ControlNet SD 2 1 Base v1
These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning.
Modellquelle
- Art
- ControlNet
- Dateigröße
- 1,36 GB
- Basismodell
- stabilityai/stable-diffusion-2-1-base
- Version
- vdiffusion_pytorch_model
- Aktualisiert
- 7. Okt. 2026
- Downloads
- 10
- Quelle
- Hugging Face
Quellenbeschreibung
controlnet-liuch37/controlnet-sd-2-1-base-v1
These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning.
Intended uses & limitations
How to use
from PIL import Image
from diffusers import (
ControlNetModel,
StableDiffusionControlNetPipeline,
UniPCMultistepScheduler,
)
checkpoint = "liuch37/controlnet-sd-2-1-base-v1"
prompt = "YOUR_FAVORITE_PROMPT"
control_image = Image.open("YOUR_SEMANTIC_IMAGE")
controlnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float32)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1-base", controlnet=controlnet, torch_dtype=torch.float32
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
generator = torch.manual_seed(0)
image = pipe(prompt, num_inference_steps=30, generator=generator, image=control_image).images[0]
image.save("YOUR_OUTPUT_IMAGE")
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
Train the ControlNet with semantic maps as the condition. Cityscapes training set is used for training (https://huggingface.co/datasets/liuch37/controlnet-cityscapes). Only 2 epochs are trained for the current version.
Quellen
1 Quelle
Quellen
1 QuelleVerifiziert 7. Okt.
Modellartefakte
1 Artefakt
Modellartefakte
1 Artefaktcheckpoint-1200/controlnet/diffusion_pytorch_model.safetensors
safetensors · 1,36 GB · SHA-256 c12c94361077…6dae · Hugging Face
Download fileQuellenauszüge
3 Auszüge
Quellenauszüge
3 Auszüge--- base_model: stabilityai/stable-diffusion-2-1-base library_name: diffusers license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet - diffusers-training - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet - diffusers-training inference: true --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # controlnet-liuch37/controlnet-sd-2-1-base-v1 These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning. ## Intended uses & limitations #### How to use ```python from PIL import Image from diffusers import ( ControlNetModel, StableDiffusionControlNetPipeline, UniPCMultistepScheduler, ) checkpoint = "liuch37/controlnet-sd-2-1-base-v1" prompt = "YOUR_FAVORITE_PROMPT" control_image = Image.open("YOUR_SEMANTIC_IMAGE") controlnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float32) pipe = StableDiffusionControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-2-1-base", controlnet=controlnet, torch_dtype=torch.float32 ) pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) generator = torch.manual_seed(0) image = pipe(prompt, num_inference_steps=30, generator=generator, image=control_image).images[0] image.save("YOUR_OUTPUT_IMAGE") ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details Train the ControlNet with semantic maps as the condition. Cityscapes training set is used for training (https://huggingface.co/datasets/liuch37/controlnet-cityscapes). Only 2 epochs are trained for the current version.
Source context: 10 downloads · 0 likes · Pipeline text-to-image · Library diffusers · Repo liuch37/controlnet-sd-2-1-base-v1
Source context: 11 downloads · 0 likes · Pipeline text-to-image · Library diffusers · Repo liuch37/controlnet-sd-2-1-base-v1
