Azis1 LoRA for Qwen-Image

A LoRA (Low-Rank Adaptation) model trained on the Qwen-Image base model for generating images of a specific person.

Model Details

Property	Value
Base Model	Qwen/Qwen-Image
LoRA Rank	64
LoRA Alpha	64
Training Resolution	512x512
Training Epochs	5
Training Images	~2400
Trigger Word	azis1

Usage

Trigger Word

Always include azis1 at the beginning of your prompt for best results.

With Diffusers (Python)

import torch
from diffusers import DiffusionPipeline

# Load base model
pipe = DiffusionPipeline.from_pretrained(
    "Qwen/Qwen-Image",
    torch_dtype=torch.bfloat16,
)
pipe.enable_model_cpu_offload()

# Load LoRA
pipe.load_lora_weights("YOUR_USERNAME/azis1-qwen-lora")

# Generate
image = pipe(
    prompt="azis1, portrait of a man, professional photography, studio lighting",
    num_inference_steps=50,
    guidance_scale=4.0,
    height=1024,
    width=1024,
).images[0]

image.save("output.png")

With ComfyUI

1. Download the .safetensors file to ComfyUI/models/loras/

2. Use this node setup:

   • UNETLoader → qwen_image_fp8_e4m3fn.safetensors
   • LoraLoaderModelOnly → azis1.safetensors (strength: 0.85)
   • CLIPLoader → qwen_2.5_vl_7b_fp8_scaled.safetensors
   • VAELoader → qwen_image_vae.safetensors

3. Recommended settings:

   • Steps: 50
   • CFG Scale: 4.0
   • Sampler: Euler

Example Prompts

azis1, portrait of a man, professional photography, studio lighting, high quality, 4K
azis1, man in casual clothes, outdoor, natural lighting
azis1, close-up portrait, dramatic lighting, artistic
azis1, man with beard, formal attire, corporate headshot

Training Details

• Framework: OneTrainer
• Optimizer: AdamW 8-bit
• Learning Rate: 0.0001 (Cosine schedule)
• Batch Size: 2 (with gradient accumulation of 2)
• Precision: bfloat16
• Hardware: NVIDIA RTX 5090 (32GB VRAM)

Limitations

• This LoRA only works with Qwen-Image base model
• It will NOT work with Stable Diffusion 1.5, SDXL, or Flux models
• Best results at 512x512 to 1024x1024 resolution

License

Apache 2.0 - Same as the base Qwen-Image model.

Acknowledgments

• Qwen-Image by Alibaba
• OneTrainer for training