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Browse files- README.md +12 -6
- safety_checker/config.json +4 -15
- smash_config.json +2 -0
- text_encoder/config.json +2 -1
- unet/config.json +1 -1
- vae/config.json +1 -1
README.md
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---
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library_name: diffusers
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tags:
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-
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---
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# Model Card for PrunaAI/test-save-tiny-stable-diffusion-pipe-smashed-pro
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First things first, you need to install the pruna library:
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```bash
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pip install
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```
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You can [use the diffusers library to load the model](https://huggingface.co/PrunaAI/test-save-tiny-stable-diffusion-pipe-smashed-pro?library=diffusers) but this might not include all optimizations by default.
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To ensure that all optimizations are applied, use the pruna library to load the model using the following code:
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```python
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from
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loaded_model = PrunaProModel.
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"PrunaAI/test-save-tiny-stable-diffusion-pipe-smashed-pro"
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)
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```
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## Smash Configuration
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"distributer": null,
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"enhancer": null,
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"factorizer": null,
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"pruner": null,
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"quantizer": null,
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"recoverer": null,
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"pruner": null,
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"quantizer": null,
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"distiller": null,
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"cacher": null,
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"recoverer": null,
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"distributer": null,
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[](https://github.com/PrunaAI)
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[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
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[](https://discord.com/invite/rskEr4BZJx)
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-
[](https://www.reddit.com/r/PrunaAI/)
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---
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library_name: diffusers
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tags:
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- pruna_pro-ai
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- safetensors
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---
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# Model Card for PrunaAI/test-save-tiny-stable-diffusion-pipe-smashed-pro
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First things first, you need to install the pruna library:
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```bash
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pip install pruna_pro
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```
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You can [use the diffusers library to load the model](https://huggingface.co/PrunaAI/test-save-tiny-stable-diffusion-pipe-smashed-pro?library=diffusers) but this might not include all optimizations by default.
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To ensure that all optimizations are applied, use the pruna library to load the model using the following code:
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```python
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from pruna_pro import PrunaProModel
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loaded_model = PrunaProModel.from_pretrained(
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"PrunaAI/test-save-tiny-stable-diffusion-pipe-smashed-pro"
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)
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# we can then run inference using the methods supported by the base model
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```
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For inference, you can use the inference methods of the original model like shown in [the original model card](https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe?library=diffusers).
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Alternatively, you can visit [the Pruna documentation](https://docs.pruna.ai/en/stable/) for more information.
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## Smash Configuration
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"distributer": null,
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"enhancer": null,
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"factorizer": null,
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"kernel": null,
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"pruner": null,
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"quantizer": null,
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"recoverer": null,
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"pruner": null,
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"quantizer": null,
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"distiller": null,
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"kernel": null,
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"cacher": null,
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"recoverer": null,
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"distributer": null,
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[](https://github.com/PrunaAI)
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[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
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[](https://discord.com/invite/rskEr4BZJx)
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[](https://www.reddit.com/r/PrunaAI/)
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safety_checker/config.json
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{
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"architectures": [
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"StableDiffusionSafetyChecker"
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],
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"dropout": 0.1,
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"eos_token_id": 2,
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"gradient_checkpointing": false,
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"hidden_act": "quick_gelu",
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"hidden_size": 32,
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"initializer_factor": 1.0,
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"initializer_range": 0.02,
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"intermediate_size": 37,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 512,
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"model_type": "clip_text_model",
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"num_attention_heads": 4,
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"num_hidden_layers": 5,
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"projection_dim": 512,
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"torch_dtype": "float32",
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"vocab_size": 99
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},
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"torch_dtype": "float32",
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"transformers_version": "4.
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"vision_config": {
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"attention_dropout": 0.1,
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"dropout": 0.1,
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"gradient_checkpointing": false,
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"hidden_act": "quick_gelu",
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"hidden_size": 32,
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"image_size": 30,
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"initializer_factor": 1.0,
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"initializer_range": 0.02,
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"intermediate_size": 37,
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"layer_norm_eps": 1e-05,
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"model_type": "clip_vision_model",
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"num_attention_heads": 4,
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"num_channels": 3,
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"num_hidden_layers": 5,
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"patch_size": 2
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"projection_dim": 512,
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"torch_dtype": "float32"
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"vocab_size": 1000
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}
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{
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"_name_or_path": "/Users/davidberenstein/.cache/huggingface/hub/models--hf-internal-testing--tiny-stable-diffusion-pipe/snapshots/3ee6c9f225f088ad5d35b624b6514b091e6a4849/safety_checker",
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"architectures": [
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"StableDiffusionSafetyChecker"
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],
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"dropout": 0.1,
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"eos_token_id": 2,
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"gradient_checkpointing": false,
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"hidden_size": 32,
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"intermediate_size": 37,
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"max_position_embeddings": 512,
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"model_type": "clip_text_model",
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"num_attention_heads": 4,
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"num_hidden_layers": 5,
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"vocab_size": 99
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},
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"torch_dtype": "float32",
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"transformers_version": "4.48.0",
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"vision_config": {
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"_attn_implementation_autoset": true,
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"attention_dropout": 0.1,
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"dropout": 0.1,
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"gradient_checkpointing": false,
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"hidden_size": 32,
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"image_size": 30,
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"intermediate_size": 37,
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"model_type": "clip_vision_model",
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"num_attention_heads": 4,
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"num_hidden_layers": 5,
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"patch_size": 2
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},
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"vocab_size": 1000
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}
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smash_config.json
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"distributer": null,
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"enhancer": null,
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"factorizer": null,
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"pruner": null,
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"quantizer": null,
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"recoverer": null,
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"pruner": null,
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"quantizer": null,
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"distiller": null,
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"cacher": null,
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"recoverer": null,
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"distributer": null,
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"distributer": null,
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"enhancer": null,
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"factorizer": null,
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"pruner": null,
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"pruner": null,
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text_encoder/config.json
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{
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"architectures": [
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"pad_token_id": 1,
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"projection_dim": 512,
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"transformers_version": "4.
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"vocab_size": 1000
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"_name_or_path": "/Users/davidberenstein/.cache/huggingface/hub/models--hf-internal-testing--tiny-stable-diffusion-pipe/snapshots/3ee6c9f225f088ad5d35b624b6514b091e6a4849/text_encoder",
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"architectures": [
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"CLIPTextModel"
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"pad_token_id": 1,
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"projection_dim": 512,
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"torch_dtype": "float32",
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"transformers_version": "4.48.0",
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"vocab_size": 1000
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unet/config.json
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"act_fn": "silu",
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"_name_or_path": "/Users/davidberenstein/.cache/huggingface/hub/models--hf-internal-testing--tiny-stable-diffusion-pipe/snapshots/3ee6c9f225f088ad5d35b624b6514b091e6a4849/unet",
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"act_fn": "silu",
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"addition_embed_type_num_heads": 64,
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vae/config.json
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"_class_name": "AutoencoderKL",
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"_name_or_path": "/Users/davidberenstein/.cache/huggingface/hub/models--hf-internal-testing--tiny-stable-diffusion-pipe/snapshots/3ee6c9f225f088ad5d35b624b6514b091e6a4849/vae",
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"act_fn": "silu",
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"block_out_channels": [
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32,
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