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----
-license: openrail++
-tags:
-- stable-diffusion
-- image-to-image
----
-# SD-XL 1.0-refiner Model Card
-![row01](01.png)
-
-## Model
-
-![pipeline](pipeline.png)
-
-[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
-
-
-## Evaluation
-![comparison](comparison.png)
-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.
-
-
-### 🧨 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.