Instructions to use kiel2/KielForge-fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kiel2/KielForge-fast with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("kiel2/KielForge-fast") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
KielForge-fast
KielForge-fast is a LoRA fine-tuned on the FLUX.1-schnell model.
Model Description
- Model Type: LoRA (Low-Rank Adaptation) for Image Generation
- Base Model: FLUX.1-schnell
- Training Method: Dreambooth LoRA
- Description: [Briefly describe what your LoRA is trained on—e.g., a specific style, character, or aesthetic.]
Usage
You can use this model with the diffusers library:
from diffusers import DiffusionPipeline
import torch
# Load the base model
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
pipe.to("cuda")
# Load your LoRA
pipe.load_lora_weights("kiel2/KielForge-fast")
# Generate
prompt = "a high quality photo of [your subject]"
image = pipe(prompt, num_inference_steps=20, guidance_scale=3.5).images[0]
image.save("output.png")
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Hardware compatibility
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Model tree for kiel2/KielForge-fast
Base model
black-forest-labs/FLUX.1-schnell