HighCWu / sd-control-lora-face-landmarks

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Total runs: 31
24-hour runs: 0
7-day runs: 2
30-day runs: 3
Model's Last Updated: February 20 2024
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Introduction of sd-control-lora-face-landmarks

Model Details of sd-control-lora-face-landmarks

ControlLoRA - Face Landmarks Version

ControlLoRA is a neural network structure extended from Controlnet to control diffusion models by adding extra conditions. This checkpoint corresponds to the ControlLoRA conditioned on Face Landmarks.

ControlLoRA uses the same structure as Controlnet. But its core weight comes from UNet, unmodified. Only hint image encoding layers, linear lora layers and conv2d lora layers used in weight offset are trained.

The main idea is from my ControlLoRA and sdxl control-lora .

Example
  1. Clone ControlLoRA from Github :
$ git clone https://github.com/HighCWu/control-lora-v2
  1. Enter the repo dir:
$ cd control-lora-v2
  1. Run code:
from PIL import Image
from diffusers import StableDiffusionControlNetPipeline, UNet2DConditionModel, UniPCMultistepScheduler
import torch
from PIL import Image
from models.control_lora import ControlLoRAModel

image = Image.open('<Your Conditioning Image Path>')

base_model = "runwayml/stable-diffusion-v1-5"

unet = UNet2DConditionModel.from_pretrained(
    base_model, subfolder="unet", torch_dtype=torch.float16
)
control_lora = ControlLoRAModel.from_pretrained(
    "HighCWu/sd-control-lora-face-landmarks", torch_dtype=torch.float16
)
control_lora.tie_weights(unet)

pipe = StableDiffusionControlNetPipeline.from_pretrained(
    base_model, unet=unet, controlnet=control_lora, safety_checker=None, torch_dtype=torch.float16
)

pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)

# Remove if you do not have xformers installed
# see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers
# for installation instructions
pipe.enable_xformers_memory_efficient_attention()

pipe.enable_model_cpu_offload()

image = pipe("Girl smiling, professional dslr photograph, high quality", image, num_inference_steps=20).images[0]

image.show()

You can find some example images below.

prompt: High-quality close-up dslr photo of man wearing a hat with trees in the background images_0) prompt: Girl smiling, professional dslr photograph, dark background, studio lights, high quality images_1) prompt: Portrait of a clown face, oil on canvas, bittersweet expression images_2)

Runs of HighCWu sd-control-lora-face-landmarks on huggingface.co

31
Total runs
0
24-hour runs
4
3-day runs
2
7-day runs
3
30-day runs

More Information About sd-control-lora-face-landmarks huggingface.co Model

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sd-control-lora-face-landmarks huggingface.co is an AI model on huggingface.co that provides sd-control-lora-face-landmarks's model effect (), which can be used instantly with this HighCWu sd-control-lora-face-landmarks model. huggingface.co supports a free trial of the sd-control-lora-face-landmarks model, and also provides paid use of the sd-control-lora-face-landmarks. Support call sd-control-lora-face-landmarks model through api, including Node.js, Python, http.

sd-control-lora-face-landmarks huggingface.co Url

https://huggingface.co/HighCWu/sd-control-lora-face-landmarks

HighCWu sd-control-lora-face-landmarks online free

sd-control-lora-face-landmarks huggingface.co is an online trial and call api platform, which integrates sd-control-lora-face-landmarks's modeling effects, including api services, and provides a free online trial of sd-control-lora-face-landmarks, you can try sd-control-lora-face-landmarks online for free by clicking the link below.

HighCWu sd-control-lora-face-landmarks online free url in huggingface.co:

https://huggingface.co/HighCWu/sd-control-lora-face-landmarks

sd-control-lora-face-landmarks install

sd-control-lora-face-landmarks is an open source model from GitHub that offers a free installation service, and any user can find sd-control-lora-face-landmarks on GitHub to install. At the same time, huggingface.co provides the effect of sd-control-lora-face-landmarks install, users can directly use sd-control-lora-face-landmarks installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

sd-control-lora-face-landmarks install url in huggingface.co:

https://huggingface.co/HighCWu/sd-control-lora-face-landmarks

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