Text-to-Image
Diffusers
TensorBoard
Safetensors
English
StableDiffusionPipeline
diffusers-training
lora
template:sd-lora
stable-diffusion-xl
stable-diffusion-xl-diffusers
Instructions to use ZB-Tech/Text-to-Image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ZB-Tech/Text-to-Image with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ZB-Tech/Text-to-Image") prompt = "Draw a picture of two female boxers fighting each other." image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Create vae/config.json
Browse files- vae/config.json +31 -0
vae/config.json
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{
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"_class_name": "AutoencoderKL",
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"_diffusers_version": "0.9.0.dev0",
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"_name_or_path": "/content/stable-diffusion-v1-5/vae",
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"act_fn": "silu",
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"block_out_channels": [
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128,
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256,
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512,
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512
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],
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"down_block_types": [
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"DownEncoderBlock2D",
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"DownEncoderBlock2D",
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"DownEncoderBlock2D",
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"DownEncoderBlock2D"
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],
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"in_channels": 3,
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"latent_channels": 4,
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"layers_per_block": 2,
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"norm_num_groups": 32,
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"out_channels": 3,
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"sample_size": 512,
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"scaling_factor": 0.18215,
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"up_block_types": [
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"UpDecoderBlock2D",
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"UpDecoderBlock2D",
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"UpDecoderBlock2D",
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"UpDecoderBlock2D"
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]
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}
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