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 config.json
Browse files- config.json +0 -1
config.json
CHANGED
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@@ -1,7 +1,6 @@
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{
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"_class_name": "FluxControlNetModel",
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"_diffusers_version": "0.30.0.dev0",
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"_name_or_path": "/pfs/mt-BzjfJP/wanghaofan/workspace/flux/controlnet/flux-controlnet-depth-v1/checkpoint-50000/controlnet",
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"attention_head_dim": 128,
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"axes_dims_rope": [
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16,
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{
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"_class_name": "FluxControlNetModel",
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"_diffusers_version": "0.30.0.dev0",
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"attention_head_dim": 128,
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"axes_dims_rope": [
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16,
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