ModelListadosExterno

ControlNet Zp11 Depth v1

Recommended version of diffusers is 0.20.2 with torch 2.

Fontehuggingface.co/sudo-ai/controlnet-zp11-depth-v1/tree/df826762d94790ee915ef61966f311c8c0faf7e3

TipoControlNetVersãodiffusion_pytorch_modelBnota

Fonte do modelo

Tipo: ControlNet
Tamanho do arquivo: 1,36 GB
Versão: vdiffusion_pytorch_model
Atualizado: 6 de out. de 2026
Downloads: 12
Fonte: Hugging Face

Descrição da fonte

Recommended version of diffusers is 0.20.2 with torch 2.

Usage Example:

import torch
import requests
from PIL import Image
from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler, ControlNetModel

# Load the pipeline
pipeline = DiffusionPipeline.from_pretrained(
    "sudo-ai/zero123plus-v1.1", custom_pipeline="sudo-ai/zero123plus-pipeline",
    torch_dtype=torch.float16
)
pipeline.add_controlnet(ControlNetModel.from_pretrained(
    "sudo-ai/controlnet-zp11-depth-v1", torch_dtype=torch.float16
), conditioning_scale=0.75)
# Feel free to tune the scheduler
pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
    pipeline.scheduler.config, timestep_spacing='trailing'
)
pipeline.to('cuda:0')
# Run the pipeline
cond = Image.open(requests.get("https://d.skis.ltd/nrp/sample-data/0_cond.png", stream=True).raw)
depth = Image.open(requests.get("https://d.skis.ltd/nrp/sample-data/0_depth.png", stream=True).raw)
result = pipeline(cond, depth_image=depth).images[0]
result.show()
result.save("output.png")

Fontes

1 fonte

Hugging FacePrimárioVersão df826762d94790ee915ef61966f311c8c0faf7e3

huggingface.co/sudo-ai/controlnet-zp11-depth-v1/tree/df826762d94790ee915ef61966f311c8c0faf7e3

Verificado 6 de out.

Artefatos de modelo

1 artefato

diffusion_pytorch_model.safetensors

safetensors · 1,36 GB · SHA-256 f8e5e2fd90b6…2a13 · Hugging Face

Download file

Trechos de fonte

3 trechos

Readme29 de jul.huggingface.co/sudo-ai/controlnet-zp11-depth-v1

--- license: openrail --- Recommended version of `diffusers` is `0.20.2` with `torch` `2`. Usage Example: ```python import torch import requests from PIL import Image from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler, ControlNetModel # Load the pipeline pipeline = DiffusionPipeline.from_pretrained( "sudo-ai/zero123plus-v1.1", custom_pipeline="sudo-ai/zero123plus-pipeline", torch_dtype=torch.float16 ) pipeline.add_controlnet(ControlNetModel.from_pretrained( "sudo-ai/controlnet-zp11-depth-v1", torch_dtype=torch.float16 ), conditioning_scale=0.75) # Feel free to tune the scheduler pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config( pipeline.scheduler.config, timestep_spacing='trailing' ) pipeline.to('cuda:0') # Run the pipeline cond = Image.open(requests.get("https://d.skis.ltd/nrp/sample-data/0_cond.png", stream=True).raw) depth = Image.open(requests.get("https://d.skis.ltd/nrp/sample-data/0_depth.png", stream=True).raw) result = pipeline(cond, depth_image=depth).images[0] result.show() result.save("output.png") ```

Note6 de out.huggingface.co/sudo-ai/controlnet-zp11-depth-v1

Source context: 12 downloads · 5 likes · Library diffusers · Repo sudo-ai/controlnet-zp11-depth-v1

Note29 de jul.huggingface.co/sudo-ai/controlnet-zp11-depth-v1

Source context: 72 downloads · 5 likes · Library diffusers · Repo sudo-ai/controlnet-zp11-depth-v1