Instructions to use hf-internal-testing/tiny-modular-cache-reference with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hf-internal-testing/tiny-modular-cache-reference with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-modular-cache-reference", 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
Upload folder using huggingface_hub
Browse files- modular_model_index.json +19 -0
- vae/config.json +28 -0
- vae/diffusion_pytorch_model.safetensors +3 -0
modular_model_index.json
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{
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"_blocks_class_name": "StableDiffusionXLAutoBlocks",
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"_class_name": "StableDiffusionXLModularPipeline",
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"_diffusers_version": "0.41.0.dev0",
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"vae": [
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"diffusers",
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"AutoencoderKL",
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{
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"pretrained_model_name_or_path": "hf-internal-testing/tiny-modular-cache-source",
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"revision": "907560ec007d1f62a5924d3fbc6fae7be49d30c4",
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"subfolder": "vae",
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"type_hint": [
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"diffusers",
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"AutoencoderKL"
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],
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"variant": null
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}
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]
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}
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vae/config.json
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{
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"_class_name": "AutoencoderKL",
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"_diffusers_version": "0.41.0.dev0",
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"act_fn": "silu",
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"block_out_channels": [
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4
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],
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"down_block_types": [
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"DownEncoderBlock2D"
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],
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"force_upcast": true,
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"in_channels": 3,
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"latent_channels": 2,
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"latents_mean": null,
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"latents_std": null,
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"layers_per_block": 1,
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"mid_block_add_attention": true,
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"norm_num_groups": 1,
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"out_channels": 3,
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"sample_size": 8,
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"scaling_factor": 0.18215,
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"shift_factor": null,
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"up_block_types": [
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"UpDecoderBlock2D"
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],
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"use_post_quant_conv": true,
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"use_quant_conv": true
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}
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vae/diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:e80191130ab0ad73d7fce9c674fa551ce9feb348a6530f8c7f09446727980a3b
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size 20372
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