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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: kandinsky-community/kandinsky-2-2-prior
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+ datasets:
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+ - lambdalabs/pokemon-blip-captions
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+ tags:
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+ - kandinsky
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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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+ # Finetuning - YiYiXu/kandinsky_prior_pokemon
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+
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+ This pipeline was finetuned from **kandinsky-community/kandinsky-2-2-prior** on the **lambdalabs/pokemon-blip-captions** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A robot pokemon, 4k photo']:
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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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+ pipe_prior = DiffusionPipeline.from_pretrained("YiYiXu/kandinsky_prior_pokemon", torch_dtype=torch.float16)
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+ pipe_t2i = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16)
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+ prompt = "A robot pokemon, 4k photo"
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+ image_embeds, negative_image_embeds = pipe_prior(prompt, guidance_scale=1.0).to_tuple()
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+ image = pipe_t2i(image_embeds=image_embeds, negative_image_embeds=negative_image_embeds).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: 2
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+ * Learning rate: 1e-05
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+ * Batch size: 1
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+ * Gradient accumulation steps: 1
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+ * Image resolution: 768
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+ * Mixed-precision: fp16
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+
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+
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+ More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/yiyixu/text2image-fine-tune/runs/90ljjfwe).
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