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.
- 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.
Make sure to upgrade diffusers to >= 0.18.0:
pip install diffusers --upgrade
In addition make sure to install
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
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.to("cuda") + pipe.enable_model_cpu_offload()
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.
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.
- 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.
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
"SDXL 0.9 is licensed under the SDXL Research License, Copyright (c) Stability AI Ltd. All Rights Reserved."
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