Instructions to use fhai50032/flux-controlnet-1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fhai50032/flux-controlnet-1000 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fhai50032/flux-controlnet-1000", 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
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# Flux-ControlNet: Text-to-Image Diffusion Model with Caption Alignment
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---
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## Key Features
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---
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# Training Parameters for Flux-ControlNet
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General Parameters:
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Model Architecture: Flux-based ControlNet Model
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Image Resolution: 512x512
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Learning Rate: 1e-5 (with cosine schedular)
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Weight Decay: 0.01
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Gradient Clipping: 1.0
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# Flux-ControlNet: Text-to-Image Diffusion Model with Caption Alignment
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This repository hosts **Flux-ControlNet**, a customized ControlNet-based diffusion model designed for generating text-embedded images.
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---
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## Key Features
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---
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# Training Parameters for Flux-ControlNet
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```
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General Parameters:
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Model Architecture: Flux-based ControlNet Model
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Image Resolution: 512x512
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Learning Rate: 1e-5 (with cosine schedular)
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Weight Decay: 0.01
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Gradient Clipping: 1.0
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```
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# Inference Code
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Soon to be added
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