You can deploy this model and start earning money today!

gxkok/
layer-diffusion-xl-transparent-attn-lora

Name: layer-diffusion-xl-transparent-attn-lora
Availability: InStock

2402.17113

1910.09700

apache-2.0

Views

No views yet

Model Card for layer_xl_transparent_attn

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Model Details

LoRA weights for SDXL using huggingface diffusers converted from this weight.

Model Description

Developed by: Lvmin Zhang et al
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Model type: SDXL LoRA-256
Language(s) (NLP): [More Information Needed]
License: Apache 2.0
Finetuned from model [optional]: SDXL

Model Sources [optional]

Repository: https://github.com/layerdiffusion/LayerDiffuse
Paper [optional]: https://arxiv.org/abs/2402.17113
Demo [optional]: [More Information Needed]

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

bash

from diffusers import StableDiffusionXLPipeline
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file

pipe = StableDiffusionXLPipeline.from_pretrained(
  "stabilityai/stable-diffusion-xl-base-1.0",
  use_safetensors=True, 
  variant="fp16",
  torch_dtype=torch.float16, 
)
# pipe.enable_xformers_memory_efficient_attention()
pipe.to("cuda")

pipe.load_lora_weights(hf_hub_download("gxkok/layer-diffusion-xl-transparent-attn-lora", "pytorch_lora_weights.safetensors"))

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]