| --- |
| license: openrail++ |
| tags: |
| - text-to-image |
| - stable-diffusion |
| - Neuron |
| - Inferentia |
| --- |
| # SD-XL 1.0-base Model Card - Neuron |
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| ## Model |
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| [SDXL](https://arxiv.org/abs/2307.01952) consists of an [ensemble of experts](https://arxiv.org/abs/2211.01324) pipeline for latent diffusion: |
| In a first step, the base model is used to generate (noisy) latents, |
| which are then further processed with a refinement model (available here: https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/) specialized for the final denoising steps. |
| Note that the base model can be used as a standalone module. |
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| Alternatively, we can use a two-stage pipeline as follows: |
| First, the base model is used to generate latents of the desired output size. |
| In the second step, we use a specialized high-resolution model and apply a technique called SDEdit (https://arxiv.org/abs/2108.01073, also known as "img2img") |
| to the latents generated in the first step, using the same prompt. This technique is slightly slower than the first one, as it requires more function evaluations. |
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| Source code is available at https://github.com/Stability-AI/generative-models . |
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| ### Model Description |
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| - **Developed by:** Stability AI |
| - **Model type:** Diffusion-based text-to-image generative model |
| - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md) |
| - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses two fixed, pretrained text encoders ([OpenCLIP-ViT/G](https://github.com/mlfoundations/open_clip) and [CLIP-ViT/L](https://github.com/openai/CLIP/tree/main)). |
| - **Resources for more information:** Check out our [GitHub Repository](https://github.com/Stability-AI/generative-models) and the [SDXL report on arXiv](https://arxiv.org/abs/2307.01952). |
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| ### Model Sources |
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| For research purposes, we recommend our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), which implements the most popular diffusion frameworks (both training and inference) and for which new functionalities like distillation will be added over time. |
| [Clipdrop](https://clipdrop.co/stable-diffusion) provides free SDXL inference. |
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| - **Repository:** https://github.com/Stability-AI/generative-models |
| - **Demo:** https://clipdrop.co/stable-diffusion |
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| ## Evaluation |
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| The chart above evaluates user preference for SDXL (with and without refinement) over SDXL 0.9 and Stable Diffusion 1.5 and 2.1. |
| The SDXL base model performs significantly better than the previous variants, and the model combined with the refinement module achieves the best overall performance. |
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| ### Usage |
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| ```py |
| from diffusers import DPMSolverMultistepScheduler |
| from optimum.neuron import NeuronStableDiffusionXLPipeline |
| |
| pipeline = NeuronStableDiffusionXLPipeline.from_pretrained("Shekswess/stable-diffusion-xl-base-1.0-neuron", device_ids=[0, 1]) |
| pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config) |
| |
| prompt = "A swirling beautiful exploding scene of magical wonders and surreal ideas and objects with portraits of beautiful woman with silk back to camera, flowers, light, cosmic wonder, nebula, high-resolution" |
| image = pipeline(prompt=prompt).images[0].save("output.png) |
| ``` |
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| For more information on how to use Stable Diffusion XL with `diffusers`, please have a look at [the Stable Diffusion XL Docs](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl). |
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| ## Uses |
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| ### Direct Use |
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| The model is intended for research purposes only. Possible research areas and tasks include |
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| - Generation of artworks and use in design and other artistic processes. |
| - Applications in educational or creative tools. |
| - Research on generative models. |
| - Safe deployment of models which have the potential to generate harmful content. |
| - Probing and understanding the limitations and biases of generative models. |
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| Excluded uses are described below. |
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| ### Out-of-Scope Use |
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| The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. |
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| ## Limitations and Bias |
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| ### Limitations |
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| - The model does not achieve perfect photorealism |
| - The model cannot render legible text |
| - The model struggles with more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” |
| - Faces and people in general may not be generated properly. |
| - The autoencoding part of the model is lossy. |
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| ### Bias |
| While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. |
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| ## Original Model |
| [Model](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) |
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| ## Precision |
| BFloat16 (bf16) |
| For Matrix Multiplication Operations. |
