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End of training

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README.md ADDED
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+
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+ ---
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+ license: creativeml-openrail-m
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+ base_model: ekshat/stable-diffusion-anime-style
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+ datasets:
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+ - lambdalabs/naruto-blip-captions
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+ tags:
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+ - stable-diffusion
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+ - stable-diffusion-diffusers
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+ - text-to-image
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+ - diffusers
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+ inference: true
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+ ---
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+
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+ # Text-to-image finetuning - ekshat/Stable_Diffussion_Anime_Style
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+
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+ This pipeline was finetuned from **ekshat/stable-diffusion-anime-style** on the **lambdalabs/naruto-blip-captions** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A person with blue eyes.']:
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+
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+ ![val_imgs_grid](./val_imgs_grid.png)
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+
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+
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+ ## Pipeline usage
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+
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+ You can use the pipeline like so:
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+
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+ ```python
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+ from diffusers import DiffusionPipeline
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+ import torch
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+
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+ pipeline = DiffusionPipeline.from_pretrained("ekshat/Stable_Diffussion_Anime_Style", torch_dtype=torch.float16)
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+ prompt = "A person with blue eyes."
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+ image = pipeline(prompt).images[0]
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+ image.save("my_image.png")
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+ ```
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+
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+ ## Training info
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+
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+ These are the key hyperparameters used during training:
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+
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+ * Epochs: 1
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+ * Learning rate: 2e-06
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+ * Batch size: 2
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+ * Gradient accumulation steps: 1
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+ * Image resolution: 512
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+ * Mixed-precision: fp16
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+
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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": "CLIPImageProcessor",
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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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+ }
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+ "_class_name": "StableDiffusionPipeline",
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+ "_diffusers_version": "0.22.0.dev0",
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+ "_name_or_path": "ekshat/stable-diffusion-anime-style",
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+ "feature_extractor": [
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+ "transformers",
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+ "CLIPImageProcessor"
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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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+ "scheduler": [
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+ "diffusers",
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+ "PNDMScheduler"
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+ "text_encoder": [
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+ "transformers",
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+ "CLIPTextModel"
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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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+ "diffusers",
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+ "UNet2DConditionModel"
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+ ],
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+ "vae": [
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+ "diffusers",
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+ "AutoencoderKL"
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+ ]
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+ }
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+ adam_beta1: 0.9
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+ adam_beta2: 0.999
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+ adam_epsilon: 1.0e-08
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+ adam_weight_decay: 0.01
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+ allow_tf32: false
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+ caption_column: text
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+ learning_rate: 2.0e-06
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