ControlNetXL LaserCode
This ControlNet model is a fine-tuned version of diffusers/controlnet-canny-sdxl-1.0, optimized for generating realistic synthetic images of laser-marked industrial packaging. It was trained to align edge-guided...
Source du modèle
- Type
- ControlNet
- Taille du fichier
- 2,33 GB
- Version
- vdiffusion_pytorch_model
- Mis à jour
- 7 oct. 2026
- Téléchargements
- 20
- Source
- Hugging Face
Description de la source
🧠 Model Card: ControlNet-SDXL Fine-Tuned for Industrial Laser Marking
This ControlNet model is a fine-tuned version of diffusers/controlnet-canny-sdxl-1.0, optimized for generating realistic synthetic images of laser-marked industrial packaging. It was trained to align edge-guided structure with high visual fidelity for Optical Character Verification (OCV) tasks in manufacturing and traceability pipelines.
Laser Marked Packaging
Figure 1: Synthetic images generated with ControlNet
🧩 Model Details
Model Description
- Model Type: ControlNet SDXL (Canny version), diffusion-based conditional image generator
- Finetuned From:
diffusers/controlnet-canny-sdxl-1.0 - Base Model: Stable Diffusion XL
stabilityai/stable-diffusion-xl-base-1.0 - Pretrained VAE:
madebyollin/sdxl-vae-fp16-fix - Use Case: Synthetic image generation for defect simulation and dataset augmentation
- Developed by: Julia Garcia
- Supervised by: Dr. David Castells Rufas, Universitat Autònoma de Barcelona
- Project Type: Final Degree Project
Model Sources
- Repository: Synthetic-Data-Generation-for-Robust-Optical-Character-Verification-in-Industrial-Laser-Marking
✅ Direct Use
This model can be used with the Diffusers library to generate grayscale, structurally accurate synthetic images of codes printed on real-world surfaces using Canny edge maps as guidance. It is ideal for:
- Optical Character Verification (OCV) dataset augmentation
- Industrial defect simulation (e.g., misplacement, beam distortion)
- Training robust recognition models for food/pharma packaging
Recommendations
- Use clear, well-preprocessed edges (800x800 recommended)
- For novel packaging types, consider retraining with few-shot LoRA/IP-Adapter modules
- Do not use in safety-critical real-time environments without human review
🚀 Getting Started
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
from diffusers.utils import load_image
import torch
# Load fine-tuned ControlNet
controlnet = ControlNetModel.from_pretrained("juliagartor/ControlNetXL_LaserCode", torch_dtype=torch.float16)
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,
torch_dtype=torch.float16
).to("cuda")
# Example generation
generated_img = pipe(
prompt,
image=control_img.convert("RGB").resize((800, 800)),
num_inference_steps=20,
num_images_per_prompt=1,
controlnet_conditioning_scale=1.2,
).images[0]
generated_img.save("generated.png")
Sources
1 source
Sources
1 sourceVérifié 7 oct.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactdiffusion_pytorch_model.safetensors
safetensors · 2,33 GB · SHA-256 b23e5819598e…587b · Hugging Face
Download fileExtraits de sources
3 extraits
Extraits de sources
3 extraits--- library_name: diffusers --- # 🧠 Model Card: ControlNet-SDXL Fine-Tuned for Industrial Laser Marking This ControlNet model is a fine-tuned version of `diffusers/controlnet-canny-sdxl-1.0`, optimized for generating realistic synthetic images of laser-marked industrial packaging. It was trained to align edge-guided structure with high visual fidelity for Optical Character Verification (OCV) tasks in manufacturing and traceability pipelines.  *Figure 1: Synthetic images generated with ControlNet* --- ## 🧩 Model Details ### Model Description - **Model Type:** ControlNet SDXL (Canny version), diffusion-based conditional image generator - **Finetuned From:** `diffusers/controlnet-canny-sdxl-1.0` - **Base Model:** Stable Diffusion XL `stabilityai/stable-diffusion-xl-base-1.0` - **Pretrained VAE:** `madebyollin/sdxl-vae-fp16-fix` - **Use Case:** Synthetic image generation for defect simulation and dataset augmentation - **Developed by:** Julia Garcia - **Supervised by:** Dr. David Castells Rufas, Universitat Autònoma de Barcelona - **Project Type:** Final Degree Project ### Model Sources - **Repository:** [Synthetic-Data-Generation-for-Robust-Optical-Character-Verification-in-Industrial-Laser-Marking](https://github.com/juliagartor/Synthetic-Data-Generation-for-Robust-Optical-Character-Verification-in-Industrial-Laser-Marking) --- ## ✅ Direct Use This model can be used with the Diffusers library to generate grayscale, structurally accurate synthetic images of codes printed on real-world surfaces using Canny edge maps as guidance. It is ideal for: - Optical Character Verification (OCV) dataset augmentation - Industrial defect simulation (e.g., misplacement, beam distortion) - Training robust recognition models for food/pharma packaging - --- ## Recommendations - Use clear, well-preprocessed edges (800x800 recommended) - For novel packaging types, consider retraining with few-shot LoRA/IP-Adapter modules - Do not use in safety-critical real-time environments without human review --- ## 🚀 Getting Started ```python from diffusers import StableDiffusionControlNetPipeline, ControlNetModel from diffusers.utils import load_image import torch # Load fine-tuned ControlNet controlnet = ControlNetModel.from_pretrained("juliagartor/ControlNetXL_LaserCode", torch_dtype=torch.float16) vae = AutoencoderKL.from...
Source context: 20 downloads · 0 likes · Library diffusers · Repo juliagartor/ControlNetXL_LaserCode
Source context: 2 downloads · 0 likes · Library diffusers · Repo juliagartor/ControlNetXL_LaserCode
