Instructions to use condzero/flux1d-merge-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use condzero/flux1d-merge-model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("condzero/flux1d-merge-model", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
flux1d-merge-model
The following merged model would not have been possible were it not for the efforts of people devoted to further advancing text to image modeling and generation.
float16 transformer weights of a 50/50 merge of the following (2) models:
https://civitai.com/models/978314 (UltraReal Fine-Tune)
https://civitai.com/models/966796 (Lumiere Alpha from Aixonlab)
This author gratefully acknowledges the authors for the above models!!
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