Text-to-Image
Diffusers
Safetensors
English
quantized
fp8
e4m3
Text-to-Image
ControlNet
Diffusers
Flux.1-dev
image-generation
Stable Diffusion
quantization
reduced-precision
Instructions to use ABDALLALSWAITI/FLUX.1-dev-ControlNet-Union-Pro-2.0-fp8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ABDALLALSWAITI/FLUX.1-dev-ControlNet-Union-Pro-2.0-fp8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ABDALLALSWAITI/FLUX.1-dev-ControlNet-Union-Pro-2.0-fp8", 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
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- reduced-precision
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base_model:
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- Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0
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inference:
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parameters:
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torch_dtype: torch.float8_e4m3fn
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- reduced-precision
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base_model:
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- Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0
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base_model_relation: quantized
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inference:
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parameters:
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torch_dtype: torch.float8_e4m3fn
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