mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
366 lines
17 KiB
Python
366 lines
17 KiB
Python
import secrets
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import warnings
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from dataclasses import dataclass
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from typing import List, Optional, Union, Callable
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import torch
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from diffusers.models import AutoencoderKL, UNet2DConditionModel
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from diffusers.pipeline_utils import DiffusionPipeline
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from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
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from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
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from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
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from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
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from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
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from ldm.modules.encoders.modules import WeightedFrozenCLIPEmbedder
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@dataclass
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class PipelineIntermediateState:
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run_id: str
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step: int
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timestep: int
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latents: torch.Tensor
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predicted_original: Optional[torch.Tensor] = None
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class StableDiffusionGeneratorPipeline(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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Implementation note: This class started as a refactored copy of diffusers.StableDiffusionPipeline.
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Hopefully future versions of diffusers provide access to more of these functions so that we don't
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need to duplicate them here: https://github.com/huggingface/diffusers/issues/551#issuecomment-1281508384
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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 latens. 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 offsensive or harmful.
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Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
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feature_extractor ([`CLIPFeatureExtractor`]):
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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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ID_LENGTH = 8
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def __init__(
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self,
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vae: AutoencoderKL,
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text_encoder: CLIPTextModel,
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tokenizer: CLIPTokenizer,
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unet: UNet2DConditionModel,
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scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
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safety_checker: StableDiffusionSafetyChecker,
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feature_extractor: CLIPFeatureExtractor,
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):
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super().__init__()
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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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# InvokeAI's interface for text embeddings and whatnot
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self.clip_embedder = WeightedFrozenCLIPEmbedder(
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tokenizer=self.tokenizer,
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transformer=self.text_encoder
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)
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self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward)
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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_xformers_memory_efficient_attention(self):
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r"""
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Enable memory efficient attention as implemented in xformers.
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When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference
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time. Speed up at training time is not guaranteed.
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Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention
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is used.
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"""
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self.unet.set_use_memory_efficient_attention_xformers(True)
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def disable_xformers_memory_efficient_attention(self):
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r"""
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Disable memory efficient attention as implemented in xformers.
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"""
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self.unet.set_use_memory_efficient_attention_xformers(False)
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def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
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text_embeddings: torch.Tensor, unconditioned_embeddings: torch.Tensor,
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guidance_scale: float,
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*, callback: Callable[[PipelineIntermediateState], None]=None,
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extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo=None,
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run_id=None,
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**extra_step_kwargs) -> StableDiffusionPipelineOutput:
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r"""
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Function invoked when calling the pipeline for generation.
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:param latents: Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for
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image generation. Can be used to tweak the same generation with different prompts.
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:param num_inference_steps: The number of denoising steps. More denoising steps usually lead to a higher quality
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image at the expense of slower inference.
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:param text_embeddings:
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:param guidance_scale: 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 Paper](https://arxiv.org/pdf/2205.11487.pdf).
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Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate
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images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
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:param callback:
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:param extra_conditioning_info:
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:param run_id:
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:param extra_step_kwargs:
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"""
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self.scheduler.set_timesteps(num_inference_steps, device=self.unet.device)
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result = None
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for result in self.generate_from_embeddings(
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latents, text_embeddings, unconditioned_embeddings, guidance_scale,
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extra_conditioning_info=extra_conditioning_info,
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run_id=run_id, **extra_step_kwargs):
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if callback is not None and isinstance(result, PipelineIntermediateState):
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callback(result)
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if result is None:
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raise AssertionError("why was that an empty generator?")
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return result
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def generate(
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self,
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prompt: Union[str, List[str]],
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*,
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opposing_prompt: Union[str, List[str]] = None,
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height: Optional[int] = 512,
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width: Optional[int] = 512,
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num_inference_steps: Optional[int] = 50,
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guidance_scale: Optional[float] = 7.5,
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generator: Optional[torch.Generator] = None,
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latents: Optional[torch.FloatTensor] = None,
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run_id: str = None,
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**extra_step_kwargs,
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):
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if isinstance(prompt, str):
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batch_size = 1
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else:
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batch_size = len(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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# 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 = guidance_scale > 1.0
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text_embeddings, unconditioned_embeddings = self.get_text_embeddings(prompt, opposing_prompt, do_classifier_free_guidance, batch_size)\
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.to(self.unet.device)
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self.scheduler.set_timesteps(num_inference_steps)
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latents = self.prepare_latents(latents, batch_size, height, width, generator, self.unet.dtype)
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yield from self.generate_from_embeddings(latents, text_embeddings, unconditioned_embeddings,
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guidance_scale, run_id=run_id, **extra_step_kwargs)
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def generate_from_embeddings(
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self,
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latents: torch.Tensor,
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text_embeddings: torch.Tensor,
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unconditioned_embeddings: torch.Tensor,
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guidance_scale: float,
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*,
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run_id: str = None,
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extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo = None,
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**extra_step_kwargs):
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if run_id is None:
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run_id = secrets.token_urlsafe(self.ID_LENGTH)
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if extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control:
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self.invokeai_diffuser.setup_cross_attention_control(extra_conditioning_info,
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step_count=len(self.scheduler.timesteps))
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else:
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self.invokeai_diffuser.remove_cross_attention_control()
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# scale the initial noise by the standard deviation required by the scheduler
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latents *= self.scheduler.init_noise_sigma
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yield PipelineIntermediateState(run_id=run_id, step=-1, timestep=self.scheduler.num_train_timesteps,
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latents=latents)
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batch_size = latents.shape[0]
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batched_t = torch.full((batch_size,), self.scheduler.timesteps[0],
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dtype=self.scheduler.timesteps.dtype, device=self.unet.device)
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# NOTE: Depends on scheduler being already initialized!
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for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
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batched_t.fill_(t)
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step_output = self.step(batched_t, latents, guidance_scale,
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text_embeddings, unconditioned_embeddings,
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i, **extra_step_kwargs)
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latents = step_output.prev_sample
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predicted_original = getattr(step_output, 'pred_original_sample', None)
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yield PipelineIntermediateState(run_id=run_id, step=i, timestep=int(t), latents=latents,
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predicted_original=predicted_original)
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# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
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torch.cuda.empty_cache()
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image = self.decode_to_image(latents)
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output = StableDiffusionPipelineOutput(images=image, nsfw_content_detected=[])
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yield self.check_for_safety(output)
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@torch.inference_mode()
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def step(self, t: torch.Tensor, latents: torch.Tensor, guidance_scale: float,
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text_embeddings: torch.Tensor, unconditioned_embeddings: torch.Tensor,
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step_index:int | None = None,
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**extra_step_kwargs):
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# invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
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timestep = t[0]
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# TODO: should this scaling happen here or inside self._unet_forward?
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# i.e. before or after passing it to InvokeAIDiffuserComponent
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latent_model_input = self.scheduler.scale_model_input(latents, timestep)
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# predict the noise residual
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noise_pred = self.invokeai_diffuser.do_diffusion_step(
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latent_model_input, t,
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unconditioned_embeddings, text_embeddings,
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guidance_scale,
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step_index=step_index)
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# compute the previous noisy sample x_t -> x_t-1
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return self.scheduler.step(noise_pred, timestep, latents, **extra_step_kwargs)
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def _unet_forward(self, latents, t, text_embeddings):
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# predict the noise residual
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return self.unet(latents, t, encoder_hidden_states=text_embeddings).sample
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@torch.inference_mode()
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def check_for_safety(self, output):
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if not getattr(self, 'feature_extractor') or not getattr(self, 'safety_checker'):
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return output
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images = output.images
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safety_checker_output = self.feature_extractor(self.numpy_to_pil(images),
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return_tensors="pt").to(self.device)
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screened_images, has_nsfw_concept = self.safety_checker(
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images=images, clip_input=safety_checker_output.pixel_values)
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return StableDiffusionPipelineOutput(screened_images, has_nsfw_concept)
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@torch.inference_mode()
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def decode_to_image(self, latents):
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# scale and decode the image latents with vae
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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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image = image.cpu().permute(0, 2, 3, 1).numpy()
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return image
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@torch.inference_mode()
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def get_text_embeddings(self,
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prompt: Union[str, List[str]],
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opposing_prompt: Union[str, List[str]],
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do_classifier_free_guidance: bool,
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batch_size: int):
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# get prompt text embeddings
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text_input = self._tokenize(prompt)
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text_embeddings = self.text_encoder(text_input.input_ids)[0]
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# get unconditional embeddings for classifier free guidance
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if do_classifier_free_guidance:
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# opposing prompt defaults to blank caption for everything in the batch
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text_anti_input = self._tokenize(opposing_prompt or [""] * batch_size)
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uncond_embeddings = self.text_encoder(text_anti_input.input_ids)[0]
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else:
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uncond_embeddings = None
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return text_embeddings, uncond_embeddings
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@torch.inference_mode()
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def get_learned_conditioning(self, c: List[List[str]], *, return_tokens=True, fragment_weights=None):
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"""
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Compatibility function for ldm.models.diffusion.ddpm.LatentDiffusion.
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"""
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return self.clip_embedder.encode(c, return_tokens=return_tokens, fragment_weights=fragment_weights)
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@property
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def cond_stage_model(self):
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warnings.warn("legacy compatibility layer", DeprecationWarning)
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return self.clip_embedder
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@torch.inference_mode()
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def _tokenize(self, prompt: Union[str, List[str]]):
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return 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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@property
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def channels(self) -> int:
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"""Compatible with DiffusionWrapper"""
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return self.unet.in_channels
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def prepare_latents(self, latents, batch_size, height, width, generator, dtype):
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# get the initial random noise unless the user supplied it
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# Unlike in other pipelines, latents need to be generated in the target device
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# for 1-to-1 results reproducibility with the CompVis implementation.
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# However this currently doesn't work in `mps`.
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latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8)
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if latents is None:
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latents = torch.randn(
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latents_shape,
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generator=generator,
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device=self.unet.device,
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dtype=dtype
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)
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else:
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if latents.shape != latents_shape:
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raise ValueError(
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f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
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if latents.device != self.unet.device:
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raise ValueError(f"Unexpected latents device, got {latents.device}, "
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f"expected {self.unet.device}")
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# scale the initial noise by the standard deviation required by the scheduler
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latents *= self.scheduler.init_noise_sigma
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return latents
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