README.md

---
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](https://huggingface.co/stabilityai/stable-diffusion-xl-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`)
```python
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)
```Python
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")
```

3. 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.