Add Composable diffusion to community pipeline examples (#951)
* Initial composable diffusion pipeline * add composable stable diffusion to readme table * Update examples/community/README.md * Apply suggestions from code review * Update examples/community/README.md * Update examples/community/README.md * Update examples/community/README.md * up * Update examples/community/README.md Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
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@ -15,6 +15,8 @@ If a community doesn't work as expected, please open an issue and ping the autho
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| Long Prompt Weighting Stable Diffusion | **One** Stable Diffusion Pipeline without tokens length limit, and support parsing weighting in prompt. | [Long Prompt Weighting Stable Diffusion](#long-prompt-weighting-stable-diffusion) | - | [SkyTNT](https://github.com/SkyTNT) |
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| Speech to Image | Using automatic-speech-recognition to transcribe text and Stable Diffusion to generate images | [Speech to Image](#speech-to-image) | - | [Mikail Duzenli](https://github.com/MikailINTech)
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| Wild Card Stable Diffusion | Stable Diffusion Pipeline that supports prompts that contain wildcard terms (indicated by surrounding double underscores), with values instantiated randomly from a corresponding txt file or a dictionary of possible values | [Wildcard Stable Diffusion](#wildcard-stable-diffusion) | - | [Shyam Sudhakaran](https://github.com/shyamsn97) |
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| Composable Stable Diffusion| Stable Diffusion Pipeline that supports prompts that contain "|" in prompts (as an AND condition) and weights (separated by "|" as well) to positively / negatively weight prompts. | [Composable Stable Diffusion](#composable-stable-diffusion) | - | [Mark Rich](https://github.com/MarkRich) |
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To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.
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```py
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@ -322,3 +324,50 @@ out = pipe(
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wildcard_files=["object.txt", "animal.txt"],
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num_prompt_samples=1
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)
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```
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### Composable Stable diffusion
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```python
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import torch as th
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import numpy as np
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import torchvision.utils as tvu
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from diffusers import DiffusionPipeline
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has_cuda = th.cuda.is_available()
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device = th.device('cpu' if not has_cuda else 'cuda')
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pipe = DiffusionPipeline.from_pretrained(
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"CompVis/stable-diffusion-v1-4",
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use_auth_token=True,
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custom_pipeline="composable_stable_diffusion",
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).to(device)
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def dummy(images, **kwargs):
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return images, False
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pipe.safety_checker = dummy
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images = []
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generator = th.Generator("cuda").manual_seed(0)
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seed = 0
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prompt = "a forest | a camel"
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weights = " 1 | 1" # Equal weight to each prompt. Cna be negative
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images = []
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for i in range(4):
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res = pipe(
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prompt,
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guidance_scale=7.5,
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num_inference_steps=50,
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weights=weights,
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generator=generator)
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image = res.images[0]
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images.append(image)
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for i, img in enumerate(images):
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img.save(f"./composable_diffusion/image_{i}.png")
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```
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@ -0,0 +1,329 @@
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"""
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modified based on diffusion library from Huggingface: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
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"""
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import inspect
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import warnings
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from typing import List, Optional, Union
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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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class ComposableStableDiffusionPipeline(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 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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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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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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@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: 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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eta: Optional[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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weights: Optional[str] = "",
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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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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 `nd.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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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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if "torch_device" in kwargs:
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device = kwargs.pop("torch_device")
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warnings.warn(
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"`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
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" Consider using `pipe.to(torch_device)` instead."
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)
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# Set device as before (to be removed in 0.3.0)
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if device is None:
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device = "cuda" if torch.cuda.is_available() else "cpu"
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self.to(device)
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if isinstance(prompt, str):
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batch_size = 1
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elif isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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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 "|" in prompt:
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prompt = [x.strip() for x in prompt.split("|")]
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print(f"composing {prompt}...")
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# get prompt text embeddings
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text_input = 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_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
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if not weights:
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# specify weights for prompts (excluding the unconditional score)
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print("using equal weights for all prompts...")
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pos_weights = torch.tensor(
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[1 / (text_embeddings.shape[0] - 1)] * (text_embeddings.shape[0] - 1), device=self.device
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).reshape(-1, 1, 1, 1)
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neg_weights = torch.tensor([1.0], device=self.device).reshape(-1, 1, 1, 1)
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mask = torch.tensor([False] + [True] * pos_weights.shape[0], dtype=torch.bool)
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else:
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# set prompt weight for each
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num_prompts = len(prompt) if isinstance(prompt, list) else 1
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weights = [float(w.strip()) for w in weights.split("|")]
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if len(weights) < num_prompts:
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weights.append(1.0)
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weights = torch.tensor(weights, device=self.device)
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assert len(weights) == text_embeddings.shape[0], "weights specified are not equal to the number of prompts"
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pos_weights = []
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neg_weights = []
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mask = [] # first one is unconditional score
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for w in weights:
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if w > 0:
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pos_weights.append(w)
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mask.append(True)
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else:
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neg_weights.append(abs(w))
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mask.append(False)
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# normalize the weights
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pos_weights = torch.tensor(pos_weights, device=self.device).reshape(-1, 1, 1, 1)
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pos_weights = pos_weights / pos_weights.sum()
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neg_weights = torch.tensor(neg_weights, device=self.device).reshape(-1, 1, 1, 1)
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neg_weights = neg_weights / neg_weights.sum()
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mask = torch.tensor(mask, device=self.device, dtype=torch.bool)
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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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# get unconditional embeddings for classifier free guidance
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if do_classifier_free_guidance:
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max_length = text_input.input_ids.shape[-1]
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if torch.all(mask):
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# no negative prompts, so we use empty string as the negative prompt
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uncond_input = self.tokenizer(
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[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
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)
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uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
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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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# update negative weights
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neg_weights = torch.tensor([1.0], device=self.device)
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mask = torch.tensor([False] + mask.detach().tolist(), device=self.device, dtype=torch.bool)
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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_device = "cpu" if self.device.type == "mps" else self.device
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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=latents_device,
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)
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else:
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if latents.shape != latents_shape:
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raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
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latents = latents.to(self.device)
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# set timesteps
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accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
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extra_set_kwargs = {}
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if accepts_offset:
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extra_set_kwargs["offset"] = 1
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self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
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# if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas
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if isinstance(self.scheduler, LMSDiscreteScheduler):
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latents = latents * self.scheduler.sigmas[0]
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# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
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# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
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# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
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# and should be between [0, 1]
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accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
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extra_step_kwargs = {}
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if accepts_eta:
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extra_step_kwargs["eta"] = eta
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for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
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# expand the latents if we are doing classifier free guidance
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latent_model_input = (
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torch.cat([latents] * text_embeddings.shape[0]) if do_classifier_free_guidance else latents
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)
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if isinstance(self.scheduler, LMSDiscreteScheduler):
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sigma = self.scheduler.sigmas[i]
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# the model input needs to be scaled to match the continuous ODE formulation in K-LMS
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latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
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# reduce memory by predicting each score sequentially
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noise_preds = []
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# predict the noise residual
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for latent_in, text_embedding_in in zip(
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torch.chunk(latent_model_input, chunks=latent_model_input.shape[0], dim=0),
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torch.chunk(text_embeddings, chunks=text_embeddings.shape[0], dim=0),
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):
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noise_preds.append(self.unet(latent_in, t, encoder_hidden_states=text_embedding_in).sample)
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noise_preds = torch.cat(noise_preds, dim=0)
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# perform guidance
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if do_classifier_free_guidance:
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noise_pred_uncond = (noise_preds[~mask] * neg_weights).sum(dim=0, keepdims=True)
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noise_pred_text = (noise_preds[mask] * pos_weights).sum(dim=0, keepdims=True)
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noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
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# compute the previous noisy sample x_t -> x_t-1
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if isinstance(self.scheduler, LMSDiscreteScheduler):
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latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample
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else:
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latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
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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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# run safety checker
|
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safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
|
||||
image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values)
|
||||
|
||||
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)
|
||||
Loading…
Reference in New Issue