SkyAsl/Pixel-artist-Z
586
🎨 Pixel-artist-Z
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This model is fine-tuned on top of **Tongyi-MAI/Z-Image-Turbo** to improve pixel art character generation from text prompts.
The LoRA is optimized for prompts that start with or include:
"a pixel art character ..."
🚀 Model Description
- Base model:
Tongyi-MAI/Z-Image-Turbo - Fine-tuning method: LoRA (Low-Rank Adaptation)
- Task: Text-to-image generation
- Specialization: Pixel art characters
🧠 Why Pixel Art?
Pixel art differs significantly from natural images:
- Sharp, discrete edges
- Limited color palettes
- Low-resolution spatial structure
Generic diffusion models often blur these characteristics. This LoRA improves:
- Structural sharpness
- Style consistency
- Prompt–image alignment for pixel art descriptions
🧩 How to Use
import torch
from diffusers import DiffusionPipeline
from peft import PeftModel
pipe = DiffusionPipeline.from_pretrained(
"Tongyi-MAI/Z-Image-Turbo",
dtype=torch.bfloat16,
device_map="cuda"
)
pipe.load_lora_weights("SkyAsl/Pixel-artist-Z")
pipe.fuse_lora(lora_scale=0.85)
prompt = "a pixel art character with square orange glasses, a chef hat-shaped head and a purple-colored body on a cool background"
image = pipe(
prompt=prompt,
num_inference_steps=4,
guidance_scale=0.0
).images[0]
image.save("pixel_art.png")🧪 Evaluation
CLIPScore (Prompt–Image Alignment)
- +1.02 CLIPScore improvement ---
📈 Training Details
- Dataset: **m1guelpf/nouns**
- Image resolution: 512×512
- Epochs: 1
- Optimizer: AdamW (
lr=1e-4) - Precision: bfloat16
- Noise scheduler: DDPM (300 steps)
- Slope: -1.1016515720251527e-05
LoRA Configuration
r = 16
lora_alpha = 32
lora_dropout = 0.05
target_modules = [to_q, to_k, to_v, to_out.0]🖼️ Example Prompts
a pixel art character with a wizard hat and glowing blue eyes
a pixel art character holding a sword and wearing red armor
a pixel art character with a robot body and green visor⚠️ Limitations
- Optimized primarily for pixel art characters
- May not improve (or may slightly degrade) photorealistic prompts
