Text-to-Image
Diffusers
TensorBoard
GGUF
stable-diffusion-xl
stable-diffusion-xl-diffusers
lora
image-to-image
art
custom-character
Instructions to use kiel2/KielForge-HD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kiel2/KielForge-HD with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("kiel2/KielForge-HD") prompt = "A cinematic portrait of the man, highly detailed skin texture, sharp focus, natural lighting" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
KielForge-HD
KielForge-HD is a custom-trained Stable Diffusion XL (SDXL) LoRA model designed for high-definition, cinematic character generation. It supports both Text-to-Image and Image-to-Image workflows, delivering rich skin textures, dramatic lighting, and sharp stylistic consistency.
Model Details
- Base Model: stabilityai/stable-diffusion-xl-base-1.0
- Model Type: Diffusion-based text-to-image and image-to-image LoRA / Checkpoint
- Training Resolution: SDXL Standard (1024x1024)
- Creator: KielTech
Files Provided in this Repository
pytorch_lora_weights.safetensors: Standard PEFT LoRA weights (recommended for dynamic loading and blending).kielforge_sdxl_merged_fp16.safetensors: Full 16-bit standalone merged checkpoint.kielforge_sdxl_Q4_K_M.gguf: 4-bit quantized GGUF format for memory-efficient local inference (e.g., ComfyUI).
Quick Start (Python / Diffusers)
You can load and use the LoRA weights directly with the Diffusers library for both text-to-image and image-to-image tasks:
import torch
from diffusers import StableDiffusionXLPipeline
# Load base SDXL pipeline
pipeline = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True
)
pipeline.enable_model_cpu_offload()
# Load KielForge-HD LoRA weights
pipeline.load_lora_weights("kiel2/KielForge-HD", weight_name="pytorch_lora_weights.safetensors")
# Text-to-Image Generation
prompt = "A cinematic close-up portrait of the man, detailed skin texture, natural lighting, masterpiece"
negative_prompt = "blurry, low quality, distorted, deformed face, plastic skin, waxy"
image = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=35,
guidance_scale=7.5,
cross_attention_kwargs={"scale": 0.75}
).images[0]
image.save("output.png")
Image-to-Image Workflow Example
import torch
from diffusers import StableDiffusionXLImg2ImgPipeline
from diffusers.utils import load_image
# Load pipeline for Image-to-Image
pipeline = StableDiffusionXLImg2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True
)
pipeline.enable_model_cpu_offload()
# Load LoRA weights
pipeline.load_lora_weights("kiel2/KielForge-HD", weight_name="pytorch_lora_weights.safetensors")
# Load input source image
init_image = load_image("[https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_file_sd.png](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_file_sd.png)").resize((1024, 1024))
prompt = "A cinematic portrait of the man in an outdoor canyon setting"
image = pipeline(
prompt=prompt,
image=init_image,
strength=0.75,
guidance_scale=7.5,
cross_attention_kwargs={"scale": 0.75}
).images[0]
image.save("img2img_output.png")
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Base model
stabilityai/stable-diffusion-xl-base-1.0