license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-xl-base-1.0
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- image-to-image
- diffusers
- lora
- kielforge-fast
inference: true
library_name: diffusers
model_name: KielForge-fast
KielForge-fast (SDXL LoRA)
KielForge-fast is a fine-tuned Stable Diffusion XL (SDXL) LoRA designed to generate and modify high-fidelity, intricately detailed futuristic portraits, advanced sci-fi characters, and concept art via both Text-to-Image and Image-to-Image workflows with remarkable realism and sharp textures.
🎨 Model Details
- Developer/Creator: KielTech
- Base Architecture: StabilityAI SDXL Base 1.0
- Model Type: Text-to-Image & Image-to-Image / Fine-tuned SDXL LoRA & GGUF variants
- Available Formats: GGUF (
kielforge-fast_q4_0.gguf,kielforge-fast_f16.gguf) & 16-bit LoRA weights (pytorch_lora_weights.safetensors) - Quantization Level: Q4_0 (4-bit round-to-nearest quantization for optimal balance between VRAM footprint and generation quality) alongside full 16-bit precision options
- Language: English
⚙️ Processing & Conversion Pipeline
The weights for this model were trained, merged, and processed inside a Kaggle development environment. The custom fine-tuned LoRA was optimized and converted into both 16-bit and GGUF standards to ensure maximum compatibility, flexibility, and efficient execution on consumer hardware running local inference stacks as well as cloud notebooks.
🚀 Recommended Usage & Parameters
- Resolution: 1024 × 1024 pixels (Native SDXL resolution)
- Sampling Steps: 25 – 35 steps
- Sampler: DPM++ 2M Karras, DPM++ SDE Karras, or Euler a
- CFG Scale (Guidance): 5.0 – 8.0
- Img2Img Strength: 0.45 – 0.6 (Sweet spot for modifying details while preserving composition)
💻 How to Use
1. Text-to-Image (StableDiffusionXLPipeline)
import torch
from diffusers import StableDiffusionXLPipeline
base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
pipe = StableDiffusionXLPipeline.from_pretrained(
base_model_id,
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True
).to("cuda")
pipe.load_lora_weights(
"kiel2/KielForge-fast",
weight_name="pytorch_lora_weights.safetensors"
)
pipe.enable_attention_slicing()
prompt = "A stunning portrait of a futuristic warrior, highly detailed armor, masterwork"
negative_prompt = "blurry, distorted, low quality, low resolution"
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=30,
guidance_scale=7.5
).images[0]
image.save("generated_image.png")
2. Image-to-Image (StableDiffusionXLImg2ImgPipeline)
import torch
import gc
from diffusers import StableDiffusionXLImg2ImgPipeline
from PIL import Image
torch.cuda.empty_cache()
gc.collect()
init_image = Image.open("test_output.png").convert("RGB")
init_image = init_image.resize((1024, 1024))
base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
base_model_id,
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True
)
pipe.enable_model_cpu_offload()
pipe.load_lora_weights(
"kiel2/KielForge-fast",
weight_name="pytorch_lora_weights.safetensors"
)
prompt = "A stunning portrait of a futuristic warrior with glowing neon cybernetic implants on her face, highly detailed armor, masterwork"
negative_prompt = "blurry, distorted, low quality, low resolution"
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
image=init_image,
strength=0.5,
num_inference_steps=30,
guidance_scale=7.5
).images[0]
image.save("generated_image_amended.png")
- Local Inference UIs (ComfyUI / WebUI Forge) Download your preferred weight variant (kielforge-fast_q4_0.gguf, kielforge-fast_f16.gguf, or the LoRA files) directly from this repository.
Place the file into your local inference UI's appropriate directory (e.g., ComfyUI/models/unet/ for GGUF files or ComfyUI/models/loras/ for the LoRA adapter weights).
Load the model through your text-to-image or image-to-image workflow to generate or transform your images!
⚠️ Limitations & Bias When using the 4-bit quantized Q4_0 version, users may occasionally notice minor quantization artifacts compared to the full 16-bit float variants, traded off for significantly faster generation speeds and a dramatically lower memory footprint.
This model inherits the general capabilities, constraints, and safety profiles of the underlying SDXL base architecture.