FFusionXL-09-SDXL

FFXL Model Card

<div style="display: flex; flex-wrap: wrap; gap: 2px;"> <img src="https://img.shields.io/badge/%F0%9F%94%A5%20Refiner%20Compatible-Yes-success"> <img src="https://img.shields.io/badge/%F0%9F%92%BB%20CLIP--ViT%2FG%20and%20CLIP--ViT%2FL%20tested-Yes-success"> <img src="https://img.shields.io/badge/%F0%9F%A7%A8%20FFXL%20Diffusers-available-brightgreen"> </div>

Model

FFXL based on SDXL consists of a two-step pipeline for latent diffusion: First, we use a base model 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.

Model Description

• Trained by: FFusion AI
• Model type: Diffusion-based text-to-image generative model
• License: FFXL Research License
• Model Description: This is a trained model based on SDXL that can be used to generate and modify images based on text prompts. It is a Latent Diffusion Model that uses two fixed, pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).
• Resources for more information: SDXL paper on arXiv.

Model Sources

• Demo:

  <div style="display: flex; flex-wrap: wrap; gap: 2px;"> <a href="https://huggingface.co/FFusion/FFusion-BaSE" target="_new" rel="ugc"><img src="https://img.shields.io/badge/Hugging%20Face-FFusion--BaSE-blue" alt="Hugging Face Model"></a> <a href="https://github.com/1e-2" target="_new" rel="ugc"><img src="https://img.shields.io/badge/GitHub-1e--2-green" alt="GitHub"></a> <a href="https://www.facebook.com/FFusionAI/" target="_new" rel="ugc"><img src="https://img.shields.io/badge/Facebook-FFusionAI-blue" alt="Facebook"></a> <a href="https://civitai.com/models/82039/ffusion-ai-sd-21" target="_new" rel="ugc"><img src="https://img.shields.io/badge/Civitai-FFusionAI-blue" alt="Civitai"></a>

</div>

🧨 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

from diffusers import DiffusionPipeline
import torch

pipe = DiffusionPipeline.from_pretrained("FFusion/FFusionXL-09-SDXL", 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:

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"):

- 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.

Attribution:

"SDXL 0.9 is licensed under the SDXL Research License, Copyright (c) Stability AI Ltd. All Rights Reserved."

License

SDXL 0.9 Research License" FFXL 0.9 Research License"

SAMPLES