base_model: black-forest-labs/FLUX.1-dev
library_name: diffusers
tags:
- flux
- flux-diffusers
- text-to-image
- diffusers
- controlnet
- diffusers-training
- flux
- flux-diffusers
- text-to-image
- diffusers
- controlnet
- diffusers-training
inference: true
promeai/FLUX.1-controlnet-lineart-promeai
promeai/FLUX.1-controlnet-lineart-promeai holds controlnet weights trained on black-forest-labs/FLUX.1-dev with lineart condition.
Here are some example images.
prompt: cute anime girl with massive fluffy fennec ears and a big fluffy tail blonde messy long hair blue eyes wearing a maid outfit with a long black gold leaf pattern dress and a white apron mouth open holding a fancy black forest cake with candles on top in the kitchen of an old dark Victorian mansion lit by candlelight with a bright window to the foggy forest and very expensive stuff everywhere
| input-control | result image |
|---|---|
 |
 |
Intended uses & limitations
How to use
with diffusers
# TODO: add an example code snippet for running this diffusion pipeline
import torch
from diffusers.utils import load_image
from diffusers.pipelines.flux.pipeline_flux_controlnet import FluxControlNetPipeline
from diffusers.models.controlnet_flux import FluxControlNetModel
base_model = 'black-forest-labs/FLUX.1-dev'
controlnet_model = 'promeai/FLUX.1-controlnet-lineart-promeai'
controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16)
pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16)
pipe.to("cuda")
control_image = load_image("./images/example-control.jpg")
prompt = "cute anime girl with massive fluffy fennec ears and a big fluffy tail blonde messy long hair blue eyes wearing a maid outfit with a long black gold leaf pattern dress and a white apron mouth open holding a fancy black forest cake with candles on top in the kitchen of an old dark Victorian mansion lit by candlelight with a bright window to the foggy forest and very expensive stuff everywhere"
image = pipe(
prompt,
control_image=control_image,
controlnet_conditioning_scale=0.6,
num_inference_steps=28,
guidance_scale=3.5,
).images[0]
image.save("./image.jpg")
with comfyui
An example comfyui workflowis also provided.
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
This controlnet is trained on one A100-80G GPU, with carefully selected proprietary real-world images dataset, with imagesize 512 + batchsize 3 (earlier period), and imagesize 1024 + batchsize 1 (after 512 training). With above configs, the GPU memory was about 70G and takes around 3 days to get this 14000steps-checkpoint.