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README.md

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+# SD-XL 1.0-refiner Model Card
+![row01](01.png)
+
+## Model
+
+![pipeline](pipeline.png)
+
+[SDXL](https://arxiv.org/abs/2307.01952) consists of a mixture-of-experts 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
+```
+
+You can use the model then as follows
+```py
+from diffusers import DiffusionPipeline
+import torch
+
+pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
+pipe.to("cuda")
+
+# if using torch < 2.0
+# pipe.enable_xformers_memory_efficient_attention()
+
+prompt = "An astronaut riding a green horse"
+
+images = pipe(prompt=prompt).images[0]
+```
+
+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()
+```
+
+
+## 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.