ControlNet Depth SDXL 1.0
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler from PIL import Image import torch import random import...
Fuente del modelo
- Tipo
- ControlNet
- Tamaño del archivo
- 2,33 GB
- Versión
- vdiffusion_pytorch_model
- Actualizado
- 6 oct 2026
- Descargas
- 8
- Fuente
- Hugging Face
Descripción de la fuente
ControlNet Depth SDXL, support zoe, midias
images
Example
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How to use it
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import random
import numpy as np
import cv2
from controlnet_aux import MidasDetector, ZoeDetector
processor_zoe = ZoeDetector.from_pretrained("lllyasviel/Annotators")
processor_midas = MidasDetector.from_pretrained("lllyasviel/Annotators")
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-depth-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
img = cv2.imread("your original image path")
if random.random() > 0.5:
controlnet_img = processor_zoe(img, output_type='cv2')
else:
controlnet_img = processor_midas(img, output_type='cv2')
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 6 oct
Artefactos del modelo
1 artefacto
Artefactos del modelo
1 artefactodiffusion_pytorch_model.safetensors
safetensors · 2,33 GB · SHA-256 ea3770af4c33…d344 · Hugging Face
Download fileExtractos de fuentes
3 extractos
Extractos de fuentes
3 extractos--- license: apache-2.0 pipeline_tag: text-to-image --- # ***ControlNet Depth SDXL, support zoe, midias***  # Example           # How to use it ```python from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler from PIL import Image import torch import random import numpy as np import cv2 from controlnet_aux import MidasDetector, ZoeDetector processor_zoe = ZoeDetector.from_pretrained("lllyasviel/Annotators") processor_midas = MidasDetector.from_pretrained("lllyasviel/Annotators") controlnet_conditioning_scale = 1.0 prompt = "your prompt, the longer the better, you can describe it as detail as possible" negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality' eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler") controlnet = ControlNetModel.from_pretrained( "xinsir/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16 ) # when test with other base model, you need to change the vae also. vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) pipe = StableDiffusionXLControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, vae=vae, safety_checker=None, torch_dtype=torch.float16, scheduler=eulera_scheduler, ) # need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance img = cv2.imread("your original image path") if random.random() > 0.5: controlnet_img = processor_zoe(img, output_type='cv2') else: controlnet_img = processor_midas(img, output_type='cv2') height, width, _ = controlnet_img.shape ratio = np.sqrt(1024. * 1024. / (width * height)) new_width, new_height = int(width * ratio), int(height * ratio) controlnet_i...
Source context: 8 downloads · 0 likes · Pipeline text-to-image · Library diffusers · Repo kristian1515/controlnet-depth-sdxl-1.0
Source context: 17 downloads · 0 likes · Pipeline text-to-image · Library diffusers · Repo kristian1515/controlnet-depth-sdxl-1.0
