Image-to-Image
Diffusers
Safetensors
flux
semantic-segmentation
instance-segmentation
referring-segmentation
depth-estimation
surface-normal-estimation
Instructions to use FunAILab/Open-Vision-Banana with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FunAILab/Open-Vision-Banana with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("FunAILab/Open-Vision-Banana", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Point quickstart at new 9B nonzeroshot merge
Browse files
README.md
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@@ -34,7 +34,7 @@ dtype = torch.bfloat16
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transformer = Flux2Transformer2DModel.from_pretrained(
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"FunAILab/Open-Vision-Banana",
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-
subfolder="final_models/9B/nonzeroshot/checkpoint-
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torch_dtype=dtype,
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)
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pipe = Flux2KleinPipeline.from_pretrained(
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transformer = Flux2Transformer2DModel.from_pretrained(
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"FunAILab/Open-Vision-Banana",
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subfolder="final_models/9B/nonzeroshot/checkpoint-avg82500-85000-87500-92500-97500_uniform/transformer",
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torch_dtype=dtype,
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)
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pipe = Flux2KleinPipeline.from_pretrained(
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