Text-to-Image
Diffusers
Safetensors
English
Text-to-Image
ControlNet
Diffusers
Flux.1-dev
image-generation
Stable Diffusion
Instructions to use Shakker-Labs/FLUX.1-dev-ControlNet-Depth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Shakker-Labs/FLUX.1-dev-ControlNet-Depth with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Shakker-Labs/FLUX.1-dev-ControlNet-Depth", 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 Settings
- Draw Things
- DiffusionBee
Update README.md
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README.md
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@@ -24,7 +24,7 @@ This repository contains a Depth ControlNet for FLUX.1-dev model jointly trained
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# Model Cards
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- The model consists of 4 FluxTransformerBlock and 1 FluxSingleTransformerBlock.
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- This checkpoint is trained on both real and generated image datasets. with 16*A800 for 50K steps. The batch size 16*4=64 with resolution=1024. The learning rate is set to 5e-6.
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- The recommended controlnet_conditioning_scale is 0.3-0.7.
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# Showcases
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# Model Cards
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- The model consists of 4 FluxTransformerBlock and 1 FluxSingleTransformerBlock.
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- This checkpoint is trained on both real and generated image datasets. with 16\*A800 for 50K steps. The batch size 16\*4=64 with resolution=1024. The learning rate is set to 5e-6.
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- The recommended controlnet_conditioning_scale is 0.3-0.7.
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# Showcases
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