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wuerstchen-prior-meme-lora-4

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Diffusers
wuerstchen
diffusers-training
lora
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  1. README.md +54 -0
  2. checkpoint-1000/optimizer.bin +3 -0
  3. checkpoint-1000/pytorch_lora_weights.safetensors +3 -0
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README.md ADDED
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+
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+ ---
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+ license: mit
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+ base_model: warp-ai/wuerstchen-prior
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+ datasets:
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+ - dongOi071102/meme-pretreatment-dataset
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+ tags:
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+ - wuerstchen
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+ - text-to-image
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+ - diffusers
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+ - diffusers-training
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+ - lora
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+ inference: true
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+ ---
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+
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+ # LoRA Finetuning - dongOi071102/wuerstchen-prior-meme-lora-4
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+
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+ This pipeline was finetuned from **warp-ai/wuerstchen-prior** on the **dongOi071102/meme-pretreatment-dataset** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['a catton cat with angry face']:
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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 = AutoPipelineForText2Image.from_pretrained(
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+ "warp-ai/wuerstchen", torch_dtype=float32
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+ )
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+ # load lora weights from folder:
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+ pipeline.prior_pipe.load_lora_weights("dongOi071102/wuerstchen-prior-meme-lora-4", torch_dtype=float32)
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+
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+ image = pipeline(prompt=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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+ * LoRA rank: 4
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+ * Epochs: 100
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+ * Learning rate: 0.0001
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+ * Batch size: 8
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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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+
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+ More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/2111818-no/text2image-fine-tune/runs/xmsqasc9).
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