Instructions to use markod0925/yarn_flux with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use markod0925/yarn_flux with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("markod0925/yarn_flux", 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
- Local Apps
- Draw Things
- DiffusionBee
| library_name: diffusers | |
| datasets: | |
| - Norod78/Yarn-art-style | |
| base_model: | |
| - black-forest-labs/FLUX.1-dev | |
| # Model Card for Model ID | |
| Make everything as Yarn Art Syle. | |
| ## Uses | |
| Make everything as Yarn Art Syle. | |
| ## How to Get Started with the Model | |
| ```python | |
| !pip install -U diffusers bitsandbytes | |
| from diffusers import AutoPipelineForText2Image, BitsAndBytesConfig | |
| import torch | |
| repo_id = "markod0925/yarn_flux" | |
| bnb_4bit_compute_dtype = torch.float16 | |
| nf4_config = BitsAndBytesConfig( | |
| load_in_4bit=True, | |
| bnb_4bit_quant_type="nf4", | |
| bnb_4bit_compute_dtype=bnb_4bit_compute_dtype, | |
| ) | |
| pipeline = AutoPipelineForText2Image.from_pretrained(repo_id, | |
| torch_dtype=bnb_4bit_compute_dtype | |
| ) | |
| pipeline.enable_model_cpu_offload() | |
| ``` | |
| ```python | |
| image = pipeline( | |
| "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", num_inference_steps=28, guidance_scale=3.5, height=768 | |
| ).images[0] | |
| image | |
| ``` | |
|  | |
| ```python | |
| image = pipeline( | |
| "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k, yarn art style", num_inference_steps=28, guidance_scale=3.5, height=768 | |
| ).images[0] | |
| image | |
| ``` | |
|  |