Instructions to use matiasfz/colc-style-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use matiasfz/colc-style-lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("undefined", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("matiasfz/colc-style-lora") prompt = "greenstyle" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("undefined", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("matiasfz/colc-style-lora")
prompt = "greenstyle"
image = pipe(prompt).images[0]colc style lora
Model description
LoRA trained on 353 frames from colc. Captures green moody lighting, baroque industrial set design, foggy atmosphere and cinematic shadows.
Trigger words
You should use greenstyle to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
Training at fal.ai
Training was done using fal.ai/models/fal-ai/z-image-base-trainer.
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