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README.md

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----
-license: openrail++
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+---
+license: openrail++
+tags:
+- stable-diffusion
+- image-to-image
+---
+# SD-XL 1.0-refiner Model Card
+
+> **Note:** This repository is a **mirror** and **not** the original upstream source.
+> The original model, weights, and documentation are developed and maintained by **Stability AI**.
+>
+> The model weights hosted here are **unmodified** and redistributed **as-is**.
+> Only minor editorial changes to this README (e.g. formatting or clarification) have been made and do **not** affect the model, its behavior, or its licensing.
+>
+> The model is released under the **CreativeML Open RAIL++-M License**, which permits use and redistribution **subject to explicit use-based restrictions** (see *Attachment A*).
+> A full copy of the license is included in this repository and applies to all distributions of the model and its derivatives.
+>
+> Users of this mirror are responsible for complying with all terms of the CreativeML Open RAIL++-M License.
+>
+> This repository is **not affiliated with or endorsed by Stability AI**.
+> The maintainer is willing to cooperate in good faith with the original rights holder regarding reasonable requests.
+
+## Model
+
+[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 (available here: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) is used to generate (noisy) latents, 
+which are then further processed with a refinement model specialized for the final denoising steps.
+Note that the base model can be used as a standalone module.
+
+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.
+
+Source code is available at https://github.com/Stability-AI/generative-models .
+
+### Model Description
+
+- **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-refiner-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).
+
+### Model Sources
+
+For research purposes, we recommned our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), which implements the most popoular 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.
+
+- **Repository:** https://github.com/Stability-AI/generative-models
+- **Demo:** https://clipdrop.co/stable-diffusion
+
+### 🧨 Diffusers 
+
+Make sure to upgrade diffusers to >= 0.18.0:
+```
+pip install diffusers --upgrade
+```
+
+In addition make sure to install `transformers`, `safetensors`, `accelerate` as well as the invisible watermark:
+```
+pip install invisible_watermark transformers accelerate safetensors
+```
+
+Yon can then use the refiner to improve images.
+
+```py
+import torch
+from diffusers import StableDiffusionXLImg2ImgPipeline
+from diffusers.utils import load_image
+pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+)
+pipe = pipe.to("cuda")
+url = "https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png"
+init_image = load_image(url).convert("RGB")
+prompt = "a photo of an astronaut riding a horse on mars"
+image = pipe(prompt, image=init_image).images
+```
+
+When using `torch >= 2.0`, you can improve the inference speed by 20-30% with torch.compile. Simple wrap the unet with torch compile before running the pipeline:
+```py
+pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+```
+
+If you are limited by GPU VRAM, you can enable *cpu offloading* by calling `pipe.enable_model_cpu_offload`
+instead of `.to("cuda")`:
+
+```diff
+- pipe.to("cuda")
++ pipe.enable_model_cpu_offload()
+```
+
+For more advanced use cases, please have a look at [the docs](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl).
+
+## Uses
+
+### Direct Use
+
+The model is intended for research purposes only. Possible research areas and tasks include
+
+- 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.
+
+Excluded uses are described below.
+
+### Out-of-Scope Use
+
+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.
+
+## Limitations and Bias
+
+### Limitations
+
+- 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.
+
+### Bias
+While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.