Model Card for SDXL Sketch-Style LoRA
Model Details
Model Description
This model is a LoRA fine-tuning module for SDXL-based anime and art-style image generation models.
It transforms polished renders into sketch-like / draft illustration variants by emphasizing linework, rough shading, and reduced color saturation while preserving composition and subject structure.
- Developed by: WorthyHuman1
- Model type: LoRA (Low-Rank Adaptation) for SDXL
- License: CreativeML Open RAIL-M
- Finetuned from model: SDXL 1.0–compatible anime/art checkpoints
Uses
Direct Use
Load this LoRA into any SDXL-compatible pipeline that supports LoRA weights to produce sketch-style outputs from text prompts.
Common use cases:
- Generating draft/concept-art sketches
- Exploring stylistic variations
- Educational demonstrations of style fine-tuning
Downstream Use
Can be combined with:
- Other LoRAs (character, pose, lighting)
- Custom SDXL pipelines and workflows
Out-of-Scope Use
- Photorealistic image generation
- High-fidelity full-color illustration
- Deceptive or misleading content generation
Bias, Risks, and Limitations
- Output quality depends on base model and prompt phrasing.
- Strong LoRA weights may oversimplify textures or reduce color fidelity.
- Style reflects patterns in the training data and may not generalize uniformly across all art styles.
Recommendations
- Experiment with LoRA strengths between
0.6 and 1.0.
- Use sketch-related keywords:
sketch, rough lineart, pencil drawing, unfinished illustration.
- Combine with an appropriate SDXL base model for best results.
How to Get Started
Suggested prompt keywords:
sketch_style, rough lineart, pencil drawing, unfinished illustration
Recommended LoRA strength: 0.6 – 1.0
Compatible with common SDXL tools (Diffusers, AUTOMATIC1111, ComfyUI, etc.).
Training Details
Training Data
Trained on a curated dataset of sketch-style and draft illustration images, filtered to emphasize line-based structure and reduced rendering polish. Training focused on style transformation rather than subject memorization.
Training Procedure
Parameter-efficient fine-tuning using LoRA techniques on SDXL-compatible layers.
Training Hyperparameters
- Precision / regime: fp16 mixed precision
Compute
- Hardware: NVIDIA GeForce RTX 4050
- Approximate training time: 16 hours
Evaluation
Testing Data, Factors & Metrics
Evaluation performed qualitatively across multiple SDXL base models and diverse prompts covering characters, portraits, and scenes. Assessment prioritized visual coherence, stylistic consistency, and prompt adherence via side-by-side comparisons.
Results
Produces consistent sketch-like outputs while preserving core composition across a variety of prompts when paired with suitable base models.
Environmental Impact
- Hardware: NVIDIA GeForce RTX 4050
- Hours used: ~16 hours
- Compute region: local / on-prem
(Estimate only)
Technical Specifications
Model Architecture and Objective
- Architecture: SDXL LoRA
- Objective: Style adaptation toward sketch/draft aesthetics
Software
- SDXL-compatible inference/training tools
- LoRA training framework (PyTorch-based)
Model Card Authors
Model Card Contact
- Hugging Face:
@WorthyHuman1
