| |
|
| | --- |
| | license: creativeml-openrail-m |
| | base_model: kandinsky-community/kandinsky-2-2-decoder |
| | datasets: |
| | - AhmetTek41/logo |
| | prior: |
| | - kandinsky-community/kandinsky-2-2-prior |
| | tags: |
| | - kandinsky |
| | - text-to-image |
| | - diffusers |
| | - diffusers-training |
| | inference: true |
| | --- |
| | |
| | # Finetuning - AhmetTek41/output |
| | |
| | This pipeline was finetuned from **kandinsky-community/kandinsky-2-2-decoder** on the **AhmetTek41/logo** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A logo for a zero waste club featuring a simple image of a closed loop of arrows, with each arrow made from different recyclable materials like paper, plastic, and metal.']: |
| |
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| |  |
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| |
|
| | ## Pipeline usage |
| |
|
| | You can use the pipeline like so: |
| |
|
| | ```python |
| | from diffusers import DiffusionPipeline |
| | import torch |
| | |
| | pipeline = AutoPipelineForText2Image.from_pretrained("AhmetTek41/output", torch_dtype=torch.float16) |
| | prompt = "A logo for a zero waste club featuring a simple image of a closed loop of arrows, with each arrow made from different recyclable materials like paper, plastic, and metal." |
| | image = pipeline(prompt).images[0] |
| | image.save("my_image.png") |
| | ``` |
| |
|
| | ## Training info |
| |
|
| | These are the key hyperparameters used during training: |
| |
|
| | * Epochs: 77 |
| | * Learning rate: 1e-05 |
| | * Batch size: 16 |
| | * Gradient accumulation steps: 1 |
| | * Image resolution: 512 |
| | * Mixed-precision: None |
| |
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| |
|
| | More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/ahmetberketekin-kocaeli-university/text2image-fine-tune/runs/b6yh1o8b). |
| |
|
