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README.md

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+---
+library_name: diffusers
+tags:
+- pruna-ai
+---
+
+# Model Card for PrunaAI/Segmind-Vega-smashed
+
+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.
+
+## Usage
+
+First things first, you need to install the pruna library:
+
+```bash
+pip install pruna
+```
+
+You can [use the diffusers library to load the model](https://huggingface.co/PrunaAI/Segmind-Vega-smashed?library=diffusers) but this might not include all optimizations by default.
+
+To ensure that all optimizations are applied, use the pruna library to load the model using the following code:
+
+```python
+from pruna import PrunaModel
+
+loaded_model = PrunaModel.from_hub(
+    "PrunaAI/Segmind-Vega-smashed"
+)
+```
+
+After loading the model, you can use the inference methods of the original model. Take a look at the [documentation](https://pruna.readthedocs.io/en/latest/index.html) for more usage information.
+
+## Smash Configuration
+
+The compression configuration of the model is stored in the `smash_config.json` file, which describes the optimization methods that were applied to the model.
+
+```bash
+{
+    "batcher": null,
+    "cacher": null,
+    "compiler": null,
+    "factorizer": "qkv_diffusers",
+    "pruner": null,
+    "quantizer": "torchao",
+    "torchao_excluded_modules": "none",
+    "torchao_quant_type": "int4dq",
+    "batch_size": 1,
+    "device": "mps",
+    "save_fns": [
+        "save_before_apply",
+        "save_before_apply"
+    ],
+    "load_fns": [
+        "diffusers"
+    ],
+    "reapply_after_load": {
+        "factorizer": "qkv_diffusers",
+        "pruner": null,
+        "quantizer": "torchao",
+        "cacher": null,
+        "compiler": null,
+        "batcher": null
+    }
+}
+```
+
+## 🌍 Join the Pruna AI community!
+
+[![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI)
+[![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI)
+[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
+[![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.com/invite/rskEr4BZJx)
+[![Reddit](https://img.shields.io/reddit/subreddit-subscribers/PrunaAI?style=social)](https://www.reddit.com/r/PrunaAI/)

dtype_info.json

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+{"dtype": "bfloat16"}

model_index.json

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+{
+  "_class_name": "StableDiffusionXLPipeline",
+  "_diffusers_version": "0.33.1",
+  "_name_or_path": "segmind/Segmind-Vega",
+  "feature_extractor": [
+    null,
+    null
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+  "force_zeros_for_empty_prompt": true,
+  "image_encoder": [
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+    null
+  ],
+  "scheduler": [
+    "diffusers",
+    "EulerDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "text_encoder_2": [
+    "transformers",
+    "CLIPTextModelWithProjection"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "tokenizer_2": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

scheduler/scheduler_config.json

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+  "_class_name": "EulerDiscreteScheduler",
+  "_diffusers_version": "0.33.1",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "final_sigmas_type": "zero",
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+  "prediction_type": "epsilon",
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+  "timestep_spacing": "leading",
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+  "trained_betas": null,
+  "use_beta_sigmas": false,
+  "use_exponential_sigmas": false,
+  "use_karras_sigmas": false
+}

smash_config.json

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+    "cacher": null,
+    "compiler": null,
+    "factorizer": "qkv_diffusers",
+    "pruner": null,
+    "quantizer": "torchao",
+    "torchao_excluded_modules": "none",
+    "torchao_quant_type": "int4dq",
+    "batch_size": 1,
+    "device": "mps",
+    "save_fns": [
+        "save_before_apply",
+        "save_before_apply"
+    ],
+    "load_fns": [
+        "diffusers"
+    ],
+    "reapply_after_load": {
+        "factorizer": "qkv_diffusers",
+        "pruner": null,
+        "quantizer": "torchao",
+        "cacher": null,
+        "compiler": null,
+        "batcher": null
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+}

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tokenizer/special_tokens_map.json

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unet/config.json

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