Image-to-Image
Diffusers
Safetensors
PyTorch
flux2
flux
image-upscaling
latent-upscaling
super-resolution
diffusion
flow-matching
rectified-flow
generative-models
image-generation
Instructions to use MinhNH232331M/FlowUpscaler-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MinhNH232331M/FlowUpscaler-diffusers 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("MinhNH232331M/FlowUpscaler-diffusers", torch_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
File size: 262 Bytes
2043d66 | 1 2 3 4 5 6 7 8 | {
"_class_name": ["flow_upscaler_pipeline", "FlowUpscalerPipeline"],
"_diffusers_version": "0.37.1",
"scheduler": ["diffusers", "FlowMatchEulerDiscreteScheduler"],
"unet": ["upscaler_unet", "UpscalerUNet"],
"vae": ["diffusers", "AutoencoderKLFlux2"]
}
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