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README.md ADDED
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+ ---
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+ license: creativeml-openrail-m
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+ tags:
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+ - stable-diffusion
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+ - text-to-image
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+ - openvino
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+
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+ ---
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+
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+ # OpenVINO Stable Diffusion
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+
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+ ## andite/anything-v4.0
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+
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+ This repository contains the models from [andite/anything-v4.0](https://huggingface.co/andite/anything-v4.0) converted to
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+ OpenVINO, for accelerated inference on CPU or Intel GPU with OpenVINO's integration into Optimum:
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+ [optimum-intel](https://github.com/huggingface/optimum-intel#openvino). The model weights are stored with FP16
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+ precision, which reduces the size of the model by half.
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+
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+ Please check out the [source model repository](https://huggingface.co/andite/anything-v4.0) for more information about the model and its license.
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+
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+ To install the requirements for this demo, do `pip install optimum[openvino]`. This installs all the necessary dependencies,
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+ including Transformers and OpenVINO. For more detailed steps, please see this [installation guide](https://github.com/helena-intel/optimum-intel/wiki/OpenVINO-Integration-Installation-Guide).
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+
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+ The simplest way to generate an image with stable diffusion takes only two lines of code, as shown below. The first line downloads the
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+ model from the Hugging Face hub (if it has not been downloaded before) and loads it; the second line generates an image.
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+
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+ ```python
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+ from optimum.intel.openvino import OVStableDiffusionPipeline
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+
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+ stable_diffusion = OVStableDiffusionPipeline.from_pretrained("andite/anything-v4.0")
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+ images = stable_diffusion("a random image").images
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+ ```
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+
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+ The following example code uses static shapes for even faster inference. Using larger image sizes will
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+ require more memory and take longer to generate.
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+
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+ If you have an 11th generation or later Intel Core processor, you can use the integrated GPU for inference, and if you have an Intel
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+ discrete GPU, you can use that. Add the line `stable_diffusion.to("GPU")` before `stable_diffusion.compile()` in the example below.
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+ Model loading will take some time the first time, but will be faster after that, because the model will be cached. On GPU, for stable
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+ diffusion only static shapes are supported at the moment.
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+
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+
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+ ```python
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+ from optimum.intel.openvino.modeling_diffusion import OVStableDiffusionPipeline
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+
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+ batch_size = 1
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+ num_images_per_prompt = 1
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+ height = 256
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+ width = 256
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+
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+ # load the model and reshape to static shapes for faster inference
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+ model_id = "andite/anything-v4.0"
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+ stable_diffusion = OVStableDiffusionPipeline.from_pretrained(model_id, compile=False)
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+ stable_diffusion.reshape( batch_size=batch_size, height=height, width=width, num_images_per_prompt=num_images_per_prompt)
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+ stable_diffusion.compile()
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+
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+ # generate image!
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+ prompt = "a random image"
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+ images = stable_diffusion(prompt, height=height, width=width, num_images_per_prompt=num_images_per_prompt).images
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+ images[0].save("result.png")
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+ ```
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+
feature_extractor/preprocessor_config.json ADDED
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+ {
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+ "crop_size": {
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+ "height": 224,
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+ "width": 224
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+ },
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+ "do_center_crop": true,
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+ "do_convert_rgb": true,
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+ "do_normalize": true,
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+ "do_rescale": true,
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+ "do_resize": true,
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+ "feature_extractor_type": "CLIPFeatureExtractor",
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+ "image_mean": [
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+ 0.48145466,
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+ 0.4578275,
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+ 0.40821073
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+ ],
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+ "image_processor_type": "CLIPFeatureExtractor",
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+ "image_std": [
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+ 0.26862954,
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+ 0.26130258,
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+ 0.27577711
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+ ],
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+ "resample": 3,
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+ "rescale_factor": 0.00392156862745098,
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+ "size": {
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+ "shortest_edge": 224
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+ }
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+ }
inference.py ADDED
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+ from optimum.intel.openvino.modeling_diffusion import OVStableDiffusionPipeline
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+
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+ batch_size = 1
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+ num_images_per_prompt = 1
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+ height = 256
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+ width = 256
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+
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+ # load the model and reshape to static shapes for faster inference
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+ model_id = "helenai/andite-anything-v4.0-ov"
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+ stable_diffusion = OVStableDiffusionPipeline.from_pretrained(model_id, compile=False)
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+ stable_diffusion.reshape( batch_size=batch_size, height=height, width=width, num_images_per_prompt=num_images_per_prompt)
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+ stable_diffusion.compile()
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+
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+ # generate image!
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+ prompt = "a random image"
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+ images = stable_diffusion(prompt, height=height, width=width, num_images_per_prompt=num_images_per_prompt).images
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+ images[0].save("result.png")
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+
model_index.json ADDED
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+ {
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+ "_class_name": "OVStableDiffusionPipeline",
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+ "_diffusers_version": "0.13.1",
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+ "feature_extractor": [
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+ "transformers",
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+ "CLIPFeatureExtractor"
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+ ],
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+ "requires_safety_checker": true,
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+ "safety_checker": [
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+ "stable_diffusion",
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+ "StableDiffusionSafetyChecker"
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+ ],
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+ "scheduler": [
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+ "diffusers",
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+ "PNDMScheduler"
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+ ],
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+ "text_encoder": [
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+ "optimum",
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+ "OVModelTextEncoder"
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+ ],
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+ "tokenizer": [
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+ "transformers",
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+ "CLIPTokenizer"
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+ ],
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+ "unet": [
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+ "optimum",
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+ "OVModelUnet"
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+ ],
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+ "vae_decoder": [
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+ "optimum",
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+ "OVModelVaeDecoder"
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+ ]
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+ }
scheduler/scheduler_config.json ADDED
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+ {
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+ "_class_name": "PNDMScheduler",
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+ "_diffusers_version": "0.13.1",
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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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+ "clip_sample": false,
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+ "num_train_timesteps": 1000,
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+ "prediction_type": "epsilon",
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+ "set_alpha_to_one": false,
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+ "skip_prk_steps": true,
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+ "steps_offset": 1,
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+ "trained_betas": null
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+ }
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