Added Community pipeline for comparing Stable Diffusion v1.1-4 checkpoints (#1584)
* Added Community pipeline for comparing Stable Diffusion v1.1-4 Signed-off-by: Suvaditya Mukherjee <suvadityamuk@gmail.com> * Made changes to provide support for current iteration of from_pretrained and added example Signed-off-by: Suvaditya Mukherjee <suvadityamuk@gmail.com> * updated a small spelling error Signed-off-by: Suvaditya Mukherjee <suvadityamuk@gmail.com> * added pipeline entry to table Signed-off-by: Suvaditya Mukherjee <suvadityamuk@gmail.com> Signed-off-by: Suvaditya Mukherjee <suvadityamuk@gmail.com>
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@ -23,7 +23,8 @@ If a community doesn't work as expected, please open an issue and ping the autho
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| Text Based Inpainting Stable Diffusion | Stable Diffusion Inpainting Pipeline that enables passing a text prompt to generate the mask for inpainting| [Text Based Inpainting Stable Diffusion](#image-to-image-inpainting-stable-diffusion) | - | [Dhruv Karan](https://github.com/unography) |
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| Bit Diffusion | Diffusion on discrete data | [Bit Diffusion](#bit-diffusion) | - |[Stuti R.](https://github.com/kingstut) |
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| K-Diffusion Stable Diffusion | Run Stable Diffusion with any of [K-Diffusion's samplers](https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/sampling.py) | [Stable Diffusion with K Diffusion](#stable-diffusion-with-k-diffusion) | - | [Patrick von Platen](https://github.com/patrickvonplaten/) |
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| Checkpoint Merger Pipeline | Diffusion Pipeline that enables merging of saved model checkpoints | [Checkpoint Merger Pipeline](#checkpoint-merger-pipeline) | - | [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) |
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| Checkpoint Merger Pipeline | Diffusion Pipeline that enables merging of saved model checkpoints | [Checkpoint Merger Pipeline](#checkpoint-merger-pipeline) | - | [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) |
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Stable Diffusion v1.1-1.4 Comparison | Run all 4 model checkpoints for Stable Diffusion and compare their results together | [Stable Diffusion Comparison](#stable-diffusion-comparisons) | - | [Suvaditya Mukherjee](https://github.com/suvadityamuk) |
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@ -774,3 +775,44 @@ Some examples along with the merge details:
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3. "CompVis/stable-diffusion-v1-4" + "hakurei/waifu-diffusion" + "prompthero/openjourney"; Add Difference interpolation; alpha = 0.5
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### Stable Diffusion Comparisons
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This Community Pipeline enables the comparison between the 4 checkpoints that exist for Stable Diffusion. They can be found through the following links:
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1. [Stable Diffusion v1.1](https://huggingface.co/CompVis/stable-diffusion-v1-1)
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2. [Stable Diffusion v1.2](https://huggingface.co/CompVis/stable-diffusion-v1-2)
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3. [Stable Diffusion v1.3](https://huggingface.co/CompVis/stable-diffusion-v1-3)
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4. [Stable Diffusion v1.4](https://huggingface.co/CompVis/stable-diffusion-v1-4)
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```python
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from diffusers import DiffusionPipeline
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import matplotlib.pyplot as plt
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pipe = DiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4', custom_pipeline='suvadityamuk/StableDiffusionComparison')
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pipe.enable_attention_slicing()
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pipe = pipe.to('cuda')
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prompt = "an astronaut riding a horse on mars"
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output = pipe(prompt)
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plt.subplots(2,2,1)
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plt.imshow(output.images[0])
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plt.title('Stable Diffusion v1.1')
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plt.axis('off')
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plt.subplots(2,2,2)
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plt.imshow(output.images[1])
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plt.title('Stable Diffusion v1.2')
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plt.axis('off')
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plt.subplots(2,2,3)
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plt.imshow(output.images[2])
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plt.title('Stable Diffusion v1.3')
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plt.axis('off')
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plt.subplots(2,2,4)
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plt.imshow(output.images[3])
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plt.title('Stable Diffusion v1.4')
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plt.axis('off')
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plt.show()
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```python
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As a result, you can look at a grid of all 4 generated images being shown together, that captures a difference the advancement of the training between the 4 checkpoints.
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@ -0,0 +1,405 @@
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from typing import Any, Callable, Dict, List, Optional, Union
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import torch
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from diffusers import (
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AutoencoderKL,
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DDIMScheduler,
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DiffusionPipeline,
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LMSDiscreteScheduler,
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PNDMScheduler,
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StableDiffusionPipeline,
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UNet2DConditionModel,
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)
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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 transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
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pipe1_model_id = "CompVis/stable-diffusion-v1-1"
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pipe2_model_id = "CompVis/stable-diffusion-v1-2"
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pipe3_model_id = "CompVis/stable-diffusion-v1-3"
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pipe4_model_id = "CompVis/stable-diffusion-v1-4"
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class StableDiffusionComparisonPipeline(DiffusionPipeline):
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r"""
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Pipeline for parallel comparison of Stable Diffusion v1-v4
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This pipeline inherits from DiffusionPipeline and depends on the use of an Auth Token for
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downloading pre-trained checkpoints from Hugging Face Hub.
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If using Hugging Face Hub, pass the Model ID for Stable Diffusion v1.4 as the previous 3 checkpoints will be loaded
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automatically.
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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 ([`StableDiffusionMegaSafetyChecker`]):
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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 ([`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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requires_safety_checker: bool = True,
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):
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super()._init_()
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self.pipe1 = StableDiffusionPipeline.from_pretrained(pipe1_model_id)
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self.pipe2 = StableDiffusionPipeline.from_pretrained(pipe2_model_id)
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self.pipe3 = StableDiffusionPipeline.from_pretrained(pipe3_model_id)
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self.pipe4 = StableDiffusionPipeline(
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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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requires_safety_checker=requires_safety_checker,
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)
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self.register_modules(pipeline1=self.pipe1, pipeline2=self.pipe2, pipeline3=self.pipe3, pipeline4=self.pipe4)
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@property
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def layers(self) -> Dict[str, Any]:
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return {k: getattr(self, k) for k in self.config.keys() if not k.startswith("_")}
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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 text2img_sd1_1(
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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: Optional[int] = 1,
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**kwargs,
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):
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return self.pipe1(
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prompt=prompt,
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height=height,
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width=width,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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negative_prompt=negative_prompt,
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num_images_per_prompt=num_images_per_prompt,
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eta=eta,
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generator=generator,
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latents=latents,
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output_type=output_type,
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return_dict=return_dict,
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callback=callback,
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callback_steps=callback_steps,
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**kwargs,
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)
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@torch.no_grad()
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def text2img_sd1_2(
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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: Optional[int] = 1,
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**kwargs,
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):
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return self.pipe2(
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prompt=prompt,
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height=height,
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width=width,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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negative_prompt=negative_prompt,
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num_images_per_prompt=num_images_per_prompt,
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eta=eta,
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generator=generator,
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latents=latents,
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output_type=output_type,
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return_dict=return_dict,
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callback=callback,
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callback_steps=callback_steps,
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**kwargs,
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)
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@torch.no_grad()
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def text2img_sd1_3(
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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: Optional[int] = 1,
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**kwargs,
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):
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return self.pipe3(
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prompt=prompt,
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height=height,
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width=width,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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negative_prompt=negative_prompt,
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num_images_per_prompt=num_images_per_prompt,
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eta=eta,
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generator=generator,
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latents=latents,
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output_type=output_type,
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return_dict=return_dict,
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callback=callback,
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callback_steps=callback_steps,
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**kwargs,
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)
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@torch.no_grad()
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def text2img_sd1_4(
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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: Optional[int] = 1,
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**kwargs,
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):
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return self.pipe4(
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prompt=prompt,
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height=height,
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width=width,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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negative_prompt=negative_prompt,
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num_images_per_prompt=num_images_per_prompt,
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eta=eta,
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generator=generator,
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latents=latents,
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output_type=output_type,
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return_dict=return_dict,
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callback=callback,
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callback_steps=callback_steps,
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**kwargs,
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)
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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: Optional[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. This function will generate 4 results as part
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of running all the 4 pipelines for SD1.1-1.4 together in a serial-processing, parallel-invocation fashion.
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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 `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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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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device = "cuda" if torch.cuda.is_available() else "cpu"
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self.to(device)
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# Checks if the height and width are divisible by 8 or not
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if height % 8 != 0 or width % 8 != 0:
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raise ValueError(f"`height` and `width` must be divisible by 8 but are {height} and {width}.")
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# Get first result from Stable Diffusion Checkpoint v1.1
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res1 = self.text2img_sd1_1(
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prompt=prompt,
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height=height,
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width=width,
|
||||
num_inference_steps=num_inference_steps,
|
||||
guidance_scale=guidance_scale,
|
||||
negative_prompt=negative_prompt,
|
||||
num_images_per_prompt=num_images_per_prompt,
|
||||
eta=eta,
|
||||
generator=generator,
|
||||
latents=latents,
|
||||
output_type=output_type,
|
||||
return_dict=return_dict,
|
||||
callback=callback,
|
||||
callback_steps=callback_steps,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# Get first result from Stable Diffusion Checkpoint v1.2
|
||||
res2 = self.text2img_sd1_2(
|
||||
prompt=prompt,
|
||||
height=height,
|
||||
width=width,
|
||||
num_inference_steps=num_inference_steps,
|
||||
guidance_scale=guidance_scale,
|
||||
negative_prompt=negative_prompt,
|
||||
num_images_per_prompt=num_images_per_prompt,
|
||||
eta=eta,
|
||||
generator=generator,
|
||||
latents=latents,
|
||||
output_type=output_type,
|
||||
return_dict=return_dict,
|
||||
callback=callback,
|
||||
callback_steps=callback_steps,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# Get first result from Stable Diffusion Checkpoint v1.3
|
||||
res3 = self.text2img_sd1_3(
|
||||
prompt=prompt,
|
||||
height=height,
|
||||
width=width,
|
||||
num_inference_steps=num_inference_steps,
|
||||
guidance_scale=guidance_scale,
|
||||
negative_prompt=negative_prompt,
|
||||
num_images_per_prompt=num_images_per_prompt,
|
||||
eta=eta,
|
||||
generator=generator,
|
||||
latents=latents,
|
||||
output_type=output_type,
|
||||
return_dict=return_dict,
|
||||
callback=callback,
|
||||
callback_steps=callback_steps,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# Get first result from Stable Diffusion Checkpoint v1.4
|
||||
res4 = self.text2img_sd1_4(
|
||||
prompt=prompt,
|
||||
height=height,
|
||||
width=width,
|
||||
num_inference_steps=num_inference_steps,
|
||||
guidance_scale=guidance_scale,
|
||||
negative_prompt=negative_prompt,
|
||||
num_images_per_prompt=num_images_per_prompt,
|
||||
eta=eta,
|
||||
generator=generator,
|
||||
latents=latents,
|
||||
output_type=output_type,
|
||||
return_dict=return_dict,
|
||||
callback=callback,
|
||||
callback_steps=callback_steps,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
|
||||
return StableDiffusionPipelineOutput([res1[0], res2[0], res3[0], res4[0]])
|
||||
Loading…
Reference in New Issue