FFXL Model Card
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
🧨 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"
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Model tree for FFusion/FFusionXL-09-SDXL
Base model
diffusers/stable-diffusion-xl-base-0.9