Sketch_LoRA

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

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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

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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.

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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.).

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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

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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.

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Environmental Impact

• Hardware: NVIDIA GeForce RTX 4050
• Hours used: ~16 hours
• Compute region: local / on-prem

(Estimate only)

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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)

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Model Card Authors

• WorthyHuman1

Model Card Contact

• Hugging Face: @WorthyHuman1