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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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# Finetuning - YiYiXu/kandinsky_prior_pokemon |
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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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## Pipeline usage |
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You can use the pipeline like so: |
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```python |
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from diffusers import DiffusionPipeline |
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import torch |
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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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## Training info |
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These are the key hyperparameters used during training: |
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* Epochs: 13 |
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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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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/pxc1exfh). |
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