Olive cuda for juggernaut-xl-v7

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.data filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,24 @@
 ---
-license: creativeml-openrail-m
+license: openrail++
+base_model: stablediffusionapi/juggernaut-xl-v7
+language:
+  - en
+tags:
+  - stable-diffusion
+  - stable-diffusion-xl
+  - onnxruntime
+  - onnx
+  - text-to-image
 ---
+
+
+# Juggernaut XL v7 for ONNX Runtime CUDA provider
+
+## Introduction
+
+This repository hosts the optimized versions of **Juggernaut XL v7** to accelerate inference with ONNX Runtime CUDA execution provider for Nvidia GPUs. It cannot run in other providers like CPU or DirectML.
+
+The models are generated by [Olive](https://github.com/microsoft/Olive/tree/main/examples/stable_diffusion) with command like the following:
+```
+python stable_diffusion_xl.py --provider cuda --optimize --model_id stablediffusionapi/juggernaut-xl-v7
+```

model_index.json

@@ -0,0 +1,46 @@
+{
+  "_class_name": "ORTStableDiffusionXLPipeline",
+  "_diffusers_version": "0.24.0",
+  "_name_or_path": "stablediffusionapi/juggernaut-xl-v7",
+  "feature_extractor": [
+    null,
+    null
+  ],
+  "force_zeros_for_empty_prompt": true,
+  "image_encoder": [
+    null,
+    null
+  ],
+  "scheduler": [
+    "diffusers",
+    "EulerDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "diffusers",
+    "OnnxRuntimeModel"
+  ],
+  "text_encoder_2": [
+    "diffusers",
+    "OnnxRuntimeModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "tokenizer_2": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "OnnxRuntimeModel"
+  ],
+  "vae_decoder": [
+    "diffusers",
+    "OnnxRuntimeModel"
+  ],
+  "vae_encoder": [
+    "diffusers",
+    "OnnxRuntimeModel"
+  ]
+}

scheduler/scheduler_config.json

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+{
+  "_class_name": "EulerDiscreteScheduler",
+  "_diffusers_version": "0.24.0",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "interpolation_type": "linear",
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "sample_max_value": 1.0,
+  "set_alpha_to_one": false,
+  "sigma_max": null,
+  "sigma_min": null,
+  "skip_prk_steps": true,
+  "steps_offset": 1,
+  "timestep_spacing": "leading",
+  "timestep_type": "discrete",
+  "trained_betas": null,
+  "use_karras_sigmas": false
+}

text_encoder/model.onnx

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text_encoder_2/model.onnx

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text_encoder_2/model.onnx.data

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tokenizer/special_tokens_map.json

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+}

tokenizer/tokenizer_config.json

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+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": "<|endoftext|>"
+}

tokenizer_2/special_tokens_map.json

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+}

tokenizer_2/tokenizer_config.json

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+  "pad_token": "!",
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+}

unet/model.onnx

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unet/model.onnx.data

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vae_decoder/model.onnx

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vae_encoder/model.onnx

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