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Browse files- README.md +5 -3
- model_index.json +1 -1
- safety_checker/config.json +1 -2
- scheduler/scheduler_config.json +1 -1
- smash_config.json +2 -0
- text_encoder/config.json +1 -1
- unet/config.json +1 -1
- vae/config.json +1 -1
README.md
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- pruna-ai
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---
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# Model Card for PrunaAI/test-tiny-stable-diffusion-pipe-smashed
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This model was created using the [pruna](https://github.com/PrunaAI/pruna) library. Pruna is a model optimization framework built for developers, enabling you to deliver more efficient models with minimal implementation overhead.
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pip install pruna
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```
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You can [use the diffusers library to load the model](https://huggingface.co/PrunaAI/test-tiny-stable-diffusion-pipe-smashed?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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from pruna import PrunaModel
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loaded_model = PrunaModel.from_hub(
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"PrunaAI/test-tiny-stable-diffusion-pipe-smashed"
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)
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```
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"batcher": null,
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"cacher": null,
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"compiler": null,
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"pruner": null,
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"quantizer": null,
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"batch_size": 1,
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"diffusers"
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],
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"reapply_after_load": {
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"pruner": null,
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"quantizer": null,
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"cacher": null,
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- pruna-ai
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---
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+
# Model Card for PrunaAI/test-load-tiny-stable-diffusion-pipe-smashed
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This model was created using the [pruna](https://github.com/PrunaAI/pruna) library. Pruna is a model optimization framework built for developers, enabling you to deliver more efficient models with minimal implementation overhead.
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pip install pruna
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```
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+
You can [use the diffusers library to load the model](https://huggingface.co/PrunaAI/test-load-tiny-stable-diffusion-pipe-smashed?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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from pruna import PrunaModel
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loaded_model = PrunaModel.from_hub(
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"PrunaAI/test-load-tiny-stable-diffusion-pipe-smashed"
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)
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```
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"batcher": null,
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"cacher": null,
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"compiler": null,
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"factorizer": null,
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"pruner": null,
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"quantizer": null,
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"batch_size": 1,
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"diffusers"
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],
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"reapply_after_load": {
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"factorizer": null,
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"pruner": null,
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"quantizer": null,
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"cacher": null,
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model_index.json
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{
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"_class_name": "StableDiffusionPipeline",
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"_diffusers_version": "0.
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"_name_or_path": "hf-internal-testing/tiny-stable-diffusion-pipe",
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"feature_extractor": [
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"transformers",
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{
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"_class_name": "StableDiffusionPipeline",
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"_diffusers_version": "0.33.1",
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"_name_or_path": "hf-internal-testing/tiny-stable-diffusion-pipe",
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"feature_extractor": [
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"transformers",
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safety_checker/config.json
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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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"_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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"vocab_size": 99
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},
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"torch_dtype": "float32",
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"transformers_version": "4.52.4",
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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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scheduler/scheduler_config.json
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{
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"_class_name": "DDIMScheduler",
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"_diffusers_version": "0.
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"beta_end": 0.012,
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"beta_schedule": "scaled_linear",
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"beta_start": 0.00085,
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{
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"_class_name": "DDIMScheduler",
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"_diffusers_version": "0.33.1",
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"beta_end": 0.012,
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"beta_schedule": "scaled_linear",
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"beta_start": 0.00085,
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smash_config.json
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"batcher": null,
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"cacher": null,
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"compiler": null,
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"pruner": null,
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"quantizer": null,
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"batch_size": 1,
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"diffusers"
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],
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"reapply_after_load": {
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"pruner": null,
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"quantizer": null,
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"cacher": null,
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"batcher": null,
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"cacher": null,
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"compiler": null,
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"factorizer": null,
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"pruner": null,
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"quantizer": null,
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"batch_size": 1,
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"diffusers"
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],
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"reapply_after_load": {
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"factorizer": null,
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"pruner": null,
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"quantizer": null,
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"cacher": null,
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text_encoder/config.json
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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.
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"vocab_size": 1000
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}
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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": "4.52.4",
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"vocab_size": 1000
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}
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unet/config.json
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{
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"_class_name": "UNet2DConditionModel",
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"_diffusers_version": "0.
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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": null,
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{
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"_class_name": "UNet2DConditionModel",
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"_diffusers_version": "0.33.1",
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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": null,
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vae/config.json
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"_class_name": "AutoencoderKL",
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"_diffusers_version": "0.
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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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{
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"_class_name": "AutoencoderKL",
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"_diffusers_version": "0.33.1",
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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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