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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: OFA-Sys/small-stable-diffusion-v0
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+ datasets:
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+ - jwl25b/final_project_dataset
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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 - arpachat/stable-diffusion_unclip-small-v21-th-800-e4
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+
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+ This pipeline was finetuned from **OFA-Sys/small-stable-diffusion-v0** on the **jwl25b/final_project_dataset** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ["Tommy Hilfiger men's Regular Fit Round Logo Grey Polo"]:
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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("arpachat/stable-diffusion_unclip-small-v21-th-800-e4", torch_dtype=torch.float16)
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+ prompt = "Tommy Hilfiger men's Regular Fit Round Logo Grey Polo"
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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: 400
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+ * Learning rate: 0.0001
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+ * Batch size: 8
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+ * Gradient accumulation steps: 4
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+ * Image resolution: 128
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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](wandb_run_url).
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+ "image_processor_type": "CLIPImageProcessor",
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+ adam_beta1: 0.9
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+ adam_weight_decay: 0.01
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+ allow_tf32: false
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+ output_dir: stable-diffusion_unclip-small-v21-th-800-e4
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+ use_ema: false
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