Instructions to use JosefKuchar/svg-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JosefKuchar/svg-generator with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Tongyi-MAI/Z-Image", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("JosefKuchar/svg-generator") 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
Upload flow-matching/config.json
Browse files- flow-matching/config.json +23 -0
flow-matching/config.json
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{
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"architectures": [
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"FlowMatchingTransformer"
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],
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"model_type": "svg-flow-matching-vectorizer",
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"model_config": {
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"input_dim": 13,
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"cond_dim": 384,
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"hidden_size": 768,
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"max_len": 256,
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"num_layers": 16,
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"num_heads": 12,
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"dropout": 0.1,
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"cond_drop_prob": 0.1,
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"learning_rate": 5e-05,
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"warmup_steps": 1000
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},
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"conditioning_model": "facebook/dinov3-vits16-pretrain-lvd1689m",
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"format": "safetensors",
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"source_checkpoint": "svg-generator/bgno3qml/checkpoints/epoch0627.ckpt",
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"source_epoch": 627,
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"source_global_step": 1669852
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}
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