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@@ -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
+unet/openvino_model.xml filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,100 @@
+# AnimagineXL-v3-openvino
+
+This is an *unofficial* [OpenVINO](https://github.com/openvinotoolkit/openvino) variant of [cagliostrolab/animagine-xl-3.0](https://huggingface.co/cagliostrolab/animagine-xl-3.0). 
+
+The repo is provided for convenience of running the Animagine XL v3 model on Intel CPU/GPU, as loading & converting a SDXL model to openvino can be pretty slow (dozens of minutes).
+
+Table of contents:
+- [Usage](#usage)
+- [How the conversion was done](#how-the-conversion-was-done)
+- [Appendix](#appendix)
+
+
+## Usage
+
+Take CPU for example:
+
+```python
+from optimum.intel.openvino import OVStableDiffusionXLPipeline
+from diffusers import (
+    EulerAncestralDiscreteScheduler,
+    DPMSolverMultistepScheduler
+)
+
+model_id = "CodeChris/AnimagineXL-v3-openvino"
+pipe = OVStableDiffusionXLPipeline.from_pretrained(model_model)
+# Fix output image size & batch_size for faster speed
+img_w, img_h = 832, 1216  # Example
+pipe.reshape(width=img_w, height=img_h,
+             batch_size=1, num_images_per_prompt=1)
+
+## Change scheduler
+# AnimagineXL recommand Euler A:
+# pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(
+    pipe.scheduler.config,
+    use_karras_sigmas=True,
+    algorithm_type="dpmsolver++"
+)  # I prefer DPM++ 2M Karras
+# Turn off the filter
+pipe.safety_checker = None
+
+# If run on a GPU, you need:
+# pipe.to('cuda')
+```
+
+After the pipe is prepared, a txt2img task can be executed as below:
+```python
+prompt = "1girl, dress, day, masterpiece, best quality"
+negative_prompt = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name"
+
+images = pipe(
+    prompt,
+    negative_prompt,
+    # If reshaped, image size must equal the reshaped size
+    width=img_w, height=img_h,
+    guidance_scale=7,
+    num_inference_steps=20
+)
+img = images[0]
+img.save('sample.png')
+```
+
+For convenience, here is the recommended image sizes from the official AnimagineXL doc:
+
+```
+# Or their transpose
+896 x 1152
+832 x 1216
+768 x 1344
+640 x 1536
+1024 x 1024
+```
+
+## How the conversion was done
+
+First, install optimum:
+
+```powershell
+pip install --upgrade-strategy eager optimum[openvino,nncf]
+```
+
+Then, the repo is converted using the following command:
+
+```powershell
+optimum-cli export openvino --model 'cagliostrolab/animagine-xl-3.0' 'models/openvino/AnimagineXL-v3' --task 'stable-diffusion-xl'
+```
+
+## Appendix
+
+Push large files:
+
+```
+git lfs install
+huggingface-cli lfs-enable-largefiles .
+```
+
+Other notes:
+
+* The conversion was done using `optimum==1.16.1` and `openvino==2023.2.0`.
+* You may query `optimum-cli export openvino --help` for more usage details.

model_index.json

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+{
+  "_class_name": "StableDiffusionXLImg2ImgPipeline",
+  "_diffusers_version": "0.25.1",
+  "_name_or_path": "cagliostrolab/animagine-xl-3.0",
+  "feature_extractor": [
+    null,
+    null
+  ],
+  "force_zeros_for_empty_prompt": true,
+  "image_encoder": [
+    null,
+    null
+  ],
+  "requires_aesthetics_score": false,
+  "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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+  "_diffusers_version": "0.25.1",
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+  "beta_schedule": "scaled_linear",
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+  "interpolation_type": "linear",
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "rescale_betas_zero_snr": false,
+  "sample_max_value": 1.0,
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+  "sigma_min": null,
+  "skip_prk_steps": true,
+  "steps_offset": 1,
+  "timestep_spacing": "leading",
+  "timestep_type": "discrete",
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+  "use_karras_sigmas": false
+}

text_encoder/config.json

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+  "torch_dtype": "float16",
+  "transformers_version": "4.36.2",
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+}

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text_encoder_2/config.json

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tokenizer/special_tokens_map.json

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tokenizer/tokenizer_config.json

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tokenizer_2/special_tokens_map.json

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tokenizer_2/tokenizer_config.json

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unet/config.json

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