|
image = self.decode_latents(latents)
|
|
|
|
# 9. Run safety checker
|
|
image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)
|
|
|
|
# 10. Convert to PIL
|
|
if output_type == "pil":
|
|
image = self.numpy_to_pil(image)
|
|
|
|
if not return_dict:
|
|
return (image, has_nsfw_concept)
|
|
|
|
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|