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README.md CHANGED
@@ -5,7 +5,7 @@ tags:
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  - safetensors
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  ---
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- # Model Card for PrunaAI/test-save-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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@@ -17,7 +17,7 @@ First things first, you need to install the pruna library:
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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-save-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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@@ -25,7 +25,7 @@ To ensure that all optimizations are applied, use the pruna library to load the
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  from pruna import PrunaModel
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  loaded_model = PrunaModel.from_pretrained(
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- "PrunaAI/test-save-tiny-stable-diffusion-pipe-smashed"
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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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  ```
@@ -71,5 +71,5 @@ The compression configuration of the model is stored in the `smash_config.json`
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  [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI)
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  [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI)
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  [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
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- [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.com/invite/rskEr4BZJx)
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  [![Reddit](https://img.shields.io/reddit/subreddit-subscribers/PrunaAI?style=social)](https://www.reddit.com/r/PrunaAI/)
 
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  - safetensors
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  ---
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+ # Model Card for pruna-test/test-save-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/pruna-test/test-save-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_pretrained(
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+ "pruna-test/test-save-tiny-stable-diffusion-pipe-smashed"
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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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  [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI)
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  [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI)
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  [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
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+ [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/JFQmtFKCjd)
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  [![Reddit](https://img.shields.io/reddit/subreddit-subscribers/PrunaAI?style=social)](https://www.reddit.com/r/PrunaAI/)
model_index.json CHANGED
@@ -1,6 +1,6 @@
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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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  {
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  "_class_name": "StableDiffusionPipeline",
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+ "_diffusers_version": "0.34.0",
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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",
safety_checker/config.json CHANGED
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  "vocab_size": 99
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  },
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  "torch_dtype": "float32",
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- "transformers_version": "4.52.3",
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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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  "vocab_size": 99
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  },
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  "torch_dtype": "float32",
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+ "transformers_version": "4.53.2",
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  "vision_config": {
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  "attention_dropout": 0.1,
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  "dropout": 0.1,
scheduler/scheduler_config.json CHANGED
@@ -1,6 +1,6 @@
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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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  {
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  "_class_name": "DDIMScheduler",
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+ "_diffusers_version": "0.34.0",
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  "beta_end": 0.012,
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  "beta_schedule": "scaled_linear",
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  "beta_start": 0.00085,
text_encoder/config.json CHANGED
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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": "4.52.3",
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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": "4.53.2",
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  "vocab_size": 1000
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  }
unet/config.json CHANGED
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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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  "addition_embed_type_num_heads": 64,
 
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  {
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  "_class_name": "UNet2DConditionModel",
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+ "_diffusers_version": "0.34.0",
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+ "_name_or_path": "/home/runner/.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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  "addition_embed_type_num_heads": 64,
vae/config.json CHANGED
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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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  32,
 
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  {
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  "_class_name": "AutoencoderKL",
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+ "_diffusers_version": "0.34.0",
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+ "_name_or_path": "/home/runner/.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,