Flux.1-dev ControlNets
Collection
A collection of ControlNet models for Flux.1-dev by Jasper Research
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4 items
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Updated
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This is Flux.1-dev ControlNet for Depth map developed by Jasper research team.
This model can be used directly with the diffusers
library
import torch
from diffusers.utils import load_image
from diffusers import FluxControlNetModel
from diffusers.pipelines import FluxControlNetPipeline
# Load pipeline
controlnet = FluxControlNetModel.from_pretrained(
"jasperai/Flux.1-dev-Controlnet-Depth",
torch_dtype=torch.bfloat16
)
pipe = FluxControlNetPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
controlnet=controlnet,
torch_dtype=torch.bfloat16
)
pipe.to("cuda")
# Load a control image
control_image = load_image(
"https://huggingface.co/jasperai/Flux.1-dev-Controlnet-Depth/resolve/main/examples/depth.jpg"
)
prompt = "a statue of a gnome in a field of purple tulips"
image = pipe(
prompt,
control_image=control_image,
controlnet_conditioning_scale=0.6,
num_inference_steps=28,
guidance_scale=3.5,
height=control_image.size[1],
width=control_image.size[0]
).images[0]
image
💡 Note: You can compute the conditioning map using for instance the MidasDetector
from the controlnet_aux
library
from controlnet_aux import MidasDetector
from diffusers.utils import load_image
midas = MidasDetector.from_pretrained("lllyasviel/Annotators")
midas.to("cuda")
# Load an image
im = load_image(
"https://huggingface.co/jasperai/Flux.1-dev-Controlnet-Depth/resolve/main/examples/output.jpg"
)
depth = midas(im)
This model was trained with depth maps computed with Clipdrop's depth estimator model as well as open-souce depth estimation models such as Midas or Leres.
This model falls under the Flux.1-dev licence.
Base model
black-forest-labs/FLUX.1-dev